A Review on Researches and Applications of sEMG-based Motion Intent Recognition Methods
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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.Abstract: Surface electromyography (sEMG) signals are human's own resources, which contain a wealth of information associated with one's movement. Thus, there is a natural advantage in utilizing sEMG signals as interface media to construct human-robot interaction (HRI) systems. The key to realize a natural HRI with sEMG is recognizing human's motion intention from sEMG signals, which usually involves three aspects, i.e., classifying discrete motion modes, estimating continuous movements of joints, and estimating stiffness or impedance of joints. This paper fully collects the researches on methods of sEMG-based motion recognition, and summarizes the features of current studies. Afterwards, this paper introduces the application status of sEMG-based motion recognition technology, and discusses the key issues constraining its marketing applications. Finally, future development of the technology is presented.
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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 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 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
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] WPT LDA+MLP 4 9种手/腕
部动作否 97.4 LDA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, WPTPCA+LDA 4 6种上
肢动作否 $>96$ WPT等时频域特征
能提高动作分类精度[24] DFT幅值,
ARCKNN 2 5种手指
按压动作否 $>93$ 利用GA算法可自主选择
与动作最相关sEMG特征[25] MAV, SSC,
ARCANFIS 4 6种手/腕
部动作否 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,
RMSGMM 4 6种上
肢动作否 $>95$ 设计并优化GMM, 其分
类效果优于LDA、MLP[27] RMS, ARC SVM 4 5种手部
姿势否 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, ARC HMM 4 6种上
肢动作否 94.6 设计并优化HMM, 其动
作分类效果优于MLP[29] MAV, ZC,
SSC, WLMCLPBoot 6 7种上肢动
作/姿势否 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, WLLDA+ANN 8 8种手/腕
部动作是 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, SSCLDA 12 10种手/腕
部动作是 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LLGMN 5 6种手/腕
部动作否 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, 8 4~12种
手/腕单一/
联合运动是 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 2 5~9种
上肢动作是 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,
RMSLDA, KNN 57(高密
度电极)6种手部
抓取动作是 $>97$ 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). 
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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 否 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 否 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 否 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 否 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 否 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 否 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 是 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 是 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 否 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 否 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.
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