Multi-channel mechanomyography (MMG) signals were acquired from the forearm when the subjects were performing eight classes of hand movements related to rehabilitation training. Ten time domain (TD) features and wavelet packet node energy (WPNE) features were extracted from each channel of MMG, and the hand movements were classified by support vector machine (SVM), extreme learning machine (ELM), linear discriminant analysis (LDA) and [Formula: see text]-nearest neighborhood (KNN) and the classifying results of three methods of collecting MMG (sensors directly on skin, sensors on cotton fabric and sensors on acrylic fiber) were compared. When all TD features were selected and SVM was adopted as the classifier, the total recognition rates of hand movements were 94.0%, 93.9% and 93.6%, respectively, of three collection methods. Using ELM can obtain similar results as SVM, with the recognition rates of 94.3%, 94.3% and 94.1%, respectively, better than using LDA (88.5%, 88.6% and 88.0%) or KNN (88.9%, 89.4% and 89.0%). For each algorithm, using TD features can acquire the highest recognition rates. Once the feature set and the classifier were selected, the total recognition rates were almost equally among three collection methods (especially for some feature sets, the differences are smaller than 1%). The results confirmed that satisfactory effects could be acquired even when the MMG was collected from sensors on fabrics with specific material, thus indicating that MMG has a unique potential value for developing wearable devices.

中文翻译：

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一项基于力学的手部动作识别的试点研究，强调织物对传感器和皮肤之间的影响

当受试者进行八类与康复训练相关的手部动作时，从前臂采集多通道机械肌谱 (MMG) 信号。从MMG的每个通道中提取十个时域（TD）特征和小波包节点能量（WPNE）特征，并通过支持向量机（SVM）、极限学习机（ELM）、线性判别分析（LDA）对手部动作进行分类。 ) 和 [公式：见正文]-最近邻域 (KNN) 以及三种采集 MMG 的方法（直接在皮肤上的传感器、在棉织物上的传感器和在腈纶上的传感器）的分类结果进行了比较。选择所有TD特征并采用SVM作为分类器时，手动运动的总识别率分别为3个收集方法的94.0％，93.9％和93.6％。使用 ELM 可以获得与 SVM 相似的结果，识别率分别为 94.3%、94.3% 和 94.1%，优于使用 LDA（88.5%、88.6% 和 88.0%）或 KNN（88.9%、89.4% 和 89.0%） ）。对于每种算法，使用 TD 特征可以获得最高的识别率。一旦选择了特征集和分类器，三种收集方法的总识别率几乎相等（特别是对于某些特征集，差异小于1%）。结果证实，即使从具有特定材料的织物上的传感器收集MMG也可以获得令人满意的效果，从而表明MMG在开发可穿戴设备方面具有独特的潜在价值。0%）或 KNN（88.9%、89.4% 和 89.0%）。对于每种算法，使用 TD 特征可以获得最高的识别率。一旦选择了特征集和分类器，三种收集方法的总识别率几乎相等（特别是对于某些特征集，差异小于1%）。结果证实，即使从具有特定材料的织物上的传感器收集MMG也可以获得令人满意的效果，从而表明MMG在开发可穿戴设备方面具有独特的潜在价值。0%）或 KNN（88.9%、89.4% 和 89.0%）。对于每种算法，使用 TD 特征可以获得最高的识别率。一旦选择了特征集和分类器，三种收集方法的总识别率几乎相等（特别是对于某些特征集，差异小于1%）。结果证实，即使从具有特定材料的织物上的传感器收集MMG也可以获得令人满意的效果，从而表明MMG在开发可穿戴设备方面具有独特的潜在价值。差异小于 1%）。结果证实，即使从具有特定材料的织物上的传感器收集MMG也可以获得令人满意的效果，从而表明MMG在开发可穿戴设备方面具有独特的潜在价值。差异小于 1%）。结果证实，即使从具有特定材料的织物上的传感器收集MMG也可以获得令人满意的效果，从而表明MMG在开发可穿戴设备方面具有独特的潜在价值。