Screen Guided Training Does Not Capture Goal-Oriented Behaviors: Learning Myoelectric Control Mappings From Scratch Using Context Informed Incremental Learning

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 33; pp. 332 - 342
• Main Authors: Campbell, Evan, Eddy, Ethan, Isabel, Xavier, Bateman, Scott, Gosselin, Benoit, Cote-Allard, Ulysse, Scheme, Erik
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2025; Institute of Electrical and Electronics Engineers
• Subjects: Adaptation models; Calibration; Context modeling; Data models; deep learning; Electromyography; Electromyography (EMG); Games; gesture recognition; incremental learning; Pipelines; Predictive models; Training; Wrist; zero-shot
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2024.3518059

Human-machine interfaces based on myoelectric signals typically use screen-guided training (SGT) for model calibration, but this approach fails to capture realistic user behaviors. This study evaluates a user-in-the-loop context-informed incremental learning (CIIL) framework, comparing SGT, SGT followed by CIIL adaptation (SGT-A), and a novel zero-shot adaptation (ZS-A) CIIL approach that begins adapting with no prior training. Sixteen participants completed a Fitts' Law targeting task using these control schemes, with performance measured via online throughput and offline classification accuracy. Despite lower offline accuracy, the ZS-A model achieved the highest online throughput (<inline-formula> <tex-math notation="LaTeX">1.47~\pm ~0.46 </tex-math></inline-formula> bits/s), significantly outperforming the SGT baseline (<inline-formula> <tex-math notation="LaTeX">1.15~\pm ~0.37 </tex-math></inline-formula> bits/s) and reached competitive performance within 200 seconds. To further enhance control performance, a novel adaptive sigmoid-based proportional control mapping was introduced, dynamically adjusting control signals to allow precise control near neutral positions and rapid movements at higher activation levels, better aligning with natural user behaviors. These findings demonstrate that CIIL can surpass traditional SGT methods in online performance and emphasize the value of real-time user-in-the-loop data for developing adaptable and intuitive myoelectric interfaces, with implications for prosthetics, rehabilitation, and telerobotics.