Investigating the Effect of Signal Channels and Features in Various Domains on the Emg-Based Hand Gesture Classification

Abstract

A variety of artificial intelligence (AI) approaches are applied for the classification of hand movements in systems that use electromyography (EMG), which measures the electrical activity of muscles. In AI approaches, machine learning (ML) is frequently preferred and researched for this classification issue. In this study, hand gesture classification was performed with ML algorithms using EMGs of 10 hand movements. Features were extracted from the time domain (TD), frequency domain (FD), time-frequency domain (TFD) (via Wavelet-based Synchrosqueezing Transform), and Fractional Fourier Transform (FrFT) domain. After training 31 ML models with all features, Subspace k-Nearest Neighbor (kNN), which is ensemble-based learning, was determined as the best model. This model was trained with different feature and channel combinations, and the classification performances were examined as channel-based and domain-based, separately. In all cases, an accuracy of 97.10% was obtained as the highest via the TD-FD-FrFT domain feature combination, including all channels. When all the results are examined, an alternative classification approach is presented to the literature by proving that the computational load decreases while the accuracy value increases by determining and utilizing the channels and features that contain more related information about hand movement.