EMGCipher: Decoding Electromyography for Upper-limb Gesture Classification with Explainable AI for Resource Optimization.

Assistive limb devices often employ surface electromyography (sEMG) and deep learning (DL) models for gesture classification. While DL models effectively classify diverse upper-limb gestures, their decision-making mechanisms often lack transparency. To address this, we introduce EMGCipher, an interpretable DL framework for upper-limb gesture classification using sEMG. It aims to bridge the gap between interpretability and performance by combining low-level sEMG feature representations with DL model-derived knowledge, quantitatively assessing the probabilistic significance of input sensors and features in gesture classification. Experiments on the Ninapro DB5 dataset demonstrate EMGCipher's effectiveness in sensor-wise and feature-wise interpretation, demonstrating its potential to optimize the usage of sensors and features for improved gesture classification performance and efficiency.