1. Extracting Spatio-Temporal Informationfrom Inertial Body Sensor Networks for Gait Speed Estimation Shanshan Chen, Christopher L. Cunningham, Bradford C. Bennett, John LachUVA Center for Wireless Health University of VirginiaBSN, 20111

2. Research Statement Signal processing challenge to obtain accurate spatialinformation from inertial BSNs Gait speed as an example to extract accurate spatio-temporalinformation Gait speed is the No. 1 predictor in frailty assessment require high gait speed accuracy desire for continuous, longitudinal gait speed monitoring2 3. Prevailing Technology --for Gait Speed Estimation Nike+ Fit-Bit:Pedometer, cadence Accelerometer, cadence Garmin Forerunner 301Wearable wrist GPS, velocity Stopwatchand Tape3 4. Inertial BSN for Gait Speed EstimationTEMPO 3.1 inertial BSN platform developed at the University of Virginia4 5. Contributions Refined human gait model by leveraging biomechanicsknowledge Improve accuracy without increasing signal processingcomplexity Mounting calibration procedure to correct mounting error Practical in experiments Improved gait speed estimation accuracy by combining thetwo methods5 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 & Results6 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 drift7 8. Stride Length ComputationReference ModelS. Miyazaki, Long-Term Unrestrained Measurement of Stride Lengthand Walking Velocity Utilizing a Piezoelectric Gyroscope8 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 Results9 10. Inspection of Gait Phase10 11. 11 12. Refined Compound ModelReference Model12 13. 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 & Results13 14. Mounting Calibration Nodes could be rotated 20~30 from ideal orientation Attenuate the signal of interest on the sensitive axis Ideal Mounting14 Non-ideal Mounting 15. Mounting Calibration Methods 15 16. Validation of Mounting Calibration Algorithm Mounting Measured by MeasurementPosition Rotated Proposed Algorithm Error of Angle Around Y-axis 0-0.0720.07215 16.286 1.28630 27.896 2.10445 43.954 1.04660 58.078 1.92275 74.737 0.263 Pendulum Model to simulate 90 90.461 0.461 node rotation on shank Rotate around z-axis with Measurement Error of Angle 2.5 controlled degree2 Determine the rotation by 1.5 Mounting Calibration Algorithm 1 0.5 Achieve an average error of ~100 15 30 45 60 75 90 Measurement Error of Angle16 17. 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 & Results17 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 ground18 19. Experiments on Treadmill Subject with poorly mounted Inertial BSN nodes performing mounting calibration 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 speed19 20. Results 21. Before/After Mounting CalibrationBefore Mounting Calibration After Mounting Calibration Badly mounted nodes causes underestimation of gait speed attenuation of signal due to bad mounting Mounting Calibration has correct the significant estimation error21 22. Results of Two Subjects 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 subject22 23. 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 requirement23 24. Results of Two ApproachesDouble Pendulum at Initial Swing Single Pendulum at Toe-OffSingle Pendulum Model at Toe-off Better than the reference model Still overestimate the gait speed24 25. 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 subject25 26. 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 processing26 27. Thanks! Q&A