EMG Signal Classification for Human Hand Rehabilitation via Two Machine Learning Techniques: kNN and SVM

Sami Briouza, H. Gritli, N. Khraief, S. Belghith, Dilbag Singh
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引用次数: 6

Abstract

In the last few years, electromyography (EMG) has shown a lot of potential in therapy and rehabilitation applications of the human limbs though exoskeletons and prosthesis. The use of Machine Learning (ML) techniques has made huge contributions to biomedical signals classification. Many ML methods have been adopted and the results were very promising. In this paper, we choose two different ML classifiers: the k-Nearest Neighbors (kNN) and the Support Vector Machine (SVM). The main goal is to compare their performance using different combinations of time-domain features. This crucial strategy allows to choose the adequate features in order to obtain good model performance. The experimental results demonstrate that the SVM classifier is more efficient where it gives a higher accuracy compared to the kNN technique.
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基于kNN和SVM两种机器学习技术的手部康复肌电信号分类
近年来,肌电图(electromyography, EMG)通过外骨骼和假肢在人体肢体的治疗和康复方面显示出很大的潜力。机器学习(ML)技术的使用为生物医学信号分类做出了巨大贡献。许多机器学习方法已经被采用,结果非常有希望。在本文中,我们选择了两种不同的ML分类器:k近邻(kNN)和支持向量机(SVM)。主要目标是使用不同的时域特征组合来比较它们的性能。这一关键策略允许选择适当的特征,以获得良好的模型性能。实验结果表明,与kNN技术相比,SVM分类器具有更高的准确率。
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