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基于结构优化的DDAG-SVM上肢康复训练动作识别方法

左国玉 徐兆坤 卢佳豪 龚道雄

左国玉, 徐兆坤, 卢佳豪, 龚道雄. 基于结构优化的DDAG-SVM上肢康复训练动作识别方法. 自动化学报, 2020, 46(3): 549-561. doi: 10.16383/j.aas.c170724
引用本文: 左国玉, 徐兆坤, 卢佳豪, 龚道雄. 基于结构优化的DDAG-SVM上肢康复训练动作识别方法. 自动化学报, 2020, 46(3): 549-561. doi: 10.16383/j.aas.c170724
ZUO Guo-Yu, XU Zhao-Kun, LU Jia-Hao, GONG Dao-Xiong. A Structure-optimized DDAG-SVM Action Recognition Method for Upper Limb Rehabilitation Training. ACTA AUTOMATICA SINICA, 2020, 46(3): 549-561. doi: 10.16383/j.aas.c170724
Citation: ZUO Guo-Yu, XU Zhao-Kun, LU Jia-Hao, GONG Dao-Xiong. A Structure-optimized DDAG-SVM Action Recognition Method for Upper Limb Rehabilitation Training. ACTA AUTOMATICA SINICA, 2020, 46(3): 549-561. doi: 10.16383/j.aas.c170724

基于结构优化的DDAG-SVM上肢康复训练动作识别方法

doi: 10.16383/j.aas.c170724
基金项目: 

国家自然科学基金 61873008

国家自然科学基金 61673003

北京市自然科学基金 4182008

北京工业大学智能制造领域大科研推进计划 JZ041001201702

详细信息
    作者简介:

    徐兆坤  北京工业大学信息学部硕士研究生.主要研究方向为模式识别和机器学习. E-mail: 21xzk@sina.com.cn

    卢佳豪  北京工业大学信息学部硕士研究生.主要研究方向为模式识别和机器人学习. E-mail: ljh lujiahao@163.com

    龚道雄  博士, 北京工业大学信息学部副教授.主要研究方向为计算智能与机器人学. E-mail: gongdx@bjut.edu.cn

    通讯作者:

    左国玉  博士, 北京工业大学信息学部副教授.主要研究方向为智能技术系统, 机器人学习和机器人控制.本文通信作者. E-mail: zuoguoyu@bjut.edu.cn

A Structure-optimized DDAG-SVM Action Recognition Method for Upper Limb Rehabilitation Training

Funds: 

National Natural Science Foundation of China 61873008

National Natural Science Foundation of China 61673003

Beijing Natural Science Foundation 4182008

Beijing University of Technology Big Scientific Promoting Plan on Intelligent Manufacturing JZ041001201702

More Information
    Author Bio:

    XU Zhao-Kun Master student at the Faculty of Information Technology, Beijing University of Technology. His research interest covers pattern recognition and machine learning

    LU Jia-Hao Master student at the Faculty of Information Technology, Beijing University of Technology. His research interest covers pattern recognition and robot learning

    GONG Dao-Xiong Ph. D., associate professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers computational intelligence and robotics

    Corresponding author: ZUO Guo-Yu Ph. D., associate professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent technology systems, robot learning, and robot control. Corresponding author of this paper
  • 摘要: 针对上肢康复训练系统中训练评估方法核心的动作识别问题, 提出一种面向Brunnstrom 4~5期患者上肢康复训练动作的SODDAG-SVM (Structure-optimized decision directed acyclic graph-support vector machine)多分类识别方法.首先将多分类问题分解成一组二分类问题, 并使用支持向量机构建各二分类器, 分别采用遗传算法和特征子集区分度准则对各二分类器的核函数参数及特征子集进行优化.然后使用类对的SVM二分类器泛化误差来衡量每个类对的易被分离程度, 并由其建立类对泛化误差上三角矩阵.最后由根节点开始, 依次根据各节点的泛化误差矩阵, 通过选择其中最易被分离类对的SVM分类器构成该节点的方式, 来构建SODDAG-SVM多分类器结构.当待预测的实例较少时, 直接构建实例经过的SODDAG-SVM部分结构并对实例进行预测; 当待预测的实例较多时, 先构建完整的SODDAG-SVM结构, 再代入所有实例进行预测.通过人体传感技术获得Brunnstrom 4~5阶段上肢康复训练的常用动作样本集, 进行SODDAG-SVM动作识别实验, 准确率达到了95.49%, 结果均优于常规的决策有向无环图(Decision directed acyceic graph, DDAG)和MaxWins方法, 实验表明本文方法能有效地提高上肢康复训练动作识别的准确率.
    Recommended by Associate Editor BAI Xiang
    1)  本文责任编委 白翔
  • 图  1  投票策略产生的不可分区域响

    Fig.  1  An inseparable region in voting strategy

    图  2  三分类器DDAG结构示意图

    Fig.  2  A DDAG structure of a three-class classifier

    图  3  消除了不可分区域的DDAG分类

    Fig.  3  Elimination of inseparable region in a DDAG classifier

    图  4  SODDAG-SVM预测算法示意图

    Fig.  4  Diagram of SODDAG-SVM prediction algorithm

    图  5  SODDAG-SVM完整结构示意图

    Fig.  5  Diagram of the complete structure of SODDAG-SVM

    图  6  起始动作及6组动作示意图

    Fig.  6  Start action and six types of training actions

    图  7  特征选择对于SVM二分类器性能的影响

    Fig.  7  Effect of feature selection on the accuracy of SVM binary classifier

    图  8  SODDAG-SVM完整结构图

    Fig.  8  Complete structure of SODDAG-SVM

    图  9  消除不可分区域示意图

    Fig.  9  Inseparable region elimination diagram

    表  1  $C$, $g$参数寻优结果

    Table  1  Optimization results of the parameters of $C$, $g$

    类对 最优$C$ 最优$g$ 最优准确率(%)
    1v2 0.009793572 0.0022394 100
    1v3 0.33971 1.08$\times 10^6$ 100
    1v4 1 9.73$\times 10^6$ 95.5517
    1v5 0.613049119 0.001941097 89.6552
    1v6 1.10$\times 10^6$ 0.00721982 95.4371
    2v3 1 0.000019687 88.6836
    2v4 0.023029107 0.019702158 83.8836
    2v5 0.967574582 0.003192183 100
    2v6 1 7.00$\times 10^13$ 100
    3v4 0.5 0.000033106 90
    3v5 0.25 6.03$\times 10^6$ 96.6689
    3v6 1 0.000047963 86.6545
    4v5 0.26493253 0.008829793 83.3582
    4v6 0.825159853 0.020969172 88.6883
    5v6 3.1249 0.000052136 86.6667
    下载: 导出CSV

    表  2  特征子集选择结果

    Table  2  Feature subset selection results

    类对 最优特征子集 测试集准确率(%)
    1v2 全部特征 100
    1v3 全部特征 100
    1v4 16, 1, 38, 25, 29, 7, 11, 20, 33, 50, 17, 30, 47 100
    1v5 26, 39, 1 95.4767
    1v6 26, 37 95
    2v3 37, 24, 46 100
    2v4 16, 29, 47, 37, 24, 21 100
    2v5 21, 16, 29, 47, 34, 50, 37, 24, 46, 39, 26, 43, 13, 1, 52, 25, 32, 38, 45, 19, 4, 11, 41, 20, 28, 15, 42 100
    2v6 39, 37, 24, 47, 50, 26, 21 100
    3v4 37, 46, 42 94.6536
    3v5 27, 14, 46, 42, 39, 26, 21, 17, 41, 13, 51, 30, 50, 16, 8, 7, 32, 19, 45 95.3467
    3v6 13, 39, 26, 46, 17, 30, 42 100
    4v5 29, 16, 39, 26, 19 100
    4v6 39, 16, 26, 13, 29, 24 100
    5v6 16 100
    下载: 导出CSV

    表  3  分类准确率对比(%)

    Table  3  Comparison of classification accuracy(%)

    DDAG SODDAG MaxWins
    平均值 92.09 95.49 91.04
    最大值 96.89 / /
    最小值 89.83 / /
    下载: 导出CSV

    表  4  时间开销对比(s)

    Table  4  Comparison of time cost (s)

    DDAG SODDAG MaxWins
    时间 0.7459 1.0167 1.5723
    下载: 导出CSV

    表  5  SODDAG-SVM混淆矩阵

    Table  5  SODDAG-SVM confusion matrix

    类别 1 2 3 4 5 6
    1 180 0 0 0 12 8
    2 0 200 0 0 0 0
    3 0 0 182 9 9 0
    4 0 0 6 194 0 0
    5 3 0 4 0 193 0
    6 3 0 0 0 0 197
    下载: 导出CSV

    表  6  类对1v5混淆矩阵

    Table  6  1v5 class pair confusion matrix

    类别 1 5
    1 186 14
    5 4 196
    下载: 导出CSV

    表  7  类对1v6混淆矩阵

    Table  7  1v6 class pair confusion matrix

    类别 1 6
    1 187 13
    6 7 193
    下载: 导出CSV

    表  8  类对3v4混淆矩阵

    Table  8  3v4 class pair confusion matrix

    类别 3 4
    3 191 9
    4 12 188
    下载: 导出CSV

    表  9  类对3v5混淆矩阵

    Table  9  3v5 class pair confusion matrix

    类别 3 5
    3 190 10
    5 9 191
    下载: 导出CSV
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  • 收稿日期:  2017-12-23
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