2.624

2020影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于有监督Kohonen神经网络的步态识别

郭欣 王蕾 宣伯凯 李彩萍

郭欣, 王蕾, 宣伯凯, 李彩萍. 基于有监督Kohonen神经网络的步态识别. 自动化学报, 2017, 43(3): 430-438. doi: 10.16383/j.aas.2017.c160114
引用本文: 郭欣, 王蕾, 宣伯凯, 李彩萍. 基于有监督Kohonen神经网络的步态识别. 自动化学报, 2017, 43(3): 430-438. doi: 10.16383/j.aas.2017.c160114
GUO Xin, WANG Lei, XUAN Bo-Kai, LI Cai-Ping. Gait Recognition Based on Supervised Kohonen Neural Network. ACTA AUTOMATICA SINICA, 2017, 43(3): 430-438. doi: 10.16383/j.aas.2017.c160114
Citation: GUO Xin, WANG Lei, XUAN Bo-Kai, LI Cai-Ping. Gait Recognition Based on Supervised Kohonen Neural Network. ACTA AUTOMATICA SINICA, 2017, 43(3): 430-438. doi: 10.16383/j.aas.2017.c160114

基于有监督Kohonen神经网络的步态识别

doi: 10.16383/j.aas.2017.c160114
基金项目: 

河北省青年自然基金 F2016202327

河北省高等学校科学技术研究项目 ZC2016020

河北省高等学校科学技术研究项目 Q2012079

详细信息
    作者简介:

    王蕾  河北工业大学控制科学与工程学院硕士研究生.主要研究方向为模式识别.E-mail:lei.wang2@siat.ac.cn

    宣伯凯  河北工业大学控制科学与工程学院博士研究生.主要研究方向为智能下肢假肢.E-mail:xuanbokai@126.com

    李彩萍  河北工业大学控制科学与工程学院硕士.主要研究方向为智能假肢.E-mail:licaiping0113@163.com

    通讯作者:

    郭欣  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康复装置和计算机控制.本文通信作者.E-mail:gxhebut@aliyun.com

Gait Recognition Based on Supervised Kohonen Neural Network

Funds: 

Natural Science Foundation of Hebei Province F2016202327

Science & Technology Research Project of Higher Education of Hebei Province ZC2016020

Science & Technology Research Project of Higher Education of Hebei Province Q2012079

More Information
    Author Bio:

     Master student at the School of Control Science and Engineering, Hebei University of Technology. Her main research interest is pattern recognition.

     Ph.D. candidate at the School of Control Science and Engineering, Hebei University of Technology. His main research interest is intelligent prostheses.

     Master at the School of Control Science and Engineering, Hebei University of Technology. Her main research interest is intelligent prostheses.

    Corresponding author: GUO Xin  Ph.D., professor at the School of Control Science and Engineering, Hebei University of Technology. His research interest covers rehabilitation device and computer control. Corresponding author of this paper.
  • 摘要: 表面肌电信号随着时间的变化而改变,这将影响运动模式的分类精度.传统人体下肢假肢运动模式的识别算法不能保证在整个肌电控制时间内达到对运动模式的有效识别.为了解决这些问题,本文提取步态初期200ms的信号的特征值,将无监督和有监督的Kohonen神经网络算法应用到大腿截肢者残肢侧的步态识别中,并与传统BP神经网络进行了对比.结果表明,有监督的Kohonen神经网络算法将五种路况下步态的平均识别率提高到88.4%,优于无监督的Kohonen神经网络算法和BP神经网络.
  • 图  1  大腿主要肌肉

    Fig.  1  The thigh muscles

    图  2  5种步态下肌电采集实验

    Fig.  2  EMG acquisition under five gait conditions

    图  3  Trigno采集系统

    Fig.  3  Trigno acquisition system

    图  4  定制的假肢接受腔

    Fig.  4  Customized prosthetic socket

    图  5  波形预处理前后对比图

    Fig.  5  Pre-pretreatment and post-pretreatment curves

    图  6  FPE函数阶数准则曲线

    Fig.  6  FPE function order criterion curve

    图  7  特征值筛选结果

    Fig.  7  The selection of eigenvalue

    图  8  Kohonen算法流程图

    Fig.  8  Flowchart of Kohonen algorithm

    图  9  确定初始权值流程图

    Fig.  9  The flowchart of determination of initial weight value

    图  10  有监督Kohonen聚类算法流程图

    Fig.  10  The flowchart of S_Kohonen clustering algorithm

    图  11  不同特征向量对步态识别结果

    Fig.  11  The gait recognition results of different feature vectors

    图  12  S_Kohonen、BP、Kohonen三种算法的平均识别率对比

    Fig.  12  Comparison of average recognition rate of S_Kohonen, BP and Kohonen algorithm

    表  1  大腿截肢者5种步态的功率谱比值

    Table  1  Power spectrum ratio of five gaits

    平地 上楼梯 下楼梯 上斜坡 下斜坡
    股直肌 6.7530 5.2074 4.2859 3.7062 2.0640
    股外侧肌 2.6390 3.0697 2.8058 3.5630 3.0875
    股二头肌 1.5962 2.4860 3.5804 2.2074 3.6307
    半腱肌 4.8403 5.2830 4.7083 5.0642 4.0974
    阔筋膜张肌 2.3746 3.4390 5.0261 5.9803 6.7549
    臀大肌 3.9827 3.7650 3.0548 3.2769 2.9067
    下载: 导出CSV

    表  2  大腿截肢者股外侧肌的4阶模型参数

    Table  2  The 4th order model parameters of vastus

    ${\boldsymbol A\boldsymbol R}_1$ ${\boldsymbol A\boldsymbol R}_2$ ${\boldsymbol A\boldsymbol R}_3$ ${\boldsymbol {AR}}_4$
    平地 4.3927 4.2654 4.3761 4.1862
    上楼梯 -2.9846 -2.7062 -2.6873 -2.8306
    下楼梯 4.6834 4.7635 4.5980 2.2074
    上斜坡 3.1370 3.2537 3.3207 3.1752
    下斜坡 -0.8349 -0.7859 -0.8263 -0.7952
    下载: 导出CSV

    表  3  步态识别结果

    Table  3  The results of gait recognition

    平地 上楼梯 下楼梯 上斜坡 下斜坡
    训练样本数 100 100 100 100 100
    测试样本数 50 50 50 50 50
    识别样本数 47 45 42 43 44
    识别率 (%) 94 90 84 86 88
    平均识别率 (%) 88.4
    下载: 导出CSV
  • [1] Chen B J, Wang Q N. Combining human volitional control with intrinsic controller on robotic prosthesis: a case study on adaptive slope walking. In: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Milan, Italy: IEEE, 2015. 4777-4780
    [2] Cherelle P, Mathijssen G, Wang Q N, Vanderborght B, Lefeber D. Advances in propulsive bionic feet and their actuation principles. Advances in Mechanical Engineering, 2014, 2014: 984046 https://www.researchgate.net/publication/266384382_Review_Article_Advances_in_Propulsive_Bionic_Feet_and_Their_Actuation_Principles
    [3] Meier M R, Hansen A H, Gard S A, McFadyen A K. Obstacle course: users' maneuverability and movement efficiency when using Otto Bock C-Leg, Otto Bock 3R60, and the CaTech SNS prosthetic knee joints. Journal of Rehabilitation Research and Development, 2012, 49(4): 583-596 doi: 10.1682/JRRD.2010.05.0094
    [4] Hoover C D, Fulk G D, Fite K B. The design and initial experimental validation of an active myoelectric transfemoral prosthesis. Journal of Medical Devices, 2012, 6(1): 011005 doi: 10.1115/1.4005784
    [5] 田彦涛, 孙中波, 李宏扬, 王静.动态双足机器人的控制与优化研究进展.自动化学报, 2016, 42(8): 1143-1157 http://www.aas.net.cn/CN/abstract/abstract18904.shtml

    Tian Yan-Tao, Sun Zhong-Bo, Li Hong-Yang, Wang Jing. A review of optimal and control strategies for dynamic walking bipedal robots. Acta Automatica Sinica, 2016, 42(8): 1143-1157 http://www.aas.net.cn/CN/abstract/abstract18904.shtml
    [6] Hoover C D, Fulk G D, Fite K B. Stair ascent with a powered transfemoral prosthesis under direct myoelectric control. IEEE/ASME Transactions on Mechatronics, 2013, 18(3): 1191-1200 doi: 10.1109/TMECH.2012.2200498
    [7] Kim D H, Cho C H, Ryu J. Real-time locomotion mode recognition employing correlation feature analysis using EMG pattern. ETRI Journal, 2014, 36(1): 99-105 doi: 10.4218/etrij.14.0113.0064
    [8] Zhang F, Huang H. Source selection for real-time user intent recognition toward volitional control of artificial legs. IEEE Journal of Biomedical and Health Informatics, 2013, 17(5): 907-914 doi: 10.1109/JBHI.2012.2236563
    [9] Huang H, Zhang F, Hargrove L J, Dou Z, Rogers D R, Englehart K B. Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion. IEEE Transactions on Biomedical Engineering, 2011, 58(10): 2867-2875 doi: 10.1109/TBME.2011.2161671
    [10] 陈国兴, 耿艳利, 刘作军, 杨鹏.假肢跌倒预警中基于相关性分析的模糊自适应反馈调节.机器人, 2015, 37(6): 732-737, 747 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201506012.htm

    Chen Guo-Xing, Geng Yan-Li, Liu Zuo-Jun, Yang Peng. Fuzzy adaptive feedback regulation for stumble pre-warning of lower limb prosthesis based on the correlation analysis. Robot, 2015, 37(6): 732-737, 747 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201506012.htm
    [11] 赵丽娜, 刘作军, 苟斌, 杨鹏.基于隐马尔可夫模型的动力型下肢假肢步态预识别.机器人, 2014, 36(3): 337-341 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201403012.htm

    Zhao Li-Na, Liu Zuo-Jun, Gou Bin, Yang Peng. Gait pre-recognition of dynamic lower limb prosthesis based on hidden Markov model. Robot, 2014, 36(3): 337-341 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201403012.htm
    [12] Peng L, Hou Z G, Kasabov K, Hu J, Peng L, Wang W Q. sEMG-based torque estimation for robot-assisted lower limb rehabilitation. In: Proceedings of the 2015 International Joint Conference on Neural Networks. Ireland, County Kerry: IEEE, 2015. DOI: 10.1109/IJCNN.2015.7280449.
    [13] 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 http://www.aas.net.cn/CN/abstract/abstract18792.shtml

    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 http://www.aas.net.cn/CN/abstract/abstract18792.shtml
    [14] Vallery H, Burgkart R, Hartmann C, Mitternacht J, Riener R, Buss M. Complementary limb motion estimation for the control of active knee prostheses. Biomedizinische Technik. Biomedical Engineering, 2011, 56(1): 45-51 doi: 10.1515/bmt.2010.057
    [15] 刘洪涛, 曹玉珍, 谢小波, 胡勇.表面肌电信号的时变AR模型参数评估肌疲劳程度的研究.中国生物医学工程学报, 2007, 26(4): 493-497 http://www.cnki.com.cn/Article/CJFDTOTAL-ZSWY200704002.htm

    Liu Hong-Tao, Cao Yu-Zhen, Xie Xiao-Bo, Hu Yong. Estimation of muscle fatigue degree using time-varying autoregressive model parameter estimation of surface electromyography. Chinese Journal of Biomedical Engineering, 2007, 26(4): 493-497 http://www.cnki.com.cn/Article/CJFDTOTAL-ZSWY200704002.htm
    [16] 张培林, 李胜.基于小波包变换和GA-PLS算法的故障特征选择方法.振动、测试与诊断, 2014, 34(2): 385-391 http://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS201402035.htm

    Zhang Pei-Lin, Li Sheng. Fault feature selection method based on wavelet and GA-PLS algorithm. Journal of Vibration, Measurement & Diagnosis, 2014, 34(2): 385-391 http://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS201402035.htm
    [17] Zhou S, Lawson D L, Morrison W E, Fairweather I. Electromechanical delay in isometric muscle contractions evoked by voluntary, reflex and electrical stimulation. European Journal of Applied Physiology and Occupational Physiology, 1995, 70(2): 138-145 doi: 10.1007/BF00361541
    [18] Lawson B E, Varol H A, Huff A, Erdemir E, Goldfarb M. Control of stair ascent and descent with a powered transfemoral prosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012, 21(3): 466-473 https://www.researchgate.net/publication/232699496_Control_of_Stair_Ascent_and_Descent_With_a_Powered_Transfemoral_Prosthesis
    [19] Huang S, Ferris D P. Muscle activation patterns during walking from transtibial amputees recorded within the residual limb-prosthetic interface. Journal of Neuroengineering and Rehabilitation, 2012, 9: 55. DOI: 10.1186/1743-0003-9-55
    [20] Hargrove L J, Simon A M, Lipschutz R D, Finucane S B, Kuiken T A. Real-time myoelectric control of knee and ankle motions for transfemoral amputees. JAMA, 2011, 305(15): 1542-1544 doi: 10.1001/jama.2011.465
    [21] Sagawa Y Jr, Turcot K, Armand S, Thevenon A, Vuillerme N, Watelain E. Biomechanics and physiological parameters during gait in lower-limb amputees: a systematic review. Gait & Posture, 2011, 33(4): 511-526 http://www.academia.edu/17420300/Biomechanics_and_physiological_parameters_during_gait_in_lower-limb_amputees_A_systematic_review
  • 加载中
图(12) / 表(3)
计量
  • 文章访问数:  2131
  • HTML全文浏览量:  465
  • PDF下载量:  1012
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-02-04
  • 录用日期:  2016-10-10
  • 刊出日期:  2017-03-20

目录

    /

    返回文章
    返回