Decoding Muscle Force From Motor Unit Firings Using Encoder-Decoder Networks

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 2484 - 2495
• Main Authors: Tang, Xiao, Zhang, Xu, Chen, Maoqi, Chen, Xiang, Chen, Xun
• Format: Journal Article
• Language: English
• Published: United States IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Coders; Computer applications; Decoding; Decomposition; Deep learning; Electrodes; Electromyography; EMG decomposition; Encoders-Decoders; Estimation; Force; Humans; Mechanical Phenomena; motor unit; Movement; Muscle Contraction; Muscle force estimation; Muscle, Skeletal; Muscles; neural drive information; Thumb; Tracking; Waveforms
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2021.3126752

Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of partial MU information resulting from incomplete decomposition. In light of these issues, this study presents a novel framework for interpreting MU activities and applies it to decode muscle force. The resulting MUs were clustered and classified into different categories by characterizing their spatially distributed firing waveforms. The process served as a general MU tracking method. On this basis, after transferring the MU firing trains to twitch force trains by a twitch force model, a deep network was designed to predict the normalized force. In addition, MU category distribution was examined to calibrate the actual force level, while functions of some unavailable MUs were compensated. To investigate the effectiveness of this framework, high-density sEMG signals were recorded using an <inline-formula> <tex-math notation="LaTeX">8\times8 </tex-math></inline-formula> electrode array from the abductor pollicis brevis muscles of eight subjects, while thumb abduction force was measured. The proposed method outperformed three common methods (<inline-formula> <tex-math notation="LaTeX">{p} < {0.001} </tex-math></inline-formula>) yielding the lowest root mean square deviation of 6.68% ± 1.29% and the highest fitness (<inline-formula> <tex-math notation="LaTeX">{R}^{{2}} </tex-math></inline-formula>) of 0.94 ± 0.04 between the predicted force and the actual force. This study offers a valuable, computational solution for interpreting individual MU activities, and its effectiveness was confirmed in muscle force estimation.