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浙江大学学报(工学版)
浙江大学学报(工学版)
计算机科学技术     
面向双侧训练的前臂外骨骼肌肉力-电关系识别模型
杨钟亮1, 唐智川2, 陈育苗3, 高增桂2
1. 东华大学 机械工程学院 上海 201620；2. 浙江大学 现代工业设计研究所,浙江 杭州 310027；3.东华大学 服装·艺术设计学院 上海 200051
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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摘要：

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.
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.
出版日期: 2014-12-01
:  TP 391.4  
基金资助:

国家自然科学基金资助项目(51305077)；中央高校基本科研业务费专项资金资助项目（2232013D3-31）.
作者简介: 杨钟亮（1982—）,男,博士,研究方向为生物启发设计、人机工程等.E-mail: yzl@dhu.edu.cn
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引用本文:

杨钟亮, 唐智川, 陈育苗, 高增桂. 面向双侧训练的前臂外骨骼肌肉力-电关系识别模型[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2014.12.008.

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), 10.3785/j.issn.1008-973X.2014.12.008.

链接本文:

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

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