Design of a Wearable System Providing Visual Biofeedback on Anterior Ground Reaction Forces to Increase Lower Limb Propulsion of Stroke Patients

Abstract

After stroke, individuals often have a limited ability to generate limb propulsive forces which have been shown to be correlated with increased fall risk and lower walking speed. Biofeedback on anterior-posterior ground reaction force (AGRF) is a promising approach for  improving limb propulsion. However, collecting data on ground reaction forces typically requires the use of expensive instrumented treadmills which have limited clinical applicability. Instead, Inertial Measurement Units (IMUs) have been utilized to estimate ground reaction forces of gait, presenting a portable, and low-cost alternative. But to date this approach has primarily been used for prognosis of gait impairments and has yet to be employed in biofeedback for gait retraining. In this study, we present the initial design of a wearable system for AGRF biofeedback gait retraining using seven IMUs strapped to the foot, shank, thigh, and pelvis. The IMU data was processed by a MATLAB application in real time to output the AGRF values of the paretic leg which were transmitted via Wi-Fi to a single board computer fastened to the waist. AGRF values were graphed in Python using the Matplotlib library in reference to the target AGRF value. Wearable display glasses were connected to the single board computer via cable to project the graph into the lens in real time. This system represents an alternative to bulky, and expensive equipment. Further similar systems could be developed that incorporate auditory or haptic feedback on AGRFs. Further, future work will look to compare the efficacy of this approach in real world settings in addition to the efficacy of other feedback modalities on gait retraining.