Plug-and-Play sEMG-Driven Hand Gesture Recognition With Subdomain Adaptation for Exoskeleton Rehabilitation Gloves

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 10
• Main Authors: Zhong, Xiao-Cong, Wang, Qisong, Liu, Dan, Wang, Xuefu, Li, Rui, Wang, Yunfei, Zhang, Meiyan, Sun, Jinwei
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptation models; Alignment; Classification; Computational modeling; Decoding; Electromyography; Exoskeletons; Feature extraction; Gesture recognition; Gloves; Hand gesture recognition; Misalignment; Motors; Plug & play; Rehabilitation; rehabilitation gloves; Rehabilitation robots; Robotics; Statistical methods; subdomain adaptation; surface electromyography (sEMG); Target recognition; Training; Transfer learning
• DOI: 10.1109/TIM.2024.3502881
• ISSN: 0018-9456

Surface electromyography (sEMG)-based hand gesture recognition has garnered widespread attention in rehabilitation robotics due to its noninvasive measurement and intuitive motion decoding. However, affected by various factors such as individual differences, achieving cross-user adaptability and long-term reliability for sEMG classification poses a significant challenge. Existing domain adaptation (DA) methods primarily focus on global distribution alignment to mitigate statistical distribution discrepancies across domains, yielding significant achievements. Nevertheless, these methods often overlook fine-grained category-level subdomain distribution alignment, leading to discriminative structure confusion and subdomain misalignment, which hinder cross-subject and cross-session gesture recognition. To tackle these issues, this article proposes a plug-and-play subdomain adaptation method called PPSDA to enhance the classification performance and generalization ability for gesture recognition across domains. Specifically, handcrafted features are extracted and utilized for source domain supervised training to preserve discriminative structures. Subsequently, source and target domains co-training is performed, wherein the local maximum mean discrepancy (LMMD) is minimized to capture fine-grained information on relevant subdomains for subdomain distribution alignment. To validate the performance of the proposed PPSDA, we recruited 12 healthy subjects and developed an sEMG-driven exoskeleton rehabilitation glove for cross-subject and cross-session evaluations. Extensive experimental results demonstrate the effectiveness and superiority of the proposed PPSDA over existing DA approaches.