A Novel Hybrid Model for Drawing Trace Reconstruction from Multichannel Surface Electromyographic Activity.
A Novel Hybrid Model for Drawing Trace Reconstruction from Multichannel Surface Electromyographic Activity.
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DOI：
10.3389/fnins.2017.00061
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发表时间：
2017
影响因子：
4.3
通讯作者：
Yang Z
中科院分区：
文献类型：
--
作者：
Chen Y;Yang Z
最近，一些研究人员已经考虑了从表面肌电图（sEMG）重建笔迹和其他有意义的手臂和手部运动的问题。虽然已经取得了很大的进展，一些实际的局限性仍然可能影响基于表面肌电信号的技术的临床适用性。本文提出了一种新的坐标状态转换、表面肌电信号特征提取和基因表达式编程（GEP）预测的三步混合模型，用于从多通道表面肌电信号中重建12种基本笔画形状的绘制轨迹。利用专门设计的坐标数据采集系统，记录了按照时间序列采集的绘画轨迹坐标数据，同时记录了7通道肌电信号。均方根（RMS）作为一种广泛使用的时域特征，通过分析窗口提取。GEP可以建立初步的重建模型。然后，可以通过构造的预测模型来近似原始绘制轨迹。应用三步混合模型，我们能够将从手臂肌肉记录的EMG活动的七个通道转换为绘画痕迹的平滑重建。混合模型可以在组内设计（所有形状的一组预测模型）中产生74%的平均准确度，在组间设计（每个形状的一组单独的预测模型）中产生86%的平均准确度，对重建的x和y坐标进行平均。实验结果表明，该三步混合模型能够有效地提高表面肌电信号的迹线重构能力。
Recently, several researchers have considered the problem of reconstruction of handwriting and other meaningful arm and hand movements from surface electromyography (sEMG). Although much progress has been made, several practical limitations may still affect the clinical applicability of sEMG-based techniques. In this paper, a novel three-step hybrid model of coordinate state transition, sEMG feature extraction and gene expression programming (GEP) prediction is proposed for reconstructing drawing traces of 12 basic one-stroke shapes from multichannel surface electromyography. Using a specially designed coordinate data acquisition system, we recorded the coordinate data of drawing traces collected in accordance with the time series while 7-channel EMG signals were recorded. As a widely-used time domain feature, Root Mean Square (RMS) was extracted with the analysis window. The preliminary reconstruction models can be established by GEP. Then, the original drawing traces can be approximated by a constructed prediction model. Applying the three-step hybrid model, we were able to convert seven channels of EMG activity recorded from the arm muscles into smooth reconstructions of drawing traces. The hybrid model can yield a mean accuracy of 74% in within-group design (one set of prediction models for all shapes) and 86% in between-group design (one separate set of prediction models for each shape), averaged for the reconstructed x and y coordinates. It can be concluded that it is feasible for the proposed three-step hybrid model to improve the reconstruction ability of drawing traces from sEMG.