Design of a Bio-Inspired Gait Phase Decoder Based on Temporal Convolution Network Architecture With Contralateral Surface Electromyography Toward Hip Prosthesis Control

Abstract

Inter-leg coordination is of great importance to guarantee the safety of the prostheses wearers, especially for the subjects at high amputation levels. The mainstream of current controllers for lower-limb prostheses is based on the next motion state estimation by the past motion signals at the prosthetic side, which lacks immediate responses and increases falling risks. A bio-inspired gait pattern generation architecture was proposed to provide a possible solution to the bilateral coordination issue. The artificial movement pattern generator (MPG) based on the temporal convolution network, fusing with the motion intention decoded from the surface electromyography (sEMG) measured at the impaired leg and the motion status from the kinematic modality of the prosthetic leg, can predict four sub gait phases. Experiment results suggested that the gait phase decoder exhibited a relatively high intra-subject consistency in the gait phase inference, adapted to various walking speeds with mean decoding accuracy ranging from 89.27 to 91.16% across subjects, and achieved an accuracy of 90.30% in estimating the gait phase of the prosthetic leg in the hip disarticulation amputee at the self-selected pace. With the proof of concept and the offline experiment results, the proposed architecture improves the walking coordination with prostheses for the amputees at hip level amputation.

A gait phase prediction model trained on benchmark datasets for evaluating a controller for prosthetic legs

“…Deep neural networks are highly capable of determining the non-linear relationship between the input and output mapping. The discretized gait phase was predicted using electromyography signals based on a temporal convolutional network (Chen et al, 2022 ). A recurrent neural network with a shank-mounted inertial measurement unit (IMU) was used to predict the gait phase for controlling an ankle exoskeleton (Seo et al, 2019 ).…”

Bio-inspired design of a self-aligning, lightweight, and highly-compliant cable-driven knee exoskeleton

“…Since exoskeletons are systems developed to work in parallel with the biological muscles, it is crucial that the robot joint remains aligned with the human joint during movement. Otherwise, the misalignment contributes to detrimental effects both on the comfort and on the effectiveness of the assistance, since it restricts the free movements of the user and deteriorates the power transmission efficiency from the actuator to the human limb (Zanotto et al, 2015 ; Sarkisian et al, 2021 ; Bulea et al, 2022 ; Chang et al, 2022 ; Chen et al, 2022 ).…”