Multi-source domain generalization and adaptation toward cross-subject myoelectric pattern recognition

• Published in: Journal of neural engineering Vol. 20; no. 1; pp. 16050 - 16062
• Main Authors: Zhang, Xuan, Wu, Le, Zhang, Xu, Chen, Xiang, Li, Chang, Chen, Xun
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.02.2023
• Subjects: Adaptation, Physiological; cross-subject; deep learning; electromyography; Electromyography - methods; Humans; multi-source domain adaptation; Pattern Recognition, Automated - methods; Reproducibility of Results; robust EMG control; deep learning; robust EMG control; cross-subject; electromyography; multi-source domain adaptation
• DOI: 10.1088/1741-2552/acb7a0
• ISSN: 1741-2560; 1741-2552

Objective. Myoelectric pattern recognition (MPR) has shown satisfactory performance under ideal laboratory conditions. Nevertheless, the individual variances lead to dramatic performance degradation in cross-user MPR applications. It is crucial to enable the myoelectric interface to adapt to multiple users’ surface electromyography (sEMG) distributions in practical. Approach. Domain adaptation (DA) is a promising approach to tackle cross-user challenges due to its ability to diminish the divergence between individual users’ EMG distributions and escalate model generalization performance. However, existing DA methods in sEMG control are based on single-source domain adaptation (SDA). SDA solely mixes multiple training users’ data as a combined source domain and attempts to align with a novel user. This simple data mixing manner ignores the sEMG distribution variations between disparate training users, leading to an insufficient variance elimination and lower performance. To this end, this paper proposes a multi-source synchronize domain adaptation framework with both DA and domain generalization (DG) capability. This multi-source framework aligns each source user and the new user in individual feature spaces, which better transfers the knowledge of existing users to the new user. Moreover, we retain the source-combined data to preserve the effectiveness of SDA. The property was further confirmed by evaluating the performance of the proposed method on data from nine subjects performing six tasks. Main results. Experiment results prove that the proposed multi-source framework achieved both positive DG and DA performance in a cross-user classification manner. Significance. This work demonstrates the usability and feasibility of the proposed multi-source framework in cross-user myoelectric control.