🤖 AI Summary
This study addresses the challenge of deploying hand gesture recognition systems on low-power embedded devices, where existing approaches relying on multi-channel electromyographic (EMG) signals and complex models are impractical. For the first time, it systematically demonstrates the feasibility of achieving high-accuracy classification of ten distinct gestures using only a single-channel surface EMG (sEMG) signal combined with lightweight machine learning. By integrating multi-domain features—including time-domain, frequency-domain, higher-order zero-crossing rate, and relative intensity—and applying Pearson correlation-based feature selection followed by PCA or LDA for dimensionality reduction, the authors evaluate classification performance using neural networks (NN), k-nearest neighbors (KNN), and support vector machines (SVM). The optimal configuration achieves 90% accuracy, substantially reducing both hardware requirements and computational overhead, thereby offering an effective solution for low-cost, low-power gesture-based interaction.
📝 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.