(Un)supervised (Co)adaptation via Incremental Learning for Myoelectric Control: Motivation, Review, and Future Directions

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 33; pp. 3565 - 3582
• Main Authors: Campbell, Evan, Egle, Fabio, OsWald, Marius, Cote-Allard, Ulysse, Pilarski, Patrick M., Boccardo, Nicolo, Meattini, Roberto, Vujaklija, Ivan, Hargrove, Levi, Canepa, Michele, Eddy, Ethan, Vecchio, Alessandro Del, Castellini, Claudio, Scheme, Erik
• Format: Journal Article
• Language: English
• Published: United States IEEE 2025
• Subjects: Accuracy; Adaptation models; Algorithms; Calibration; Computational modeling; Electromyography; Electromyography - methods; human-computer-interaction; Humans; Incremental learning; Machine Learning; Motivation; Myoelectric control; Pattern recognition; Pattern Recognition, Automated; Prosthesis Design; Prosthetics; Supervised Machine Learning; Taxonomy; Unsupervised Machine Learning
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2025.3602397

This paper presents a narrative review of incremental learning methods for myoelectric control, outlining both the historical trajectory and potential of adaptive prosthetic systems. Traditional myoelectric control has evolved from direct control techniques to advanced pattern recognition, yet persistent challenges such as signal non-stationarities and, consequently, the need for frequent recalibration remain. Incremental learning may enable a paradigm shift by continuously updating control models based on real-time, user-in-the-loop data, thereby addressing user-specific variations, environmental changes, and challenges from screen-guided-training based calibration. A central contribution of the paper is its taxonomy of incremental learning strategies, which divides the field into four categories: dedicated on-demand recalibration, unsupervised incremental learning, predictor-dependent incremental learning, and environment-dependent incremental learning. The methodology, strengths, and limitations of each category are discussed, providing a clear framework for evaluating current research and guiding future innovations. Further, this work establishes three settings for incremental learning: domain-incremental, task-incremental, and class-incremental continual learning. In addition, the paper highlights emerging trends such as transfer learning, domain adaptation, and self-supervised regression. It also emphasizes the potential of physiologically-inspired algorithms, novel end-effector designs to enhance prosthetic performance, and human-device co-adaptation. Finally, this paper discusses open challenges for incremental learning like attribution of signal changes to noise vs. behaviours, model complexity vs. data requirements, and user vs. model adaptation. Collectively, these insights pave the way for next-generation myoelectric systems that are more robust, intuitive, and adaptable to the dynamic needs and behaviours of users.