人体下肢行走关节连续运动表面肌电解码方法

陈江城; 张小栋

doi:10.7652/xjtuxb201606010

您当前的位置：

首页 >

文章列表页 >

人体下肢行走关节连续运动表面肌电解码方法

更新时间：2025-07-09

- 人体下肢行走关节连续运动表面肌电解码方法
- Surface Electromyography Decoding for Continuous Movement of Human Lower Limb During Walking
- 西安交通大学学报 2016年50卷第6期页码：61-67
- 作者机构：
  
  西安交通大学现代设计及转子轴承系统教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2015-10-19。作者简介: 陈江城(1987—),男,博士生;张小栋(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金重大研究计划资助项目(91420301)
- DOI：10.7652/xjtuxb201606010
  中图分类号： TP242
- 网络首发：2016-06-10，
  
  纸质出版：2016
- 稿件说明：
移动端阅览
陈江城, 张小栋. 人体下肢行走关节连续运动表面肌电解码方法[J]. 西安交通大学学报, 2016,50(6):61-67.

Surface Electromyography Decoding for Continuous Movement of Human Lower Limb During Walking[J]. 2016, 50(6): 61-67.
陈江城, 张小栋. 人体下肢行走关节连续运动表面肌电解码方法[J]. 西安交通大学学报, 2016,50(6):61-67. DOI： 10.7652/xjtuxb201606010.

Surface Electromyography Decoding for Continuous Movement of Human Lower Limb During Walking[J]. 2016, 50(6): 61-67. DOI： 10.7652/xjtuxb201606010.

摘要

为实现人体下肢步态动作的连续识别

提出了一种利用表面肌电信号进行下肢关节运动角度连续解码的方法。首先利用光学运动捕捉实现下肢关节运动角度的计算

然后采集下肢运动相关主力肌肉的表面肌电信号并提取其活动强度信息; 在此基础上

基于受限玻尔兹曼机构建深度自动编码器(DAE)

实现多路表面肌电信号强度时间序列的低维空间编码和最优特征提取; 最后

利用BP神经网络建立特征量与关节矢状面运动角度之间的非线性回归模型。实验结果表明:该方法提取的信号特征信息优于传统的主量分析方法

采用提出的模型能够更精确地估计下肢关节连续运动角度

其估计值与真实值的均方误差较传统方法降低25%～35%。研究结果为人机交互接口技术的开发、实现下肢可穿戴智能装备的生物电连续控制、提高人机运动平稳性奠定了基础。

Abstract

To estimate the continuous movement of human lower limb during walking

a regression model which relates the surface electromyography(EMG)and the movement variables of the lower limb joints is constructed. The joint movement angles of lower limb are calculated accurately based on optical motion capture system

then the surface EMG signals are sampled from the main muscles directly concerned with the lower limb motion; the muscle activities are extracted

and a deep auto-encoder(DAE)network with restricted Boltzmann machines(RBM)is realized

by which the multi-channel processed surface EMG signals are encoded in low dimensional space and the optimal features are extracted. The nonlinear model mapping the EMG features to sagittal surface movement angles is established with back propagation(BP)neural network. Extensive experiments indicate that the features extracted with the deep auto-encoder(DAE)network are outperformed principal components analysis(PCA); the movement angles of lower limb joints can be estimated continuously and precisely with the regression models and the mean square error(MSE)between the estimated values and real values is reduced by 25%-35% compared with the traditional method. The proposed strategy is expected to develop human-machine interaction interface technology for the achievement of continuous bioelectric control and the improvement of motion stability between human and machine

especially for lower limb wearable intelligent equipment.

关键词

Keywords

references

DÍAZ I, GIL J J, SÁNCHEZ E. Lower-limb robotic rehabilitation: literature review and challenges [J]. Journal of Robotics, 2011, 2011: Article ID 759764.

杨启志, 曹电锋, 赵金海. 上肢康复机器人研究现状的分析 [J]. 机器人, 2013, 35(5): 630-640.

YANG Qizhi, CAO Dianfeng, ZHAO Jinhai. Analysis on state of the art of upper limb rehabilitation robots [J]. Robot, 2013, 35(5): 630-640.

SANKAI Y. HAL: hybrid assistive limb based on cybernics [M]∥Robotics Research. Berlin, Germany: Springer, 2011: 25-34.

PHINYOMARK A, PHUKPATTARANONT P, LIMSAKUL C. Feature reduction and selection for EMG signal classification [J]. Expert Systems with Applications, 2012, 39(8): 7420-7431.

PHINYOMARK A, QUAINE F, CHARBONNIER S, et al. Feature extraction of the first difference of EMG time series for EMG pattern recognition [J]. Computer Methods and Programs in Biomedicine, 2014, 117(2): 247-256.

TSAI A C, LUH J J, LIN T T. A novel STFT-ranking feature of multi-channel EMG for motion pattern recognition [J]. Expert Systems with Applications, 2015, 42(7): 3327-3341.

LAUER R, SMITH B, BETZ R. Application of a neuro-fuzzy network for gait event detection using electromyography in the child with cerebral palsy [J]. IEEE Transactions on Biomedical Engineering, 2005, 52(9): 1532-1540.

DING Q C, XIONG A B, ZHAO X G, et al. A novel EMG-driven state space model for the estimation of continuous joint movements [C]∥IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, USA: IEEE, 2011: 2891-2897.

ZHANG F, LI P F, HOU Z G, et al. sEMG-based continuous estimation of joint angles of human legs by using BP neural network [J]. Neurocomputing, 2012, 78(1): 139-148.

戴虹, 钱晋武, 张震, 等. GRNN在肌电估计踝关节运动中的应用 [J]. 仪器仪表学报, 2013, 34(4): 845-852.

DAI Hong, QIAN Jinwu, ZHANG Zhen, et al. Application of GRNN in ankle movement prediction based on surface electromyography [J]. Chinese Journal of Scientific Instrument, 2013, 34(4): 845-852.

AUNG Y M, AL-JUMAILY A. sEMG based ANN for shoulder angle prediction [J]. Procedia Engineering, 2012, 41: 1009-1015.

丁其川, 赵新刚, 韩建达. 基于肌电信号的上肢多关节连续运动估计 [J]. 机器人, 2014, 36(4): 469-476.

DING Qichuan, ZHAO Xingang, HAN Jianda. EMG-based estimation for multi-joint continuous movement of human upper limb [J]. Robot, 2014, 36(4): 469-476.

田国会, 尹建芹, 韩旭, 等. 一种基于关节点信息的人体行为识别新方法 [J]. 机器人, 2014, 36(3): 285-292.

TIAN Guohui, YIN Jianqin, HAN Xu, et al. A novel human activity recognition method using joint points information [J]. Robot, 2014, 36(3): 285-292.

HARRINGTON M E, ZAVATSKY A B, LAWSON S E, et al. Prediction of the hip joint centre in adults, children, and patients with cerebral palsy based on magnetic resonance imaging [J]. Journal of Biomechanics, 2007, 40(3): 595-602.

HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313: 504-507.

余凯, 贾磊, 陈雨强, 等. 深度学习的昨天、今天和明天 [J]. 计算机研究与发展, 2013, 50(9): 1799-1804.

YU Kai, JIA Lei, CHEN Yuqiang, et al. Deep learning: yesterday, today, and tomorrow [J]. Journal of Computer Research and Development, 2013, 50(9): 1799-1804.

张春霞, 姬楠楠, 王冠伟. 受限波尔兹曼机 [J]. 工程数学学报, 2015, 32(2): 159-173.

ZHANG Chunxia, JI Nannan, WANG Guanwei. Restricted Boltzmann machines [J]. Chinese Journal of Engineering Mathematics, 2015, 32(2): 159-173.

陈江城, 张小栋, 李睿, 等. 利用表面肌电信号的下肢动态关节力矩预测模型 [J]. 西安交通大学学报, 2015, 49(12): 33-36.

CHEN Jiangcheng, ZHANG Xiaodong, LI Rui, et al. Prediction model for dynamic joint torque of lower limb with surface EMG [J]. Journal of Xi'an Jiaotong University, 2015, 49(12): 33-36.

TD C, SN G, DJ E, et al. A six degrees-of-freedom marker set for gait analysis: repeatability and comparison with a modified Helen Hayes set [J]. Gait Posture, 2009, 30(2): 173-180.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

利用肌肉协同研究上肢简单和复杂运动的关联性

采用变分模态分解与领域自适应的表面肌电信号手势识别

电子级超纯氮深冷空分流程节能优化研究

多工况下离心压气机叶轮-机匣一体联合设计优化

离子液体改性燃料的电流体动力学效应研究