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Shanshan Chen, Christopher L. Cunningham , John Lach

Extracting Spatio -Temporal Information from Inertial Body Sensor Networks for Gait Speed Estimation. Shanshan Chen, Christopher L. Cunningham , John Lach UVA Center for Wireless Health University of Virginia BSN, 2011. Bradford C. Bennett,.

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Shanshan Chen, Christopher L. Cunningham , John Lach

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  1. Extracting Spatio-Temporal Information from Inertial Body Sensor Networks for Gait Speed Estimation Shanshan Chen, Christopher L. Cunningham, John Lach UVA Center for Wireless Health University of Virginia BSN, 2011 BradfordC. Bennett,

  2. Research Statement • 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

  3. Prevailing Technology --for Gait Speed Estimation • Nike+® Pedometer, cadence • Fit-Bit®: • Accelerometer, cadence • Stopwatch and Tape • Garmin Forerunner ®301 • Wearable wrist GPS, velocity

  4. Inertial BSN for Gait Speed Estimation • Portable Solution for Gait Related Analysis • Also provide other spatio-temporal parameters • Challenges • Spatial Information • Integration Drift • Mounting Uncertainty • Minimizing Invasiveness • How? • Calculating Stride Length/Gait Cycle • Distance = • Average Gait Speed = Distance / Travelling time TEMPO 3.1 inertial BSN platform developed at the University of Virginia

  5. Contributions • 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

  6. Outline • 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

  7. Gait Cycle & Integration Drift Cancelation • 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

  8. Stride Length Computation = sin( = sin()× Stride Length =+ ++ Reference Model • S. Miyazaki, “Long-Term Unrestrained Measurement of Stride Length • and Walking Velocity Utilizing a Piezoelectric Gyroscope”

  9. Outline • 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

  10. Inspection of Gait Phase

  11. Refined Compound Model Reference Model , : Shank Length = sin( = sin()× Stride Length = +++

  12. Outline • 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

  13. Mounting Calibration • Nodes could be rotated 20°~30° from ideal orientation • Attenuate the signal of interest on the sensitive axis • Essence of Mounting Calibration • Mapping inertial frame () to global frame() : • Finding -- the x, y, z axis (global frame) represented by the inertial frame • Accelerometer readings are the orthogonal bases of the inertial frame Ideal Mounting Non-ideal Mounting

  14. Mounting Calibration Methods • 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

  15. Validation of Mounting Calibration Algorithm • 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°

  16. Outline • 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

  17. 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. 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 Subject with poorly mounted Inertial BSN nodes performing mounting calibration on treadmill

  19. Results

  20. Before/After Mounting Calibration After Mounting Calibration Before Mounting Calibration • Badly mounted nodes causes underestimation of gait speed – attenuation of signal due to bad mounting • Mounting Calibration has correct the significant estimation error

  21. Results of Two Subjects • Significantly reduced RMSE compared to the reference model • Overestimate at lower speeds and underestimate at higher speeds • Overestimate taller subject’s speeds more than the shorter subject

  22. Gait Model at Different Speeds • The thigh angle can be critical for controlling the step length • Elimination of thigh angle results in underestimation of stride length at high speed • Vice versa at low speed High Speed • Use thigh nodes to increase accuracy if invasiveness is not a concern • How accurate is accurate enough? • Depends on application requirement

  23. Results of Two Approaches Double Pendulum at Initial Swing Single Pendulum at Toe-Off • Single Pendulum Model at Toe-off • Better than the reference model • Still overestimate the gait speed

  24. Future Work • 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

  25. Conclusion • 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

  26. Thanks! Q&A

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