An Exploratory Study of Single Channel Surface Electromyography for Hand Gesture Classification
Machine Learning
Summary
The authors explored how well hand gestures can be recognized using signals from just one muscle sensor instead of many. They converted the raw signals into different types of features and removed any overlapping information. They tested three simple machine learning methods and found that a small neural network could correctly identify hand gestures with about 90% accuracy. This shows it might be possible to build cheaper, low-power devices for gesture recognition using just one sensor.
Authors
Daanish Hindustani
Abstract
Accurate hand gesture recognition using surface electromyography (sEMG) typically relies on multichannel sensor arrays and computationally intensive models. This limits practical deployment in low-power and embedded systems. This study investigates the feasibility of classifying ten hand gestures using a single sEMG channel combined with lightweight machine learning architectures. Raw sEMG signals were transformed into a comprehensive feature-based representation, including time-domain, frequency-domain, higher-order-crossing, and relative-intensity features. Feature redundancy was reduced using Pearson correlation filtering and the removal of highly correlated features, while dimensionality-reduction techniques (LDA and PCA) were applied selectively. Three classifiers, a feed-forward neural network (NN), k-nearest neighbors (KNN), and a support vector machine (SVM), were systematically evaluated across four experiments. Results demonstrate that combining time and frequency features with Pearson filtering and a compact NN can achieve up to 90 percent accuracy, even with limited temporal and spatial information. These findings highlight the potential of single-channel sEMG systems for cost-effective, low-power gesture-recognition applications.