Enhancing and Shaping Closed-Loop Co-Adaptive Myoelectric Interfaces With Scenario-Guided Adaptive Incremental Learning

• Published in: IEEE journal of biomedical and health informatics Vol. PP; pp. 1 - 12
• Main Authors: Li, Wei, Shao, Jiang, Shi, Ping, Li, Sujiao, Yu, Hongliu
• Format: Journal Article
• Language: English
• Published: United States IEEE 07.05.2025
• Subjects: augmented reality (AR); domain adversarial training; incremental learning; Myoelectric interface; surface electromyography (sEMG)
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2025.3567713

Virtual environments have been employed in the myoelectric prosthetics field as effective training and assessment tools to enhance intrinsic motivation, thereby encouraging sustained engagement in neuromuscular rehabilitation. However, motivating amputees to maintain consistent participation and perseverance in long-term training remains a critical challenge. To address this, we propose a scenario-guided adaptive incremental learning strategy that leverages contextual information in unknown environments to improve pseudo-label prediction accuracy. This strategy integrates two core components: Augmented Reality (AR) environment and Multimodal Progressive Domain Adversarial Neural Network (MPDANN). AR enables amputees to perform virtual prosthesis control and holographic object manipulation tasks in realistic, interactive scenarios, bridging the gap between laboratory training and daily-life usability. MPDANN Employs dual-domain classifiers through domain adversarial training, utilizing surface electromyography (sEMG) and inertial measurement unit (IMU) data to facilitate knowledge transfer across multi-source domains and achieve robust adaptation to unseen environments. A total of 16 able-bodied subjects and 2 amputee subjects completed a 5-day assessment protocol involving 10 holographic object manipulation tasks under 8 limb position conditions, using either a convolutional neural network (CNN) or MPDANN. Experimental results showed that able-bodied subjects using MPDANN achieved a 10 rate compared to the CNN baseline, reaching over 80 proficiency. While amputee subjects exhibited lower average completion rates than able-bodied subjects on the final day, the MPDANN strategy still demonstrated consistent performance improvements across both groups. This study substantiates the efficacy of integrating real-time visual feedback with a closed-loop domain adaptation algorithm, thereby enhancing sEMG recognition performance in untrained environments.