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UNDERSTANDING AND PREVENTING
ANTERIOR CRUCIATE LIGAMENT INJURIES
USING NOVEL MOTION ANALYSIS SYSTEMS
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF
MECHANICAL ENGINEERING
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Ariel Veronica Dowling May 2011
http://creativecommons.org/licenses/by-nc/3.0/us/
This dissertation is online at: http://purl.stanford.edu/tj428wy3646
© 2011 by Ariel Veronica Dowling. All Rights Reserved.
Re-distributed by Stanford University under license with the author.
This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.
ii
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Thomas Andriacchi, Primary Adviser
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Mark Cutkosky
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Nicholas Giori
Approved for the Stanford University Committee on Graduate Studies.
Patricia J. Gumport, Vice Provost Graduate Education
This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.
iii
~iv~
Abstract The overall goal of this dissertation is to use novel motion analysis systems to
investigate the underlying mechanisms that cause an anterior cruciate ligament (ACL)
injury and then to explore movement modification methods that might prevent ACL
injuries from occurring. This injury causes immediate functional impairment and also
increases the long term risk of developing osteoarthritis (OA), a degenerative joint
disease. Thus, understanding the causes of this injury and investigating methods to
prevent it from occurring are important goals and could lead to improved health and
quality of life. Additionally, novel motion analysis systems can provide new
information about ACL injuries and therefore should be used to help analyze these
injuries from a different perspective. This thesis provides the results from multiple
experimental studies that used two novel motion analysis systems to investigate the
underlying causes of ACL injury and potential injury prevention methods. These
results add to the understanding of the ACL injury mechanism and also suggest
potential preventative methods that could decrease the overall incidence of ACL
injury.
Using a markerless motion capture system, the first investigation determined
that increasing the coefficient of friction of the shoe-surface condition will change a
subject’s movement strategies during a sidestep cutting task in specific ways that may
increase the risk of ACL injury. Additionally, increased running speed combined with
increased floor friction further alters a subject’s movement in biomechanical measures
associated with risk for ACL injury, and these changes are different between females
and males. This investigation provides a biomechanical basis for the increased
incidence of ACL injuries on high friction surfaces, and suggests that the
biomechanical causes change based on the speed of the maneuver. In terms of gender,
this investigation suggests that females are more at risk for ACL injury when cutting
on high friction surfaces at different speeds.
In terms of novel motion analysis systems, there is a need for simple, cost
effective methods to identify athletes at a higher risk for ACL injury during jumping
~v~
tasks. Wearable systems offer many advantages over traditional motion capture
systems: they are simpler to use, do not require complex post-processing, and make it
feasible to test subjects in a natural environment. As such, the second study assessed
the capacity of a wearable inertial-based system to evaluate ACL injury risk during
jumping tasks. This system accurately detected crucial temporal events and measured
total jump height with a precision comparable to dedicated optical devices.
Additionally, the proposed system measured the knee flexion and the trunk lean, and
demonstrated good concurrent validity and discriminative performance in terms of the
known risk factors for ACL injury. This study also reported the angular velocity of the
thigh and shank segments during bilateral and unilateral drop jumps for the first time,
and showed that angular velocity was consistent between subjects. Furthermore, this
study illustrated there is an association between the coronal segment angular velocity
and knee abduction moment, and that the coronal segment angular velocity can
differentiate between subjects at higher risk for ACL injury.
Recent studies have shown that the incidence of ACL injury can be decreased
through the use of intervention programs, but the quality of the feedback provided to
the participants in these programs can vary depending on the skill of the observer.
Therefore, the objective for the final study was to determine if an independent inertial-
based system can be used to modify jump landing mechanics in order to decrease the
risk for ACL injury by providing real-time feedback based on known kinematic and
kinetic injury risk factors. This study found that the subjects reduced their risk for
ACL injury after training with the system because there were significant increases in
the maximum knee flexion angle and the maximum trunk lean. The subjects also
reduced their risk for injury by decreasing their thigh coronal angular velocity, which
was correlated with a decrease in their knee abduction moment. This study suggests
that an inertial-based system could be used for interventional training aimed at
reducing the risk for ACL injury.
~vi~
Acknowledgements
There have been many people who have helped me throughout my time at
Stanford. I would like to start by sincerely thanking my advisor, Tom Andriacchi, for
providing me with guidance, mentorship, and for allowing me to pursue research that
truly excited my passion for science and engineering. He has made me a better
researcher by providing advice and direction to my work while at the same time giving
me the freedom to learn and grow on my own. In a similar vein I would like to extend
a very special thanks to Julien Favre, who has been an amazing mentor, coauthor, and
friend from the day he walked in our office door. His assistance and guidance have
been invaluable, and over the past two years he has helped me to refocus my thesis in
order to make a more impactful contribution to science, to conduct a year-long jump
study extravaganza, and to write many papers and abstracts. I am truly grateful that he
has shared his knowledge and friendship with me. I would also like to thank Ajit
Chaudhari for introducing me to the world of scientific research and for helping me
through my first major study, and to Stefano Corazza for being my office mate and
teaching me about the world of markerless motion capture. I would also like to thank
the many members of the BioMotion Lab, both past and present, for making the last 6
years of my life educational, interesting, and exciting; I truly value all the time I have
spent with everyone and will enjoy keeping up with everyone’s lives in the years to
come.
Furthermore, thank are definitely due to Melinda Cromie and Melanie Fox for
working with me on countless problem sets and projects, providing me with advice on
all things biomechanics, and for being amazing friends since my very first day at
Stanford. I would also like to express my appreciation to all of the subjects (most of
them my friends) that have volunteered their time to participate in my studies at the
Biomotion Lab. Additionally, I would like to thank Stanford University, the Palo Alto
VA, and the NSF as the funding sources for these projects.
Finally, I would like to thank those closest to me for their unwavering love and
support. I would not be the person I am today without my family, Paul, Laurie, and
~vii~
Russell. Their unconditional love and support, as well as their belief that I can
accomplish anything, have given me the confidence to achieve and the knowledge that
I am loved regardless of what I do achieve. I would also like to thank my very special
person Adrienne Diebold, who has been the yin to my yang for almost 10 years. Her
advice has helped me in both science and life, and I am truly grateful to her for sharing
her opinions, advice, workouts, and friendship with me over the last decade. Finally, I
will forever be thankful for my wonderful partner in life, Aron Levin. His steadfast
support, humor, time management skills, and assistance in all aspects of my life have
enriched my life beyond measure. I look forward to our next big adventure together
and all the years to follow. ני אוהבת אותו היום ובכל יוםא
~viii~
Table of Contents Abstract .................................................................................................... iv
Acknowledgements .................................................................................. vi
Table of Contents ................................................................................... viii
List of Tables ........................................................................................... xii
List of Figures ........................................................................................ xiii
11 Introduction ......................................................................................... 1
1.1. Overview ........................................................................................ 1
1.2. Anterior Cruciate Ligament Injury ................................................ 1 1.2.1. Description ............................................................................... 1 1.2.2. Prevalence ................................................................................ 3 1.2.3. Osteoarthritis ............................................................................ 4
1.3. Statement of Purpose ..................................................................... 5 1.4. Outline of Upcoming Chapters ...................................................... 6
22 Review of Relevant Literature ........................................................... 8
2.1. Mechanisms of ACL Injury ........................................................... 8 2.2. Risk Factors for Injury ................................................................... 9
2.2.1. Biomechanical: Kinematics ..................................................... 9 2.2.2. Biomechanical: Kinetics ........................................................ 10 2.2.3. Environmental Factors ........................................................... 13
2.3. Prevention Strategies and Programs ............................................ 14 2.3.1. Knee Flexion Angle Modification ......................................... 14 2.3.2. Real-Time Feedback Modifications ...................................... 15
2.4. Novel Motion Analysis Systems .................................................. 15 2.4.1. Markerless Motion Capture ................................................... 16 2.4.2. Inertial Sensors ...................................................................... 17
~ix~
33 Shoe-Surface Friction Influences Movement Strategies During a Sidestep Cutting Task: Implications for Anterior Cruciate Ligament Injury Risk ........................................................................ 19
3.1. Overview ...................................................................................... 19 3.2. Introduction .................................................................................. 20 3.3. Methods ........................................................................................ 21
3.3.1. Subjects .................................................................................. 21 3.3.2. Experimental Design ............................................................. 22 3.3.3. Data Collection ...................................................................... 23 3.3.4. Data Analysis ......................................................................... 24 3.3.5. Statistical Analysis ................................................................ 25
3.4. Results .......................................................................................... 26 3.5. Discussion .................................................................................... 30 3.6. Conclusion ................................................................................... 34 3.7. Acknowledgments ........................................................................ 34
44 Running Speed and Gender Influence Movement Strategies During a Sidestep Cutting Task on Different Friction Surfaces: Implications for ACL Injury Risk ................................................... 35
4.1. Overview ...................................................................................... 35 4.2. Introduction .................................................................................. 36 4.3. Methods ........................................................................................ 37
4.3.1. Subjects .................................................................................. 37 4.3.2. Experimental Design ............................................................. 38 4.3.3. Data Collection ...................................................................... 39 4.3.4. Data Analysis ......................................................................... 40 4.3.5. Statistical Analysis ................................................................ 41
4.4. Results .......................................................................................... 41 4.5. Discussion .................................................................................... 46 4.6. Conclusion ................................................................................... 49 4.7. Acknowledgments ........................................................................ 50
~x~
55 A Wearable System to Assess Risk for ACL Injury During Jump Landing: Measurements of Temporal Events, Jump Height, and Sagittal Plane Kinematics ................................................................. 51
5.1. Overview ...................................................................................... 51 5.1.1. List of Definitions .................................................................. 52
5.2. Introduction .................................................................................. 53 5.3. Methods ........................................................................................ 54
5.3.1. Subjects .................................................................................. 54 5.3.2. Experimental Design ............................................................. 54 5.3.3. Wearable System ................................................................... 56 5.3.3.1. Hardware ............................................................................ 56 5.3.3.2. Angle measurements .......................................................... 56 5.3.3.3. Temporal events detection .................................................. 57 5.3.3.4. Vertical jump height ........................................................... 57 5.3.4. Reference System .................................................................. 58 5.3.5. Data Analysis ......................................................................... 59
5.4. Results .......................................................................................... 61 5.5. Discussion .................................................................................... 66 5.6. Conclusions .................................................................................. 69 5.7. Acknowledgments ........................................................................ 69
66 Characterization of Jump Landing Mechanics Based on Thigh and Shank Segment Angular Velocity: Implications for ACL Injury Risk ..................................................................................................... 70
6.1. Overview ...................................................................................... 70 6.2. Introduction .................................................................................. 71 6.3. Methods ........................................................................................ 72
6.3.1. Subjects .................................................................................. 72 6.3.2. Experimental Design ............................................................. 73 6.3.3. Segment angular velocity ...................................................... 74 6.3.4. External knee moments ......................................................... 76 6.3.5. Data Analysis ......................................................................... 76
6.4. Results .......................................................................................... 77 6.5. Discussion .................................................................................... 83 6.6. Conclusions .................................................................................. 87 6.7. Acknowledgments ........................................................................ 87
~xi~
77 Real Time Inertial-Based Feedback Can Reduce Risk for ACL Injury During Jump Landings ......................................................... 88
7.1. Overview ...................................................................................... 88 7.2. Introduction .................................................................................. 89 7.3. Methods ........................................................................................ 90
7.3.1. Subjects .................................................................................. 90 7.3.2. Jump Task .............................................................................. 90 7.3.3. Feedback ................................................................................ 91 7.3.3.1. Hardware ............................................................................ 91 7.3.3.2. Parameters .......................................................................... 91 7.3.3.3. Relative Risk....................................................................... 92 7.3.4. Experimental Design ............................................................. 92 7.3.4.1. Training Session ................................................................. 95 7.3.5. Knee Abduction Moment Measurement ............................... 96 7.3.6. Statistical Analysis ................................................................ 97
7.4. Results .......................................................................................... 97 7.5. Discussion .................................................................................. 103 7.6. Conclusions ................................................................................ 107 7.7. Acknowledgments ...................................................................... 107
88 Summary .......................................................................................... 108
8.1. Overall Conclusions ................................................................... 108 8.2. Contributions .............................................................................. 110
References .............................................................................................. 112
~xii~
List of Tables
Table 3-1: Variables of Interest at Foot Contact for Low and High Friction Surfaces 27
Table 4-1: Variables of interest at foot contact for males and females on both low and high friction surfaces. ............................................................................................. 45
Table 5-1: Jump height measured with wearable and reference systems. .................... 62
Table 5-2: Similarities of the patterns (R) between the systems .................................. 64
Table 5-3: Knee kinematic parameters measured at specific time points with both measurement systems. ............................................................................................ 65
Table 6-1: Coefficients of multiple correlation (CMC) and ranges (SD) for the angular velocities of the shank and thigh segments in all three planes. .............................. 80
Table 6-2: Angular velocity parameters measured at specific time points. ................. 81
Table 6-3: Correlation (R) between knee abduction moment and coronal plane angular velocity, as well as receiver operating curves (ROC). ........................................... 82
Table 7-1: Standardized set of movement modifications for training session ............. 95
Table 7-2: Knee flexion angle, trunk lean, and jump height at baseline and follow-up. Number at risk indicates subjects outside the low risk ranges. .............................. 98
Table 7-3: Thigh coronal angular velocity and knee abduction moment for both systems at baseline and follow-up. For thigh coronal angular velocity, change calculated as the average difference between baseline and follow-up (absolute value). Knee abduction moment split into at-risk (ABD Baseline) ........................ 99
~xiii~
List of Figures
Figure 1-1: Normal knee anatomy, front view ............................................................... 2
Figure 1-2: Arthroscopic view (left) and cadaveric dissection (right) of the anteromedial (AM) and posterolateral (PL) functional bundles of the ACL (Seibold 2008) .......................................................................................................... 3
Figure 1-3: Scattergram of the proportion of individuals with radiographic osteoarthritis (OA) plotted against time after ACL injury or reconstructive surgery. Each data point represents a data set from 1 of 127 individual publications. Symbols: • represents nonsurgical treatment; ▾ represents primary suture or enhancement; ▪ represents reconstruction by autograft; ♦ represents reconstruction by synthetic graft or allograft (Lohmander 2007). ................................................... 4
Figure 2-1: ACL injury through a combination of knee valgus and anterior tibial translation force during a side-cut maneuver in soccer players (Alentorn-Geli 2009a) ....................................................................................................................... 9
Figure 2-2: Knee Abduction Moment .......................................................................... 12
Figure 2-3: Construction of a subject’s image from a markerless motion capture system. The silhouettes of the subject from different cameras are projected into space, and their intersection forms an approximation of the volume occupied by the subject’s body (Corazza 2006) ......................................................................... 17
Figure 2-4: Inertial sensor measurement system (Physilog®, BioAGM, CH) ............. 18
Figure 3-1: Knee flexion angle during the entire recorded sequence. Data represent average of all trials on each surface for one subject. .............................................. 26
Figure 3-2: External knee adduction/abduction moment during the entire recorded sequence. Data represent average of all trials on each surface for one subject. Stance phase begins at frame 0. Negative values indicate abduction moment. ..... 29
Figure 3-3: Example measurements for relative medial and posterior center of mass (COM) distance from the support limb (defined as ankle joint center). ................ 30
Figure 4-1: Knee flexion angle at foot contact by total and by gender. ....................... 43
Figure 4-2: Knee moments at foot contact by total and by gender. ............................. 44
Figure 5-1: Proposed wearable system. ........................................................................ 55
Figure 5-2: Bland and Altman analysis of jump height. Solid line corresponds to bias and dashed lines correspond to 66% limits of agreement. ..................................... 62
Figure 5-3: Example of continuous knee flexion angle and trunk lean for one subject during one bilateral jumping task, with the discrete time point parameters identified. ................................................................................................................ 64
~xiv~
Figure 6-1: Experimental setup of the wearable system and the camera-based system markers. Wearable system IMUs identified with white oval. Positive axes convention for SAV identified for medial/lateral (M-L), posterior/anterior (P-A), and inferior/superior (I-S) axes. ............................................................................. 74
Figure 6-2: Bilateral jump angular velocity curves for shank and thigh segments in sagittal, coronal, and transverse planes (axes are according to Figure 6-1). Initial contact (IC) is indicated by black circle, and maximum stance (MAX) is indicated by white star. Difference (DIF) is range between IC and MAX. ........................... 78
Figure 6-3: Unilateral jump angular velocity curves for shank and thigh segments in sagittal, coronal, and transverse planes (axes are according to Figure 6-1). Initial contact (IC) indicated by black circle, maximum stance (MAX) indicated by white star. Difference (DIF) is range between IC and MAX. .......................................... 79
Figure 6-4: Illustration of the relationship between coronal SAV and external knee abduction moment. A) positive thigh SAV tends to increase the knee abduction moment, B) positive shank SAV tends to decrease the knee abduction moment, C) positive difference between thigh and shank SAVs tends to increase knee abduction moment. ................................................................................................. 83
Figure 7-1: Experimental protocol for entire testing session. ...................................... 94
Figure 7-2: Entire testing session for one subject. For each parameter, blue circle indicates mean baseline measurements, red triangles indicate training jump values, and black X indicates mean follow-up measurements. Green shading indicates low risk range. ............................................................................................................... 96
Figure 7-3: Change in knee flexion angle, trunk lean, and thigh coronal angular velocity by subject ................................................................................................ 100
Figure 7-4: Change in knee abduction moment by subject from baseline to follow-up, split into at-risk and not-at-risk cohorts. At-risk cohort (top) had a positive (abduction) peak moment at baseline while not-at-risk cohort (bottom) had a negative (adduction) peak moment at baseline. ................................................... 102
Figure 7-5: Intra-subject association between the change (baseline to follow-up) in the thigh coronal angular velocity and the knee abduction moment. ......................... 103
~1~
11 Introduction
1.1. Overview
The overall goal of this project is to use novel motion analysis systems to
investigate the underlying mechanisms that cause an anterior cruciate ligament (ACL)
injury and then to explore movement modification methods that might prevent ACL
injuries from occurring. An ACL injury is one of the most common musculoskeletal
injuries sustained during sports participation. This injury causes immediate functional
impairment and also increases the long term risk of developing osteoarthritis (OA), a
degenerative joint disease. Thus, understanding the causes of this injury and
investigating methods to prevent it from occurring are important goals and could lead
to improved health and quality of life for recreational athletes. Additionally, novel
motion analysis systems can provide new information about ACL injuries and
therefore should be used to help analyze these injuries from a different perspective.
The remainder of this chapter provides the motivation for investigating ACL injuries,
describes the statement of purpose for this study, and gives an outline for the
following chapters.
1.2. Anterior Cruciate Ligament Injury
1.2.1. Description
The anterior cruciate ligament (ACL) is one of the four major ligaments of the
knee. On the proximal side, the ACL attaches to the posteromedial edge of the lateral
femoral condyle. It then follows an oblique course in the anteromedial direction and
distally attaches to the anterior intercondylar fossa on the tibia plateau (Bicer 2009)
(Figure 1-1). The cross-sectional area of the ACL is irregular and varies throughout
the knee; the ligament “fans out” at the tibial attachment.
The
anteromedia
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originates at
inserts at th
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f the tibial at
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~2~
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~3~
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1-3: Scatterthritis (OA)ry. Each dans. Symbolsenhanceme
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~4~
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~5~
Many injured patients elect to undergo ACL reconstruction surgery, which is
accepted as the standard of care and can successfully treat the initial loss of stability
and function (Tashman 2008). However, ACL reconstruction does not reduce the
incidence of OA (Barrack 1990; Daniel 1994; Kannus 1989; Lohmander 2004;
Lohmander 2007; Maletius 1999; Meunier 2007; von Porat 2004). Lohmander et al.
(2004) found that in a population of female soccer players who suffered ACL ruptures
at an average age of 19 years, 51% of the injured knees showed radiographic knee OA
just 12 years after injury (at age 31), compared to only 7% of the uninjured
contralateral knees. Additionally, this study showed that there was no significant
difference in the incidence of radiographic knee OA between ACL reconstructed
knees and ACL deficient knees, suggesting that the ACL reconstruction was unable to
reduce the rates of OA in this population. Another study by von Porat et al. (2004)
showed similar results in male soccer players 14 years after injury. Radiographic
changes were found in 78% of the 122 subjects studied, and advanced radiographic
changes (Kellgren-Lawrence grade 2 or higher) were observed in 41% of the subjects.
Again, there were no differences in incidence of radiographic changes between
surgically and non-surgically treated subjects, further supporting that reconstruction
does not prevent OA from occurring. All these studies suggest that ACL injury leads
to degeneration of the articular cartilage in the injured knee, and standard ACL
reconstruction procedures do not protect the knee from developing OA.
1.3. Statement of Purpose
As stated previously, ACL injury is a growing problem among athletes,
particularly among women. An injury often leads to premature degenerative arthritis,
and there is no known treatment that can reduce this increased risk. In order to reduce
the risk for an ACL injury, it is first important to understand how the injuries occur;
specifically, how subjects adapt their movement to different conditions, and how these
adaptations change their risk for ACL injury. Additionally, prevention or reduction of
the risk for ACL injuries is important for long term joint health, and therefore it is
~6~
important to determine effective methods to alter the subjects’ movements so that they
are less at risk for injury.
The underlying goal of this thesis is to fill critical gaps in the available
knowledge on the causes of ACL injury, and then to investigate methods to prevent
these injuries from occurring. Novel motion analysis systems were used for this thesis
in order to examine parameters that might affect risk for ACL injury but are difficult
to measure with standard motion analysis systems. To achieve these goals, multiple
studies were conducted of healthy subjects performing movement tasks that replicate
known ACL injury mechanisms while data was collected with two different types of
novel motion analysis systems. The causes for ACL injury were investigated,
specifically how subjects adapt their movement strategies (and therefore their risk for
injury) as a response to the coefficient of friction of the floor surface. Next, a novel,
inertial-based motion analysis system was characterized for use during jumping tasks.
This system was then used as a real time feedback system to reduce the risk for ACL
injury. The system instructed subjects how to modify their movement and then
measured how effectively the subjects were able to alter their movement technique as
well as the change in their risk for ACL injury.
1.4. Outline of Upcoming Chapters
Chapter 2 is a review of the relevant literature that pertains to understanding
and preventing ACL injuries, specifically the mechanisms of ACL injury, risk factors
for injury, and injury prevention strategies and programs. The novel motion analysis
systems used for this thesis are also discussed in this chapter.
Chapter 3 analyzes how subjects change their movement strategies for shoe-
surface conditions with a high coefficient of friction relative to a low friction condition
and how these changes in movement strategies affected their risk for ACL injury. The
study demonstrated that for the high coefficient of friction surface, the subjects
adopted a movement strategy which increased their risk for ACL injury.
Chapter 4 investigates how increasing running speed prior to a single limb
landing combined with increased floor friction alters a subject’s movement as well as
~7~
how these alterations are different between males and females. The results from this
study were that increasing the running speed on a high friction surface alters the
subjects’ risk of injury; some of the alterations are protective and some increase the
risk of injury. In terms of gender, females are more at risk for injury than males during
all the test conditions.
Chapter 5 explains the development and assessment of a wearable inertial-
based system to measure jumping tasks in terms of temporal event detection, jump
height, and knee angles. The wearable system accurately detected temporal events and
measured total jump height. It also measured the knee joint angles in all three planes
and demonstrated good concurrent validity and discriminative performance in terms of
the known risk factors for ACL injury.
Chapter 6 describes the characterization of the thigh and shank angular
velocity during a jump landing and the association between coronal angular velocity
and knee abduction moment. The coronal angular velocities were significantly
correlated with the knee abduction moment, showing that angular velocity could be a
useful parameter to analyze jump landing movements.
Chapter 7 illustrates that an independent inertial-based system can be used to
modify jump landing mechanics in order to decrease the risk for ACL injury by
providing real-time feedback based on known kinematic and kinetic injury risk
factors. This study determined that the subjects can effectively modify their jumping
technique based on feedback from the inertial system and that these movement
modifications caused a reduction in their risk for ACL injury.
Chapter 8 is a summary of the above studies (Chapter 3 though 7) and presents
the results in a unified manner. The major scientific contributions of the thesis are also
described in this chapter.
~8~
22 Review of Relevant Literature
2.1. Mechanisms of ACL Injury
The first part of this thesis focuses on understanding how ACL injuries occur;
therefore it is critical to examine the previous literature defining the main mechanisms
of non-contact ACL injuries. Qualitative analyses of ACL injuries captured on videos
taken during sports events suggest that many injuries occur at foot contact during a
landing from a jump with either one or two legs or a deceleration movement before a
change in direction (Boden 2000; Kimura 2010; Krosshaug 2007; Myklebust 1998;
Olsen 2004). Additionally, the affected knee appears to be near full extension (below
30° of flexion) at the time of injury (Boden 2000; Cochrane 2007; McNair 1990;
Olsen 2004; Teitz 2001). Boden et al. (2000) used retrospective video analysis to
define the most common kinematic positions that resulted in an ACL injury during
sports. They reported that ACL injury occurred during a deceleration movement when
the knee was close to full extension, the tibia was externally rotated, and the foot was
planted. During/after injury, a valgus collapse of the knee has been observed, most
commonly among female athletes (Boden 2000; Krosshaug 2007; Olsen 2004; Teitz
2001). Olsen et al. (2004) concluded that the ACL injury mechanism in women’s
handball was a valgus collapse combined with tibial rotation when the knee was close
to full extension. Additionally, Teitz (2001) suggested that the position of the center of
mass (COM) of the subject during injury was posterior and far from the location of the
foot-to-ground contact (support limb).
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~9~
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~10~
a risk factor for injury because this signifies an overly upright posture (Blackburn
2008; Blackburn 2009; Griffin 2000). Increasing trunk flexion during landing leads to
increased hip and knee flexion angles but does not alter transverse or coronal plane
kinematics (Blackburn 2008).
In the coronal plane, many studies have suggested that an increased abduction
angle of the knee at both initial contact and maximum value during deceleration is a
risk factor for injury (Borotikar 2008; Ford 2003; Ford 2005; Ford 2006; Ford 2010;
Kanamori 2000; Pappas 2007; Russell 2006; Withrow 2006; Yu 2005). A collapse
into abduction of the lower limb is typically seen in the video evidence of actual
injuries (Boden 2000; Koga 2010; Krosshaug 2007; Olsen 2004). In a prospective
study, Hewett et al. (2005a) found that athletes that sustained an ACL injury had 8°
more knee abduction angle during landing from a jump when compared to uninjured
athletes. Ford et al. (2005) showed that females exhibited greater knee abduction
angles during cutting maneuvers than comparable male athletes. Also, knee abduction
angle has been suggested as a strong predictor of future injury (Hewett 2005a; Padua
2009a).
Finally, increased rotation of the tibia in the internal direction at initial contact
and maximum value during deceleration has been suggested to increase the risk for
ACL injury (Borotikar 2008; Kiriyama 2009; McLean 2007), and increased rotation in
both the internal and external directions has been observed during actual ACL injuries
(Koga 2010; Krosshaug 2007; Olsen 2004). Also, female athletes had an increased
knee internal rotation angle compared to male athletes during the landing preparation
of a stop-jump task (Chappell 2007).
2.2.2. Biomechanical: Kinetics
Multiple investigations of ACL injuries have suggested that specific kinetic
measures of the knee can be used to identify a higher risk for ACL injury (Alentorn-
Geli 2009a). The primary kinetic risk factor for ACL injury is the external knee
abduction moment. Biomechanical studies of both cutting tasks and jumping tasks
have indicated that subjects with an increased knee abduction moment during
~11~
deceleration have an increased risk of ACL injury (Besier 2001b; Ford 2010; McLean
2007; Renstrom 2008). In terms of gender, females typically display greater abduction
moments than men during cutting and jumping (Chappell 2002; Hewett 2006;
Renstrom 2008). Similarly, simulations of jump landings and cadaveric studies have
suggested that increased load in abduction increases the strain in the ACL (Fukuda
2003; Kanamori 2000; Markolf 1995; Shin 2009; Shin 2010; Withrow 2005). The
knee abduction moment during landing can also be used to evaluate an athlete’s risk of
injury by stratifying athletes into low-risk or high-risk categories (Myer 2007).In
terms of actual injuries, a prospective study found that female athletes that sustained
an ACL injury had a 2.5 greater peak knee abduction moment during landing than
uninjured athletes (Hewett 2005a). Furthermore, this study showed that knee
abduction moment was a stronger predictor for ACL injury than knee flexion angle.
Given this known ACL injury risk factor, it would be beneficial to have a
simple method to predict whether or not an athlete will sustain an ACL injury based
on measuring knee abduction moment during a landing after a jump. A prospective
study by Hewett et al. found that knee abduction moment during landing predicts
future ACL injury with a sensitivity of 78% and a specificity of 73% and that the
combination of knee abduction moment and knee abduction angle predicted injury
with an R2 of 0.88 (Hewett 2005a). However, measuring the knee abduction moment
during landing is a complex calculation requiring both a camera based measurement
system and a force plate to record ground reaction forces, as well as extensive time
necessary to prepare the subject for subsequent testing. Therefore, simpler methods to
predict the knee abduction moment have been investigated. Another study has
suggested that specific biomechanical parameters can predict 78% of the variance in
the knee abduction moment during landing using the peak knee abduction angle, the
peak knee flexion moment, the knee flexion angle range of motion, BMI, and length of
the tibia (Myer 2010a). These same parameters could predict a high knee abduction
moment status with 85% sensitivity and 93% specificity (Myer 2010a). Additionally, a
simpler method using clinical correlates to the previously identified laboratory
measures successfully predicted high knee abduction moment status (Myer 2010b,
Myer 2010c
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~12~
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~13~
2.2.3. Environmental Factors
The primary environmental risk factor for ACL injury examined in this thesis
is the coefficient of friction of the shoe-surface interface. It has been widely
hypothesized that an increased coefficient of friction (COF) of the shoe-surface
interaction leads to increased incidence of ACL injury during sporting events
involving run-to-cut maneuvers (Alentorn-Geli 2009a; Torg 1974). This has been
suggested by studies comparing weather conditions, different types of surfaces, and
footwear. Studies have shown that weather conditions that produced dry fields are
associated with more injuries than wet fields and that most injuries occur on dry fields
(Orchard 2001; Scranton 1997). For example, 95.2% of noncontact ACL injuries
observed in the National Football League (NFL) over 5 seasons occurred on a dry
field, which has a higher COF than a wet field; similarly, weather conditions that led
to dry fields (low amounts of rainfall and high evaporation rates) had a higher relative
risk (2.87 and 2.55 greater risk, respectively) of noncontact ACL injury among
Australian football players over 7 years of play (Orchard 2001; Scranton 1997). Other
studies that examined the effect of weather on lower limb injuries in NFL games
found that there were significantly fewer knee and ankle injuries in cold weather than
warm weather, and the authors concluded that this could be a result of the reduced
shoe-surface traction in the cold climate (Orchard 2003). Further investigations on
injury rates in the NFL for artificial turf surfaces versus grass surfaces determined that
there was a higher rate of ACL injury on older versions of AstroTurf, which has a
much larger COF than natural grass (Orchard 2003; Powell 1992). Additionally, in a
video examination of ACL injury events in team handball, Olsen et al. (Olsen 2003)
determined that more ACL injuries occurred on high COF rubber floor surfaces than
wooden floor surfaces; this relationship was especially high for female athletes.
Additionally, footwear that has a higher COF has been associated with a greater risk of
ACL injury (Lambson 1996). For all these studies, the surface with the higher COF
was shown to also have a higher incidence of ACL injury.
~14~
2.3. Prevention Strategies and Programs
Recent articles have shown that the incidence of ACL injury can be decreased
among athletes through the use of intervention programs that focus on modifying
lower extremity biomechanics (Alentorn-Geli 2009b; Brophy 2010, Hewett 2006b;
Renstrom 2008; Silvers 2007). Most of these programs combine various different
intervention modifications (e.g. kinematic modifications strength training,
plyometrics, balance training, etc), and so it is unclear how each individual
modification contributes to the changes observed after the intervention and the
corresponding decrease in the incidence of injury. These intervention programs are
generally six to eight weeks in duration and require 2 to 3 training sessions per week
where the participants perform a variety of neuromuscular, plyometric, and strength
exercises. Furthermore, most of the training sessions occur during team practices
because the participants cannot perform the intervention training independently; as a
result, compliance rates for these intervention programs can be as low as 28%
(Myklebust 2003). During the training sessions, either coaches or physical therapists
must be present to provide feedback to the participants in order to ensure they are
properly performing the training intervention (Alentorn-Geli 2009b; Brophy 2010,
Hewett 2006b; Renstrom 2008; Silvers 2007). However, this feedback generally
consists of verbal instructions based on real-time visual observation; therefore, the
feedback is not quantitative in nature and can vary depending on the skill of the
observer. Additionally, not all intervention programs have proved to be successful.
2.3.1. Knee Flexion Angle Modification
Many of the successful intervention programs emphasize proper landing
technique after a landing from a jump, specifically an increase in the knee flexion
angle during landing. This focus on increasing knee flexion angle during landing
stems from previous research showing that a small knee flexion angle at both the
initial contact with the ground and the maximum angle achieved during landing is a
risk factor for ACL injury (section 2.2.1). Furthermore, investigations of actual ACL
~15~
injuries suggest that the injury occurs at a low knee flexion angle immediately
following initial contact with the ground (section 2.2.1).
Increasing the knee flexion angle during jump landing has been the primary
modification in several previous studies focused on altering lower extremity
biomechanics in order to reduce the risk for ACL injury (Herman 2009; Mizner 2008;
Myers 2010; Oñate 2005). After the intervention, the subjects were reported to have
significantly increased their knee flexion angles at both initial contact and peak value,
and also exhibited changes in other saggital plane parameters, specifically an increase
in hip flexion angle and decreases in hip flexion moment, knee flexion moment, and
anterior tibial shear force (Herman 2009; Mizner 2008; Myers 2010; Oñate 2005).
However, the correlation between the change in knee flexion angle and the change in
other risk factors has not been investigated.
2.3.2. Real-Time Feedback Modifications
Real-time training interventions have been developed primarily for repetitive
exercises such as walking or running (Barrios 2010, Crowell 2011, Dowling 2010,
Hunt 2011, Noehren 2010, Shull 2011, Wheeler 2011) and rehabilitation (Bachlin
2010, Tate 2010). In these investigations, subjects were provided with visual, auditory,
or haptic feedback that instructed them as to how to modify their movements. While
most interventional studies used real-time marker-based motion capture (Barrios 2010,
Hunt 2011, Noehren 2010, Shull 2011, Wheeler 2011), this method is limited in scope
because of the difficulties in tracking markers and processing position data in real-
time.
2.4. Novel Motion Analysis Systems
Skin marker-based motion capture, otherwise known as stereophotogrammetry,
has been widely used in studies of human movement (Andriacchi 2000), and can have
an accuracy of less than 1 mm (Chiari 2005). However, instrumental errors (Chiari
2005), soft tissue artifact (Leardini 2005), and marker misplacement (Della Croce
2005) can all affect the estimation of the skeletal system movement and are critical
~16~
sources of measurement error. Additionally, these systems require skilled operators
and complex instrumentation (e.g., multiple cameras synchronized with a force plate),
which restrict their usage for routine applications. Because of these difficulties, other
types of motion analysis systems have been proposed to study human movement. The
following sections discuss the two novel motion analysis systems used in this thesis.
2.4.1. Markerless Motion Capture
Motion capture based only on video data, known as markerless motion capture,
has become increasingly popular in the last few years because it and can be used for a
broad range of applications. Thus far, markerless motion capture has been used for
animation in the entertainment industry, sports performance evaluation, surveillance,
and biomechanical analysis for clinical applications; however, only sports
performance and biomechanical analysis require a high degree of accuracy of the
system. Additionally, markerless motion capture enables the subjects to move
naturally, minimizes the subject preparation time, and reduces inter-operator
variability since no markers are placed on the subject (Corazza 2009; Mündermann
2006). This type of motion capture is well suited to measuring movements that occur
quickly, as traditional markers have a tendency to fall off the subject during fast
movements. For example, a recent study used markerless motion capture to evaluate
different types of tennis serves in order to determine which serve places the most
stress on the body (Abrams 2011). Furthermore, markerless motion capture can be
used in situations where marker-based motion capture is impossible, such as motion
capture of animals. Due to the wide variety of animal skin, marker attachment can be
impossible or may significantly alter the animal’s natural movement, as illustrated in a
study by Zelman et al. (Zelman 2009) that used a markerless motion capture system to
track octopus arm movements in 3D space.
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~17~
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~18~
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~19~
33 Shoe-Surface Friction Influences Movement Strategies During a Sidestep Cutting Task: Implications for Anterior Cruciate Ligament Injury Risk
3.1. Overview Increasing the coefficient of friction of the shoe-surface interaction has been
shown to lead to increased incidence of anterior cruciate ligament (ACL) injuries, but
the causes for this increase are unknown. Previous studies indicate that specific
biomechanical measures during landing are associated with an increased risk for ACL
injury. At foot contact during a sidestep cutting task, subjects use different movement
strategies for shoe-surface conditions with a high coefficient of friction (COF) relative
to a low friction condition. Specifically, the study tested for significant differences in
knee kinematics, external knee moments, and the position of the center of mass for
different COFs. Twenty-two healthy subjects (11 male) were evaluated performing a
30° sidestep cutting task on a low friction surface (0.38) and a high friction surface
(0.87) at a constant speed. An 8-camera markerless motion capture system combined
with 2 force plates was used to measure full-body kinematics, kinetics, and center of
mass. At foot contact, subjects had a lower knee flexion angle (P = .01), lower
external knee flexion moment (P < .001), higher external knee abduction moment (P <
.001), and greater medial distance of the center of mass from the support limb (P <
~20~
.001) on the high friction surface relative to the low friction surface. The high COF
shoe-surface condition was associated with biomechanical conditions that can increase
the risk of ACL injury. The higher incidence of ACL injury observed on high friction
surfaces could be a result of these biomechanical changes. The differences in the
biomechanical variables were the result of an anticipated stimulus due to different
surface friction, with other conditions remaining constant. The risk analysis of ACL
injury should consider the biomechanical movement changes that occur for a shoe-
surface condition with high friction.
Portions of this chapter were previous published in the American Journal of
Sports Medicine in 2010 (Dowling 2010). The final, definitive version of this paper
has been published in The American Journal of Sports Medicine, 38/3, Mar/2010 by
SAGE Publications, Inc. All rights reserved. ©2010. The author contributed to this
paper by collecting all of the data from the subjects, processing the data, analyzing the
data, and writing the manuscript of the paper.
3.2. Introduction As described in Chapter 1, the ACL is frequently injured and can lead to
premature knee osteoarthritis with or without reconstruction. Qualitative analysis of
ACL injuries suggest that these injuries commonly occur at foot contact during a
landing or deceleration movement before a change in direction with the position of the
center of mass (COM) posterior and far from the location of the foot-to-ground contact
(support limb). Quantitative studies indicate that specific biomechanical measures
during landing can be used to identify an increased risk for ACL injury, specifically a
small knee flexion angle, large abduction angle or moment, and large internal or
external rotation moment (Chapter 2). One main environmental factor, an increased
coefficient of friction (COF) of the shoe-surface interaction, leads to increased
incidence of ACL injury during sporting events involving run-to-cut maneuvers
(Chapter 2). For all the studies described previously, the surface with the higher COF
was shown to also have a higher incidence of ACL injury; however, the biomechanical
~21~
changes that an athlete adopts on a high friction surface that lead to the greater
incidence of injury have not been determined.
This study tested the hypothesis that subjects use different movement strategies
for shoe-surface conditions with a high COF relative to a low friction condition at foot
contact during a sidestep cutting task. Specifically, the study tested for significant
differences in knee flexion and abduction angles, external knee moments of flexion,
abduction, and internal rotation, and the position of the COM, all between the high and
low COF conditions. These biomechanical variables were chosen to quantify the
movement strategies because all of them are associated with increased risk of ACL
injury.
3.3. Methods 3.3.1. Subjects
Twenty-two total participants volunteered for this investigation. There were 11
male and 11 female subjects with an average age of 23.6 ± 2.7 years and body mass
index (BMI) of 23.2 ± 1.9. Subjects were regular participants at the
recreational/intramural level in sports involving sidestep cutting maneuvers, as it was
assumed that these subjects would be familiar with the sidestep cutting task. Of the 22
subjects, 11 played intramural or collegiate level soccer, 5 played collegiate lacrosse,
and 6 played collegiate competitive Ultimate Frisbee. Subjects with a history of lower
limb musculoskeletal injuries requiring surgery or any current symptoms of pain or
injury were excluded from the study. Informed written consent was obtained from all
subjects before data collection and approved by the Institutional Review Board. After
the informed consent was obtained, height, mass, and age were measured and
recorded, and it was noted if the subject chose to cut from their right or left leg during
the running task (the dominant leg).
~22~
3.3.2. Experimental Design
Subjects were asked to perform a 30° sidestep cut off from their self-selected
dominant leg under 2 different surface conditions (low and high COF) in a gait
laboratory. The running task used for this study was a sidestep cut of 30° from the
direction of travel, a common task used in studies designed to assess ACL injury risk
(Besier 2001a; Besier 2001b; Cochrane 2007; Dempsey 2007; McLean 2004a;
McLean 2005; McLean 2004b; Olsen 2004). Subjects were asked to cut on both their
left and right legs during familiarization trials to determine which leg they preferred or
their dominant leg. The 30° angle was marked by tape on the floor of the laboratory
and a marker on the wall of the laboratory to give the subjects definitive points of
reference, and the subjects practiced the task until they could hit the predefined marks.
Two different surfaces were chosen for this investigation. The first surface was
a low friction surface (COF = 0.38 ± 0.03), which was achieved by placing disposable
shoe covers inside-out over the subject’s shoes and having the subject run on the high-
pressure laminate floor surface of the gait laboratory. The second surface was a high
friction surface (COF = 0.87 ± 0.19), which was achieved by taping a thick rubber mat
to the floor of the laboratory and the force plate where the subject would be running.
The rubber mat was affixed to the floor and to the force plate with tape to prevent
motion; the section placed over the force plate was separate from the floor section to
prevent transfer of force. No shoe coverings were used in the high friction trials. The
subjects were asked to wear their own comfortable athletic shoes during the test. The
COF was calculated for each individual subject’s shoe by putting the shoe in each
surface friction condition on a force plate and then conducting a horizontal pull test of
the shoe with a 25-lb weight placed on top of it. The horizontal force (F) required to
pull the shoe across the surface divided by the normal force (N) of the shoe-weight
combination was used to calculate the COF for that shoe on that surface (F = COF *
N).
Surfaces tests were randomized. The subjects were allowed to practice on each
surface before their trials were recorded. Immediately before testing, the subjects
completed a training session on the low friction surface. During this session, the
~23~
subjects were asked to perform the cutting task several times to find the fastest
possible comfortable speed in the low friction condition. This self-selected running
speed was then chosen as the standard running speed for all surface conditions. This
protocol ensured the safety of the subjects during the low friction trials. The subjects
then completed 5 acceptable trials of the running task on each surface and were given
a 1-minute interval of rest between each trial to prevent fatigue. A trial was considered
acceptable if the subject completed the task within 0.2 m/s of the standard running
speed, achieved approximately a 30° angle during the cut (±5° by visual inspection),
and was fully recorded by the data collection system.
3.3.3. Data Collection
A markerless motion capture (MMC) system combined with 2 force plates was
used to collect full body kinematics and lower limb kinetics; the MMC system was
chosen because it does not require placing markers/fixtures on the body that could
affect the natural motion of the subject (Corazza 2009; Mündermann 2006). Video
recordings of the subject trials were captured at a frequency of 120 Hz by 8 VGA
color cameras, resolution 640 by 480 pixels (Allied Vision Technologies, Stadtroda,
Germany), and a multiple video stream acquisition system (Simi Motion Analysis,
Unterschleissheim, Germany). A 3-dimensional representation of the subject, or visual
hull, was created using a previously described volume intersection method at every
frame (Mündermann 2005). A full-body laser scan (Cyberware, Monterey, California)
provided an accurate description of the subject’s outer body surface and was used to
create a subject-specific model. The body scan was automatically divided into 15 rigid
segments with 6 degrees of freedom between adjacent segments, and the joint centers
between these body segments were identified (Corazza 2009). This model was then
matched to the visual hulls throughout the entire recorded sequence and used to extract
the locations of the joint centers of the subject using a previously described matching
process (Corazza 2006). Ground-reaction forces and moments were collected using 2
multi-component force plates (Bertec, Columbus, Ohio) recording at 120 Hz and
synchronized with the video camera system.
~24~
3.3.4. Data Analysis
Once the joint centers for the entire sequence were identified, the kinematic
and kinetic calculations were completed based on previously described methods
(Andriacchi 2003; Andriacchi 2004). Knee rotations were expressed as the angles
between 2 vectors, created along the long axes of the shank and thigh segments,
projected onto the global reference planes (Andriacchi 2003). This method of angle
calculation was validated against marker-based motion capture data as accurate at the
instant of foot contact during stance (Andriacchi 2003). To calculate external moments
at each joint center, each lower limb segment (foot, shank, thigh) was idealized to be a
rigid body. The foot was assumed to be massless, and the shank and thigh segment
inertial properties were taken from the literature (Dempster 1967). External
intersegmental moments for each trial were calculated from the joint center locations
from the MMC system, force plate data, and inertial segment data using an inverse
dynamics approach (Andriacchi 2004). Moments were normalized to body weight and
height (%Bw*Ht) to allow for comparison between subjects. Last, the COM of the
subject was approximated, assuming homogeneous density of the body, by measuring
the center of volume of the visual hull for every frame of the recorded trial and then
assuming this location as the COM. The difference between the global position of the
COM and the global position of the ankle joint center was calculated (normalized to
height, %Ht) for each frame in the sagittal and coronal planes to give a relative
measure of distance of the COM that could be compared between subjects.
The stance phase of the sidestep cut was defined as the interval when the
ground-reaction force was greater than 10 N. The kinetic measurements were
calculated during the weight acceptance phase of stance, defined as the phase from
foot contact until the first trough in the total ground-reaction force (vector summation
of Fx, Fy, and Fz) (Besier 2001b). These measurements were used because previous
work has suggested that during a single-limb landing task, the strain in the ACL
reaches a maximum value at the beginning of stance (Cerulli 2003). The kinematic
and COM measurements were calculated during the final 20% of the flight phase
~25~
preceding foot contact plus the weight acceptance phase; this was done to accurately
record peaks and troughs in the data that sometimes occurred slightly before foot
contact. Minimum knee flexion angle, maximum knee flexion moment, minimum
posterior COM, and maximum medial COM were measured. Maxima and minima,
rather than means, were measured for these variables because definitive peaks and
troughs were observed, and these extremes could represent dangerous loading
patterns. For the remaining variables, the data were averaged across the phase of
interest: weight acceptance phase for the kinetic measurements, and final 20% of
stance plus weight acceptance phase for kinematic and COM measurements. For each
biomechanical variable, one datum point per subject was calculated by measuring all 5
recorded trials and averaging these 5 values to determine the subject’s overall
performance during the testing. The approach running speed of the subject was
determined by calculating the horizontal distance traveled by the joint center of the
abdomen before initial foot contact divided by the amount of time to traverse this
distance. The final cutting angle was calculated by determining the anterior/posterior
and medial/lateral displacement of the abdomen joint center for each time point from
toe-off until the end of the recorded trial and then averaging the calculated angle
created by these displacements from the approach axis (Besier 2001b).
3.3.5. Statistical Analysis
The data for this statistical analysis were the knee flexion and abduction
angles, the 3 external knee joint moments (flexion, abduction, internal rotation), and
the relative position of the COM in the medial and lateral directions, all at foot
contact. Paired 2-tailed Student t tests with friction surface as the intertest factor were
used to detect significant differences between the 2 surface friction conditions in the
variables stated above. All statistical tests were performed in MATLAB version
R2007b (Mathworks, Natick, Massachusetts), and the significance level was set a
priori to α = .05 with a Bonferroni correction for multiple comparisons.
3.4. Re
The
between mo
was signific
trials condu
(Table 3-1,
Figure r
esults high and low
ovement stra
cantly less du
ucted on the
Figure 3-1).
3-1: Knee frepresent av
w COF cond
ategies durin
uring the fin
e high frictio
flexion anglverage of all
~26~
ditions were
ng the run-to
nal 20% of s
on condition
le during thl trials on ea
~
e associated
-cut trials. T
stance plus w
n relative to
he entire recach surface
with signific
The peak kne
weight accep
o the low fri
corded sequfor one sub
cant differen
ee flexion an
ptance phase
iction condi
ence. Data bject.
nces
ngle
e for
tion
~27~
Kinetic, Kinematic, or COM Variable
Friction Surface
Significant Difference
Low Friction High Friction
Mean SD Mean SD
Knee Flexion Angle (°) 23.38 7.6 20.60 8.3 *(p < 0.01) Knee Abduction Angle (°) 6.93 3.3 6.52 3.8 Knee Flexion Moment (%BW*Ht) 5.80 2.4 3.39 1.6 *(p < 0.001) Knee Adduction/Abduction Moment (ADDUCTION+) (%BW*Ht) 1.10 1.1 -0.10 1.8 *(p < 0.001) Knee Internal Rotation Moment (%BW*Ht) 0.50 0.4 0.53 0.5 Medial Distance COM (%Ht) 9.18 2.0 10.42 2.0 *(p < 0.001) Posterior Distance COM (%Ht) 17.70 3.0 18.14 4.0 Speed (m/s) 3.17 0.4 3.23 0.5 Cutting Angle (°) 24.09 4.4 29.35 4.0 *(p < 0.001)
Table 3-1: Variables of Interest at Foot Contact for Low and High Friction Surfaces
~28~
The average difference in knee flexion angle for the total population was a
decrease of 2.8° on the high friction condition. The individual subject response to the
change from the low to high condition ranged from a 5° increase in knee flexion angle to
a 10.5° decrease in knee flexion angle. Five subjects had a greater knee flexion angle on
the high friction surface. The trials conducted on the high friction condition were
associated with a significantly lower peak knee flexion moment during the weight
acceptance phase (Table 3-1). The average difference for the flexion moment was a
decrease of 2.4 %BW*Ht on the high friction condition. The individual subject response
to the change from the low to high condition ranged from a 3.0 %BW*Ht increase in
knee flexion moment to a 7.4 %BW*Ht decrease in knee flexion moment. Two subjects
had a greater knee flexion moment on the high friction surface. Additionally, the average
knee abduction moment was significantly higher for the high friction condition during the
weight acceptance phase; on the low friction condition, there was an average adduction
moment, while on the high friction condition, there was an average abduction moment.
The average difference in the abduction moment was an increase of 1.2 %BW*Ht on the
high friction surface. The individual subject response to the change from the low to high
condition ranged from a 4.5 %BW*Ht increase in knee abduction moment to a 2.5
%BW*Ht decrease in knee abduction moment. Two subjects had a lower knee abduction
moment on the high friction surface (Table 3-1, Figure 3-2).
Fse
m
fr
1
co
d
th
st
su
ru
3
av
Figure 3-2: equence. Da
phas
The lo
medial direct
riction condi
, Figure 3-3
ondition ran
ecrease in d
he medial d
tatistically s
ubject was s
unning task,
0° off the v
verage cuttin
External knata represene begins at
ocation of th
tion (1.2 %
ition during
3). The indi
nged from a
distance in th
direction on
ignificant (P
significantly
the subjects
vertical axis
ng angle of 2
nee adductiont average oframe 0. Ne
he COM wa
%Ht larger) f
the final 20
ividual subj
4.7 %Ht inc
he medial dir
n the high f
P = .3) betw
y different b
s were able
for the high
24° for the lo
~29~
on/abductioof all trials oegative valu
s positioned
for the high
0% of stance
ject respons
crease in dis
rection. Thr
friction surf
ween surface
between fric
to obtain the
h friction co
ow friction c
~
on moment on each sur
ues indicate
d at a signific
h friction co
e plus weigh
se to the ch
stance in the
ree subjects h
face. The d
es (Table 3-
ction conditi
e desired cu
ondition but
condition.
during the face for oneabduction m
cantly greate
ondition rel
ht acceptance
hange from
medial dire
had a decrea
difference in
-1). The cutt
ions (Table
utting angle
were only a
entire recore subject. Stmoment.
er distance i
lative to the
e phase (Tab
the low to
ection to 0.8
ase in distan
n speed wa
ting angle o
3-1). Durin
of approxim
able to obta
rded tance
in the
e low
ble 3-
high
%Ht
nce in
s not
of the
ng the
mately
ain an
F
3
du
T
fl
m
ch
in
b
2
w
ro
h
kn
an
K
et
w
m
m
Figure 3-3: E(COM
3.5. Dis
This s
uring a side
The biomech
lexion angle
medial distan
hanges assoc
njury. For ex
een reported
004; Teitz 2
with decrease
otation loadi
The re
igh COF con
nee abductio
nalysis, vide
Kanamori 20
t al. (Hewett
were at great
moments as t
moment than
Example meM) distance
scussion study suppo
estep cutting
hanical chan
and knee fl
nce of the C
ciated with a
xample, a de
d as increasi
2001). Addit
ed knee flexi
ing (Hame 2
esults showi
ndition are i
on moment
eo evidence,
00; Lloyd 2
t 2005a) fou
ter risk for
the athletes
the noninjur
easurementfrom the su
orted the hyp
g task influen
nges associa
flexion mom
COM from
a higher CO
ecreased kne
ing the risk
tionally, it h
ion angle, es
002; Markol
ing that the
important be
as increasin
, simulation
001; Marko
und that fema
an ACL in
who injured
red athletes.
~30~
ts for relativupport limb
pothesis that
nces a subje
ated with a
ment, a highe
the support
OF condition
e flexion an
of ACL inj
has been sho
specially wh
lf 1995).
abduction m
ecause nume
ng the risk
ns, and cadav
lf 1995; Shi
ale athletes w
njury than th
d their ACL
Hewett also
~
ve medial anb (defined as
at the COF o
ect’s movem
high COF
er knee abdu
t limb. Sev
n suggest an
ngle, specific
jury (Boden
own that the
hen combine
moment was
erous studies
for ACL inj
veric studie
in 2008). A
with large pe
hose with s
L had a 2.5 t
o found that
nd posteriors ankle joint
of the shoe-
ment strategy
surface we
uction mome
veral of thes
increase in
cally betwee
n 2000; McN
e strain in th
ed with abdu
s substantial
s have identi
njury, throug
s (Ford 200
prospective
eak knee ab
smaller peak
times greate
knee abduc
r center of mt center).
-surface inte
y at foot con
ere a lower
ent, and a gr
se biomecha
the risk for
n 0° and 30°
Nair 1990; O
he ACL incr
uction or inte
lly higher fo
ified an incre
gh biomecha
05; Fukuda 2
study by H
duction mom
k knee abdu
er knee abdu
tion momen
mass
erface
ntact.
knee
reater
anical
ACL
°, has
Olsen
reases
ernal-
or the
eased
anical
2003;
ewett
ments
uction
uction
nt was
~31~
a stronger predictor for ACL injury than knee flexion angle. While an increase of 1.2
%BW*Ht is smaller than previously reported values, it is clinically important because 20
of the 22 subjects responded in similar fashion, suggesting that the increase in the
abduction moment associated with the increased COF represents a strong trend that
would definitely change the risk of injury. Considering the previous studies concluding
that a higher abduction moment presents a risk factor for ACL injury, the results of this
study suggest that the increase in the abduction moment on the high friction surface could
help to explain the greater incidence of ACL injury observed in conditions that result in
high shoe-surface friction.
Some subjects exhibited different biomechanical changes at the knee from the
presented results. For knee flexion angle, 5 subjects were opposite the trend; for knee
flexion and abduction moments, 2 subjects were opposite the trend, and for medial
distance of the COM, 3 subjects were opposite the trend. These were not the same
subjects, as only 2 subjects exhibited opposite reactions in 2 or more biomechanical
variables. The differences in these subjects were not correlated with any of the other
variables measured during the study. Determining why certain subjects responded in a
different manner to the same change in stimulus would improve the clinical
understanding of movement adaptation and injury risk, but the cohort of opposite
responders in this study was too small to draw any significant or relevant conclusions.
However, because ACL injury is still a relatively rare occurrence among athletes, the
changes exhibited by the opposite responders may be indicative of why certain athletes
will eventually tear their ACL and others will not.
Overall, the results of this study suggest that subjects change their landing
mechanics before foot contact as a result of the anticipation of the change in COF. All the
biomechanical variables were evaluated at foot contact because this is the instant during
the stance phase where most ACL injuries occur, as previously reported. Evaluating the
subject at foot contact allowed us to determine the subject’s anticipatory movements that
position the limb at landing. The differences in the biomechanical variables were the
result of an anticipated stimulus due to different surface friction, with other conditions
remaining constant. Given the same initial conditions of running speed, running distance,
~32~
and so on, anticipation of the change in the COF of the surface was the only stimulus
necessary to affect a significant change in the subjects’ movement strategies.
This study showed that changing the COF of the cutting surface resulted in a
change in the subjects’ movement strategies based on the anticipation of the COF and
that the strategy adopted for the higher COF surface could increase the risk of ACL
injury. The observation that subjects can modify their movement strategies in anticipation
of the landing surface friction has several important implications when considering
methods to prevent or reduce the risk of ACL injury. The subjects required minimal
exposure to a different COF condition to adapt their patterns of movement and produce
the anticipatory changes observed during the run-to-cut task. These findings suggest that
different training mechanisms could be quickly adopted by an athlete to lower the risk of
injury on a high COF surface.
While it might be difficult to train an athlete to control a quantity such as the
abduction moment at the knee, the position of the COM might be a more feasible target
for developing training strategies to prevent injury. It has been shown that poor
neuromuscular control of the trunk during a sudden force-release task predicts knee
injury risk (Hewett 2005b; Zazulak 2007), and given the mass of the torso, poor
neuromuscular control could manifest itself in altered COM position. Our study showed
that the subjects adopted a more medial position of the COM on the higher COF
condition; it has been previously reported that during actual ACL injury events, the
position of the COM of the subject was posterior and farther from the location of the
support limb (Teitz 2001). Subtle arm movements have also been observed to influence
knee abduction moments during a run-to-cut movement, which could be indicative of a
change in the position of the COM (Chaudhari 2005). Thus, training programs focused on
positioning the COM could help an athlete run on a high COF surface with the movement
strategy and protective adaptations associated with a low COF surface. Anterior cruciate
ligament injury prevention programs focused on neuro-muscular training and control
have been successfully implemented (Gilchrist 2008; Mandelbaum 2005), suggesting that
a neuromuscular training program specifically focused on COM positioning could result
in a much lower risk of injury on high friction surfaces.
~33~
One consideration in evaluating the results of this study is that only 2 different
surface COF conditions were tested, while athletes in playing situations can experience a
much broader range of friction conditions. Additionally, the high COF surface was not as
high as some previously reported values for artificial playing surfaces (McNitt 2008).
Further, the subjects were studied in a controlled laboratory environment moving at much
slower speeds than typically observed during competitive sports play, and the sidestep
cutting task was not as taxing as the typical deceleration and pivot maneuvers seen during
athletic competitions. Even with the limitations, the statistically significant effects of
surface friction on knee kinematics, kinetics, and COM observed in this study should be
considered as potential contributing factors to the increased incidence of ACL injury in
high friction conditions.
The suggestion that the biomechanical changes observed on the high COF
condition are associated with an increased risk for ACL injury is supported by studies
describing the frequency of ACL injury on various surface conditions. For example,
95.2% of noncontact ACL injuries observed in the National Football League (NFL) over
5 seasons occurred on a dry field, which has a higher COF than a wet field; similarly,
weather conditions that led to dry fields (low amounts of rainfall and high evaporation
rates) had a higher relative risk (2.87 and 2.55 greater risk, respectively) of noncontact
ACL injury among Australian football players over 7 years of play (Orchard 2001;
Scranton 1997). Other studies that examined the effect of weather on lower limb injuries
in NFL games found that there were significantly fewer knee and ankle injuries in cold
weather than warm weather, and the authors concluded that this could be a result of the
reduced shoe-surface traction in the cold climate (Orchard 2003). Further investigations
on injury rates in the NFL for artificial turf surfaces versus grass surfaces determined that
there was a higher rate of ACL injury on older versions of AstroTurf, which has a much
larger COF than natural grass (Orchard 2003; Powell 1992). Additionally, in a video
examination of ACL injury events in team handball, Olsen et al. (Olsen 2004) determined
that more ACL injuries occurred on high COF rubber floor surfaces than wooden floor
surfaces. Thus, reducing the COFs of these artificial surfaces may result in a significant
reduction in ACL injuries. The results of this study suggest that an analysis of the
~34~
movement strategies implemented on different COF conditions could help to determine
the potential risk for ACL injury of these various shoe-surface conditions.
3.6. Conclusion Taken together with existing literature, this study supports the hypothesis that
increasing the COF of the shoe-surface condition will change a subject’s movement
strategies during a sidestep cutting task in specific ways that may increase the risk of
ACL injury, providing a biomechanical basis for the increased incidence of ACL injuries
on high friction surfaces. This study found that a high COF condition was associated with
a lower knee flexion angle, higher external knee flexion and knee abduction moments,
and greater medial distance of the COM from the support limb, all of which suggest an
increased risk for ACL injury. Therefore, the higher incidence of ACL injury observed on
high friction conditions could be a result of these biomechanical changes. The subjects
exhibited the ability to adapt quickly to surface conditions after minimal training,
suggesting that focused training mechanisms could be developed to help lower the risk of
injury on high COF surfaces. This study provided additional insight into the influence of
shoe-surface friction on the risk for ACL injury.
3.7. Acknowledgments The authors thank the volunteer subjects for their participation. Special thanks to
students Erica Holland, Nathan Fenner, Katerina Blazek, and Jenssy Rojina for their
assistance in collecting and processing the data. This research was supported by the
National Science Foundation grant #03225715.
~35~
44 Running Speed and Gender Influence Movement Strategies During a Sidestep Cutting Task on Different Friction Surfaces: Implications for ACL Injury Risk
4.1. Overview Female athletes are 4-6 times more likely to suffer an anterior cruciate
ligament (ACL) injury than their male counterparts in the same landing and cutting
sports. Also, increasing the coefficient of friction of the shoe-surface interaction has
been shown to lead to increased incidence of ACL injuries. This study tested the
hypotheses that increasing running speed prior to a single limb landing combined with
increased floor friction would alter a subject’s movement and these changes will be
altered more in females than males. Twenty-two healthy subjects (11 male) were
evaluated performing a 30° sidestep cutting task under three different conditions; on a
low friction surface at low speed, on a high friction surface at the same low speed, and
on the same high friction surface at a high speed. An 8-camera markerless motion
capture system combined with 2 force plates was used to measure full-body
kinematics, kinetics, and center of mass. The biomechanical changes associated with a
greater running speed on a high friction surface were increased knee flexion angle
(increase of 6°), increased knee flexion, adduction, and internal rotation moments
~36~
(between 0.4 %BW*Ht and 1.1 %BW*Ht), and a greater medial and posterior distance
(approximately 4 %Ht) of the center of mass from the support limb. For every
condition females exhibited significantly lower knee flexion angles (approximately 6°
lower) than their male counterparts and showed a trend towards an increased knee
abduction angle (approximately 3° greater). Increasing the running speed on a high
friction surface prior to a single limb landing alters movement in the biomechanical
variables associated with ACL injury risk. Some of these alterations suggest that that
the subjects are adopting protective mechanisms to reduce their risk of injury during
this condition while other alterations suggest that the subjects are increasing their risk.
Furthermore, the differing adaptations to the high friction surface observed at different
speeds suggest that the biomechanical causes for the higher incidence of ACL injury
on high friction surfaces change based on the speed of the maneuver. In terms of
gender, the differences in the movement strategies between females and males suggest
that women are more at risk for ACL injury during all three trial conditions.
4.2. Introduction As described in Chapter 1, the ACL is frequently injured and can lead to
premature knee osteoarthritis with or without reconstruction. Previous research has
shown that gender is a risk factor for ACL injury as female athletes are 4-6 times more
likely to suffer an ACL injury than their male counterparts in the same landing and
cutting sports. Qualitative analysis of ACL injuries suggest that these injuries
commonly occur at foot contact during a landing or deceleration movement before a
change in direction with the position of the center of mass (COM) posterior and far
from the location of the foot-to-ground contact (support limb). Quantitative studies
indicate that biomechanical measures during landing can be used to identify an
increased risk for ACL injury, specifically a small knee flexion angle, large abduction
angle or moment, and large internal or external rotation moment (Chapter 2).
One main extrinsic factor, an increased coefficient of friction (COF) of the
shoe-surface interaction, leads to increased incidence of ACL injury during sporting
events involving run-to-cut maneuvers especially among females (Chapter 2). For all
~37~
the studies described previously, the surface with the higher COF was shown to also
have a higher incidence of ACL injury. Another extrinsic factor affected ACL injury
risk is speed of movement. The influence of speed during a run to cut maneuver has
not been fully investigated as a possible risk factor for ACL injury. Previous literature
has indicated that running speed is not correlated with increased incidence of injury
(Cochrane 2007), as ACL injuries were observed at speeds ranging from a slow
jogging to sprinting. However, other studies have suggested that higher speed
movements result in more ACL injuries (Myklebust 1998; Pope 2002).
Previous research on the influence of the shoe surface friction on movement
strategies during a run to cut maneuver suggested that a high COF condition was
associated with a lower knee flexion angle, higher external knee flexion and knee
abduction moment, and greater medial distance of the center of mass from the support
limb, all of which suggest an increased risk for ACL injury and might be the cause of
the higher incidence of ACL injury on high friction surfaces (Dowling 2010).
However, this study focused on subjects cutting at low speed, and did not differentiate
the results by gender. Thus gender, landing biomechanics, and friction are all potential
risk factors for ACL injury, but the interaction of these risk factors in the context of
running speed has not yet been addressed. As such, this study tested the hypotheses
that increasing running speed prior to a single limb landing combined with increased
floor friction would alter a subject’s movement, specifically in the knee flexion and
abduction angles, external knee moments of flexion, abduction, and internal rotation,
and the position of the center of mass, and these changes will be altered more in
females than males.
4.3. Methods 4.3.1. Subjects
Twenty-two total participants volunteered for this investigation (Dowling
2010). There were 11 male and 11 female subjects with an average age of 23.6 ± 2.7
years and BMI of 23.2 ± 1.9. Subjects were regular participants at the
recreational/intramural level in sports involving sidestep cutting maneuvers. Subjects
~38~
with a history of lower limb musculoskeletal injuries requiring surgery or any current
symptoms of pain or injury were excluded from the study. Informed written consent
was obtained from all subjects prior to data collection and approved by the
Institutional Review Board. After the informed consent was obtained, height, mass,
and age were measured and recorded, and it was noted whether the subject chose to
cut off their right or left leg during the running task (the dominant leg).
4.3.2. Experimental Design
Subjects were asked to perform a 30° sidestep cut off of their self-selected
dominant leg in a gait laboratory under three different conditions; on a low friction
surface at low speed, on a high friction surface at the same low speed, and on the same
high friction surface at a high speed. The running task used for this study was a
sidestep cut of 30° from the direction of travel, a common task used in studies
designed to assess ACL injury risk (Besier 2001a; Besier 2001b; Cochrane 2007;
Dempsey 2007; McLean 2004; McLean 2005). Subjects were asked to cut on both
their left and right legs to determine their dominant leg. The 30° angle was marked in
the lab, and the subjects practiced the task until they could hit the predefined marks.
Two different surfaces were chosen for this investigation. The first surface was a low
friction surface (COF = 0.38 ± 0.03) which was achieved by placing disposable shoe
covers inside-out over the subject’s shoes. The second surface was a high friction
surface (COF = 0.87 ± 0.19) which was achieved by taping a thick rubber mat to the
floor of the laboratory and the force plate where the subject would be running. The
subjects were asked to wear their own comfortable athletic shoes during the test. The
coefficient of friction was calculated for each individual subject’s shoe by putting the
shoe in each surface friction condition on a force plate and then conducting a
horizontal pull test of the shoe with a 25 lb weight placed on top of it. Surfaces tests
were randomized and the subjects were allowed to practice on each surface before
their trials were recorded.
To determine speeds, the subjects completed a training session on the low
friction surface. During this session, the subjects were asked to perform the cutting
~39~
task several times to find the fastest possible comfortable speed in the low-friction
condition, which was then chosen as the standard running speed the low speed trials.
This protocol ensured the safety of the subjects during the low friction trials. For the
high speed trials, subjects were ask to perform the cutting task at the fastest possible
speed that they could attain within the confines of the lab, and then asked to maintain
this speed during all subsequent high speed trials. The subjects completed five
acceptable trials of the running task for each condition, and were given a one-minute
interval of rest between each trial to prevent fatigue. A trial was considered acceptable
if the subject completed the task within 0.2 meters/second of the chosen running
speed, achieved approximately a 30° angle during the cut (± 5° by visual inspection),
and was fully recorded by the data collection system.
4.3.3. Data Collection
A markerless motion capture (MMC) system combined with two force plates
was used to collect full body kinematics and lower limb kinetics; the MMC system
was chosen because it does not require placing markers/fixtures on the body that could
affect the natural motion of the subject (Corazza 2006; Mündermann 2006). Video
recordings of the subject trials were captured at a frequency of 120 Hz by eight VGA
color cameras, resolution 640 by 480 pixels (Allied Vision Technologies, Stadtroda,
Germany), and a multiple video stream acquisition system (Simi Motion Analysis,
Unterschleissheim, Germany). A 3D representation of the subject, or visual hull, was
created using a previously described volume intersection method at every frame
(Mündermann 2005). A full-body laser scan (Cyberware, Monterey, CA) was used to
create a subject-specific model with the joint centers between these body segments
identified (Corazza 2009). This model was then matched to the visual hulls and used
to extract the locations of the joint centers of the subject using a previously described
matching process (Corazza 2006). Ground reaction forces and moments were collected
using two multi-component force plates (Bertec, Columbus, OH) recording at 120 Hz
and synchronized with the video camera system.
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4.3.4. Data Analysis
Once the joint centers for the entire sequence were identified, the kinematic
and kinetic calculations were completed based on previously described methods
(Andriacchi 2003; Andriacchi 2004; Dowling 2010). Knee rotations were expressed as
the angles between two vectors, created along the long axes of the shank and thigh
segments, projected onto the global reference planes (Andriacchi 2003). To calculate
external moments at each joint center, each lower limb segment (foot, shank, thigh)
was idealized to be a rigid body. The foot was assumed to be massless, and the shank
and thigh segment inertial properties were taken from the literature (Dempster 1967).
External intersegmental moments for each trial were calculated from the joint center
locations from the MMC system, force plate data, and inertial segment data using an
inverse dynamics approach (Andriacchi 2004). Moments were normalized to
bodyweight and height (%Bw*Ht) to allow for comparison between subjects. Last, the
center of mass (COM) of the subject was approximated by measuring the center of
volume of the visual hull then assuming this location as the center of mass. The
difference between the global position of the COM and the global position of the ankle
joint center was calculated (normalized to height, %Ht) in the sagittal and coronal
planes to give a relative measure of distance of the center of mass that could be
compared between subjects.
The stance phase of the sidestep cut was defined as the interval when the
ground reaction force was greater than 10N. The kinetic measurements were
calculated during the weight acceptance phase of stance, defined as the phase from
foot contact until the first trough in the total ground reaction force (Besier 2001b),
because this is when the strain in the ACL reaches a maximum value at the beginning
of stance (Cerulli 2003).The kinematic and center of mass measurements were
calculated during the final 20% of the flight phase preceding foot contact plus the
weight acceptance phase. Minimum knee flexion angle, maximum knee flexion
moment, minimum posterior COM, and maximum medial COM were measured. For
the remaining variables, the data were averaged across the phase of interest: weight
acceptance phase for the kinetic measurements, and final 20% of stance plus weight
~41~
acceptance phase for kinematic and COM measurements. For each biomechanical
variable, one datum point per subject was calculated by measuring all five recorded
trials and averaging these five values to determine the subject’s overall performance
during the testing. The approach running speed of the subject was determined by
calculating the horizontal distance traveled by the joint center of the abdomen before
initial foot contact divided by the amount of time to traverse this distance. The final
cutting angle was calculated by determining the anterior/posterior and medial/lateral
displacement of the abdomen joint center for each time point from toe-off until the end
of the recorded trial, and then averaging the calculated angle created by these
displacements from the approach axis (Besier 2001b).
4.3.5. Statistical Analysis
The data for this statistical analysis were the knee flexion and abduction
angles, the three external knee joint moments (flexion, abduction, internal rotation)
and the relative position of the center of mass in the medial and lateral directions, all at
foot contact. A mixed-model ANOVA was used to detect significant differences
between gender and the three conditions for the variables stated above. For the
statistical analysis, gender was the between-subjects independent variable while
condition type was the within-subjects independent variable. All statistical tests were
performed in MATLAB version R2007b (The Mathworks, Natick, MA), and the
significance level was set a priori to α = 0.05 with a Bonferroni correction for
multiple comparisons.
4.4. Results Increased running speed and gender were associated with significant
differences in movement during the run to cut trials. There were significant differences
in all three conditions and between males and female for the peak knee flexion angle
during the final 20% of stance plus weight acceptance phase. Both genders exhibited a
lower knee flexion angle on the high friction surface relative to the low friction
surface (decrease of 3°) for the low speed trials, and increasing the speed resulted in a
~42~
greater knee flexion angle (increase of 6°) on the high friction surface (Table 4-1,
Figure 4-1). There were statistically significant differences between the three test
conditions (p < 0.001). In terms of gender, females displayed the described pattern of
movement changes, but for every condition females exhibited significantly lower knee
flexion angles than their male counterparts (Table 4-1). Females had about 5° lower
knee flexion during the low friction, low speed condition, 6° lower knee flexion on the
high friction, low speed condition, and 6° lower knee flexion on the high friction, high
speed condition (Table 4-1).
Additionally, the knee abduction angle was not significantly different between
the three conditions, but did show a trend towards significance between genders.
Females displayed approximately 3° greater knee abduction angle on all three
conditions compare to males, but the results were not statistically significant (p = 0.1)
(Table 4-1).
The three knee moments (flexion, abduction, internal) were all significantly
different for the three test conditions during the weight acceptance phase (Table 4-1,
Figure 4-2) but not between genders. Both genders exhibited a lower knee flexion
moment on the high friction surface relative to the low friction surface (decrease of 2.4
%BW*Ht) for the low speed trials, and increasing the speed resulted in a greater knee
flexion moment (increase of 1.1 %BW*Ht) on the high friction surface (Table 4-1).
However, the greatest knee flexion moment was measured during the low friction, low
speed condition (Table 4-1). Both genders exhibited an abduction moment on the high
friction surface and an adduction moment on the low friction surface (change of 1.2
%BW*Ht) for the low speed trials, and increasing the speed resulted in a adduction
knee moment (change of 0.4 %BW*Ht) on the high speed, high friction condition
(Table 4-1, Figure 4-2). However, the adduction knee flexion moment during the low
friction, low speed condition was larger in magnitude than during the high friction,
high speed condition (Table 4-1). Last, males and females displayed no difference in
the internal rotation moment at the knee between the low and high friction surfaces,
but increasing the speed resulted in a greater internal rotation moment on the high
friction surface for both genders (increase of 0.4 %BW*Ht) (Table 4-1, Figure 4-2).
Figu
re 4-1: Kne *
** =
e flexion an= differencdifference i
~43~
ngle at foot cce in all condin males and
~
contact by tditions (p <d females (p
total and by< 0.01) p < 0.01)
y gender.
Fig
~
gure 4-2: Kn^ = dif
= difference# = dif
nee momentfference in He in Low COfference in H
~44~
ts at foot coHigh COF, OF and HigHigh COF, H
~
ontact by totLow Speed
gh COF, lowHigh Speed
tal and by g(p < 0.01)
w speed (p <d (p < 0.01)
gender.
< 0.01)
~45~
Knee Kinetic, Kinematic,
or COM Variable
Condition Surface
Low Friction, Low Speed High Friction, Low Speed High Friction, High Speed
P-Value Male Female Male Female Male Female
Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
Flex Angle (°) 25.89 9.59 20.87 4.00 23.89 9.24 17.32 5.96 29.29 8.83 23.07 6.13 ^^p < 0.05
*p < 0.001
Abd Angle (°) 6.53 3.65 7.02 2.94 5.66 3.17 6.43 3.17 7.45 6.48 5.93 1.99 ^p = 0.1
Flex Mom (%BW*Ht)
6.22 2.23
5.45 2.65
3.25 1.49 3.51 1.81
4.88 1.68 4.30 2.25 *p < 0.001
Add/Abd Mom (ADD+) (%BW*Ht)
1.24 1.08
0.98 1.14
-0.25 2.08 0.03 1.60
0.93 2.29 0.57 2.55 *p < 0.01
Int Mom (%BW*Ht)
0.55 0.30
0.47 0.47
0.55 0.57 0.51 0.47
1.01 0.42 0.82 0.69 *p < 0.001
Medial COM (%Ht)
9.42 2.32
8.95 1.77
10.60 1.60 10.25 2.39
11.99 1.74 10.69 4.37 *p < 0.001
Posterior COM (%Ht)
17.91 3.45
17.48 2.59
17.38 3.51 18.89 4.51
19.56 3.46 24.40 14.81 *p < 0.05
Speed (m/s) 3.30 0.39 3.04 0.31 3.35 0.54 3.12 0.38 3.92 0.49 3.80 0.45 *p < 0.001Cutting Angle (°)
24.13 5.17
24.05 3.76
27.70 3.43 31.01 3.98
28.66 8.28 27.79 6.39 *p < 0.001
Table 4-1: Variables of interest at foot contact for males and females on both low and high friction surfaces. ^^Significant differences between genders; ^Trend to significant differences between genders;
*Significant differences between three trial conditions for all subjects.
~46~
The location of the center of mass (COM) was positioned at a significantly greater
distance in the medial direction for each successive test condition during the final 20% of
stance plus weight acceptance phase for all the subjects. Both genders exhibited greater
distance in the medial direction on the high friction surface relative to the low friction
surface (increase of 1.2 %Ht) for the low speed trials, and increasing the speed resulted in
a greater distance in the position of the COM (increase of 1.0 %Ht) on the high friction
surface (Table 4-1). The location of the center of mass (COM) was positioned at a
significantly greater distance in the posterior direction for the high speed, high friction
condition during the final 20% of stance plus weight acceptance phase for all the
subjects. Both genders exhibited greater distance in the posterior direction in the high
friction, high speed condition (increase of 4.0 %Ht) relative to the other two conditions
(Table 4-1).
The difference in speed was not statistically significant between males and
females during any test condition (Table 4-1), but was significantly greater during the
high speed trials (3.85 m/s) compared to the low speed trials (3.20 m/s). The cutting angle
of the subject was significantly different between the low and high friction conditions but
not between gender or at higher speed (Table 4-1). During the running task, the subjects
were able to obtain the desired cutting angle of approximately 30° off the vertical axis on
the high friction surface at both low and high speeds, but were only able to obtain an
average cutting angle of 24° for the low friction condition.
4.5. Discussion This study supported the hypothesis that increasing the running speed on a high
friction surface prior to a single limb landing alters movement in the biomechanical
variables associated with ACL injury risk, and that these changes affect females more
than males. The biomechanical changes associated with a greater running speed on a high
friction surface were increased knee flexion angle, increased knee flexion, adduction, and
internal rotation moments, and a greater medial and posterior distance of the center of
mass from the support limb. Additionally, females exhibited a decreased knee flexion
angle and a trend towards an increased knee abduction angle for all three conditions when
compared to males.
~47~
Some of the adaptations that the subjects exhibited on the high friction surface at
high speed suggest that that they adopt protective mechanisms to reduce the risk of ACL
injury during this condition. In terms of kinematic variables, a decreased knee flexion
angle between 0° and 30° has been suggested as increasing the risk of ACL injury (Boden
2000; McNair 1990; Olsen 2004; Teitz 2001), and strain in the ACL increases with
decreased knee flexion angle when combined with abduction or internal-rotation loading
(Hame 2002; Markolf 1995). The subjects displayed the largest knee flexion angle at foot
contact for the high speed, high friction condition, suggesting that the subjects reduce the
risk of injury at high speed. Additionally, the subjects exhibited a adduction moment
during the high friction, high speed condition, which could also be a protection
mechanism; numerous studies have identified an increased knee abduction moment as
increasing the risk for ACL injury, through biomechanical analysis, video evidence,
simulations, and cadaveric studies (Ford 2005; Fukuda 2003; Hewett 2005a; Kanamori
2000; Lloyd 2001; Markolf 1995; McLean 2005; Shin 2008; Shin 2007).
However, the increase in external knee moments and change in position of the
COM suggest that subjects also increase the risk for ACL injury during the high friction,
high speed condition. Previous cadaveric studies have suggested that the addition of a
significant internal rotation moment can drastically increase the strain in the ACL and
therefore increase the risk of ACL injury (Markolf 1995; Kanamori 2000). Markolf et al.
determined that a combined loading state of the knee that included an internal rotation
torque was an important loading mechanism of the ACL when the knee was in an
extended position (Markolf 1995). Specifically, this study determined that a adduction
moment of the knee at less than 30° of knee flexion significantly increased the loading in
the ACL, and an internal torque moment increased the loading in the ACL at all flexion
angles (Markolf 1995). Additionally, the external knee flexion moment was also higher
during the high friction, high speed condition when compared to the high friction, low
speed condition; this is significant as an increase in the flexion moment increased the
overall loading of the knee and can contribute to the total loading of the ACL (Markolf
1995). Furthermore, the location of the center of mass was significantly different for all
three surface conditions, and increasing the speed of the maneuver resulted in a greater
distance of the center of mass from the support limb in both the medial and posterior
~48~
directions (Table 4-1). In a previous study of video footage of ACL injuries, Teitz
reported that during the injury event, the position of the COM of the subject was posterior
and farther from the location of support limb (Teitz 2001), suggesting that an increase in
the position of the COM in the medial and posterior directions might also increase the
risk for ACL injury. The results from this study suggest that the risk for ACL injury is
increased on the high friction, high speed condition because of the increase in the internal
rotation moment combined with a higher flexion moment and a large adduction moment
while the knee is flexed less than 30°, and an change in the position of the COM in the
medial and posterior directions.
Altogether, the adaptations observed in all the subjects on the high friction surface
at low and high speeds suggest that the biomechanical causes for the higher incidence of
ACL injury on high friction surfaces changes based on the speed of the maneuver.
Cochrane et al. studied injuries in Australian football and determined that running speed
was not correlated to an increase in ACL injury rates as injuries occurred at speeds from
slow jogging to fast running/sprinting (Cochrane 2007). However, other studies have
suggested that ACL injuries occur more frequently during high speed run to cut
maneuvers (Myklebust 1998), especially on a high friction rubber surface (Pope 2002),
suggesting that increased speed does increase the risk for ACL injury. The current study
suggests that the biomechanical factors that could cause the increased incidence of ACL
injuries on a high friction surface at high speeds are an increased internal rotation
moment at the knee combined with a high flexion moment and a large adduction moment
with the knee under 30° of knee flexion, along with the increase in the medial and
posterior distance of the COM from the support limb; the previous study (Dowling 2010)
suggests that at low speeds, a lower knee flexion angle, a higher external knee flexion
and knee abduction moment, and greater medial distance of the center of mass from the
support limb could cause the increased incidence of ACL injury. Therefore, the
discrepancies noted in the speed of maneuver during an injury could be related to the
different biomechanical variables that influence the risk for injury at different speeds.
The differences in the movement strategies between females and males suggest
that women are more at risk for ACL injury during all three trial conditions. These results
support the large body of previous literature suggesting that women are more at risk for
~49~
ACL injury than men (Renstrom 2008). This study showed that females had less knee
flexion at foot contact than men for all three conditions, and decreased knee flexion
during foot contact has been previous suggested as a major risk factor for ACL injury
among women (Boden 2000; McNair 1990; Olsen 2004; Renstrom 2008). Additionally,
the trend towards an increased knee abduction angle during foot contact displayed by the
subjects in this study supports previous work suggesting that an increased abduction
angle is a risk factor for ACL injury and that women display a greater abduction angle
than men for the same movement tasks (Ford 2005; Hewett 2005a; Hewett 2006; McLean
2004; McLean 2005; Olsen 2004; Renstrom 2008; Silvers 2007). Altogether, females are
at a greater risk for ACL injury than their male counterparts on low friction surfaces at
low speeds as well as on high friction surfaces at both low and high speeds because their
movement adaptations (decreased knee flexion angle, increased knee abduction angle)
are known to increase the risk of injury.
One consideration in evaluating the results of this study is that the increased speed
of the run to cut maneuver was not as fast as standard game running, and athletes in
playing situations can experience much faster running conditions. The maximum speed of
the subjects was constrained due to the smaller size of the testing facility, and to reduce
the risk of injury for the subjects. The results measured in this study can be extrapolated
to higher speeds, but clearly indicate that speed does change the movement strategies of
the subject. Additionally, when the subject cohort was split by gender the statistical
power of the data was decreased because there were significantly fewer subjects in each
category. However, the results seen for the knee flexion angle were statistically
significant and support previous research on gender and ACL injury, as does the trend
towards a change in the knee abduction angle. Even with the gender limitations, the
results observed in this study should be considered as potential contributing factors to the
increased incidence of ACL injury in high friction conditions and among females.
4.6. Conclusion This study supports the hypotheses that increasing running speed prior to a single
limb landing combined with increased floor friction alters a subject’s movement in
biomechanical measures associated with risk for ACL injury, and these changes are
~50~
altered more in females than males. This study found that the high speed, high friction
condition resulted in an increased knee flexion angle, increased knee flexion, adduction,
and internal rotation moments, and a greater medial and posterior distance of the center
of mass from the support limb. Furthermore, the differing adaptations to the high friction
surface observed at different speeds suggest that the biomechanical causes for the higher
incidence of ACL injury on high friction surfaces change based on the speed of the
maneuver. In terms of gender, for every condition females exhibited significantly lower
knee flexion angles than their male counterparts and showed a trend towards an increased
knee abduction angle, suggesting that they are more at risk for ACL injury during all the
conditions. This study provided additional insight into the influence of speed, gender, and
shoe-surface friction on the risk for ACL injury.
4.7. Acknowledgments The authors thank the volunteer subjects for their participation. Special thanks to
students Erica Holland, Nathan Fenner, Katerina Blazek, and Jenssy Rojina for their
assistance in collecting and processing the data. This research was supported by the
National Science Foundation grant #03225715.
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55 A Wearable System to Assess Risk for ACL Injury During Jump Landing: Measurements of Temporal Events, Jump Height, and Sagittal Plane Kinematics
5.1. Overview The incidence of anterior cruciate ligament injury (ACL) remains high, and
there is a need for simple, cost effective methods to identify athletes at a higher risk
for ACL injury. Wearable measurement systems offer potential methods to assess the
risk of ACL injury during jumping tasks. The objective of this study was to assess the
capacity of a wearable inertial-based system to evaluate ACL injury risk during
jumping tasks. The system accuracy for measuring temporal events (initial contact,
toe-off), jump height, and sagittal plane angles (knee, trunk) was assessed by
comparing results obtained with the wearable system to simultaneous measurements
obtained with a marker-based optoelectronic reference system. Thirty-eight healthy
participants (20 male and 18 female) performed drop jumps with bilateral and
unilateral support landing. The mean differences between the temporal events obtained
with both systems were below 5 ms and the precisions were below 24 ms. The mean
jump heights measured with both systems differed by less than 1 mm, and the
associations (Pearson correlation coefficients) were above 0.9. For the discrete angle
parameters, there was an average association of 0.91 and precision of 3.5° for the knee
~52~
flexion angle and an association of 0.77 and precision of 5.5° for the trunk lean. The
results based on receiver-operating characteristic (ROC) also demonstrated that the
proposed wearable system could identify movements at higher risk for ACL injury.
The area under the ROC plots was between 0.89 and 0.99 for the knee flexion angle
and between 0.83 and 0.95 for the trunk lean. The wearable system demonstrated good
concurrent validity with marker-based measurements and good discriminative
performance in terms of the known risk factors for ACL injury. This study suggests
that a wearable system could be a simple cost-effective tool for conducting risk
screening or for providing focused feedback.
Portions of this chapter have been accepted for publication in the Journal of
Biomechanical Engineering by ASME: “A wearable system to assess risk for ACL
injury during jump landing: measurements of temporal events, jump height, and
saggital plane kinematics.” Dowling AV, Favre J, Andriacchi TP. ©2011 (in press).
The author contributed to this paper by collecting all of the data from the subjects,
processing the data, analyzing the data, and writing the manuscript of the paper.
5.1.1. List of Definitions
ACL anterior cruciate ligament
Xw (subscript w) wearable system
Xr (subscript r) reference system
LP landing preparation prior to initial contact
IC initial contact
MAX peak value
DIF difference between peak and initial contact (DIF = MAX - IC)
ED end of the deceleration phase
TO toe-off
A1w first peak of the maximum inferior shank acceleration
A2w maximum value of the shank acceleration norm after initial contact
ΔIC systematic delay at initial contact (ΔIC = ICr - ICw)
ΔTO systematic delay at toe-off (ΔTO = TOr - TOw)
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Δh vertical displacement (h) between the normal posture and toe-off
posture
KFE knee flexion/extension angle
TL trunk lean
ROC receiver-operating characteristic
5.2. Introduction
As described in Chapter 1, the ACL is frequently injured and can lead to
premature knee osteoarthritis with or without reconstruction. Qualitative analyses of
ACL injuries during sporting events suggest that one of the major mechanisms for
non-contact ACL injuries is a landing from a jump with either one or two legs. In
order to study this injury mechanism, researchers have designed a physical task,
known as a drop jump, which can replicate the injury mechanism in a safe controlled
laboratory setting (Ford 2003; Noyes 2005). This drop jump task has been employed
in numerous studies focused on understanding ACL injuries (Hewett 2005a; Hewett
2006), and the International Olympic Committee Medical Commission recommends
using this task to identify athletes at high risk for ACL injury (Renstrom 2008).
Quantitative analyses of ACL injuries indicate that specific kinematic measures during
jumping tasks can be used to identify a higher risk for ACL injury; specifically, a
small flexion angle and a small trunk lean angle (Chapter 2).
In terms of novel motion analysis systems, numerous wearable measurement
systems have been proposed in order to simplify the measurement of human
movements and to allow monitoring of subjects in their natural environments
(Aminian 2006). Although these systems rely on both the initial contact and the toe-
off from the jump events, no temporal error was reported regarding the detection of
these events. However, these two time points, as well as the end of the deceleration
phase, are critical for reducing the continuous knee joint angle measurements into the
discrete kinematic parameters that are related to the risk of ACL injury.
The objective for this study was to assess the capacity of a wearable inertial-
based system to measure jumping tasks as well as to assess the measurement error and
~54~
the capacity of the system to evaluate ACL injury risk. The measurement errors for
temporal event detection, jump height and sagittal plane knee and trunk kinematics
were evaluated by comparing simultaneous measurements from the wearable system
with a marker-based optoelectronic system for two jumping tasks, bilateral and
unilateral drop jumps. Then, the discriminative performance of the wearable system to
identify the movements at higher risk for ACL injury was analyzed.
5.3. Methods 5.3.1. Subjects
Thirty-eight participants volunteered for this investigation. There were 20 male
and 18 female subjects with an average age of 26.9 ± 4.3 years and BMI of 23.0 ± 2.1.
The subjects were regular participants in sports involving jumping maneuvers at the
recreational/intramural level in order to ensure that they would be familiar with
jumping tasks. Subjects with previous lower limb musculoskeletal injuries requiring
surgery or any current symptoms of pain or injury were excluded. This study was
approved by the Institutional Review Board and informed written consent was
obtained from all subjects prior to data collection.
5.3.2. Experimental Design
The subjects performed two different drop jump maneuvers in a gait
laboratory: a drop jump with a bilateral support landing and a drop jump with a
unilateral support landing. For both drop jump tasks, the subject started the maneuver
by standing on a box (36 cm high for bilateral, 20.5 cm for unilateral) in their normal
standing posture. At a signal from the investigator, they dropped directly off the box
and then immediately performed a maximum height vertical jump, raising both arms
to touch a target placed above their heads (Noyes 2005, Ford 2003). For the unilateral
support landings, subjects shifted their weight to their right leg, dropped off the box,
landed, and then performed the vertical jump with their right leg. The left leg never
impacted the ground during this jump task. The landing directly after the drop from
the box wa
would impa
subjects lan
landing, onl
each jump t
trial to prev
these jumps
simultaneou
of a camera
s used for t
act the force
nded on two
ly the right
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s, measurem
usly by the p
-based motio
F
the analysis,
plates in the
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force plate
o data collec
. Three tria
ments of low
proposed we
on capture sy
Figure 5-1: P
~55~
, and the bo
e floor during
orce plates, o
was used. T
ction and w
als were rec
wer body ki
arable system
ystem comb
Proposed w
~
ox was posi
g this landin
one foot on e
The subject
were given a
orded for b
inematics an
m and by a r
bined with tw
wearable sys
itioned so th
ng. For the b
each plate; fo
ts were allow
rest interva
both jumping
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reference sy
wo force plat
stem.
hat the subj
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for the unilat
wed to prac
al between e
g tasks. Dur
were collec
ystem consis
tes (Figure 5
ects
ding,
teral
ctice
each
ring
cted
ting
5-1).
~56~
5.3.3. Wearable System
5.3.3.1. Hardware
The wearable system (hereafter referred as subscript w) used for this study
consisted of a light portable data logger and three miniature IMUs (Physilog®,
BioAGM, CH). Two IMUs, containing a three axis gyroscope (±900°/s) and a three
axis accelerometer (±7g), were fastened to rigid lightweight plastic plates, and then
each plate was affixed onto the right thigh and shank of the subjects with elastic straps
(Favre 2010, Favre 2008). The last IMU was affixed to the chest of the subject on the
manubrium with tape. This IMU contained one gyroscope (±600°/s) that measured the
angular velocity in the sagittal plane, and two accelerometers (±7g) that measured
vertical and frontal trunk accelerations. To avoid marker occlusion with the reference
system, the data logger was fixed on the upper back using a custom holder. The
inertial sensors were recorded at 240 Hz and downloaded to a host computer at the end
of the testing session. To synchronize the data, the wearable system was connected to
the reference system by a thin cable.
5.3.3.2. Angle measurements
Multiple calibrations were performed to align the IMUs with the bone
anatomical frames and to remove error related to the positioning of the IMUs on the
subjects. For the two leg-mounted sensors, a functional calibration procedure (Favre
2009), consisting of seated passive knee movements, was performed. In order to have
a common reference frame for the thigh and shank segments, an alignment procedure
consisting of a standing hip abduction movement was completed before the jumping
tasks (Favre 2008). For the trunk, the bone anatomical frame was defined by the
vertical axis (gravity) as measured by the chest accelerometers during a neutral
standing posture and by the medial/lateral axis of the chest IMU. During the
movements, the orientation of the three segments relative to their own reference
frames were calculated using a fusion algorithm based on the acceleration and angular
velocity signals (Favre 2006). The knee flexion/extension angle (KFEw) was
calculated based on the thigh and shank orientations using Cardan angles (Grood
~57~
1983), and the trunk lean (TLw) was defined as the angle between the vertical and the
inferior/superior axis of the trunk frame.
5.3.3.3. Temporal events detection
The first peak (A1w) of the maximum inferior shank acceleration was used to
identify the initial contact event (ICw). Similarly, the maximum value (A2w) of the
shank acceleration norm after ICw was selected for the toe-off event (TOw). Since
these acceleration peaks were measured on the shank, systematic delays (ΔIC and ΔTO)
were expected with the actual events (ICw = A1w + ΔIC; TOw = A2w + ΔTO). In order to
objectively estimate the ΔIC and ΔTO delays, the jumps were randomly divided into
two groups. The first group was used to determine the delays, and the second group
was used to evaluate the method. The delays were defined as the mean value of the
difference between the occurrence of the acceleration peaks and the occurrence of the
events measured with the reference system (hereafter referred as subscript r) for all
jumps in the first group (ΔIC = ICr - ICw; ΔTO = TOr - TOw). For the rest of the study,
all ICw and TOw events were identified based on the acceleration peaks combined with
the delays (ΔIC = 41.7 ms; ΔTO = 41.7 ms). Finally, the end of the deceleration phase
(EDw) was defined as the time point when the maximum knee flexion angle occurred
between ICw and TOw (Ford 2010, Yu 2006). Similarly to ICw and TOw, the systematic
delay for EDw as compared to the reference event was determined by dividing the total
number of jumps into two groups (ΔED = -16.7 ms).
5.3.3.4. Vertical jump height
For this study, the height of the vertical jump after the landing from the box
was measured. In the literature, two approaches are used to estimate the height of a
jump. The first approach consists of measuring the flight time and then using a
ballistic formula (height = 1/8 * flight time * g; g= 9.81m/s2) to calculate what is
known as the vertical jump flight height (Bosco 1983). The second approach,
considered the gold standard (Aragon 2000), consists of performing a direct
measurement of the movement of the center of mass using a motion capture system. A
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fundamental difference between the two approaches is whether or not the vertical
displacement (Δh) from the heel-off during the propulsion phase is included in the
measurement. At toe-off, the body is already in an extended position compared to the
normal standing posture, mostly as a result of ankle plantar flexion. In this study, an
intermediate approach was selected to provide the total jump height. First, the vertical
jump flight height was calculated using the ballistic formula; flight time was defined
as the duration between the take-off (TOw) and the final landing. The final landing was
determined by the same method as ICw, using the inferior shank acceleration and ΔIC.
Then, using the reference system, Δh was determined for each jump by subtracting the
sacrum height during the neutral posture from the sacrum height at take-off (TOr). No
correlation was found between Δh and the morphology of the subjects, and so all the
values for Δh were averaged for each jump type. These averages (10.6 cm for bilateral
and 12.4 cm for unilateral) were added to the vertical jump flight height to obtain the
total vertical jump height.
5.3.4. Reference System
An optoelectronic motion capture system (Qualisys Medical, Gothenburg, SE)
with ten infrared cameras collecting at 120 Hz was used to measure the motion of 28
primary reflective markers (trunk: left and right medial clavicle; pelvis: left and right
anterior superior iliac spine, left and right iliac crest, and left and right posterior
superior iliac spine; right thigh: great trochanter plus nine markers distributed on the
front and lateral sides; right shank: lateral tibial plateau plus six markers distributed on
the front and lateral sides; right foot: lateral malleolus, lateral heel, and fifth
metatarsal) and 4 auxiliary markers (medial and lateral femoral condyles, medial tibial
plateau and medial malleolus). Two multi-component force plates (Bertec, Columbus,
OH) measured the subjects’ interaction with the ground at 1200 Hz. Prior to
completing the jumping tasks, the subjects were measured during a neutral standing
reference posture and a thigh circumduction movement with both primary and
auxiliary markers. The auxiliary markers were then removed to allow unencumbered
execution of the jumping movements. The point cluster technique was used to track
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the orientation of the pelvis, thigh, shank, and foot technical frames using the
corresponding primary markers (Andriacchi 1998). The thigh bone anatomical frame
was defined according to Cappozzo et al. (Cappozzo 1995), and the shank bone
anatomical frame was based on Dyrby and Andriacchi (Dyrby 2004). The hip joint
center was estimated from the circumduction movement using an optimization method
(Halvorsen 2003). Similarly to the wearable system, Cardan angles based on the thigh
and shank bone anatomical frame (Grood 1983) were used to describe the knee
flexion/extension angle (KFEr) during the entire jumping trial. The trunk lean (TLr)
was defined as the angle between the vertical axis and the axis joining the middle
point of the clavicle markers and the middle point of the anterior superior iliac spine
markers (Foti 2000). In order to compare the wearable and the reference angles, the
reference angles were reset during the neutral posture. The ICr and TOr events were
defined as the time points when the vertical ground reaction force exceeded and fell
below 10N respectively (Ford 2010). According to Ford et al. (Ford 2010), the EDr
event was defined as the time point corresponding to the minimum vertical height of
the pelvis between ICr and TOr. To measure the height of the vertical jump, the
position of a virtual sacrum marker was calculated by averaging the left and right
posterior superior iliac spine markers. The jump height was defined as the difference
between the height of the sacrum during the neutral posture and the maximum sacrum
height achieved during the vertical jump.
5.3.5. Data Analysis
To assess the event detection method, the difference between the time points
obtained with the wearable and the reference systems (e.g., ICw and ICr) was
calculated for all the jumps in the evaluation group. Then, for the bilateral and
unilateral jumping tasks the mean (accuracy) and standard deviation (precision) of
these differences were calculated for the three events.
The total vertical jump height measured with the wearable system was
compared to the reference jump height in terms of association and level of agreement
for both bilateral and unilateral jumping tasks. The Pearson correlation coefficient (R)
~60~
was used to characterize the association, whereas a Bland and Altman plot was used to
characterize the agreement (Bland 1999). In the presence of heteroscedasticity (p<
0.05), the accuracy and precision were calculated as the summation of a constant term
and a variable term (calculated as a percentage of the measurement). To evaluate the
concurrent validity of the measurement methods (i.e., the ability to distinguish
between groups), a paired two-tailed t-test with landing task as the inter-test factor was
used to identify significant jump height differences between the two jumping tasks.
This test was performed independently for the wearable and the reference systems.
The knee flexion angle and the trunk lean were analyzed in terms of both
pattern and amplitude. For the pattern, the similarity between the curves obtained with
both systems (e.g., KFEw and KFEr) was evaluated with the Pearson coefficient of
correlation (R). This coefficient was calculated for both angles (KFE and TL) during
each jump for the entire movement, from the drop off the box until the final landing.
The median, 25th percentile, and 75th percentile of these coefficients were then
calculated for both jumping tasks. To assess the amplitude, discrete values related to
ACL injury risk were extracted from the continuous angles. Similar to the jump
height, the values obtained with the wearable and the reference systems were
compared in term of association, agreement and concurrent validity. For the knee
flexion angle, four discrete values were considered: the angle at initial contact
(KFE(IC)), the maximum flexion angle achieved during the deceleration phase of the
landing (KFE(MAX)), the difference between the maximum flexion angle and the
flexion angle at initial contact (KFE(DIF) = KFE(MAX) - KFE(IC)), and the
minimum flexion angle attained prior to initial contact, or the landing preparation
angle (KFE(LP)). Three values were extracted for the trunk lean (TL(IC), TL(MAX)
and TL(DIF)).
Since it has previously been shown that kinematics measured with different
systems are generally not interchangeable (Ferrari 2008), differences in the knee
flexion angle and trunk lean measurements between the wearable and the reference
systems were expected. Thus, the receiver-operating characteristic (ROC) was
calculated to assess the performance of the proposed wearable system in identifying
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movements at higher risk for ACL injury (Zweig 1993). Again, because the angles can
be dependent on the measurement system, it was impractical to use previous
publications to set thresholds that would differentiate between “at risk” or “not at risk”
jumps for the reference system. Therefore, based on the literature described in the
introduction, a direction of rotation associated with a higher risk for injury was
identified for the two angles (i.e., knee extension and backward trunk lean). Then, for
each discrete angular parameter for both jump tasks, the median value was calculated
for the reference system in order to divide the movements into two groups, a higher
risk and lower risk cohort. These cohorts were used to plot the ROC of the wearable
system for each discrete angular parameter for both jump tasks. Finally, the areas
under the ROC plots were calculated to characterize the discriminative performance of
the wearable system (Zweig 1993). The values for the area under a ROC plot range
from 0.5 to 1, with 1 indicating perfect separation of the two groups with the wearable
system. All statistical tests were performed in MATLAB version R2009b (The
Mathworks, Natick, MA) and the significance level was set a priori to α = 0.05.
5.4. Results The temporal differences between the events obtained with the wearable
system and the events obtained with the reference system differed by less than 1% of
stance, and the precisions varied between 2% and 4.5% of stance. Specifically, the
mean (standard deviation) differences were -3.0 (9.1) ms for IC, -4.9 (23.0) ms for
ED, and -0.1 (13.8) ms for TO, and the duration of stance averaged 485 ms.
The bilateral and unilateral jump heights measured with both systems differed
an average of less than 1 mm, and the associations (R) between the measurement
systems were above 0.9 (Table 5-1). Heteroscedasticity was present for the unilateral
jump height, as shown in Figure 5-2. The accuracies for the averaged bilateral and
unilateral jump heights were -0.6 cm and -5.9 cm, whereas the precisions were 1.9 cm
and 1.4 cm (Table 5-1). Regarding the concurrent validity, both systems reported that
the bilateral jump heights were significantly larger than the unilateral jump heights (p
< 0.001).
Jump
Bilateral Unilateral
Table ^p < 0.001**p < 0.00
Figure 5-2b
The
are presente
(Ferrari 201
high for th
Regarding t
average asso
0.77 wherea
performed
ReferencMean (cm)
38.2** 27.0**
5-1: Jump h: significant
01: significa
2: Bland andbias and das
continuous
ed for a typic
10a), the sim
he knee flex
the amplitud
ociation of 0
as the precis
similarly an
ce We
SDMea(cm
8.2 38.8*5.1 27.5*
height meast correlation
ant differenc
d Altman anshed lines co
angles obta
cal bilateral
milarity of t
xion (KFE)
de (Table 5-3
0.91 and pre
sion was 5.5
nd indicated
~62~
arable Can
m) SD
** 7.6** 4.3
sured with wn between mce between j
nalysis of juorrespond t
ained with th
drop jump in
the angular
and good
3), the best r
cision of 3.5
5°. Regarding
d the same
~
Correlation
R
0.97^ 0.94^
wearable anmeasuremenjump for sa
ump height. to 66% limit
he wearable
n Figure 5-3
patterns for
for the trun
results were
5°. For TL, t
g the concur
e significan
EAccuracy
(cm)
-0.6 -5.9 20.20
nd referencnt systems f
ame measur
Solid line cts of agreem
e and the ref
3. According
r the entire
nk lean (TL
e obtained fo
the average
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nt (p < 0.0
Error y Precisi
(cm)
1.9 0% 1.4
e systems. for same jumrement syste
correspondsment.
ference syste
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or KFE, with
association
y, both syste
01, p < 0.0
ion )
mp em
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was
5-2).
h an
was
ems
001)
~63~
differences between bilateral and unilateral jumps except for TL(MAX), where a
significant difference was measured with the wearable system but not the reference
system, and TL(DIF), where the reference system indicated a significant different but
not the wearable system. Indeed, for the unilateral jump compared to the bilateral
jump, both systems reported that subjects had less knee flexion for all discrete values.
The area under the ROC plots for the wearable system were between 0.89 and 0.99 for
KFE and between 0.83 and 0.95 for TL at identifying the jumps that were considered
as being at risk for ACL injury (Table 5-3).
Figure 5subje
T
-3: Examplect during on
Knee Flexion
Trunk Lean
Table 5-2: Si
e of continune bilateral
para
Bilatera
Unilatera
Bilatera
Unilatera
imilarities ofor the con
~64~
uous knee fll jumping taameters ide
Median
al 0.999
al 0.997
al 0.656
al 0.786
of the patterntinuous kne
~
lexion angleask, with theentified.
n 25%
0.998
0.996
0.511
0.404
rns (R) betwee joint ang
e and trunk e discrete ti
75%
0.999
0.998
0.813
0.885
ween the sysgles.
lean for onime point
stems
e
~65~
Measured Angle Values System Evaluation
Reference Wearable Association Error
Risk Determination
Parameter Jump Mean (°) SD Mean (°) SD R Accuracy (°) Precision (°) ROC
Kne
e F
lexi
on (
KF
E) Land Prep
Bilateral 12.2* 10.2 15.9* 10.1 0.97^ -3.7 2.4 0.99 Unilateral 9.2* 6.7 12.8* 6.9 0.93^ -3.6 2.6 0.96
Contact Bilateral 21.3** 8.5 26.0** 8.0 0.92^ -4.7 3.4 0.96
Unilateral 12.0** 6.2 18.1** 6.6 0.84^ -6.0 3.6 0.90
Max Stance
Bilateral 85.3** 10.5 87.7** 10.7 0.93^ -2.4 3.9 0.96 Unilateral 62.4** 7.5 66.6** 8.1 0.85^ -4.1 4.3 0.89
Difference Bilateral 64.0** 10.9 61.8** 11.4 0.94^ 2.3 3.9 0.96
Unilateral 50.4** 7.1 48.5** 7.8 0.89^ 6.6 -9.5% 3.4 0.92
Tru
nk L
ean
(TL
)
Contact Bilateral 9.3* 9.2 13.8** 10.2 0.75^ -4.6 6.8 0.83
Unilateral 11.6* 7.0 11.3** 8.9 0.65^ 3.6 -28.6% 6.3 0.83 Max
Stance Bilateral 22.3 12.6 26.9* 11.1 0.88^ -7.8 13.1% 5.8 0.93
Unilateral 22.1 12.0 24.9* 10.7 0.88^ -5.5 11.7% 5.6 0.95
Difference Bilateral 13.1** 7.6 13.1 7.1 0.72^ 0.0 5.4 0.87
Unilateral 10.3** 7.3 13.5 7.0 0.76^ -3.1 3.0 13.8% 0.89
Table 5-3: Knee kinematic parameters measured at specific time points with both measurement systems. ^p < 0.001: significant correlation between measurement systems for same jump task
*p < 0.01, **p < 0.001: significant difference between jump task for same measurement system
~66~
5.5. Discussion Specific knee kinematic parameters during drop jumps can potentially identify
subjects at higher risk for ACL injury. However, these parameters are difficult to
measure, which limits their usefulness as a tool for risk screening or to provide focused
feedback. The purpose of this study was to propose a simple, wearable IMU-based
measurement system for analyzing drop jumps and to evaluate the efficacy of this
wearable system at identifying movements associated with a higher risk for ACL injury.
To this end, approximately two hundred jumps were simultaneously recorded with both
the wearable system and a reference system. The temporal events, vertical jump height,
and sagittal plane angles were evaluated in order to characterize this new system.
The method proposed to detect the ground contact events (IC and TO) appears to
have the capacity to detect parameters related to the risk of ACL injury. The high
accuracy (below 5 ms) and precision (below 24 ms) confirmed that the selected shank
acceleration features could be reliably identified and that there was a systematic delay
between the temporal events based on shank acceleration relative to the foot-ground
event. The precision error was about twice as high for the end of the deceleration event
(ED). This was expected because this event is not associated with a shock, but is based on
the displacement of the sacrum or on the knee flexion angle, which are both smooth
curves. Other authors have previously proposed the use of accelerometers to detect the
ground contact events during jumping tasks (Quagliarella 2010, Casartelli 2010, Elvin
2007). However, these studies did not report the errors related to the detection of the
events. Correctly identifying IC is important because the angles at this particular time
point are necessary to assess the risk for ACL injury, while a larger error for ED is
acceptable because this event is only used to define the temporal window during which
the maximum rotations are determined. Therefore, this study confirmed that the proposed
wearable system can detect the temporal events that are required to evaluate the risk of
ACL injury.
The total vertical jump heights measured with both systems were strongly
associated and had a high level of agreement (Figure 5-2 and Table 5-1). These results
confirmed the efficacy of the method in detecting the temporal events, as well as the use
of constant offsets to account for the vertical displacement during heel-off (Δh). The two
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Δh used in this study were of comparable amplitude with the systematic error previously
reported between devices that measured the flight jump height and the total jump height
(Aragon-Vargas 2000). The offsets were independent of the subjects, indicating that
either the total jump height or the flight jump height could be used for evaluation. The
heteroscedasticity observed for the unilateral jump task might be a result of the
asymmetric nature of this task or because there is a difference in pelvis posture between
take-off and landing. In comparison to Castrelli et al. (Castrelli 2010), who assessed a
system consisting of one accelerometer worn at the hip, the system proposed in this study
appeared more accurate and slightly more precise. In addition, the proposed wearable
system reported comparable precision with optical devices specifically designed to
measure flight jump height (Bosquet 2009, Glattorn 2010). The 11cm difference in jump
height between the two jumping tasks was expected since the athletes generate much less
vertical force jumping from one leg as opposed to two legs. While jump height is not
specifically a risk factor for ACL injury, it is important as a performance measurement.
The athletes who would potentially be using a wearable system for risk screening or
feedback during a training program would also certainly be interested in monitoring their
jump height. In conclusion, this study demonstrated that an IMU affixed to the shank is
an appropriate alternative for the measurement of vertical jump height and has an
advantage over standard systems because this method does not rely on a specific testing
environment and so can be used anywhere.
The associations in angle patterns between the two systems suggested that the
wearable system can measure differences in angles between subjects (Table 5-2).
However, there were small differences in the amplitude of the measurements. These
differences were expected since both systems performed completely independent
measurements; each system used a different method to track the orientation of the
segments and a different definition for the bone anatomical frames. It has been previously
shown that the choice of bone anatomical frame can strongly influence the knee angles
(Favre 2010, Ferrari 2008, della Croce 1999). In addition, to measure the orientation of
the leg segments, the wearable system used a rigid plate strapped to the subject while the
reference system used two clusters of markers as well as an algorithm to reduce the soft
tissue artifacts (Andriacchi 1998). The trunk was assumed to be a rigid segment and did
~68~
not account for concavity of the body. However, the pattern similarities reported in this
study for jumping tasks were comparable to the similarities observed with other inertial-
based systems during walking tasks (Ferrari 2010b, Favre 2009, Favre 2008). The small
differences could be explained by the higher intensity of the jump tasks as well as
differences in the study design.
For both the knee flexion and the trunk lean, the angles were similar between the
wearable and the reference systems and consistent with previous studies that measured
knee and trunk sagittal angles during bilateral drop jumps (Blackburn 2008, Blackburn
2009, Hewett 2005a, McLean 2007, Ford 2010). Like the angular patterns, the precisions
reported in this study for jumping tasks were similar to the precisions observed with other
IMU-based systems during walking tasks (Favre 2009, Favre 2008, Picerno 2008). These
results agree with previous work showing that rotations measured with different methods
are not automatically interchangeable (Ferrari 2008). But in terms of risk for ACL injury,
the concurrent validity and the ROC are more critical than the actual amplitudes of
rotation.
The wearable system had a similar capacity to detect differences between the two
jump types when compared to the reference system, providing important evidence for the
application of the wearable system. Specifically, both systems reported the same
direction of change for all of the significantly different measurements except for one.
The ability of the wearable system to distinguish between two different jumping tasks
suggests that the wearable system should also be able to identify kinematic differences
between low risk and high risk jumping movements. The area under the ROC plots
confirmed this assumption and suggest that the wearable system can identify movements
classified as higher risk by the reference system. It is worth mentioning that the area
under the ROC plots is only an evaluation, since the median thresholds of the reference
system used to separate the movements into the high and low risk cohorts were not based
on data collected from actual injuries. Finally, as shown by Hewett et al. (Hewett 2005a),
combining individual risk factors together can increase the efficacy of the prediction for
ACL injury.
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5.6. Conclusions The wearable system proposed in this study extended the functionality of inertial-
based systems to analyze jumps. It accurately detected crucial temporal events and
measured total jump height with a precision comparable to dedicated optical devices.
Additionally, the proposed system measured the knee flexion and the trunk lean, and
demonstrated good concurrent validity and discriminative performance in terms of the
known risk factors for ACL injury. Wearable systems offer many advantages over
traditional motion capture systems: they are simpler to use, do not require complex post-
processing, and make it feasible to test subjects in a natural environment. These
advantages, combined with the results reported in this study, suggest that a wearable
system could be a promising tool for conducting risk screening or for providing focused
feedback.
5.7. Acknowledgments This work was supported by an NSF graduate fellowship, the Palo Alto VA, and
the Stanford Center on Longevity. Thanks to Dr. Kamiar Aminian from EPFL for his
assistance.
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66 Characterization of Jump Landing Mechanics Based on Thigh and Shank Segment Angular Velocity: Implications for ACL Injury Risk
6.1. Overview The dynamic movements associated with anterior cruciate ligament (ACL)
injury suggest that limb segment angular velocity can provide important information
for understanding the conditions that lead to an injury. Angular velocity measures
could provide a quick and simple method of assessing injury risk without the
constraints of a laboratory. The objective of this study was to test if there is an
association between the thigh and shank angular velocities and the knee abduction
moment (a measure relevant to ACL injury). Thirty-six healthy participants (18 male)
performed drop jumps with bilateral and unilateral support landing. Thigh and shank
angular velocities were measured by a wearable inertial-based system, and external
knee moments were measured using a marker-based system. The angular velocity
parameters were able to distinguish between the two types of jumping tasks.
Furthermore, the coronal angular velocities were significantly correlated with the knee
abduction moment. The receiver-operating characteristic, used to determine the ability
of the segment angular velocity to identify movements at higher risk for ACL injury,
ranged from 0.57 to 0.78. This study showed that angular velocity could be a useful
~71~
parameter to analyze ACL injuries and that the coronal angular velocity is associated
with the knee abduction moment.
Portions of this chapter were submitted for publication to the Journal of
Orthopaedic Research by Wiley Periodicals: “Characterization of jump landing
mechanisms based on thigh and shank segment angular velocity: implications for ACL
injury.” Dowling AV, Favre J, Andriacchi TP. ©2011 (in review). The author
contributed to this paper by collecting all of the data from the subjects, processing the
data, analyzing the data, and writing the manuscript of the paper.
6.2. Introduction As described in Chapter 1, the ACL is frequently injured and can lead to
premature knee osteoarthritis. Qualitative analyses of ACL injuries during sporting
events suggest that one of the major mechanisms for non-contact ACL injuries is a
landing from a jump with either one or two legs. In order to study this injury
mechanism, researchers have designed a physical task, known as a drop jump, which
can replicate the injury mechanism in a safe controlled laboratory setting (Ford 2003;
Noyes 2005). Quantitative analyses of ACL injuries indicate the external knee
abduction moment has been reported to be a strong indicator of injury risk (Chapter 2).
Like joint moments, angular velocity could also be an important metric in
understanding the neuromuscular control and the mechanism of ACL injury. For
example, Yu et al. (Yu 2006) found that sagittal hip and knee angular velocity were
correlated to the knee joint resultant anterior-posterior force during landing from a
jump, suggesting that angular velocity could be a critical factor that affects ACL
loading. Segment angular velocity (SAV), which is complementary to joint angular
velocity, might be a more important metric in understanding the mechanism of jump
landing because it describes the movement of each segment independently (Favre
2010). Lower limb SAV has been shown to be a valuable outcome parameter for
various applications of gait analysis (Mills 2001, Cham 2002, Salarian 2004, Damiano
2006, Favre 2010). Moreover, during the landing from a drop jump, the coronal thigh
and shank SAVs might be associated with knee abduction moment since they are
~72~
closely related to the medial-lateral movement of the knee. Therefore, an assessment
of the ability of the SAVs to predict the knee abduction moment could establish SAV
as a surrogate marker for the knee abduction moment, thus enabling simple and
efficient testing for ACL injury risk. Although it might provide insight into the
understanding of the ACL injury mechanism, SAV has not been previously reported
for the lower limb segments during drop jump landing tasks.
As discussed in Chapter 2, properly measuring ACL injury risk factors requires
complex instrumentation. Some protocols have been proposed to analyze drop jumps
based on manual inspection of standard video recordings (Padua 2009b; Myer 2010b;
Myer 2010c). While these methods are simple, they still require time to analyze the
videos and might not be able to quantify subtle movements. Easy-to-use wearable
systems composed of inertial measurement units (IMUs) attached on the thigh and
shank segments have been proposed to measure knee rotation (Favre 2009). Since
these IMUs contain a three axis angular rate sensor (gyroscope), they can also be used
to measure SAV with a high degree of reliability. Using an IMU-based system, Favre
et al. (Favre 2010) recently showed that three-dimensional lower limbs SAVs are
consistent among subjects during gait and that SAV could be used to compare ankle
treatments.
The primary objective of this study was to test the hypothesis that there is an
association between coronal SAV and the knee abduction moment, and if so, to
evaluate the potential of SAV to identify the movements at higher risk for ACL injury.
In addition, this study characterized the inter-subject variations of the thigh and shank
SAVs during a jump landing and evaluated the sensibility of SAV at distinguishing
different landing mechanisms.
6.3. Methods 6.3.1. Subjects
Eighteen males and eighteen females, with an average age of 26.7 ± 4.1 years
and BMI of 23.0 ± 2.2, volunteered for this study. All were regular participants in
sports and did not have any history of previous lower limb musculoskeletal injuries
~73~
requiring surgery or any current injuries. This study was approved by the Institutional
Review Board and informed written consent was obtained from all subjects prior to
data collection.
6.3.2. Experimental Design
The subjects performed bilateral and unilateral support drop jump maneuvers
in a gait laboratory (Ford 2003, Noyes 2005). The subjects dropped directly off a box
(36cm for the bilateral, 20.5cm for the unilateral) and then immediately performed a
maximum height vertical jump. For the unilateral jumps the subjects landed on their
right leg. The landing directly after the drop from the box was used for the analysis.
Two force plates were used to record the subject’s landing, one for each foot. The
subjects practiced the jump tasks prior to data collections. Three trials were recorded
for both jumping tasks. During these jumps, the movement of the lower limbs was
measured simultaneously by a wearable system and by a camera-based motion capture
system combined with the two force plates (Figure 6-1).
Figure 6-system maconvention
6.3.3. S
The
of a light po
which conta
affixed onto
calibration p
-1: Experimarkers. Wean for SAV id
Segment
wearable sy
ortable data
ained a three
o the right
procedure w
mental setuprable system
dentified forand infer
angular
ystem (Physi
logger reco
e axis gyrosc
thigh and s
was performe
~74~
p of the wearm IMUs ider medial/latrior/superio
velocity
ilog®, BioA
ording at 240
cope (±900°/
shank of th
ed in order
~
rable systementified withteral (M-L),or (I-S) axes
AGM, CH) u
0 Hz, and tw
/s) and a thre
he subjects (
to obtain th
m and the cah white oval, posterior/as.
used for this
wo miniature
ee axis acce
(Figure 6-1)
he SAVs ind
amera-basel. Positive axanterior (P-
study consis
e IMUs each
lerometer (±
). A functio
dependent fr
ed xes A),
sted
h of
±7g)
onal
from
~75~
the fixation of the IMUs on the body segments (Favre 2009, Favre 2008). As a result,
the SAVs were expressed relative to a right handed coordinate system embedded in
each body segment. The sagittal, coronal, and transverse SAVs corresponded to the
angular velocity around the medial/lateral, posterior/anterior, and inferior/superior
axes respectively of the embedded coordinate systems (Figure 6-1).
For the analysis, the continuous thigh and shank SAVs were restricted to the
stance phase following the drop off the box. The initial contact (IC) and toe-off (TO)
events were detected using characteristic peaks of the shank acceleration (Elvin 2007),
and the end of the deceleration phase was defined as 50% of stance. Based on previous
studies (Myer 2009, Hewett 2005), three critical points on the curves were selected to
reduce the continuous SAVs: the angular velocity at initial contact (IC), the maximum
angular velocity achieved during the first half of the deceleration phase (MAX), and
the difference between the maximum angular velocity and the angular velocity at
initial contact (DIF = MAX – IC). For the thigh segment, SAV(MAX) was calculated
as a maximum velocity in the sagittal and coronal planes and as a minimum velocity in
the transverse plane. For the shank segment, SAV(MAX) was calculated as a
minimum velocity in the sagittal plane and as a maximum velocity in the coronal and
transverse planes. In addition to the amplitude, the time point of the occurrence of
MAX was reported as a percentage of the stance duration. Finally, the difference
between the discrete SAV values of the two segments (thigh – shank) were calculated
for each parameter described above to estimate the relative angular velocity between
the thigh and shank segments.
In addition to the SAVs, the rotation of the segments in the coronal plane was
analyzed during the deceleration phase of landing because it is particularly relevant to
ACL injury (Hewett 2005) and because it might be associated with the knee abduction
moment. The maximum lateral angular displacement was measured because the thigh
and shank segments first rotate laterally (positive coronal SAV) after landing. To this
end, the coronal SAV was integrated over the deceleration phase, and the maximum
displacement was defined as the maximum value of the integration.
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6.3.4. External knee moments
To measure the external knee moments, a marker-based motion capture system
(Qualisys Medical, Gothenburg, SE) with ten infrared cameras collecting at 120 Hz
and two force plates (Bertec, Columbus, OH) collecting at 1200 Hz were used. The
external moments were determined as previous described (Andriacchi 2004, Dowling
2010). Moments were normalized to percent bodyweight and height (%BW*Ht) to
allow for comparison between subjects.
The external knee abduction moment was analyzed during the stance phase
following the drop off the box. The initial contact (IC) and toe-off (TO) events were
defined as the time points when the vertical ground reaction force equaled 10N, and
the end of the deceleration was defined as the time point corresponding to the
minimum vertical height of the pelvis between IC and TO (Ford 2010). The discrete
values for the knee abduction moment were the same as the discrete values for the
SAV (IC, MAX and DIF). Subjects demonstrated two landing strategies for the knee
abduction moment; some subjects landed with primarily an abduction moment while
others landed with primarily an adduction moment. To preserve these strategies, the
average moment during the deceleration phase was calculated. If this average was an
abduction moment, then the MAX value was defined as the maximum abduction
moment; otherwise the MAX value was defined as the maximum adduction moment.
6.3.5. Data Analysis
The inter-subject variations of the SAVs were analyzed in terms of pattern and
amplitude for both leg segments during both jumping tasks. The coefficient of
multiple correlation (CMC) was used to assess the similarity between all the curves
obtained for each of the six SAVs from the two drop jump tasks (Kadaba 1989). For
the amplitude, at each discrete point (IC, MAX, DIF) the standard deviation (SD) over
all the jumps was calculated, and these SDs were compared to the total range of SAV
for the axis.
Paired Student t-tests were performed between the bilateral and unilateral SAV
discrete values and between the occurrences of the MAX angular velocity in order to
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evaluate the sensitivity of SAV to distinguish between the landing mechanisms. The
association between the knee abduction moment and the SAVs at the discrete time
points was assessed with the Pearson correlation coefficient (R). This coefficient was
also used to characterize the association between the difference in knee abduction
moment between IC and TO (DIF) and the maximum coronal angular displacement of
the segments.
Finally, the receiver-operating characteristic (ROC) was plotted to assess the
ability of the SAVs to identify movements at higher risk for ACL injury (Zweig 1993).
The median value for each knee abduction moment discrete parameter was used to
divide the jumps into two groups, a higher risk and lower risk cohort. Based on the
literature, the group with the higher knee abduction moment was assumed to represent
the landings at higher risk for ACL injury. Finally, the area under the ROC plot was
calculated to characterize the discriminative performance of the SAVs (Zweig 1993).
All statistical tests were performed in MATLAB version R2010b (The Mathworks,
Natick, MA) and the significance level was set a priori to 0.05.
6.4. Results In general, the SAV curves were well defined movement patterns in all three
planes and displayed similar patterns for both jumping tasks (Figure 6-2 and Figure 6-
3). The CMC indicated high pattern similarities between subjects for the sagittal SAVs
and moderate to good similarities for the coronal and transverse SAVs (Table 6-1).
These coefficients agreed with the ranges of SAVs, which were higher for the sagittal
and transverse planes. Regarding the amplitude, the variations between subjects were
smallest for the sagittal plane with inter-subject SDs representing approximately 10%
of the range (Table 6-2). For the transverse and coronal planes, the SDs were
approximately 20% of the range.
Figure 6-2in sagitta
Initial conindicat
: Bilateral jal, coronal, ntact (IC) isted by white
jump angulaand transve
s indicated be star. Diffe
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ar velocity cerse planes by black cirerence (DIF)
~
curves for s(axes are ac
rcle, and ma) is range b
shank and tccording to aximum stanetween IC a
high segmeFigure 6-1)nce (MAX) and MAX.
nts ). is
Figuresegments in
6-1). Iniindicat
e 6-3: Unilatn sagittal, coitial contactted by white
teral jump aoronal, andt (IC) indicae star. Diffe
~79~
angular veld transverse ated by blacerence (DIF)
~
ocity curves planes (axe
ck circle, ma) is range b
s for shank es are accoraximum staetween IC a
and thigh rding to Figance (MAX)and MAX.
ure )
~80~
Thigh Shank
Plane Jump CMC Range (°/sec)
SD (°/sec)
CMC Range (°/sec)
SD (°/sec)
Sagittal Bilateral 0.94 711.5 111.7 0.97 909.03 98.6
Unilateral 0.92 587.7 83.9 0.96 707.95 100.7
Coronal Bilateral 0.67 205.6 78.2 0.46 184.73 60.8 Unilateral 0.62 196.9 58.3 0.66 174.25 57.8
Transverse Bilateral 0.70 522.2 101.1 0.71 436.39 133.1 Unilateral 0.58 460.5 103.7 0.82 663.37 188.6
Table 6-1: Coefficients of multiple correlation (CMC) and ranges (SD) for the angular velocities of the shank and thigh segments in all three planes.
For all SAVs, the amplitude was close to or crossed zero °/sec at 50% of stance
phase, indicating that the segment stopped rotating in one direction and started rotating
in the other at this time point. During deceleration, the sagittal SAVs were positive for
the thigh and negative for the shank, indicating that the thigh is rotating backwards
and the shank is rotating forwards, resulting in knee flexion. In the coronal plane, the
SAVs were positive for both segments, suggesting that the thigh and the shank were
both rotating laterally. These similar directions of rotation didn't indicate a clear
overall rotation of the knee. The transverse SAVs were negative for the thigh and
positive for the shank, indicating external rotation for the thigh and internal rotation
for the shank and suggesting that the knee was rotating internally during deceleration.
The discrete parameters extracted at specific time points were able to
distinguish between the two types of jumping tasks (Table 6-2). There were significant
differences in the sagittal SAV at IC and MAX for the thigh, shank, and difference
between thigh and shank segments, for the DIF value of the shank, and for the timing
of MAX for the thigh segment. For the coronal plane, there were significant SAV
differences at IC and MAX for the thigh segment. In this plane, the timing of MAX
for the thigh segment was also significantly different between jumping tasks.
Regarding the transverse SAV, the differences between the jumping tasks were
significant for all the discrete values for the thigh, for IC and MAX for the shank, and
for MAX and DIF for the difference between the segments. Moreover, there were
significant differences in the occurrence of MAX for the shank.
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Thigh Shank Thigh - Shank
Time
Parameter Jump
Mean (°/sec)
SD Timepoint (% stance)
SD Mean (°/sec)
SD Timepoint (% stance)
SD Mean (°/sec)
SD
Sag
itta
l
Contact Bilateral 130.4** 53.1 -309.0** 73.5 439.4** 103.7
Unilateral 93.3** 58.4 -256.8** 77.4 350.1** 115.0
Max Stance Bilateral 301.9* 82.7 16.4** 6.2 -422.3** 58.6 4.1 1.4 724.1** 103.1
Unilateral 274.8* 61.2 13.2** 5.2 -325.9** 59.1 3.6 3.4 600.7** 82.8
Difference Bilateral 171.5 95.2 -113.3** 59.8 284.7 101.7
Unilateral 181.5 83.3 -69.1** 64.3 250.6 98.1
Cor
onal
Contact Bilateral -2.1* 28.0 11.3 45.5 -13.3 62.6
Unilateral 7.6* 29.8 6.9 38.7 0.7 46.3
Max Stance Bilateral 100.7* 49.1 15.7** 5.0 61.2 44.8 9.9 6.4 39.4 80.6
Unilateral 118.0* 43.7 10.1** 3.7 64.4 31.3 9.9 6.3 53.7 64.4
Difference Bilateral 102.7 46.3 50.0 33.1 52.8 64.7
Unilateral 110.4 52.8 57.4 36.1 53.0 57.3
Tra
nsve
rse
Contact Bilateral -28.7** 91.9 35.0** 85.1 -63.7 130.8
Unilateral 104.5** 85.9 173.1** 95.6 -68.6 111.1
Max Stance Bilateral -279.7** 82.5 13.6 5.0 195.6** 73.9 8.5** 4.8 -475.3** 106.5
Unilateral -224.9** 74.6 13.0 5.1 326.1** 101.3 4.7** 2.5 -551.0** 120.8
Difference Bilateral -251.0** 126.4 160.6 85.0 -411.6** 159.0
Unilateral -329.3** 117.5 153.1 91.2 -482.4** 137.0
Table 6-2: Angular velocity parameters measured at specific time points. *p < 0.01, **p < 0.001: significant difference between jump task
~82~
The thigh and shank coronal SAVs were significantly correlated with the knee
abduction moment for most of the discrete measurements and for all of the maximum
coronal angular displacements during the two jumping tasks (Table 6-3). The
significant correlations were always positive for the thigh segment, negative for the
shank segment, and positive for the thigh – shank measurement, which agreed with the
direction of rotation (Figure 6-4). For the significant correlations, the area under the
ROC plots were between 0.61 and 0.75 for the thigh segment, between 0.59 and 0.72
for the shank segment, and between 0.57 and 0.78 for the thigh – shank measurement
at identifying the jumps that were considered as being at higher risk for ACL injury
according to knee abduction moment values (Table 6-3).
Thigh Shank Thigh – Shank
Time
Parameter Jump R ROC R ROC R ROC
Ang
ular
Vel
ocit
y Contact Bilateral -0.04 0.54 -0.08 0.59 0.04 0.58
Unilateral 0.00 0.49 0.03 0.52 -0.02 0.55
Max Stance Bilateral 0.32** 0.61 -0.16 0.52 0.28** 0.57
Unilateral 0.38** 0.71 -0.34** 0.65 0.43** 0.72
Difference Bilateral 0.46** 0.74 -0.35** 0.66 0.51** 0.78
Unilateral 0.25* 0.61 -0.20* 0.59 0.35** 0.69
Coronal Angular Displacement
Bilateral 0.26* 0.70 -0.20* 0.60 0.26* 0.68
Unilateral 0.46** 0.75 -0.41** 0.72 0.47** 0.75
Table 6-3: Correlation (R) between knee abduction moment and coronal plane angular velocity, as well as receiver operating curves (ROC).
*p < 0.01, **p < 0.001: significant correlation between angular velocity parameter and knee abduction moment
Figure 6-knee ab
abduction moment, C
6.5. Di
The
consistent i
landings. A
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obtained for
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iscussion
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ump landin
wett 2005a;
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AVs indicat
~83~
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erse
~84~
planes (flexion and internal rotation angles). No clear tendency was apparent from the
coronal SAVs, which is also supported in the literature as various studies have
reported both knee abduction and adduction angles during the deceleration phase
(Ford 2003; Ford 2010; Hewett 2005a; Kiriyama 2009; McLean 2007; Pappas 2007;
Russell 2006; Yu 2005). Contrary to joint angles, SAVs provide an analysis of the
individual movements of the thigh and shank segments as well as their relative
movement (coordination). Moreover, SAVs characterize angular dynamic movement
instead of static movement (velocity vs. displacement). For example, this study
showed that the shank segment reached its maximum angular velocity sooner than the
thigh segment in the sagittal and coronal planes for both jump tasks, showing that the
shank segment stabilized first in these two planes. This study also reported that the
thigh and shank coronal MAXs occur simultaneously and later during stance for the
shank segment. These results support using SAVs as a complement to knee angles in
the understanding of the neuromuscular control and injury mechanisms during jump
landing tasks.
Several differences in the SAVs were noticed between the bilateral and
unilateral landing mechanics (Table 6-2). Overall, out of the eleven parameters (nine
amplitudes and two timings) extracted in each plane, between four and nine were
significantly different between jumping tasks, which is comparable to previously
reported results for knee angles (Chapter 5). In general, the changes in the SAV
amplitudes between the jumping tasks were smaller than the inter-subject variations,
suggesting that SAV is a reliable characterization of landing mechanics. The timing of
the maximum values of the SAVs also differed between jumping tasks. During the
unilateral jumps, the maximum SAV (MAX) occurred sooner in stance for the thigh
segment in all three planes as well as for the shank segment in the transverse plane.
These differences are most likely a result of faster stabilization adjustments that are
necessary in order for the subjects to successfully complete the unilateral landing. This
observation illustrates the importance of understanding the dynamic landing
mechanics like SAV because the knee is most vulnerable during the early stance phase
(Boden 2000; McNair 1990; Olsen 2004). Based on these differences, SAV could
~85~
certainly be used to identify subjects with abnormal movements and might be able to
determine if a specific subject with abnormal movement is more at risk for a future
injury. However, further investigation is necessary to determine if abnormal SAVs are
correlated to an increased incidence of injury.
Furthermore, this study tested the hypothesis that there is a relationship
between the coronal SAV and knee abduction moment. There were two motivations
behind this test. First, an association with a known risk factor such as knee abduction
moment would support the idea that SAV could be an important parameter in the
identification of landing movements at higher risk for ACL injury. Second, an
association between these two metrics would suggest that SAV could be used to
predict knee abduction moment. This association would be highly beneficial in terms
of screening or feedback for risk of ACL injury, because measuring SAV is simple
and can be performed anywhere. As hypothesized, the coronal SAVs at MAX and DIF
were significantly correlated with the knee abduction moment (Table 6-3).
Furthermore, the coronal angular displacement was also significantly correlated with
knee abduction moment. The correlations were not strong, but this is to be expected
when two complex metrics are tested only for linearity. However, it is important to
note that the sign of the correlations agreed with the biomechanics, as illustrated in
Figure 6-4. Physically, when the thigh segment has a positive coronal angular velocity,
it is rotating in the direction that will increase the abduction moment, and therefore the
correlation with knee abduction moment should be positive. On the other hand, a
positive shank angular velocity will decrease the abduction moment. Finally, a
positive difference between the thigh and shank SAVs indicates that the knee is
abducting, which corresponds to an increase in knee abduction moment.
Given these associations, the potential of the coronal SAV to differentiate
between subjects at higher risk for ACL injury (based on the knee abduction moment)
was evaluated (Table 6-3) by receiver operating characteristic (ROC) plots. On
average, the area under the ROC plots for the significant correlations was 0.69 for the
thigh segment, 0.64 for the shank segment, and 0.70 for the difference between thigh
and shank segments. These values suggest that the coronal SAV can identify
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movements classified as higher risk, but with limited sensitivity and specificity. It is
important to note that the two cohorts (lower and higher risk) were not determined
based on a knee abductiom moment threshold obtained from previous research, such
as in studies by Myer et al. (Myer 2007, Myer 2010a). Therefore, the present ROC
values constitute only a first evaluation of the discriminative potential of SAV, and
better results could be expected if a threshold obtained through a prospective study
was used. These results confirmed that SAV could be a useful parameter to analyze
jump landing movements, and that coronal SAV is associated with knee abduction
moment. This association with knee abduction moment is important because research
has shown that knee abduction moment can accurately predict future ACL injury with
high sensitivity and specificity (Hewett 2005a). Furthermore, simpler methods to
predict knee abduction moment (and by extension risk for ACL injury) have been
investigated since knee abduction moment is complex to measure. (Myer 2010a, Myer
2010b, Myer 2010c). Given the association between knee abduction moment and
SAV, it is possible that a multifaceted model relying on the biomechanical parameters
suggested by Myer et al. (Myer 2010a, Myer 2010b, Myer 2010c) but with the
addition of the coronal SAV will result in a highly sensitive and specific model of
knee abduction moment and therefore will be able to accurately predict risk for a
future ACL injury.
Although joint angles and moments are the metrics that are most widely used
to describe the biomechanics of the knee, segment angular velocity has also been
shown to be important (Cham 2002; Damiano 2006; Favre 2010; Mills 2001; Radin
1991; Salarian 2004; Yu 2006). One explanation for the limited use of SAV in the past
might be because it is highly sensitive to measurement errors when it is derived from
camera-based motion capture systems. However, now SAV can be reliably and easily
measured using inertial measurement units without spatial or temporal constraints
because these units include a triaxial gyroscope which measures the angular velocity
directly. Furthermore, functional calibration procedures have been developed to align
the gyroscope axes to the bone anatomical frame of the thigh and shank segments
(Favre 2009), making the measurements independent of the sensors’ placement on the
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segments. Additionally, the computing requirements to process the angular velocity
are minimal, making SAV very attractive as a real time measurement.
6.6. Conclusions This study reported the angular velocity of the thigh and shank segments
during bilateral and unilateral drop jumps for the first time. It showed that lower limb
SAV was consistent between subjects and therefore could be reduced down to discrete
values to describe the landing movement and compare landing mechanics during drop
jump tasks. Additionally, these results showed that there is an association between the
coronal SAV and knee abduction moment, and that the coronal SAV can differentiate
between subjects at higher risk for ACL injury. In conclusion, this study demonstrated
that SAV, which is simple to measure with inertial measurement units, is a valuable
metric to describe landing biomechanics.
6.7. Acknowledgments This work was supported by an NSF graduate fellowship, the Palo Alto VA,
and the Stanford Center on Longevity. Thanks to Dr. Kamiar Aminian from EPFL for
his assistance.
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77 Real Time Inertial-Based Feedback Can Reduce Risk for ACL Injury During Jump Landings
7.1. Overview Recent studies have shown that the incidence of ACL injury can be decreased
through the use of intervention programs, but the quality of the feedback provided to
the participants in these programs can vary depending on the skill of the observer. The
objective for this investigation was to determine if an independent inertial-based
system can be used to modify jump landing mechanics in order to decrease the risk for
ACL injury by providing real-time feedback based on known kinematic and kinetic
injury risk factors. Seventeen subjects (7 male) conducted drop jump tasks while
wearing an inertial-based measurement system that provided feedback on the relative
risk of the task in terms of the knee flexion angle, trunk lean, and thigh coronal
velocity. The subjects conducted a baseline session with no landing instructions, then
a training session where they received feedback from the system, and finally a follow-
up session where they maintained the jumping technique learned during the training
session. The baseline and follow-up sessions were then compared. The subjects
increased their knee flexion angle (16.2°), and their trunk lean after the training (17.4).
The subjects also altered the thigh coronal angular velocity by 29.4°/sec and reduced
their knee abduction moment by 0.5 %BW*Ht. There was a significant correlation (R2
= 0.55) between the change in the thigh coronal angular velocity and the change in the
knee abduction moment. The subjects reduced their risk for ACL injury after training
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with the system because there were significant positive changes in the known
kinematic and kinetic injury risk factors. This study suggests that an inertial-based
system could be used for interventional training aimed at reducing the risk for ACL
injury.
7.2. Introduction As described in Chapter 1, the anterior cruciate ligament (ACL) is the most
commonly injured ligament of the knee. As such, researchers have developed
intervention programs that can successfully decrease the incidence of ACL injury
(Chapter 2), but compliance rates can be as low as 28% (Myklebust 2003).
Furthermore, either an instructor or a physical therapist must be present to coach the
participants (Alentorn-Geli 2009b; Brophy 2010; Hewett 2006b; Renstrom 2008;
Silvers 2007). An independent and quantitative feedback system could greatly
improve these intervention programs by allowing the subjects to conduct the training
sessions on their own while still receiving consistent instructions.
Many of the successful intervention programs emphasize proper jump landing
technique. Furthermore, specific kinematic and kinetic parameters, such as a small
knee flexion angle, small trunk flexion angle, and large knee abduction moment, have
been shown to increase the risk for ACL injury during a jump landing (Chapter 2).
Measuring these parameters requires a complex setup (e.g., gait laboratory) as well as
a substantial amount of time to prepare the subject and process the data.
In the past decade, inertial-based systems have been developed to simplify the
measurement of human movement and monitor subjects in their natural environment
(Chapter 2). One such system has been recently validated; this system was primarily
focused on analyzing the knee flexion angle and trunk lean during a drop jump task
(Chapter 5). Chapter 6 also described an association between the thigh coronal angular
velocity and the knee abduction moment during a drop jump task. Altogether, the
literature suggests that an inertial-based system could be used to assist subjects during
the training sessions of intervention programs by providing quantitative feedback
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(Crowell 2011). However, it is currently unknown if subjects can respond to feedback
provided by an inertial-based system during a jumping task.
The overall objective for this study was to determine if an independent
inertial-based system can be used to modify jump landing mechanics in order to
decrease the risk for ACL injury. Quantitative real-time feedback was provided for the
knee flexion angle, trunk lean, and thigh coronal angular velocity. The first specific
hypothesis tested in this study was that subjects could respond to the real-time
feedback within a short, same-day training period. The second specific hypothesis was
that by decreasing their thigh coronal angular velocity during the deceleration phase of
the landing, the subjects would also decrease their knee abduction moment.
7.3. Methods 7.3.1. Subjects
Seventeen subjects (7 male and 10 female) with an average age of 27.5 ± 2.9
years and BMI of 22.8 ± 2.3 were selected for this study. All were regular participants
in sports involving jumping maneuvers at the recreational level. Subjects with
previous lower limb musculoskeletal injuries requiring surgery or any current
symptoms of pain or injury were excluded. This study was approved by the
Institutional Review Board and informed written consent was obtained from all
subjects prior to data collection. The subjects were unaware of the goals of the study
prior to the start of the testing session.
7.3.2. Jump Task
The jump task for this study was a bilateral support drop jump maneuver in a
gait laboratory (Ford 2003; Noyes 2005). For this task, each subject dropped off a 36
cm box, landed with both feet on the ground, and then immediately performed a
maximum height vertical jump. The landing directly after the drop from the box was
used for the analysis. The jump was considered acceptable if the subject dropped off
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the box with both feet at the same time and fully impacted a force plate embedded in
the ground with their right-side foot.
7.3.3. Feedback
7.3.3.1. Hardware
The feedback system consisted of three small inertial measurement units
(Physilog®, BioAGM, CH) affixed on the chest, thigh, and shank segment
respectively (Chapter 5). These units were connected to a computer that recorded the
signal from the inertial sensors at 240 Hz during the jump task. Using custom
software, the knee flexion angle, trunk lean, coronal thigh angular velocity, and
vertical jump height were calculated immediately after the subject completed the jump
trial (Chapter 5; Chapter 6). The technical details of this system, as well as its
validation for drop jump analysis, have been presented elsewhere (Chapter 5; Chapter
6; Favre 2009; Favre 2008; Favre 2006). Finally, a projector was used to display the
results of the jump analysis. It took less than 10 minutes to place this system on a
subject and less than five seconds to analyze a jump.
7.3.3.2. Parameters
The feedback consisted of three kinematic parameters (knee flexion angle,
trunk lean, and coronal thigh angular velocity) plus the jump height. One characteristic
feature previously identified as being associated with ACL injury was extracted from
each kinematic parameter (Chapter 5; Chapter 6). For the knee flexion angle and trunk
lean, the maximum values achieved during stance were chosen as feedback parameters
because they have been suggested as risk factors for ACL injury (Blackburn 2008;
Blackburn 2009; Griffin 2000, Hewett 2005a; Huston 2001; Yu 2005) and are
common components of intervention programs (Chappell 2007; Chappell 2008;
Herman 2009; Hewett 1999; Mandelbaum 2005; Myer 2007; Myer 2005; Myklebust
2003; Olsen 2005; Pollard 2006). For the thigh coronal angular velocity, the first
maximum (inward) peak during stance was selected because it is correlated to the knee
abduction moment (Chapter 6), which is a strong predictor of ACL injury risk (Ford
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2010; Hewett 2005a; McLean 2007; Renstrom 2008). Additionally, the jump height
was included in order to ensure that the modifications of the jump landing mechanics
did not adversely affect the performance of the jump.
7.3.3.3. Relative Risk
Based on the literature described in the introduction, a direction associated
with a higher risk for injury was determined for the three kinematic feedback
parameters (i.e., knee extension, backward trunk lean and inward thigh coronal
angular velocity). Then, the actual target values for the feedback were determined
based on previous research on healthy subjects conducting drop jumps that used the
same inertial-based system (Chapter 5; Chapter 6). Knee flexion angle and trunk lean
have been widely documented in the context of ACL injury; therefore relatively small
risk ranges were defined for these two parameters and the subjects were instructed to
be to be within those ranges. The risk ranges, [88°; 120°] for the knee flexion angle
and [25°; 60°] for the trunk lean, corresponded to the upper half [median; maximum]
of the data previously collected with healthy subjects (Chapter 5). No target range was
defined for the thigh coronal angular velocity because a risk threshold has never been
reported for this parameter nor has it been used in an intervention program. Instead,
the participants were instructed to land in a neutral manner (i.e., with the first peak of
the thigh coronal angular velocity equal to 0°/sec).
7.3.4. Experimental Design
The experimental protocol consisted of seven parts (Figure 7-1). During the
preparation, the feedback system and the reflective markers for an optoelectronic
system (Andriacchi, 1998) were placed on the subjects. The subjects then performed a
short warm-up consisting of light jogging and/or squatting. When the subjects felt
ready, calibration procedures were performed for the feedback (Chapter 5; Favre
2009; Favre 2008) and optoelectronic (Andriacchi 1998; della Croce 1999) systems.
After that, the jumping task was explained to the subjects, and they were allowed to
practice until they felt confident with the task. At this point, the subjects conducted a
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baseline testing session consisting of three drop jumps. For the baseline session, no
landing instructions were provided and the subjects were not told what the feedback
parameters would be. Following the baseline testing, the subjects completed a training
session of 15 to 20 jumps within approximately 30 minutes where they received
feedback on their jumping technique. Immediately after the training session, the
subjects conducted a follow-up session also consisting of three drop jumps. For the
follow-up testing, the subjects were asked to maintain the jumping technique that they
learned during the training session. The subjects also had the opportunity to repeat a
jump during this session if they felt that they did not successfully accomplish the
movement modification.
Figure 7-1Vertical arrow
: Experimentaws indicate wh
~94~
l protocol for ehen feedback wa
entire testing seas given to the
ession. subjects.
~95~
7.3.4.1. Training Session
Once the baseline testing was complete, the average values over the three
baseline jumps were calculated for each feedback parameter. These averages were
then projected onto the wall in front of the subject (Figure 7-2) and the four feedback
parameters were verbally explained to the subject using a standardized speech. For the
knee flexion angle and trunk lean, the low risk range was shaded green. The subjects
were also given a standardized set of movement modifications for each parameter that
would reduce their risk of injury (Table 7-1) and were told they would have between
15 and 20 jumps to incorporate the modifications into their jumping technique. The
subjects were then instructed to modify their landing mechanics in order, starting with
the knee flexion angle (if necessary), then the trunk lean (if necessary), and finally the
thigh coronal angular velocity (if necessary). Regarding the jump height, the subjects
were instructed to maintain their baseline height for all the subsequent jumping trials.
After each jump, the display was immediately updated to add the results of the latest
jump to the subject’s training history (Figure 7-2). The examiners assisted the subjects
during the training by indicating when to move on to the next feedback parameter and
by suggesting changes in the landing technique using the standardized set of
movement modifications. When the subjects achieved a jumping technique that
optimized all the feedback parameters, or when they reached 20 training jumps, they
were asked to maintain that technique for the follow-up trials.
Parameter Standardized Movement Modifications
Knee Flexion Angle
Bend knees more during landing
Land softly
Trunk Lean Bend torso more during landing
Coil like a spring
Thigh Coronal Angular Velocity
Push knees outward at the beginning of landing
Increase toe-out angle
Move feet closer together during landing
Table 7-1: Standardized set of movement modifications for training session
Figure 7-2:indicatevalues, a
7.3.5. K
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~96~
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~97~
force plate (Bertec, Columbus, OH) collecting at 1200 Hz. The point cluster technique
was used to track the orientation of the shank and foot frames (Andriacchi 1998), and
the knee abduction moment was calculated using an inverse dynamic approach
(Andriacchi 2004, Dowling 2010). The subjects demonstrated two landing strategies
for the knee abduction moment: some subjects landed with primarily an abduction
moment while others landed with primarily an adduction moment. To preserve these
strategies, the average moment during the deceleration phase of the landing was
calculated. If this average was positive (mainly in abduction), then the maximum
value (abduction peak) was reported; otherwise the minimum value (adduction peak)
was reported. To allow for comparison between subjects, the knee abduction moment
was normalized to percent bodyweight and height (%BW*Ht).
7.3.6. Statistical Analysis
For each of the five parameters considered in this study (knee flexion angle,
trunk lean, thigh coronal angular velocity, knee abduction moment, and jump height)
during both the baseline and the follow-up sessions, the values from the three jumps
were averaged in order to have one mean value per subject per session. Paired Student
t-tests (baseline vs. follow-up) were used to evaluate the effects of the training.
Additionally, the association between the change in the thigh coronal angular velocity
from baseline to follow-up and the change in the knee abduction moment from
baseline to follow-up was assessed with the Pearson correlation coefficient (R). All
statistical tests were performed in MATLAB version R2010b (The Mathworks,
Natick, MA) and the significance level was set a priori to 0.05.
7.4. Results Within a 20 jump training session, all of the subjects were able to respond to
the feedback from the inertial-based system in terms of the knee flexion angle and the
trunk lean, and most of the subjects were also able to change the amplitude of their
thigh coronal angular velocity. However for some subjects, more than one training
session would have been necessary to obtain a landing technique that satisfied all three
~98~
parameters. The feedback history for a full test (baseline to follow-up) is shown for a
typical subject in Figure 7-2. In terms of the maximum knee flexion angle, at baseline
9 subjects were outside the low risk range, and at follow-up all subjects were inside
the pre-defined range (Table 7-2, Figure 7-3). All but one subject increased their knee
flexion angle during the training (average change: 16.2°, p < 0.001), and the one
subject that did not had a relatively high baseline value (104°). The results were
similar for the maximum trunk lean. At baseline, 10 subjects were outside the low risk
range, and at follow-up all subjects were inside the range (Table 7-2, Figure 7-3). All
17 subjects increased their trunk lean during the training (average change: 17.4°, p <
0.001). In terms of thigh coronal angular velocity, at baseline 16 subjects had a
positive value (indicating an inward movement of the thigh after initial contact) and
one subject had a negative value (indicating an outward movement of the thigh after
initial contact). After training, 13 subjects landed with a more neutral thigh coronal
angular velocity, and 4 subjects were not able to complete the third modification.
Overall, the subjects altered the first peak of the thigh coronal angular velocity by
29.4°/sec (p < 0.01) (Table 7-3, Figure 7-3). The subjects also maintained the same
jump height from baseline to follow-up (p = 0.6), and the average change in jump
height was 0.5 cm.
Number at Risk
Average SD
Kne
e F
lexi
on
Ang
le (
°)
Baseline 9 88.8** 8.0
Follow-Up 0 105.0** 5.6
Change -9 16.2 10.0
Tru
nk
Lea
n (°
) Baseline 10 23.6** 8.0
Follow-Up 0 41.0** 3.8
Change -10 17.4 8.4
Jum
p H
eigh
t (c
m) Baseline 35.2 6.4
Follow-Up 35.8 7.0
Change 0.5 2.7
Table 7-2: Knee flexion angle, trunk lean, and jump height at baseline and follow-up. Number at risk indicates subjects outside the low risk ranges.
**p < 0.001: Difference between baseline and follow-up
~99~
Average SD
Thigh Coronal Angular Velocity (°/sec)
Baseline 67.7* 49.7
Follow-Up 47.6* 40.5
Change 29.4 31.6
Kne
e A
bduc
tion
M
omen
t (%
BW
*Ht)
ABD Baseline
Baseline 2.0^ 0.9
Follow-Up 1.2^ 1.5
Change -0.8 1.0
ADD Baseline
Baseline -1.7 0.8
Follow-Up -2.0 1.0
Change -0.3 1.3
Table 7-3: Thigh coronal angular velocity and knee abduction moment for both systems at baseline and follow-up. For thigh coronal angular velocity, change calculated as the average difference between baseline and follow-up (absolute
value). Knee abduction moment split into at-risk (ABD Baseline) and not-at-risk (ADD Baseline) cohorts.
*p < 0.01: Difference between baseline and follow-up ^p = 0.06: Trend to significance between baseline and follow-up
FFigure 7-3: Chaange in knee flefrom baseline
exion angle, truto follow-up. G
~100~
unk lean, and thGreen shading i
high coronal anindicates low ri
ngular velocityisk range.
y by subject
~101~
Regarding the peak knee abduction moment, the average change for all the
subjects was -0.5 %BW*Ht (p < 0.001). For further analysis, the subjects were split
into two cohorts based on their baseline values because previous work (Hewett 2005a)
has shown that only an abduction moment increases the risk of ACL injury. At
baseline, 8 subjects had an abduction (positive) moment and were classified as "at-
risk", whereas 9 subjects had an adduction (negative) moment and were classified as
"not-at-risk" (Figure 7-4). For the at-risk cohort, 6 subjects decreased their knee
abduction moment during the training (-1.2 %BW*Ht) while 2 increased their knee
abduction moment (0.4 %BW*Ht). For the entire at-risk cohort, the average change
was -0.8 BW*Ht (trend to significance: p = 0.06) (Table 7-3, Figure 7-4). Moreover, 2
of the subjects in the baseline at-risk cohort had an adduction (not-at-risk) moment
after the training. None of the subjects in the baseline not-at-risk cohort had an
abduction (at-risk) moment after the training, and the average change for this cohort
was not statistically significant (Figure 7-4). Finally, a significant correlation (R2 =
0.55, p < 0.001) was obtained between the change in the knee abduction moment and
the change in the thigh coronal angular velocity (Figure 7-5).
Figure 7follow-up
positive (a
7-4: Changep, split into abduction) p
had a neG
e in knee abat-risk and peak momeegative (add
Green shadin
~102~
bduction monot-at-risk nt at baselinduction) peang indicates
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odify their ju
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~103~
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~104~
important to note that the subjects were instructed to modify their landing mechanics
one parameter at a time and that the thigh coronal angular velocity was the final
parameter. Since the majority of the subjects were able to decrease their thigh coronal
angular velocity, this result also suggests that some subjects might need more than one
training session.
At follow-up, the subjects had significantly reduced their risk for ACL injury
in term of the kinematic risk factors. After training, the subjects increased both their
maximum knee flexion angle and their maximum trunk lean during stance. The 16.2°
increase obtained for the knee flexion angle is comparable to previous intervention
programs consisting of a single training session. Mizner et al. (Mizner 2008) reported
a change of 11.3° for female athletes instructed to increase their knee flexion angle
during a drop jump landing. Another study that investigated different combinations of
feedback during a vertical jumping task reported changes in knee flexion angle
between 27° and 40° (Oñate 2005). Although no study has directly reported the
change in trunk lean after an intervention, the 17.4° increase observed in this study
agrees with the change reported by Blackburn et al. (Blackburn 2007) for trunk flexion
angle during a controlled drop jump landing task. Furthermore, it is important to note
that in this study the amplitude of change for each kinematic parameter was driven by
the low risk range; it is assumed that with enough training, any subject could land with
an exact amplitude of knee flexion angle and trunk lean.
As hypothesized, by decreasing their thigh coronal angular velocity, the
subjects also decreased their knee abduction moment. In fact, there was a strong
association between the change in these parameters from baseline to follow-up (R2 =
0.55). This result clearly indicates that the thigh coronal angular velocity could be
related to ACL injury. Whereas Chapter 6 suggested that the thigh coronal angular
velocity could be used to differentiate subjects with high or low knee abduction
moment, this study showed that the thigh coronal angular velocity could be used to
estimate the change in the knee abduction moment as a result of an intervention. This
is important because the knee abduction moment is a complex parameter to measure
and therefore cannot be used in large-scale intervention programs. While the thigh
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coronal angular velocity alone cannot accurately predict the actual value of the knee
abduction moment, it can provide a strong overall assessment of the effect of a
movement alteration (e.g., upper body positioning or foot stance) on the knee
abduction moment and therefore on the risk for ACL injury. A more complex model
needs to be developed in order to predict the actual value of the knee abduction
moment from the thigh coronal angular velocity (Meyer 2010a).
The decrease in the knee abduction moment between baseline and follow-up
also suggested that the feedback system successfully guided the subjects to decrease
their risk for ACL injury during the training (Hewett 2005a). The average decrease of
0.8 %BW*Ht for the at-risk cohort is comparable to the results from previous
intervention programs. Mizner et al. (Mizner 2007) reported a 0.65 %BW*Ht
reduction in the knee abduction moment for female athletes instructed to land softly
and avoid knee valgus during a drop jump landing. In another study, female athletes
considered at higher risk for ACL injury displayed a decrease of 0.5%BW*Ht during a
drop jump landing after a 7 week neuromuscular training program (Myer 2007).
This study demonstrated that a real-time inertial-based system could be used
for interventional training aimed at reducing the risk of ACL injury. This system
provides immediate feedback as to which movement modifications and jumping
strategies are most effective for the individual subject, and therefore the movement
modifications can be customized for each subject. Furthermore, the system provides
quantitative feedback that can be saved to monitor the subject’s progress or to
compare subjects. Another benefit of this system is that it is independent and does not
require a trained observer to administer the feedback. In terms of user-friendliness, the
system used in this study consisted of only three small inertial measurement units and
a computer, required just a few minutes to set up, and automatically analyzed the data
after each jump. Therefore it would be ideal for decentralized use, such as in a home
setting. This system could increase the compliance rates of interventional training
because the participant would not have to travel to receive the training. To improve
the usability of this system in a home setting, future iterations might not require a
~106~
computer for the analysis or could suggest movement modifications in the form of a
video game.
One limitation of this study was that it included only a small number of healthy
subjects. Further research should be conducted with a larger cohort of potentially at-
risk subjects, such as young female athletes, to determine how these subjects respond
to training with the feedback system. Furthermore, the target values for the
intervention (low risk ranges for the knee flexion angle and trunk lean, neutral landing
for the thigh coronal angular velocity) were based on previous research conducted
with healthy subjects. While these target values were appropriate for the healthy
population enrolled in this study, a full prospective study using the inertial-based
system is necessary to determine the target values that correspond to actual risk for
ACL injury.
In this study only three parameters were used for the feedback because it was
anticipated that the subjects would not be able to modify more than three parameters
during a single training session. However, there are many other known ACL injury
risk factors that could be included in the feedback in order to improve the intervention.
This inertial-based system can also measure knee angles in the coronal and transverse
plane (Favre 2009) as well as velocity in all three planes (Favre 2010) without
modifications to the hardware or the data collection procedure. However, before
increasing the number of feedback parameters, an overall risk assessment “score”
should be developed in order to combine the disparate risk factors. This type of overall
score would help the subjects to understand their level of risk in a concise manner.
Additionally, this score could be used to classify the feedback parameters according to
their importance. One of the difficulties in developing an overall risk score is that a
large amount of data from many subjects is needed to build a reliable risk assessment
model. However, the system used in this study can also effectively collect quantitative
data and therefore could be used to develop an overall risk assessment score. Finally,
the inertial-based system is a simple system that can quickly and easily collect data
from many subjects in a natural environment, and therefore it would be an ideal
system to prospectively screen subjects for ACL injury risk.
~107~
7.6. Conclusions This investigation determined that an independent inertial-based system can be
used to modify jump landing mechanics in order to decrease the risk for ACL injury.
The subjects could effectively respond to feedback from this system in a short training
session. Furthermore, the subjects reduced their risk for ACL injury after training with
this system because there were significant increases in the maximum knee flexion
angle and the maximum trunk lean. The subjects also reduced their risk for injury by
decreasing their thigh coronal angular velocity, which was correlated with a decrease
in their knee abduction moment. This study suggests that an inertial-based system
could be used for interventional training aimed at reducing the risk for ACL injury.
This system is independent, simple to set up and use, and automatically provides
quantitative feedback to the subject. Therefore it would be an ideal system for use in a
home setting or to prospectively screen subjects for ACL injury risk.
7.7. Acknowledgments This work was supported by an NSF graduate fellowship, the Palo Alto VA,
and the Stanford Center on Longevity. Thanks to Dr. Kamiar Aminian from EPFL for
his assistance.
~108~
88 Summary
8.1. Overall Conclusions
The overall goal of this dissertation was to use novel motion analysis systems
to investigate the underlying mechanisms that cause an anterior cruciate ligament
(ACL) injury and then to explore movement modification methods that might prevent
ACL injuries from occurring. The results from multiple experimental studies that used
two novel motion analysis systems have been presented in the preceding chapters.
These results add to the understanding of the ACL injury mechanism and also suggest
potential preventative methods that could decrease the overall incidence of ACL
injury.
Chapter 3 analyzed how subjects change their movement strategies for shoe-
surface conditions with a high coefficient of friction relative to a low friction condition
and how these changes in movement strategies affected their risk for ACL injury. This
study found that a high COF condition was associated with a lower knee flexion angle,
higher external knee flexion and knee abduction moments, and greater medial distance
of the COM from the support limb, all of which suggest an increased risk for ACL
injury. The primary conclusions were that increasing the COF of the shoe-surface
condition will change a subject’s movement strategies during a sidestep cutting task in
specific ways that may increase the risk of ACL injury, providing a biomechanical
basis for the increased incidence of ACL injuries on high friction surfaces.
Chapter 4 investigated how increasing running speed prior to a single limb
landing combined with increased floor friction alters a subject’s movement as well as
how these alterations are different between males and females. This study found that
the high speed, high friction condition resulted in an increased knee flexion angle,
increased knee flexion, adduction, and internal rotation moments, and a greater medial
~109~
and posterior distance of the center of mass from the support limb. Furthermore, the
differing adaptations to the high friction surface observed at different speeds suggest
that the biomechanical causes for the higher incidence of ACL injury on high friction
surfaces change based on the speed of the maneuver. In terms of gender, for every
condition females exhibited significantly lower knee flexion angles than their male
counterparts and showed a trend towards an increased knee abduction angle,
suggesting that they are more at risk for ACL injury during all the conditions.
Chapter 5 explained the development and assessment of a wearable inertial-
based system to measure jumping tasks in terms of temporal event detection, jump
height, and knee angles. The wearable system proposed in this study extended the
functionality of inertial-based systems to analyze jumps. It accurately detected crucial
temporal events and measured total jump height with a precision comparable to
dedicated optical devices. Additionally, the proposed system measured the knee
flexion and the trunk lean, and demonstrated good concurrent validity and
discriminative performance in terms of the known risk factors for ACL injury.
Chapter 6 described the characterization of the thigh and shank angular
velocity during a jump landing and the association between coronal angular velocity
and knee abduction moment. This study reported the angular velocity of the thigh and
shank segments during bilateral and unilateral drop jumps for the first time. It showed
that lower limb SAV was consistent between subjects and therefore could be reduced
down to discrete values to describe the landing movement and compare landing
mechanics during drop jump tasks. Additionally, these results showed that there is an
association between the coronal SAV and knee abduction moment, and that the
coronal SAV can differentiate between subjects at higher risk for ACL injury.
Chapter 7 determined that an independent inertial-based system can be used to
modify jump landing mechanics in order to decrease the risk for ACL injury. The
subjects could effectively respond to feedback from this system in a short training
session. Furthermore, the subjects reduced their risk for ACL injury after training with
this system because there were significant increases in the maximum knee flexion
angle and the maximum trunk lean. The subjects also reduced their risk for injury by
~110~
decreasing their thigh coronal angular velocity, which was correlated with a decrease
in their knee abduction moment. This study suggests that an inertial-based system
could be used for interventional training aimed at reducing the risk for ACL injury.
8.2. Contributions
This thesis made significant contributions to the scientific knowledge on ACL
injuries and ACL injury prevention. Chapters 3 and 4 expanded the understanding of
the ACL injury mechanism on different coefficient of friction surfaces. This work
illustrated how subjects change their movement strategies (in terms of specific
biomechanical variables) as a result of changing the surface coefficient of friction
during a run to cut task. Furthermore, this work provided a biomechanical basis for the
increased incidence of ACL injuries on high friction surfaces. This thesis also
illuminated the biomechanical differences between male and female athletes during
cutting as a result of surface friction, and provided additional evidence as to why
females are more at risk for ACL injury.
Chapter 5 characterized the use of a wearable inertial-based motion analysis
system during a jumping task. This study was the first investigation to describe the use
of this type of motion analysis system during jumping tasks, specifically in terms of
temporal event detection, jump height, and sagittal plane angles. Furthermore, the
results showed that the proposed system could be used to determine increased risk for
ACL injury, suggesting that this simple system could be a promising tool for
conducting risk screening in a natural environment.
Chapter 6 established thigh and shank angular velocity as important parameters
to analyze jump landing mechanics. This work showed that angular velocity had a
distinctive pattern that characterized the dynamics of the movement and added new
information about the movement of the lower limbs. Furthermore, this study was the
first to show an association between the thigh angular velocity in the coronal plane
and the knee abduction moment. This association suggests that angular velocity could
be related to the risk of ACL injury because it is correlated with an important known
~111~
ACL injury risk factor. This study further supports the importance of measuring
angular velocity during a jump landing movement.
Chapter 7 demonstrated that an independent inertial-based system can be used
to modify jump landing mechanics. This system efficiently reduced the risk for ACL
injury by providing real-time feedback in terms of known kinematic and kinetic risk
factors. This was one of the first studies to train subjects by using quantitative
feedback primarily from an inertial-based system, showing that a simple feedback
system could be used outside of a research environment. Furthermore, this study
demonstrated an association between the change in the thigh coronal angular velocity
and the change in the knee abduction moment. These results show that thigh coronal
angular velocity can provide a strong overall assessment of the effect of a movement
alteration on the knee abduction moment and therefore on the risk for ACL injury.
~112~
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