Modelling EMG driven wrist movements using a bio-inspired neural network

Abstract

The hidden pattern within the sEMG signal has wide applications in human-robot interaction. Decoding the patterns from sEMG signal tends to use black box models, which limits the further analysis of the mechanism of human musculoskeletal system. Therefore, a bio-inspired neural network (BNN) is proposed to model the information propagation procedures from nerve-related information (i.e. EMG signal) to muscle activation to joint activation to extremity movements. Instead of random parameter initialisation, the priori knowledge, such as muscle-electrode relationship, and muscles’ functionality, are fully considered to initialise the parameters. Besides, an interpretability constraint error back propagation algorithm (ICBP) is proposed to fine-tune the model for movement prediction, without scarifying model’s interpretability. An open sEMG database ISRMyo-I is utilised to verify the proposed methods for the classification of six wrist movements. With the only input of mean absolute value (MAV) feature, the proposed approach achieves an accuracy of >82%, which outperforms the support vector machine (78%), linear discriminant analysis (80%), k-nearest neighbors (78%), multi-layer perceptron (69%), random forest (74%), and convolutional neural network (74%).