Most traditional myoelectric pattern recognition (MPR) systems are limited to recognizing a fixed set of gesture classes and are prone to performance degradation when exposed to unknown gestures. This study proposes a robust MPR framework that simultaneously enhances intra-class compactness and improves open-set rejection performance. Time-domain features of high-density surface electromyography (HD-sEMG) signals are first decomposed into pattern-specific and pattern-variant components, preserving essential muscle activations and reducing intra class variability. A unified model is then constructed by integrating dissimilarity metric learning with classification, enabling simultaneous estimation of an anomaly score and class label for the input gesture. For each known gesture, a pattern-specific decision boundary is defined based on the maximum anomaly score. This allows accurate classification of known gestures and effective rejection of unknown ones. The proposed method is evaluated on a self-collected dataset containing 17 gestures and a public benchmark dataset containing 65 gestures. In intra-session experiments on both datasets, it achieves over 99% classification accuracy for known gestures and more than 98% rejection accuracy for unknown gestures. Under challenging inter-session conditions, it still maintains over 77% open-set recognition accuracy, substantially outperforming existing open-set MPR methods. These results demonstrate the effectiveness of combining muscle synergy decomposition with dissimilarity metric learning to improve the robustness of myoelectric interfaces.
Wang et al. (Thu,) studied this question.