Explainable AI-Guided Optimization of EMG Channels and Features for Precise Hand Gesture Classification: A SHAP-Based Study

Abstract

Extraction of the correct and efficient descriptors of muscular activity plays a vital role in tackling the challenging problem of myoelectric control of powered prostheses. This work presents a feature extraction framework that aims to enhance the representation of muscular activities by increasing the amount of information that can be extracted from individual and combined electromyogram (EMG) channels. The proposed method for feature selection is based on Shapley Additive explanations (SHAP). The SHAP value is used to reduce the feature dimension. The proposed approach has been evaluated on two datasets obtained at a sampling rate of 1 kHz through a band consisting of seven EMG channels. The Standard deviation (SD) and Integrated EMG (IEMG) of electrodes 3, 5, 6, and 7 recognized four motions with a classification accuracy of 98.42%±1.16% and six gestures with a classification accuracy of 96.6%±0.91%, respectively. In the present work, an ensemble technique called bagging in the random forest algorithm has been used to obtain the optimum results.