Cross-user gesture recognition from sEMG signals using an optimal transport assisted student-teacher framework

• Published in: Computers in biology and medicine Vol. 165; p. 107327
• Main Authors: Li, Xinhui, Zhang, Xu, Chen, Xiang, Chen, Xun, Liu, Aiping
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2023; Elsevier Limited
• Subjects: Adaptation; Algorithms; Classification; Consumer electronics; Cross-user variability; Deep learning; Domain adaptation; Electromyography; Gestural recognition; Gesture recognition; Human-computer interaction; Internal Medicine; Labels; Machine learning; Muscles; Myoelectric control; Myoelectric interfaces; Myoelectricity; Neural networks; Optimization; Other; Pattern recognition; Pattern recognition systems; Physiology; Prostheses; Student-teacher framework; Teachers; Domain adaptation; Myoelectric interfaces; Cross-user variability; Student-teacher framework; Gestural recognition
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107327

The cross-user gesture recognition is a puzzle in the myoelectric control system, owing to great variability in muscle activities across different users. To address this problem, a novel optimal transport (OT) assisted student-teacher (ST) framework (termed OT-ST) was proposed in this paper to facilitate transfer across user domains in an unsupervised domain adaptation (UDA) manner. In this framework, the initial parameters of the ST models were trained with the labeled data from users in the source domain. In the model transfer stage for a new user in the target domain, the teacher model was utilized to generate pseudo labels for unlabeled testing samples, providing guidance to the adaptation of the student model. The OT algorithm was employed to optimize the pseudo labels generated from the teacher model, avoiding the model bias and further improving the effect of domain adaptation. The performance of the proposed OT-ST framework was evaluated via experiments of classifying seven hand gestures using high-density surface electromyogram (HD-sEMG) recordings from extensor digitorum muscles of eight intact-limbed subjects. The OT-ST framework yielded a high accuracy of 96.50 ± 2.88% for new users, and outperformed other common machine learning and UDA methods significantly (p < 0.01), demonstrating its effectiveness. The OT-ST framework does not require special repetitive training or any labeled data for calibration. In addition, it can incrementally learn from new testing samples and improve the recognition ability. This study provides a promising method for developing user-generic myoelectric pattern recognition, with wide applications in human-computer interaction, consumer electronics and prosthesis control. •A novel framework is proposed for cross-user myoelectric pattern-recognition control.•The proposed framework relies on a student-teacher model assisted with optimal transport.•The proposed framework allows the classifier to be adapted to new users in an incremental learning way.