🤖 AI Summary
This work addresses the significant performance degradation in surface electromyography (sEMG)-based gesture recognition caused by electrode repositioning, variations in skin condition, or changes in limb posture across sessions, which hinders real-world deployment. The authors propose the first label-free cross-session sEMG decoding framework that enables transfer without requiring new annotations. It employs a layout-invariant universal encoder combined with unsupervised feature distribution alignment during inference to adapt to new sessions. Trained on a single annotated session, the model generalizes effectively to subsequent unannotated sessions. Evaluated on NinaPro DB6, the method achieves a cross-session macro F1-score of 0.688, substantially outperforming both the conventional per-user LDA baseline (0.540) and existing approaches that rely solely on source-domain data, thereby overcoming the need for repeated calibration.
📝 Abstract
Recognition accuracy obtained during a recording session does not persist when a user puts on the electrodes again after the electrodes had previously been removed. The electrodes may have moved slightly, the skin may be drier or wetter, or the elbow may be positioned differently; these factors all contribute to day-to-day variability and therefore represent a major obstacle to implementing successful pattern-recognition based myoelectric control systems in daily practice. However, simply recalibrating a user's hand for 20 min at every doff/don event is a clearly unrealistic expectation. A montage-agnostic encoder built for cross-user, cross-montage transfer is trained here using data collected during a particular recording session, and then applied to data collected later in a different recording session without adjusting anything, on the ten intact subjects of NinaPro DB6. The performance of this approach is compared to that of a per-user LDA classification pipeline, and to that of two published approaches that only rely on source data collected from the same recording session. Carried unchanged across recording sessions, the encoder retains 0.688 macro-F1 against 0.540 for the per-user pipeline, and, on the per-window metric the published baselines use, sits above both published source-only results, a band of two points that locates the encoder rather than ranking it. Of five label-free test-time adaptations, only feature-statistic alignment improves every subject; batch-normalisation re-estimation, a standard method in the domain-adaptation literature, collapses this architecture entirely. Aligning the encoder's feature statistics to the new session recovers about what a single labelled calibration repetition would.