Chen Yumiao, Yang Zhongliang
Fashion and Art Design Institute, Donghua University Shanghai, China.
College of Mechanical Engineering, Donghua University Shanghai, China.
Front Neurosci. 2017 Feb 14;11:61. doi: 10.3389/fnins.2017.00061. eCollection 2017.
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.
最近，一些研究人员考虑了从表面肌电图（sEMG）重建笔迹以及其他有意义的手臂和手部动作的问题。尽管已经取得了很大进展，但一些实际限制可能仍会影响基于sEMG技术的临床适用性。本文提出了一种新颖的三步混合模型，即坐标状态转换、sEMG特征提取和基因表达式编程（GEP）预测，用于从多通道表面肌电图重建12种基本一笔画形状的绘图轨迹。使用专门设计的坐标数据采集系统，我们记录了按照时间序列采集的绘图轨迹的坐标数据，同时记录了7通道肌电信号。作为一种广泛使用的时域特征，均方根（RMS）通过分析窗口提取。可以通过GEP建立初步的重建模型。然后，通过构建的预测模型可以近似原始绘图轨迹。应用该三步混合模型，我们能够将从手臂肌肉记录的7通道肌电活动转换为绘图轨迹的平滑重建。在组内设计（所有形状使用一组预测模型）中，混合模型的平均准确率为74%，在组间设计（每种形状使用一组单独的预测模型）中，平均准确率为86%，这是对重建的x和y坐标进行平均得到的。可以得出结论，所提出的三步混合模型提高从sEMG重建绘图轨迹的能力是可行的。
