Separation of compound actions with wrist and finger based on EMG
Separation of compound actions with wrist and finger based on EMG
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DOI：
10.1117/12.2585334
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Eisuke Yamamoto;Momoyo Ito;S. Ito;M. Fukumi
中科院分区：
文献类型：
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作者：
Eisuke Yamamoto;Momoyo Ito;S. Ito;M. Fukumi
在本文中，我们提出了测量手腕和手指的肌电信号使用干式传感器佩戴在手腕附近，并分离到手腕和手指肌电信号的测量数据，通过使用独立分量分析（伊卡）。从而可以从复杂运动中确定手腕和手指的肌电信号，实现更复杂运动中的个体识别。本研究的最终目标是从复杂运动中识别出个体运动。作为初步尝试，本文将独立分量伊卡用于分离复合运动，并对该方法的有效性进行了评价。我们测量了三天四个动作的肌电图。分别采用FastICA、Infomax和JADE三种方法的组合，通过与原始信号的相关系数对结果进行评价。最准确的组合是FastICA + Infomax，准确率为70.5%
In this paper, we propose to measure the EMGs of the wrist and fingers using dry-type sensors worn near the wrist, and to separate the measured data into wrist and finger EMGs by using independent component analysis (ICA). Then we can confirm the EMGs of the wrist and fingers from the complex motion and realize individual identification in more complex motions. The final goal of this study is to identify individual motions from complex motions. In this paper, as a preliminary step, the ICA is used to isolate compound motions and the validity of the method is evaluated. We measured the EMGs for three days and four motions. The results of the combination of FastICA, Infomax and JADE, respectively, were evaluated by the correlation coefficient with the original signal. The most accurate combination was FastICA + Infomax with an accuracy of 70.5%