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浙江大学学报(工学版)
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
    
Force-sEMG relations recognition models of forearm exoskeleton for bilateral training
YANG Zhong-liang1, TANG Zhi-chuan2, CHEN Yu-miao3, GAO Zeng-gui2
1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China; 2. Modern Industrial Design Institute, Zhejiang University, Hangzhou 310027, China; 3. Fashion·Art Design Institute, Donghua University, Shanghai 200051, China
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Abstract  

To accurately recognize muscle forces of the non-paretic limb movement in forearm exoskeleton system, and drive the paretic limb for bilateral training in stroke, the force and surface electromyography (sEMG) relations recognition models of voluntary muscle contraction were proposed. The sEMG signals and strength values were recorded from 4 participants under 4 levels of forearm grasp forces by experiments. Using one-way ANOVA and multiple comparisons test, 11 features of sEMG time domain indices with significant differences were extracted from homogeneous subsets. The force-sEMG relations classification model and the myodynamia prediction model were constructed based on Elman network. For comparison, another prediction models using support vector machine (SVM) and gene expression programming (GEP) were presented here. Experimental results show that the Elman model can successfully recognize 4 levels of grasp forces, gives better results on modeling efficiency than the GEP model, and gives better generalization performance on myodynamia predicting than the SVM model. A forearm exoskeleton was developed to demonstrate the viability of the present modeling methods in the control of bilateral training.

Published: 01 December 2014
CLC:  TP 391.4  
  TP 241.3  
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YANG Zhong-liang, TANG Zhi-chuan, CHEN Yu-miao, GAO Zeng-gui. Force-sEMG relations recognition models of forearm exoskeleton for bilateral training. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2014, 48(12): 2152-2161.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2014.12.008     OR     http://www.zjujournals.com/eng/Y2014/V48/I12/2152


面向双侧训练的前臂外骨骼肌肉力-电关系识别模型

为了使前臂外骨骼系统能准确识别健肢运动的肌力水平,并带动患肢进行双侧训练,提出基于表面肌电的肌肉自主收缩力-电关系识别模型.通过4名被试者，实验采集前臂4种抓握力度下的肌电信号和握力值.利用单因素方差分析与多重比较产生的同类子集,提取11个具有显著性差异的肌电时域指标作为特征值.采用Elman神经网络构建力-电关系分类模型和肌力预测模型,并与基于支持向量机和基因表达式编程的预测模型进行性能比较.实验结果表明:Elman模型能够成功识别4种不同握力水平,建模效率高于基因表达式编程模型,肌力预测的泛化性能优于支持向量机模型.开发一个前臂外骨骼,在双侧训练的控制中验证了方法的有效性.

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