仅用单通道肌电图实现90%手部手势识别，适合低功耗设备。
An Exploratory Study of Single Channel Surface Electromyography for Hand Gesture Classification
- 单通道肌电信号结合轻量模型，降低硬件要求。
- 融合时域频域特征，经相关性过滤后达90%准确率。
- 适合嵌入式系统，成本低、功耗小，可推广到可穿戴设备。
使用表面肌电图（sEMG）进行精确的手势识别通常依赖多通道传感器阵列和计算量大的模型，限制了其在低功耗嵌入式系统中的实际部署。本研究探索了仅用单个sEMG通道结合轻量级机器学习架构分类十种手部手势的可行性。原始sEMG信号被转化为包含时域、频域、高阶过零点及相对强度特征的综合特征表示。通过皮尔逊相关性过滤去除冗余特征，并选择性应用线性判别分析（LDA）和主成分分析（PCA）进行降维。系统评估了前馈神经网络（NN）、k近邻（KNN）和支持向量机（SVM）三种分类器，在四个实验中表现优异。结果表明，结合时域与频域特征，经皮尔逊相关性过滤后使用紧凑型神经网络，即使在时空信息有限的情况下，仍能达到最高90%的准确率。这些发现突显了单通道sEMG系统在低成本、低功耗手势识别应用中的潜力。
原文摘要 · Abstract (English)
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.
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