1. 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,

2. 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

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

4. Inertial BSN for Gait Speed Estimation 4 TEMPO 3.1 inertial BSN platform developed at the University of Virginia

5. 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

6. 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

7. 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

8. Stride Length Computation 8 Reference Model S. Miyazaki, “Long-Term Unrestrained Measurement of Stride Length and Walking Velocity Utilizing a Piezoelectric Gyroscope”

9. 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

10. Inspection of Gait Phase 10

11. 11

12. Refined Compound Model 12 Reference Model

13. 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

14. 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

15. 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

16. Validation of Mounting Calibration Algorithm 16 Mounting Position Rotated Around Y-axis Measured by Proposed Algorithm Measurement Error of Angle 0°-0.072°0.072° 15°16.286°1.286° 30°27.896°2.104° 45°43.954°1.046° 60°58.078°1.922° 75°74.737°0.263° 90°90.461°0.461° 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°

17. 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

18. 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

19. 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

20. Results

21. 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

22. Results of Two Subjects 22 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

23. 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

24. 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

25. 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

26. 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

27. Thanks! Q&A