Research on Collaborative Quality Assessment Model of Elbow Muscles based on MC-MMG and DRSN

Abstract

The purpose of our study was to investigate the individual muscle contribution to generated force under four representative of elbow multi-muscle contraction tasks: flexion, extension, pronation, and supination. In this paper, we proposed a collaborative quality assessment model of muscles to elbow generated force based on a multi-channel mechanomyogram (MC-MMG) to explore the relationship between the elbow generated force and the individual muscles under different contraction tasks. Based on the analysis of elbow anatomy, MMG signals of brachial biceps (BB), brachial (BR), triceps (TR), brachioradialis (BRD) were collected by using MC-MMG collection platform. The Kernel Principal Component Analysis (KPCA) algorithm was used to reduce the dimension of the original MMG signal. Then, the Mean Average Value (MAV) feature of the signals was extracted as the input of the Deep Residual Shrinkage Network (DRSN), which is a new deep learning algorithm to establish the relationship between MC-MMG and generated force. Mean Impact Value (MIV) index was used to assess the contribution level of different muscles groups for estimating the generated force. The experimental results show that the single muscle with the highest MIV value can track the change of generated force better than multiple muscles under different contraction tasks. This result can provide effective guidance for estimating generated force and can be further applied to the recognition of motion intention.