New Channel Merging Methods for Multi-DoF Force Prediction of Finger Contractions

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI:10.1109/BIOCAS.2019.8919012

Yuyang Chen, Xinyu Jiang, C. Dai, Wei Chen

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引用次数: 4

Abstract

Surface electromyography (sEMG) signal is one of the widely applied biological signals in the research field of the force intention prediction. However, due to the severe crosstalk issue of sEMG signals during fine hand contractions, few studies have related sEMG to multiple degree-of-freedom (DoF) force prediction of individual fingers. Accordingly, this pilot study proposed two methodsCommon Spatial Pattern (CSP) and Softmax function to solve the cross-talk issues for the estimation of EMG-force during multiple finger contractions through weighting the significance of each selected channel. High-density sEMG signals of forearm extensor muscles were obtained, and experimental data from two able-bodied subjects were analyzed. Subjects produced 1-DoF and 3-DoF forces up to 30% maximum voluntary contraction (MVC). Then, the root-mean-square values of sEMG were related to joint force. Linear EMG-force models were trained using 1-DoF trials, then tested on 3-DoF trials. Our results showed that the proposed two novel methods had lower RMS errors than the traditional methods for index, and ring with little fingers. The results suggest that 3-DoF control for individual fingers with minimal training procedure (1-DoF trials) may be feasible for practical use.

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手指收缩多自由度力预测的信道合并新方法

表面肌电信号是力意图预测研究领域中应用最广泛的生物信号之一。然而，由于手部精细收缩过程中表面肌电信号存在严重的串扰问题，很少有研究将表面肌电信号与单个手指的多自由度力预测联系起来。因此，本试点研究提出了两种方法——共同空间模式(common Spatial Pattern, CSP)和Softmax函数，通过加权每个选择通道的重要性来解决多重手指收缩时肌电力估计的串扰问题。获取前臂伸肌高密度肌电信号，并对两名健全受试者的实验数据进行分析。受试者产生的1-DoF和3-DoF力高达30%的最大自愿收缩(MVC)。然后，表面肌电信号的均方根值与关节力的关系。线性肌电力模型采用1自由度试验进行训练，然后采用3自由度试验进行测试。结果表明，两种方法的RMS误差均低于食指和小指环的传统方法。结果表明，用最少的训练程序(1-DoF试验)对单个手指进行3-DoF控制在实际应用中是可行的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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