Continuous Gait Phase Estimation by Muscle Deformations with Speed and Ramp Adaptability

Accurate estimation of gait phases is crucial in controlling wearable robots designed to restore or enhance human walking capabilities. Asynchronous human-machine motions can lead to reduced efficiency, increased interaction loads, and potential harm to the human body. Variations in walking speeds and terrain ramps pose additional challenges to continuous phase estimation. This paper presents a novel synergy method based on muscle deformations measured by flexible wearable sensors to dynamically monitor gait phases while adapting to changes in speed and terrain ramp. Polar coordinate diagrams are used to elucidate the cyclic features of joint angles and muscle deformations during walking. The consistency of muscle deformation synergy is studied to build a phase estimation model, with parameters being determined by neural network algorithms. The average root-mean-square error for the estimated phase is less than 4.4% within a speed range of 3 to 6 km/h and less than 8.8% in a ramp range of 0 to 10°. Muscle deformation presents a viable alternative in scenarios involving changes in speed, outperforming joint kinematics in gait phase estimation amidst variations in ramp inclinations. The proposed unified muscle synergy model, adaptable across a wide spectrum of walking speeds and ramp inclinations, holds promise for enhancing human-machine coordination for robotic assistance and rehabilitation.