HD-sEMG-based research on activation heterogeneity of skeletal muscles and the joint force estimation during elbow flexion

Abstract

Objective. To investigate the activation heterogeneity of skeletal muscles and realize the joint force estimation during the elbow flexion task. Approach. When an isometric elbow flexion task was performed, high-density surface electromyography (HD-sEMG) signals from a grid covering the front and inside of the upper arm and the generated joint force were recorded synchronously. HD-sEMG signals were preprocessed and then decomposed into source signals corresponding to biceps brachhi (BB) and brachialis (BR) and their contribution vectors using a fast, independent component analysis (FastICA) algorithm. The activation heterogeneity of BB and BR was investigated from the activation level and activation region, initially. Then, the contribution combinations of two sources were classified into several major clusters using the K-means clustering method. Afterwards, input signals for force estimation were extracted from the major clusters corresponding to different combinations, and the polynomial fitting technique was adopted as the force estimation model. Finally, the force estimation results were obtained and the analysis around the force estimation performance using different input signals was conducted. Main results. Ten subjects were recruited in this research. The experimental results demonstrated that it is feasible to analyze the activation heterogeneity of muscles from the activation level and activation region, and to select the appropriate region of the HD-sEMG grid for high performance force estimation. For the isometric elbow flexion task, joint force estimation accuracy could be improved when the input signal was extracted from the specific area where the contribution difference of BB and BR to the HD-sEMG signals were relatively small. Significance. The proposed framework provided a novel way to explore the relationship between muscle activation and the generating joint force, and could be extended to multiple noteworthy research fields such as myoelectric prostheses, sports biomechanics, and muscle disease diagnosis.