Please wait a minute...
浙江大学学报(工学版)  2018, Vol. 52 Issue (10): 1980-1988    DOI: 10.3785/j.issn.1008-973X.2018.10.018
自动化技术     
下肢假肢穿戴者跑动步态识别方法
赵晓东1, 刘作军1,2, 陈玲玲1,2, 杨鹏1,2
1. 河北工业大学 控制科学与工程学院, 天津 300130;
2. 智能康复装置与检测技术教育部工程研究中心, 天津 300130
Approach of running gait recognition for lower limb amputees
ZHAO Xiao-dong1, LIU Zuo-jun1,2, CHEN Ling-ling1,2, YANG Peng1,2
1. School of Control Science and Engineering, Hebei University of Technology, Tianjin 300130, China;
2. Engineering Research Center of Intelligent Rehabilitation and Detecting Technology, Ministry of Education, Tianjin 300130, China
 全文: PDF(1201 KB)   HTML
摘要:

提出基于粒子群(PSO)优化支持向量机(SVM)的下肢假肢穿戴者跑动步态识别方法.将假肢接受腔装配的肌电(EMG)传感器、加速度计和足底的压力传感器采集的假肢穿戴者跑动运动信息进行去噪预处理,对应提取加速度的偏度、均值与肌电信号均方根多个特征参数作归一化处理,结合双下肢足底压力信息组成多维特征向量,作为SVM的输入,解决了单一特征识别步态的低准确率问题.利用PSO优化分类模型参数,建立基于SVM的次序二叉树分类模型对跑动步态进行辨识.与传统BP神经网络的步态识别方法对比表明,利用PSO优化SVM方法能够将跑动步态识别率提高到92.78%,优于SVM和BP神经网络.

Abstract:

An approach based on particle swarm optimization (PSO) and support vector machine (SVM) was proposed for running gait recognition of lower limb prosthesis wearers. The electromyography (EMG) sensors and accelerators installed in the prosthetic socket and pressure sensors installed in the plantar were used to acquire amputee's running motion information, and the sensors data were denoised correspondingly. Then the skewness and mean of motion acceleration and the root-mean-square of EMG were chosen and normalized as feature parameters. These parameters were combined with the plantar pressure information to form multi-feature vector as the input of SVM, which solved the problem of low recognition accuracy of single feature. PSO was used to optimize classification model parameters. The binary tree model based on SVM was established to identify the running gait. The experimental results show that the recognition correct rate is 92.78%, which is higher than SVM and traditional BP neural networks.

收稿日期: 2017-05-28 出版日期: 2018-10-11
CLC:  TP242  
基金资助:

国家自然科学基金资助项目(61174009,61203323);河北省青年自然科学基金资助项目(F2016202327);河北省高等学校技术研究资助项目(ZC2016020).

通讯作者: 刘作军,男,教授.orcid.org/0000-0001-7671-4665.     E-mail: liuzuojun@hebut.edu.cn
作者简介: 赵晓东(1991-),男,博士生,从事智能假肢、模式识别的研究.orcid.org/0000-0002-5855-671X.E-mail:zxd0829@163.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  

引用本文:

赵晓东, 刘作军, 陈玲玲, 杨鹏. 下肢假肢穿戴者跑动步态识别方法[J]. 浙江大学学报(工学版), 2018, 52(10): 1980-1988.

ZHAO Xiao-dong, LIU Zuo-jun, CHEN Ling-ling, YANG Peng. Approach of running gait recognition for lower limb amputees. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(10): 1980-1988.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2018.10.018        http://www.zjujournals.com/eng/CN/Y2018/V52/I10/1980

[1] LAWSON B E, VAROL H A, GOLDFARB M. Standing stability enhancement with an intelligent powered trans-femoral prosthesis[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(9):2617-2624.
[2] VANDEN A J, SAMOREZOV S, DAVIS B L, et al. Modeling and optimal control of an energy-storing prosthetic knee[J]. Journal of Biomechanical Engineering, 2012, 134(5):145-151.
[3] SHULTZ A H, LAWSON B E, GOLDFARB M. Running with a powered knee and ankle prosthesis[J]. IEEE Transactions on Neural System and Rehabilitation Engineering, 2015, 23(3):403-412.
[4] RIGNEY S, SIMMONS A, KARK L. Finite element analysis of a lower-limb running-specific prosthesis[C]//Australasian Congress on Applied Mechanics. Australia:[s. n.], 2014:297-305.
[5] 黄岩, 谢广明, 杨晓华, 等. 半被动双足机器人动态行走的位姿估算[J]. 北京大学学报:自然科学版, 2009, 45(4):565-571 HUANG Yan, XIE Guang-ming, YANG Xiao-hua, et al. Body state estimation in a quasi-passive bipedal robot during dynamic walking[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2009, 45(4):565-571
[6] 王启宁, 郑恩昊, 陈保君, 等. 面向人机融合的智能动力下肢假肢研究现状与挑战[J]. 自动化学报, 2016, 42(12):1780-1793 WANG Qi-ning, ZHENG En-hao, CHENG Bao-jun, et al. Recent progress and challenges of robotic lower-limb prostheses for human robot integration[J]. Acta Automatica Sinica, 2016, 42(12):1780-1793
[7] 杨鹏, 刘作军, 耿艳利, 等. 智能下肢假肢关键技术研究进展[J]. 河北工业大学学报, 2013, 42(1):76-80 YANG Peng, LIU Zuo-jun, GENG Yan-li, et al. Research advance on key technology of intelligent lower limb prosthesis[J]. Journal of Hebei University of Technology, 2013, 42(1):76-80
[8] 赵丽娜, 刘作军, 苟斌, 等. 基于隐马尔可夫模型的动力型下肢假肢步态预识别[J]. 机器人, 2014, 36(3):337-341 ZHAO Li-na, LIU Zuo-jun, GOU Bin, et al. Gait pre-recognition of dynamic lower limb prosthesis based on hidden Markov model[J]. Robot, 2014, 36(3):337-341
[9] HUANG H, ZHANG F, HARGROVE L J, et al. Contnuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(10):2867-2875.
[10] 喻洪流, 徐兆红, 卢博睿, 等. 基于Fuzzy-CMAC的人体假肢系统智能控制方法研究[J]. 中国生物医学工程学报, 2012, 31(1):83-88 YU Hong-liu, XU Zhao-hong, LU Bo-rui, et al. Study on intelligent control of human body prosthetic leg based on fuzzy-CMAC[J]. Chinese Journal of Biomedical Engineering, 2012, 31(1):83-88
[11] 陈国兴, 刘作军, 陈玲玲, 等. 假肢穿戴者跌倒预警系统设计[J]. 华中科技大学学报:自然科学版, 2015, 43(增1):294-297. CHEN Guo-xing, LIU Zuo-jun, CHEN Ling-ling, et al.Design of a stumble pre-warning system for lower limb amputees[J]. Journal of Huazhong University of Science and Technology:Natural Science Edition, 2015, 43(supple.1):294-297.
[12] 杨建坤. 大腿假肢穿戴者在滑倒过程中的平衡策略研究及其应用[D]. 北京:清华大学, 2006. YANG Jian-kun. Studies on human balance strategy of trans-femoral prosthes is users during slip gait and its application[D]. Beijing:Tsinghua University, 2006.
[13] 陈国兴, 耿艳利, 刘作军, 等. 假肢跌倒预警中基于相关性分析的模糊自适应反馈调节[J]. 机器人, 2015, 37(6):732-737 CHEN Guo-xing, GENG Yan-li, LIU Zuo-jun, et al. Fuzzy adaptive feedback regulation for stumble pre-warning of lower limb prosthesis based on the correlation analysis[J]. Robot, 2015, 37(6):732-737
[14] 刘磊, 杨鹏, 刘作军. 采用多核相关向量机的人体步态识别[J]. 浙江大学学报:工学版, 2017, 51(3):562-571 LIU Lei, YANG Peng, LIU Zuo-jun. Locomotion-mode using multiple kernel relevance vector machine[J]. Journal of Zhejiang University:Engineering Science, 2017, 51(3):562-571
[15] YOUNG A G, SIMON A M, HARGROVE L J. A training method for locomotion mode prediction using powered lower limb prostheses[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014, 22(3):671-677.
[16] XIA J, CHANUSSOT J, DU P, et al. Rotation-based support vector machine ensemble in classification of hyperspectral data with limited training samples[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 54(3):1519-1531.
[17] 唐明珠, 阳春华, 桂卫华. 基于改进的QBC和CS-SVM的故障检测[J]. 控制与决策, 2012, 27(10):1489-1493 TANG Ming-zhu, YANG Chun-hua, GUI Wei-hua. Fault detection based on modified QBC and CS-SVM[J]. Control and Decision, 2012, 27(10):1489-1493
[18] 钟崴, 彭梁, 周永刚, 等. 基于小波包分析和支持向量机的锅炉结渣诊断[J]. 浙江大学学报:工学版, 2016, 50(8):1499-1506 ZHONG Wei, PENG Liang, ZHOU Yong-gang, et al. Slagging diagnosis of boiler based on wavelet packet analysis and support vector machine[J]. Journal of Zhejiang University:Engineering Science, 2016, 50(8):1499-1506
[19] 袁胜发, 褚福磊. 支持向量机及其在机械故障诊断中的应用[J]. 振动与冲击, 2007, 26(11):29-35 YUAN Sheng-fa, CHU Fu-lei. Support vector machines and its applications in machine fault diagnosis[J]. Journal of Vibration and Shock, 2007, 26(11):29-35
[20] 姜明辉, 袁绪川, 冯玉强. PSO-SVM的模型的构建与应用[J]. 哈尔滨工业大学学报, 2009, 41(2):169-172 JIANG Ming-hui, YUAN Xu-chuan, FENG Yu-qiang. Construction and application of PSO-SVM model[J]. Journal of Harbin Institute of Technology, 2009, 41(2):169-172
[21] 张涛, 张明辉, 李清伟, 等. 基于粒子群-支持向量机的时间序列分类诊断模型[J]. 同济大学学报:自然科学版, 2016, 44(9):1450-1457 ZHANG Tao, ZHANG Ming-hui, LI Qing-wei, et al. Time serious classfication diagnosis model based on partical swarm optimization and support vector machine[J]. Journal of Tongji University:Natural Science, 2016, 44(9):1450-1457
[22] DONOHO D L, JOHNSTONE J M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3):425-455.

[1] 王晨学, 平雪良, 徐超. 解决约束平面偏移问题的机械臂闭环标定[J]. 浙江大学学报(工学版), 2018, 52(11): 2110-2119.
[2] 王硕朋, 杨鹏, 孙昊. 听觉定位数据库构建过程优化[J]. 浙江大学学报(工学版), 2018, 52(10): 1973-1979.
[3] 傅晓云, 雷磊, 杨钢, 李宝仁. 喷水推进型水下滑翔机的水平翼参数配置及定常运动分析[J]. 浙江大学学报(工学版), 2018, 52(8): 1499-1508.
[4] 李中雯, 王斌锐, 陈迪剑. 有并联脊柱的四足机器人步态规划[J]. 浙江大学学报(工学版), 2018, 52(7): 1267-1274.
[5] 柯显信, 张文朕, 杨阳, 温雷. 仿人机器人多传感器定位系统[J]. 浙江大学学报(工学版), 2018, 52(7): 1247-1252.
[6] 李泚泚, 田国会, 张梦洋, 张营. 基于本体的物品属性类人认知及推理[J]. 浙江大学学报(工学版), 2018, 52(7): 1231-1238.
[7] 陈迪剑, 徐一展, 王斌锐. 基于双生成函数的步行机器人最优步态生成[J]. 浙江大学学报(工学版), 2018, 52(7): 1253-1259.
[8] 吴炳龙, 曲道奎, 徐方. 基于力/位混合控制的工业机器人精密轴孔装配[J]. 浙江大学学报(工学版), 2018, 52(2): 379-386.
[9] 潘立, 鲍官军, 胥芳, 张立彬. 六自由度装配机器人的动态柔顺性控制[J]. 浙江大学学报(工学版), 2018, 52(1): 125-132.
[10] 谷雨, 李平, 韩波. 基于分层粒子滤波的地标检测与跟踪[J]. J4, 2010, 44(4): 687-691.
[11] 蒋荣欣, 张亮, 田翔, 陈耀武. 多机器人队形变换最优效率求解[J]. J4, 2010, 44(4): 722-727.
[12] 刘楚辉, 姚宝国, 柯映林. 工业机器人切削加工离线编程研究[J]. J4, 2010, 44(3): 426-431.
[13] 李强, 王宣银, 程佳. Stewart液压平台轨迹跟踪自适应滑模控制[J]. J4, 2009, 43(6): 1124-1128.
[14] 奚海燕, 牟同升, 李俊凯, 等. 平板显示器彩色运动伪像的测量与评价[J]. J4, 2009, 43(6): 1158-1162.
[15] 程邦胜, 唐孝威. Harris尺度不变性关键点检测子的研究[J]. J4, 2009, 43(5): 855-859.