Gait Step Length Classification Using Force Myography

Abstract

Step length changes with a change in walking speed, during gait initiation and termination, turning and obstacle avoidance. Clinical populations such as amputees lack the necessary muscular control to modulate their step length. Further, step length reduces in elderlies due to muscular weakness. To aide such populations in recovering normal gait, powered prostheses and assistive devices are developed. These devices require a control input regarding the upcoming step length in order to automate step length modulation. In this paper, we present a force myography-based step length classification model which can predict long and short steps before their completion. Three healthy participants walked over a surface marked with long and short steps while wearing a force myography system over their left thigh and a force-sensitive left insole. Three machine learning models were trained using the processed force myography signal to classify long and short steps. The machine learning model trained by the entire stride signal presented the highest F1-score of 86.64 % proving that the force myography signal of the thigh is a potential input signal for automated step length control in powered prostheses and assistive devices.