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基于表面肌电的下肢肌肉功能网络构建及其应用研究

陈玲玲 李珊珊 刘作军 张燕

陈玲玲, 李珊珊, 刘作军, 张燕. 基于表面肌电的下肢肌肉功能网络构建及其应用研究. 自动化学报, 2017, 43(3): 407-417. doi: 10.16383/j.aas.2017.c160230
引用本文: 陈玲玲, 李珊珊, 刘作军, 张燕. 基于表面肌电的下肢肌肉功能网络构建及其应用研究. 自动化学报, 2017, 43(3): 407-417. doi: 10.16383/j.aas.2017.c160230
CHEN Ling-Ling, LI Shan-Shan, LIU Zuo-Jun, ZHANG Yan. Construction of Lower Limb's Functional Muscle Network and Its Application Based on Surface EMG. ACTA AUTOMATICA SINICA, 2017, 43(3): 407-417. doi: 10.16383/j.aas.2017.c160230
Citation: CHEN Ling-Ling, LI Shan-Shan, LIU Zuo-Jun, ZHANG Yan. Construction of Lower Limb's Functional Muscle Network and Its Application Based on Surface EMG. ACTA AUTOMATICA SINICA, 2017, 43(3): 407-417. doi: 10.16383/j.aas.2017.c160230

基于表面肌电的下肢肌肉功能网络构建及其应用研究

doi: 10.16383/j.aas.2017.c160230
基金项目: 

河北省高等学校科学技术研究项目 Q2012079

天津市应用基础与前沿技术研究计划 13JCQNJC03400

国家自然科学基金 61174009

国家自然科学基金 61203323

详细信息
    作者简介:

    陈玲玲博士, 河北工业大学控制科学与工程学院副教授.主要研究方向为康复辅具控制, 模式识别.E-mail:chenling@hebut.edu.cn

    刘作军博士, 河北工业大学控制科学与工程学院教授.主要研究方向为智能机器人, 下肢假肢, 智能建筑.E-mail:nankailzj@eyou.com

    张燕博士, 河北工业大学控制科学与工程学院教授.主要研究方向为智能假肢, 预测控制, 多模型控制.E-mail:yzhangz@163.com

    通讯作者:

    李珊珊河北工业大学控制科学与工程学院硕士研究生.主要研究方向为复杂网络, 外骨骼机器人.本文通信作者.E-mail:18222706765@163.com

Construction of Lower Limb's Functional Muscle Network and Its Application Based on Surface EMG

Funds: 

Colleges and Universities in Hebei Province Science and Technology Research Project Q2012079

Tianjin Research Program of Application Foundation and Advanced Technology 13JCQNJC03400

National Natural Science Foundation of China 61174009

National Natural Science Foundation of China 61203323

More Information
    Author Bio:

    Ph. D., associate professor at the School of Control Science and Engineering, Hebei University of Technology. Her research interest covers rehabilitation technical aids control and pattern recognition

    Ph. D., professor at the School of Control Science and Engineering, Hebei University of Technology. His research interest covers intelligent robot, lower limb prostheses, and intelligent building

    Ph. D., professor at the School of Control Science and Engineering, Hebei University of Technology. Her research interest covers intelligent prostheses, predictive control, and multiple model control

    Corresponding author: LI Shan-ShanMaster student at the School of Control Science and Engineering, Hebei University of Technology. Her research interest covers complex networks and exoskeleton robot. Corresponding author of this paper
  • 摘要: 在肌电控制下肢康复辅具研究中,合适的肌电采集位置是运动模式识别的前提与基础.针对目前肌电采集位置缺乏成熟理论依据和统一标准的问题,选取90个下肢肌电采集点作为节点,通过计算节点间的肌电相关性,构建下肢肌肉功能网络,证明其具有小世界特性.实验结果表明:不同运动模式的网络具有明显的拓扑结构差异,通过网络特性分析可以确定与模式关联度大的肌电采集位置,取得较好的运动模式识别结果.通过构建及分析下肢肌肉功能网络,深入了解下肢运动模式更替过程中的肌肉协同工作机制,为下肢康复辅具控制中肌电采集位置的确定提供了理论支持.
    1)  本文责任编委 赵新刚
  • 图  1  左腿肌电电极放置位置

    Fig.  1  EMG electrodes placement of left leg

    图  2  一个步态周期膝关节角度与EMG

    Fig.  2  The knee angle and EMG of a gait stride

    图  3  网络稀疏度与阈值关系

    Fig.  3  The relationship of network sparsity and threshold

    图  4  三种运动模式的邻接矩阵

    Fig.  4  Adjacency matrix under three motions

    图  5  右侧大腿肌电电极放置位置

    Fig.  5  EMG electrodes placement on the right

    图  6  三种模式的肌肉功能网络

    Fig.  6  The functional muscle network of three patterns

    图  7  三种模式间具有显著差异的节点

    Fig.  7  The nodes with significant difference among three patterns

    表  1  肌电电极分区与分布情况

    Table  1  The partition and distribution of EMG electrodes

    区域 个数 节点 名称 位置 分布情况
    1 12 V01~V12 LTF1~LTF12 左侧大腿前侧 四排三列
    2 11 V13~V23 LTP1~LTP12 左腿大腿后侧 三列 (三排一列+四排两列)
    3 10 V24~V33 LTL1~LTL10 左腿大腿外侧 五排两列
    4 4 V34~V37 LCF1~LCF4 左侧小腿前侧 纵向排列
    5 8 V38~V45 LCP1~LCP8 左腿小腿后侧 四排两列
    6 12 V46~V57 RTF1~RTF12 右侧大腿前侧 四排三列
    7 11 V58~V68 RTP1~RTP12 右腿大腿后侧 三列 (三排一列+四排两列)
    8 10 V69~V78 RTL1~RTL10 右腿大腿外侧 五排两列
    9 4 V79~V82 RCF1~RCF4 右侧小腿前侧 纵向排列
    10 8 V83~V90 RCP1~RCP8 右腿小腿后侧 四排两列
    下载: 导出CSV

    表  2  受试者信息

    Table  2  The information of subjects

    实验对象 性别 年龄 身高 (cm) 体重 (kg)
    S1 24 165 48
    S2 24 170 54
    S3 45 162 65
    S4 65 155 73
    S5 23 172 80
    S6 27 180 95
    S7 42 175 70
    S8 62 170 72
    下载: 导出CSV

    表  3  三种模式复杂网络的小世界特性参量统计

    Table  3  Three models of complex networks of small world characteristic parameter statistics

    运动模式 $n$ $langlekrangle$ $edges$ $CC$ $L$ $C_{nc}$ $L_{nc}$ $C_{ER}$ $L_{ER}$ $gamma=C/{C_{ER}}$ $lambda=L/{L_{ER}}$ $sigma=gamma/lambda$
    平地 90 30.0 2487 0.8883 1.7857 0.7241 1.3833 0.3659 1.2992 2.4277 1.3745 1.7662
    上楼梯 90 13.1 1091 0.8874 2.3736 0.6880 3.1679 0.1598 1.7129 5.5532 1.3857 4.0075
    下楼梯 90 16.1 1337 0.8318 2.0372 0.7003 2.5776 0.1963 1.5902 4.2374 1.2811 3.3076
    下载: 导出CSV

    表  4  不同阈值下的网络统计特性

    Table  4  Network statistical characteristics under different threshold

    $TH$ 稀疏度$S_p$ 平均度$langlekrangle$ 聚类系数$CC$
    0.5 0.4916 40.81 0.7527
    0.55 0.4266 35.41 0.7665
    0.6 0.3648 30.28 0.7708
    0.65 0.3024 25.10 0.7899
    0.7 0.2472 20.52 0.7945
    0.75 0.1941 16.11 0.8318
    0.8 0.1418 11.77 0.8250
    0.85 0.0910 7.55 0.8193
    0.9 0.0565 4.69 0.7194
    0.95 0.0344 2.86 0.5770
    下载: 导出CSV

    表  5  不同模式下节点介数特性统计

    Table  5  Node betweenness under different model

    排序 平地 下楼 上楼
    1 RTL4h   280.1 LCP6h   1644.4 RCP4h   557.2
    2 RTF9h   277.7 RTP6h   1473.8 RCP8h   366.5
    3 RTL10h   267.0 RCP3h   1369.9 RCP6h   344.4
    4 LTP5h   236.4 LTL4h   1304.2 RCP5h   327.2
    5 RTF10h   208.8 RTP3h   1216.6 RTP6h   318.2
    6 RTF7h   180.6 RCP2h   1212.6 LTP3h   301.6
    7 LTF10h   161.4 RTL1h   1198.6 RTF8h   287.5
    8 LCP3h   151.1 RTL9h   1141.3 RTP3h   221.2
    9 RTF1h   135.9 RTP2h   1048.7 RTP1h   218.5
    10 LCP5h   128.4 RTL2h   932.4 RTL8h   193.0
    下载: 导出CSV

    表  6  不同模式下节点介数特性统计

    Table  6  Node betweenness under different model

    排序 节点标号 位置
    1 RTP5 右腿大腿后侧
    2 LTP1 左腿大腿后侧
    3 LTL7 左腿大腿后侧
    4 LTF9 左腿大腿前侧
    5 RTL6 右腿大腿外侧
    6 RCP2 右腿小腿后侧
    7 LCP5 左腿小腿后侧
    8 RTF11 右腿大腿前侧
    下载: 导出CSV

    表  7  不同特征提取方法的模式识别比较

    Table  7  Comparison of pattern recognition based on different methods of feature extraction

    序号 单侧肌肉数目 肌肉采集点 识别率 (%)
    LDA SVM NN
    S1 12 大腿:RF, VL, BF, VM, ST, SM, AL, KTF小腿:TA, PL, GM, SM 78.1 82.3 80.2
    S2 8 大腿:RF, VL, BF, VM, ST小腿:TA, GM, SM 91.4 93.1 92.2
    S3 6 大腿:RF, VL, BF, VM小腿:TA, GM 95.6 97.6 97.3
    S4 3 大腿:RF, VL小腿:SM[6] 84.4 85.7 86.4
    S5 4 大腿:VM, SM, AL, KTF[7] 87.4 90.6 89.9
    S6 4 下肢肌肉功能网络分析结果 95.3 98.4 97.9
    下载: 导出CSV
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  • 收稿日期:  2016-03-01
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