Continuous Lower Limb Multi‐Intent Prediction for Electromyography‐Driven Intrinsic and Extrinsic Control

Abstract

Currently, electromyography (EMG)‐driven lower limb control can be divided into computational intrinsic control and interactive extrinsic control based on the participation of neural information in the controllers. However, the former method lacks detailed measurements of the expected motion, whereas the latter is prone to errors in diverse movement situations. Based on this problem, a multi‐intent prediction scheme is proposed that analyzes both rhythmic locomotion and volitional movement intent for AI‐based intrinsic and extrinsic hybrid control (AIEC). A 1D residual shrinkage convolutional network is designed to extract EMG features. The motion state is continuously predicted with an accuracy of 91.66% and the angle completion is also estimated with of 0.9540 and 0.9456 for left and right knee angles, respectively. Additionally, the comparison test further indicates that the motion state classification is significantly improved in the multitask analysis compared with the single‐task approach. This work demonstrates and verifies a novel method in EMG studies that multi‐intent recognition not only compensates for the lack of information in the analysis of single rhythmic or volitional movement, but also enhances the comprehensive performance and makes AIEC feasible, which optimizes the current EMG‐driven control for ampler intent collection and more practical robotic control.