WH2014 Session: Quantifying energy expenditure of wheelchair-based physical

QUANTIFYING ENERGY EXPENDITURE OF

WHEELCHAIR-BASED PHYSICAL ACTIVITIESIN FREE-LIVING ENVIRONMENTS

SHIVAYOGI HIREMATH

UNIVERSITY OF PITTSBURGH

Quantifying Energy Expenditure of Wheelchair-based Physical Activities in Free-living Environments

Shivayogi Hiremath, Stephen Intille Rory Cooper, Dan Ding Wireless Health 2014 Bethesda, MD, USA

Introduction • Lack of regular physical activity (PA) is a major

public health concern

• Regular PA is crucial in wheelchair users as it is associated with

– Aerobic Capacity and Flexibility – Muscular strength and Endurance – Improved psychological well-being – Risk of cardiovascular disease and other chronic

conditions • Regular PA levels in wheelchair users are low

– Environmental barriers – Lack of accessible equipment – Physiological changes

© Shivayogi Hiremath 2010-14

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Introduction • Research has shown that it is possible for

wheelchair users to attain PA levels recommended by the ACSM1

– Wheelchair basketball, tennis, Handcycling

• Self-monitoring of diet, PA and body weight can assist in maintaining a healthy lifestyle

• Availability of an activity monitor will allow wheelchair users to achieve optimal regular PA leading to a healthy and active lifestyle

3 © Shivayogi Hiremath 2010-14

Background • Sensor-based PA monitors have been used

to track and quantify PAs among wheelchairs users 2-6

– None of these monitors can capture and provide real-time feedback to the user

Approach • Real-time feedback enabled by

advancements in m-Health • Meaningful feedback to wheelchair users

© Shivayogi Hiremath 2010-14 4


Physical Activity Monitoring System

Wocket7

G-WRM8

PAMS

Testing of PAMS in MWUs • 45 MWUs with SCI

– Laboratory (n=25) & NVWG 2012 (n=20) – Home (n=20)

• Activities Performed – Resting – Arm-Ergometry – Darts, Basketball – Deskwork, Watching TV – Folding Clothes, Laundry – Food Preparation, Eating Simulation – Propulsion: Carpet, Tile, Ramp, Home – Resistance: Band, Dumbbell, Handgrip – Making Bed, Floor Sweeping – Cleaning Room, Vacuuming – Wheelchair Pushups


6

Model Development • Two step classification algorithm to detect

wheelchair based PAs • Activity-specific energy expenditure

estimation models – Resting – Arm-ergometry – Household activities – PAs that might involve wheelchair movement – Wheelchair propulsion – Caretaker pushing – Basketball


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Results

-1

-0.5

0

0.5

1

1.5

2

-3

-2

-1

0

1

2

3

4

5

6

1 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7 8 8 8 8 9 9 9 9 10 10 10 10 11 11 11 11 11

Dis

tan

ce T

rave

lled

in m

/10

s

Acc

eler

atio

n in

m/s

2

Activity Trials

Mean Acceleration and Distance Travelled WocketArmDistTravelled

Resting Arm-Ergometry Darts Deskwork Folding Propulsion Invest Resist- Clothes Carpet Moderate Slow Pushing ance


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Results Demographic Characteristics

Age 41.0 ± 12.6 years

Gender 39 males; 6 females

Weight 78.1 ± 18.1 kg

Height 1.8 ± 0.1 m

SCI Level C5 to L5

Completeness 22 (Complete); 23(Incomplete)

Wheelchair use 12.6 ± 8.6 years

Smokers 14

PA participation 36 regular; 5 occasional; 4 no regular PA


Results

10

00.5

11.5

22.5

33.5

44.5

5

EE

Energy Expenditure for Various Activities

EE in kcal/min


Results: Classification 80-20CV


Training Accuracy Testing Accuracy Models

0.9356 0.9801 SVM

• First level classification

M/NM Training Accuracy Testing Accuracy Model

NM 0.8678 0.8495 J48

M 0.9557 0.9340 SVM

• Second level classification

• Classification into seven PAs showed that PAMS had an overall accuracy of 89.3%

Results: Confusion Matrix


True\Predict R AE NM Prop Push Bask MM

Resting 22 0 24 0 0 0 1

AE 0 85 6 0 0 0 1

PAs NM 3 2 135 0 0 0 0

Propulsion 0 0 0 134 1 0 0

Pushing 0 0 0 2 44 0 1

Basketball 0 0 1 5 1 17 6

MM 0 0 0 0 0 0 12 12

Results: Mean Signed Error

13

-50-40-30-20-10

01020304050

Per

cen

tage

Err

or (

%)

Wheelchair based Physical Activities

Plot of Mean Signed Error for PAMS

PAMS


EE estimation error post-classification showed that the overall EE error for PAMS was -9.8% (0.1%).

Results: Mean Absolute Error

14

05

101520253035404550

Per

cen

tage

Err

or (

%)

Wheelchair based Physical Activities

Plot of Mean Absolute Error for PAMS

PAMS


Results: Metabolic Equivalent Tasks

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METs based Intensity Actual mins Estimated mins K4b2 PAMS

Light Intensity 387 406 Moderate Intensity 116 97 High Intensity 5 3


Discussion

• Frequency based features were picked for classification algorithms for certain PAs

• Majority of the regression equations chose demographic characteristics to estimate EE

• Multi-modal information from PAMS can be utilized to quantify wheelchair-based activities in laboratory and community

• Limitations –Active population –Movement based variables


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Future Work


• Evaluation of PAMS in community settings

• Evaluation of PAMS in community along with DLW

• Expand to other population • Use social networks such as Facebook

– Combine self-monitoring with social support

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1. American College of Sports Medicine, Mitchell H. Whaley, Peter H. Brubaker, Robert Michael Otto, Lawrence E. Armstrong ACSM's guidelines for exercise testing and prescription, 7, illustrated ed.: Lippincott Williams & Wilkins, 2005.

2. C. A. Warms and B. L. Belza, "Actigraphy as a measure of physical activity for wheelchair users with spinal cord injury," Nursing Research, vol. 53, pp. 136-43, 2004.

3. S. E. Sonenblum, S. Sprigle, J. Caspall, and R. Lopez, "Validation of an accelerometer-based method to measure the use of manual wheelchairs," Medical Engineering and Physics, vol. 34, pp. 781-86, 2012.

4. M. L. Tolerico, D. Ding, R. A. Cooper, D. M. Spaeth, S. G. Fitzgerald, R. Cooper, et al., "Assessing mobility characteristics and activity levels of manual wheelchair users," Journal of Rehabilitation R and D, vol. 44, pp. 561-72, 2007.

5. M. Lee, W. Zhu, B. Hedrick, and B. Fernhall, "Estimating MET values using the ratio of HR for persons with paraplegia," Med Sci Sports Exerc, vol. 42, pp. 985-90, 2010.

6. E. H. Coulter, P. M. Dall, L. Rochester, J. P. Hasler, and M. H. Granat, "Development and validation of a physical activity monitor for use on a wheelchair," Journal of Spinal Cord, vol. 49, pp. 445-450, 2011.

7. S. S. Intille, F. Albinali, S. Mota, B. Kuris, P. Botana, and W. L. Haskell, "Design of a Wearable Physical Activity Monitoring System using Mobile Phones and Accelerometers," in Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society. , Boston, MA, 2011, pp. 3636-39.

8. S. V. Hiremath, D. Ding, and R. A. Cooper, "Development and evaluation of a gyroscope based wheel rotation monitor for manual wheelchair users.," Spinal Cord Medicine, vol. 36, pp. 347-356, 2013.


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References

Acknowledgements

• Switzer Research Fellowship (H133Fl10032), NIDRR, Department of Education

• Department of Defense (W81XWH-10-1-0816) • RERC on Interactive Exercise Technologies

and Exercise Physiology for Persons with Disabilities (H133E070029), NIDRR

• VA Center of Excellence for Wheelchairs and Associated Rehabilitation Engineering (B3142C)


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WH2014 Session: Quantifying energy expenditure of wheelchair-based physical