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基于表面肌电的运动意图识别方法研究及应用综述

丁其川 熊安斌 赵新刚 韩建达

丁其川, 熊安斌, 赵新刚, 韩建达. 基于表面肌电的运动意图识别方法研究及应用综述. 自动化学报, 2016, 42(1): 13-25. doi: 10.16383/j.aas.2016.c140563
引用本文: 丁其川, 熊安斌, 赵新刚, 韩建达. 基于表面肌电的运动意图识别方法研究及应用综述. 自动化学报, 2016, 42(1): 13-25. doi: 10.16383/j.aas.2016.c140563
DING Qi-Chuan, XIONG An-Bin, ZHAO Xin-Gang, HAN Jian-Da. A Review on Researches and Applications of sEMG-based Motion Intent Recognition Methods. ACTA AUTOMATICA SINICA, 2016, 42(1): 13-25. doi: 10.16383/j.aas.2016.c140563
Citation: DING Qi-Chuan, XIONG An-Bin, ZHAO Xin-Gang, HAN Jian-Da. A Review on Researches and Applications of sEMG-based Motion Intent Recognition Methods. ACTA AUTOMATICA SINICA, 2016, 42(1): 13-25. doi: 10.16383/j.aas.2016.c140563

基于表面肌电的运动意图识别方法研究及应用综述

doi: 10.16383/j.aas.2016.c140563
基金项目: 

国家自然科学基金 61273355, 61503374, 61573340

国家高技术研究发展计划 (863计划) 2015AA042301

机器人学国家重点实验室自主课题 2015-z06)

详细信息
    作者简介:

    熊安斌 2015年获得中国科学院大学博士学位.主要研究方向为肌电信号处理与模式识别.E-mail:xiongab@sia.cn

    赵新刚 中国科学院沈阳自动化研究所研究员.2008年获得中国科学院沈阳自动化研究所博士学位.主要研究方向为机器人控制、智能系统与康复机器人.E-mail:zhaoxingang@sia.cn

    韩建达 中国科学院沈阳自动化研究所研究员.1998年获得哈尔滨工业大学博士学位.主要研究方向为可穿戴机器人, 智能系统, 移动机器人自主控制.E-mail:jdhan@sia.cn

    通讯作者:

    丁其川 中国科学院沈阳自动化研究所副研究员.2014年获得中国科学院大学博士学位.主要研究方向为生物电信号处理, 模式识别, 穿戴机器人.本文通信作者.E-mail:dingqichuan@sia.cn

A Review on Researches and Applications of sEMG-based Motion Intent Recognition Methods

Funds: 

National Natural Science Foundation of Chin 61273355, 61503374, 61573340

Supported by National High Technology Research and Development Program of China (863 Program) 2015AA042301

and Self-planned Project of the State Key Laboratory of Robotics 2015-z06)

More Information
    Author Bio:

    He received his Ph.D. degree from University of Chinese Academy of Sciences in 2015. His research interest covers EMG signal processing and pattern recognition

    Professor at the Shenyang Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from Shenyang Institute of Automation, Chinese Academy of Sciences in 2008. His research interest covers robot control, rehabilitation robots, and intelligent systems

    Professor at the Shenyang Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from Harbin Institute of Technology in 1998. His research interest covers wearable robots, intelligent systems, and control for the autonomy of mobile robots

    Corresponding author: DING Qi-Chuan Associate professor at the Shenyang Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from University of Chinese Academy of Sciences in 2014. His research interest covers biological signal processing, pattern recognition, and wearable robots. Corresponding author of this paper
  • 摘要: 表面肌电信号 (Surface electromyography, sEMG) 是人体自身的资源, 蕴含着关联人体运动的丰富信息, 用它作为交互媒介以构建人机交互 (Human-robot interaction, HRI) 系统有天然的优势.通过肌电信号实现人机自然交互的关键是由肌电信号识别出人体运动意图, 通常包括离散动作模态分类、关节连续运动量估计及关节刚度/阻抗估计等三方面内容.本文详细归纳基于表面肌电的运动识别方法研究成果, 总结当前研究的特点; 随后, 介绍基于表面肌电的运动识别技术的应用现状, 并探讨制约其推广的主要问题; 最后, 展望该技术的未来发展.
  • 图  1  运动单元 (每条运动神经对应一个运动单元)

    Fig.  1  Motor units (One motor nerve is corresponding to one motor unit.)

    图  2  采集sEMG信号

    Fig.  2  Sampling sEMG signals

    图  3  基于sEMG的人机交互过程

    Fig.  3  The process of sEMG-based HRI

    图  4  基于sEMG的离散动作分类

    Fig.  4  sEMG-based discrate-motion classification

    图  5  一种提取肌肉活跃度u方法

    Fig.  5  A method of extracting muscle activities u

    图  6  基于sEMG的连续运动估计及机器人控制

    Fig.  6  sEMG-based continuous-movement estimation and robot control

    图  7  Hill肌肉模型

    Fig.  7  Hill-type muscle model

    图  8  MANUS-HAND灵巧手

    Fig.  8  The MANUS-HAND dextrous hand

    图  9  i-LIMB仿生手

    Fig.  9  The i-LIMB bionic hand

    图  10  7-DOF上肢外骨骼康复机器人EXO-UL3

    Fig.  10  The 7-DOF upper-limb exoskeleton rehabilitation robot EXO-UL3

    图  11  TUPLEE下肢外骨骼

    Fig.  11  The TUPLEE lower extremity exoskeleton

    图  12  HAL外骨骼

    Fig.  12  The HAL exoskeleton

    表  1  离散动作分类研究1

    Table  1  Researches on discrete-motion classification1

    文献sEMG特征降维
    分类算法
    sEMG
    通道数
    动作残疾人
    测试?
    平均分类
    精度 (%)
    小结
    [13]WPTLDA+MLP49种手/腕
    部动作
    97.4LDA投影特征能提高动作识
    别精度, 用于假手在线控制
    [23]TD, STFT,
    WT, WPT
    PCA+LDA46种上
    肢动作
    $>96$ WPT等时频域特征
    能提高动作分类精度
    [24]DFT幅值,
    ARC
    KNN25种手指
    按压动作
    $>93$ 利用GA算法可自主选择
    与动作最相关sEMG特征
    [25]MAV, SSC,
    ARC
    ANFIS46种手/腕
    部动作
    92设计一种多步强化分类
    器, 其性能优于ANN、
    贝叶斯分类器等
    [26]TD, ARC,
    RMS
    GMM46种上
    肢动作
    $>95$ 设计并优化GMM, 其分
    类效果优于LDA、MLP
    [27]RMS, ARCSVM45种手部
    姿势
    73基于双线性变换, 提出一种
    与个体无关的动作分类方法
    [28]RMS, ARCHMM46种上
    肢动作
    94.6设计并优化HMM, 其动
    作分类效果优于MLP
    [29]MAV, ZC,
    SSC, WL
    MCLPBoot67种上肢动
    作/姿势
    92, 80利用Boosting随机森林分
    类器降低未训练数据的干扰
    [30]RMS, ZC,
    SSC, WL
    LDA+ANN88种手/腕
    部动作
    59~92提出一种可以检测并修正
    动作错误分类的后处理算法
    [31]MAV, ZC,
    WL, SSC
    LDA1210种手/腕
    部动作
    84.4针对单侧截肢患者, 评估利
    用其健侧与截肢侧肌肉的
    sEMG进行肌电控制的效果
    [32]归一化
    sEMG值
    LLGMN56种手/腕
    部动作
    82.6~97.7利用任务上下文信息进
    行动作分类, 完成机械
    臂辅助进餐实验验证
    [33]MAV, ZC,
    WL, SSC
    条件并联
    分类算法
    6, 84~12种
    手/腕单一/
    联合运动
    93.4, 89.1设计条件并联分类器, 分
    类多自由度联合运动模态
    [34]RMS,
    log (RMS)
    模糊C-均值25~9种
    上肢动作
    79.9, 92.7设计一种用户自主选择动
    作类的实时肌电分类方法
    [35]TD, ARC,
    RMS
    LDA, KNN57(高密
    度电极)
    6种手部
    抓取动作
    $>97$ 针对颈脊髓损伤患者, 由
    其瘫痪肢体肌肉sEMG
    识别出运动意图
    1表 1中涉及的sEMG特征:时域特征 (Time domain feature, TD)、短时傅里叶变换 (Short-time Fourier transform, STFT)、小波变换 (Wavelet transform, WT)、小波包变换 (Wavelet packet transform, WPT)、离散傅里叶变换 (Discrete Fourier transform, DFT)、自回归模型系数 (Autoregressive model coefficients, ARC)、均方根 (Root mean square, RMS)、平均绝对值 (Mean absolute value, MAV)、零穿越次数 (Number of zero-crossings, ZC)、波长 (Wave length, WL)、斜率符号变化次数 (Slope sign changes, SSC); 特征投影算法:主元分析 (Principal components analysis, PCA)、遗传算法 (Genetic algorithm, GA); 分类模型: $K$ 近邻 ( $K$ -nearest neighbor, KNN)、线性判别分析 (Linear discriminant analysis, LDA)、隐马尔可夫模型 (Hidden Markov model, HMM)、高斯混合模型 (Gaussian mixture model, GMM)、多层感知器 (Multi layer perceptron, MLP)、人工神经网络 (Artificial neural network, ANN)、支持向量机 (Support vector machine, SVM)、对数线性高斯混合网络 (Log-linearized Gaussian mixture network, LLGMN)、自适应神经模糊交互系统 (Adaptive neuro-fuzzy inference system, ANFIS)、线性规划Boosting算法 (Linear programming boosting algorithm, MCLPBoot).
    下载: 导出CSV

    表  2  关节连续运动估计研究2

    Table  2  Researches on estimation of joints' continuous-movements2

    文献模型连续运动量sEMG
    通道数
    残疾人
    测试?
    小结
    [43]基于Hill模型的
    前向神经肌骨模型
    关节力矩、角
    速度、角度等
    10建立包括肌肉活跃度、肌肉收缩、
    肌骨几何、关节动力/运动学等子
    模型的神经肌骨动力模型
    [45]改进的Hill
    肌肉力模型
    上肢肘/腕
    关节力矩
    28改进Hill肌肉力模型, 利用
    遗传算法辨识参数, 构建
    肌电控制上肢康复系统
    [46]简化的Hill
    肌肉力模型
    下肢膝关
    节力矩
    6简化Hill肌肉力模型, 设计
    分步标定法辨识参数, 构建
    肌电控制下肢外骨骼系统
    [47]神经肌骨动
    力学模型
    下肢髋/膝
    关节力矩
    16建立"多肌肉-多肌骨"的一般化
    运动模型, 实现多关节力矩估计
    [48]基于Hill模型
    的状态空间模型
    肘关节角
    度、角速度
    1提取sEMG特征作为量测输出,
    建立估计关节运动的状态空间模型
    [49]DRNN下肢关节角
    度、角速度等
    6设计全连接自适应DRNN
    网络, 估计关节连续运动量
    [50]BPNN下肢踝/膝/髋
    关节角度
    7预测健康者与脊髓损伤患者的
    下肢关节运动, 实现下肢康复
    设备的自主肌电控制
    [51]NMF, LR, ANN腕关节角度16比较3种不同模型预测关
    节角度离线与在线性能
    [52-53]线性状态
    空间模型
    上肢关节角度及
    手部运动位置
    9建立状态空间方程, 描述肌肉活跃
    度与关节运动量映射关系, 并引入
    补偿疲劳干扰的模型自适应机制
    [54]高阶多项
    式模型
    肘关节
    屈/伸角度
    2利用高阶多项式插值方法建
    立sEMG特征与规则化肘关
    节角度的映射模型
    2表 2中涉及的算法:反向传播神经网络 (Back propagation neural network, BPNN)、动态递归神经网络 (Dynamic recurrent neural network, DRNN)、非负矩阵分解 (Non-negative matrix factorization, NMF)、线性回归 (Linear regression, LR), 其他同表 1.
    下载: 导出CSV
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  • 收稿日期:  2014-08-04
  • 录用日期:  2015-10-10
  • 刊出日期:  2016-01-01

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