An Exploratory Study of Single Channel Surface Electromyography for Hand Gesture Classification

arXiv cs.LG Papers
surface-electromyography hand-gesture-classification single-channel machine-learning feature-extraction low-power embedded-systems

Summary

This study explores the feasibility of classifying ten hand gestures using a single-channel sEMG signal combined with lightweight machine learning models, achieving up to 90% accuracy. It demonstrates potential for cost-effective, low-power gesture recognition.

arXiv:2607.15972v1 Announce Type: new 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.
Original Article
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# An Exploratory Study of Single Channel Surface Electromyography for Hand Gesture Classification
Source: [https://arxiv.org/abs/2607.15972](https://arxiv.org/abs/2607.15972)
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> 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\.

## Submission history

From: Daanish Hindustani \[[view email](https://arxiv.org/show-email/569d78e2/2607.15972)\] **\[v1\]**Fri, 17 Jul 2026 14:06:40 UTC \(1,091 KB\)

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