Towards Robust and Interpretable EMG-based Hand Gesture Recognition using Deep Metric Meta Learning

arXiv:2404.15360v1 Announce Type: cross
Abstract: Current electromyography (EMG) pattern recognition (PR) models have been shown to generalize poorly in unconstrained environments, setting back their adoption in applications such as hand gesture control. This problem is often due to limited training data, exacerbated by the use of supervised classification frameworks that are known to be suboptimal in such settings. In this work, we propose a shift to deep metric-based meta-learning in EMG PR to supervise the creation of meaningful and interpretable …

abstract adoption applications arxiv control cs.ai cs.hc cs.lg cs.sy current data eess.sp eess.sy environments gesture recognition meta pattern recognition recognition robust training training data type