Estimating Ground Reaction Force and Center of Pressure using Low-Cost Wearable Devices.

Objective Ambulatory monitoring of ground reaction force (GRF) and center of pressure (CoP) could improve management of health conditions that impair mobility. Insoles instrumented with force-sensitive resistors (FSRs) are an unobtrusive, low-cost, and low-power technology for sampling GRF and CoP in real-world environments. However, FSRs have variable response characteristics that complicate estimation of GRF and CoP. This study introduces a unique data analytic pipeline that enables accurate estimation of GRF and CoP despite relatively inaccurate FSR responses. This paper also investigates whether inclusion of a complementary knee angle sensor improves estimation accuracy. Methods Seventeen healthy subjects were equipped with an insole instrumented with six FSRs and a string-based knee angle sensor. Subjects walked in a straight line at self-selected slow, preferred, and fast speeds over an in-ground force platform. Twenty repetitions were performed for each speed. Supervised machine learning models estimated weight-normalized GRF and shoe size-normalized CoP, which were re-scaled to obtain GRF and CoP. Results Anteroposterior GRF, Vertical GRF, and Anteroposterior CoP were estimated with a normalized root mean square error (NRMSE) of less than 5%. Mediolateral GRF and CoP were estimated with an NRMSE of 8.1% and 6.4%$ respectively. Knee angle-related features slightly improved GRF estimates. Conclusion Normalized models accurately estimated GRF and CoP despite deficiencies in FSR data. Significance Ambulatory use of the proposed system could enable objective, longitudinal monitoring of severity and progression for a variety of health conditions.