AI 中文总结
研究探索单通道表面肌电图结合轻量级机器学习架构对手势分类的可行性，通过转换信号为综合特征表示、减少特征冗余等，评估三种分类器，结果表明结合相关技术和紧凑型神经网络能达90%准确率，凸显其在低功耗手势识别应用中的潜力。
AI 中文摘要
使用表面肌电图（sEMG）进行准确的手势识别通常依赖多通道传感器阵列和计算密集型模型，这限制了其在低功耗和嵌入式系统中的实际应用。本研究探讨了结合单通道sEMG与轻量级机器学习架构对手势进行分类的可行性。原始sEMG信号被转换为包含时域、频域、高阶交叉和相对强度特征的综合特征表示。利用皮尔逊相关滤波等减少特征冗余并选择性应用降维技术。通过四个实验系统评估了三种分类器。结果表明，结合时间和频率特征、皮尔逊滤波及紧凑型神经网络，即使在有限时空信息下也能达到90%的准确率。这些发现凸显了单通道sEMG系统在经济高效、低功耗手势识别应用中的潜力。
英文摘要
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.
