A novel feature extraction method for machine learning based on surface electromyography from healthy brain
A novel feature extraction method for machine learning based on surface electromyography from healthy brain
复制标题
一种基于健康大脑表面肌电图的机器学习特征提取方法
DOI：
10.1007/s00521-019-04147-3
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发表时间：
2019-12-01
影响因子：
6
通讯作者：
Kong, Jianyi
中科院分区：
文献类型：
--
作者：
Li, Gongfa;Li, Jiahan;Kong, Jianyi
特征提取是基于表面肌电模式识别的多功能假肢控制的重要步骤之一。本文提出了一种基于肌肉活动区的表面肌电信号特征提取方法。本文设计了一个用不同特征对四种手部动作进行分类的实验。该实验用于证明新特征具有更好的分类性能。实验结果表明，与均值绝对值（MAV）、波形长度（WL）、过零值（ZC）和斜率变化（SSC）等传统特征相比，该特征具有更好的分类性能。AMR、MAV、WL、ZC和SSC的平均分类误差分别为13%、19%、26%、24%和22%。新的肌电图特征是基于手部运动和前臂活动肌肉区域之间的映射关系。这种映射关系已在医学上得到证实。我们利用新的特征提取算法从原始肌电信号中提取活动肌肉区域数据。该算法能很好地表征手部运动。另一方面，新特征向量的大小比其他特征要小得多。新特性可以缩小计算成本。这证明了AMR可以提高表面肌电信号模式识别的准确率。
Feature extraction is one of most important steps in the control of multifunctional prosthesis based on surface electromyography (sEMG) pattern recognition. In this paper, a new sEMG feature extraction method based on muscle active region is proposed. This paper designs an experiment to classify four hand motions using different features. This experiment is used to prove that new features have better classification performance. The experimental results show that the new feature, active muscle regions (AMR), has better classification performance than other traditional features, mean absolute value (MAV), waveform length (WL), zero crossing (ZC) and slope sign changes (SSC). The average classification errors of AMR, MAV, WL, ZC and SSC are 13%, 19%, 26%, 24% and 22%, respectively. The new EMG features are based on the mapping relationship between hand movements and forearm active muscle regions. This mapping relationship has been confirmed in medicine. We obtain the active muscle regions data from the original EMG signal by the new feature extraction algorithm. The results obtained from this algorithm can well represent hand motions. On the other hand, the new feature vector size is much smaller than other features. The new feature can narrow the computational cost. This proves that the AMR can improve sEMG pattern recognition accuracy rate.