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.