Variations in muscular contraction are known to significantly impact the quality of the generated EMG signal and the output decision of a proposed classifier. This is an issue when the classifier is further implemented in prosthetic hand design. Therefore, this study aims to develop a deep learning classifier to improve the classification of hand motion gestures and investigate the effect of force variations on their accuracy on amputees. The contribution of this study showed that the resulting deep learning architecture based on DNN (deep neural network) could recognize the six gestures and robust against different force levels (18 combinations). Additionally, this study recommended several channels that most contribute to the classifier's accuracy. Also, the selected time domain features were used for a classifier to recognize 18 combinations of EMG signal patterns (6 gestures and three forces). The average accuracy of the proposed method (DNN) was also observed at 92.0 ± 6.1 %. Moreover, several other classifiers were used as comparisons, such as support vector machine (SVM), decision tree (DT), K-nearest neighbors, and Linear Discriminant Analysis (LDA). The increase in the mean accuracy of the proposed method compared to other conventional classifiers (SVM, DT, KNN, and LDA), was 17.86 %. Also, the study's implication stated that the proposed method should be applied to developing prosthetic hands for amputees that recognize multi-force gestures.

中文翻译：

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利用截肢者的肌电信号提高多力变化手势识别的深度学习方法

已知肌肉收缩的变化会显着影响生成的肌电图信号的质量和所提出的分类器的输出决策。当分类器在假手设计中进一步实现时，这是一个问题。因此，本研究旨在开发一种深度学习分类器来改进手部动作手势的分类，并研究力量变化对其截肢者准确性的影响。这项研究的贡献表明，由此产生的基于 DNN（深度神经网络）的深度学习架构可以识别六种手势，并且对不同的力水平（18 种组合）具有鲁棒性。此外，这项研究还推荐了几个最有助于分类器准确性的通道。此外，所选时域特征用于分类器识别 18 种 EMG 信号模式组合（6 个手势和 3 个力）。所提出的方法 (DNN) 的平均准确度也为 92.0 ± 6.1 %。此外，还使用了其他几种分类器作为比较，例如支持向量机（SVM）、决策树（DT）、K近邻和线性判别分析（LDA）。与其他传统分类器（SVM、DT、KNN 和 LDA）相比，该方法的平均准确率提高了 17.86%。此外，该研究的含义表明，所提出的方法应该应用于为截肢者开发识别多力手势的假肢。