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

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%).