Shanshan Chen, Christopher L. Cunningham, John Lach UVA Center
for Wireless Health University of Virginia BSN, 2011 Extracting
Spatio-Temporal Information from Inertial Body Sensor Networks for
Gait Speed Estimation 1 Bradford C. Bennett,
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Research Statement 2 Signal processing challenge to obtain
accurate spatial information from inertial BSNs Gait speed as an
example to extract accurate spatio-temporal information Gait speed
is the No. 1 predictor in frailty assessment require high gait
speed accuracy desire for continuous, longitudinal gait speed
monitoring
Inertial BSN for Gait Speed Estimation 4 TEMPO 3.1 inertial BSN
platform developed at the University of Virginia
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Contributions 5 Refined human gait model by leveraging
biomechanics knowledge Improve accuracy without increasing signal
processing complexity Mounting calibration procedure to correct
mounting error Practical in experiments Improved gait speed
estimation accuracy by combining the two methods
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Outline 6 Current Gait Speed Estimation Method Gait Cycle
Extraction and Integration Drift Cancelation Stride Length
Computation by Reference Model Refined Human Gait Model Mounting
Calibration Experiment & Results
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Gait Cycle & Integration Drift Cancelation 7 Gyroscope
signals on the sagittal plane Use foot on ground to find gait cycle
boundaries Numerically easy to pick up local maximum Helpful for
canceling integration drift Shank angle is near zero and does not
contribute to the stride length calculation when foot is on ground
Assume linear drift
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Stride Length Computation 8 Reference Model S. Miyazaki,
Long-Term Unrestrained Measurement of Stride Length and Walking
Velocity Utilizing a Piezoelectric Gyroscope
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Outline 9 Current Gait Speed Estimation Method Gait Cycle
Extraction & Integration Drift Cancelation Stride Length
Computation by Reference Model Refined Human Gait Model Mounting
Calibration Experiments and Results
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Inspection of Gait Phase 10
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11
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Refined Compound Model 12 Reference Model
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Outline 13 Current Gait Speed Estimation Method Gait Cycle
Extraction and Integration Drift Cancelation Stride Length
Computation by Reference Model Refined Human Gait Model Mounting
Calibration Experiment & Results
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Mounting Calibration 14 Nodes could be rotated 20~30 from ideal
orientation Attenuate the signal of interest on the sensitive axis
Ideal Mounting Non-ideal Mounting
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Mounting Calibration Methods 15 Standing straight to get vector
Lift leg and hold still to obtain the rotated Assumption: rotating
only on the sagittal plane, i.e. only y-axis of accelerometer is
rotated, z-axis remain perpendicular to sagittal plane Cross
product to obtain the third vector Apply calibration
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Validation of Mounting Calibration Algorithm 16 Mounting
Position Rotated Around Y-axis Measured by Proposed Algorithm
Measurement Error of Angle 0-0.0720.072 1516.2861.286 3027.8962.104
4543.9541.046 6058.0781.922 7574.7370.263 9090.4610.461 Pendulum
Model to simulate node rotation on shank Rotate around z-axis with
controlled degree Determine the rotation by Mounting Calibration
Algorithm Achieve an average error of ~1
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Outline 17 Current Gait Speed Estimation Method Gait Cycle
Extraction and Integration Drift Cancelation Stride Length
Computation by reference model Refined Human Gait Model Mounting
Calibration Experiment & Results
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Treadmill Control of Speed Is gait on treadmill different from
on ground? Gyroscope signals collected on treadmill show no
significant difference from those collected on ground 18
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Experiments on Treadmill Two subjects, a taller male subject
and a shorter female subject Two trials were conducted for each
subject, one with well-mounted nodes and another with
poorly-mounted nodes to validate mounting calibration Speeds
ranging from 1 to 3 MPH with a 0.2 MPH (0.1m/s) increment for 45
seconds at each speed 19 Subject with poorly mounted Inertial BSN
nodes performing mounting calibration on treadmill
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Results
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Before/After Mounting Calibration 21 Badly mounted nodes causes
underestimation of gait speed attenuation of signal due to bad
mounting Mounting Calibration has correct the significant
estimation error Before Mounting Calibration After Mounting
Calibration
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Results of Two Subjects 22 Significantly reduced RMSE compared
to the reference model Overestimate at lower speeds and
underestimate at higher speeds Overestimate taller subjects speeds
more than the shorter subject
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Gait Model at Different Speeds The thigh angle can be critical
for controlling the step length 23 Use thigh nodes to increase
accuracy if invasiveness is not a concern How accurate is accurate
enough? Depends on application requirement High Speed Elimination
of thigh angle results in underestimation of stride length at high
speed Vice versa at low speed
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Results of Two Approaches 24 Double Pendulum at Initial Swing
Single Pendulum Model at Toe-off Better than the reference model
Still overestimate the gait speed Single Pendulum at Toe-Off
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Future Work 25 Need more subjects, more gait types, and more
gait speeds For certain types of pathological gait, include those
with shuffling, a wide base, and out-of-plane motion More refined
gait models will be developed based on biomechanical knowledge
Evaluate if a training set of data can be used to calibrate the
algorithm for each individual subject
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Conclusion 26 Achieving an RMSE of 0.09m/s accuracy with a
resolution of 0.1m/s Proposed model shows significant improvement
in accuracy compared to the reference model Mounting calibration
corrected the estimation error Leveraging biomechanical domain
knowledge simplifies signal processing