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惯性动捕数据驱动下的智能下肢假肢运动意图识别方法

苏本跃 王婕 刘双庆 盛敏 向馗

苏本跃, 王婕, 刘双庆, 盛敏, 向馗. 惯性动捕数据驱动下的智能下肢假肢运动意图识别方法. 自动化学报, 2020, 46(7): 1517-1530. doi: 10.16383/j.aas.c180070
引用本文: 苏本跃, 王婕, 刘双庆, 盛敏, 向馗. 惯性动捕数据驱动下的智能下肢假肢运动意图识别方法. 自动化学报, 2020, 46(7): 1517-1530. doi: 10.16383/j.aas.c180070
SU Ben-Yue, WANG Jie, LIU Shuang-Qing, SHENG Min, XIANG Kui. An Improved Motion Intent Recognition Method for Intelligent Lower Limb Prosthesis Driven by Inertial Motion Capture Data. ACTA AUTOMATICA SINICA, 2020, 46(7): 1517-1530. doi: 10.16383/j.aas.c180070
Citation: SU Ben-Yue, WANG Jie, LIU Shuang-Qing, SHENG Min, XIANG Kui. An Improved Motion Intent Recognition Method for Intelligent Lower Limb Prosthesis Driven by Inertial Motion Capture Data. ACTA AUTOMATICA SINICA, 2020, 46(7): 1517-1530. doi: 10.16383/j.aas.c180070

惯性动捕数据驱动下的智能下肢假肢运动意图识别方法

doi: 10.16383/j.aas.c180070
基金项目: 

国家自然科学基金 61603003

国家自然科学基金 11471093

教育部科技发展中心"云数融合科教创新"基金 2017A09116

安徽省高校优秀拔尖人才培育资助项目 gxbjZD26

详细信息
    作者简介:

    王婕 安庆师范大学计算机与信息学院、安徽省智能感知与计算高校重点实验室硕士研究生.主要研究方向为机器学习, 运动意图识别.E-mail: wangjiexiaogui@126.com

    刘双庆  安庆师范大学数学与计算科学学院、安徽省智能感知与计算高校重点实验室硕士研究生.主要研究方向为机器学习, 运动意图识别.E-mail: liushuangqing11@126.com

    盛敏 安庆师范大学数学与计算科学学院、安徽省智能感知与计算高校重点实验室教授. 2009年获得合肥工业大学计算机与信息学院博士学位.主要研究方向为模式识别与图像及视频处理.E-mail: msheng0125@aliyun.com

    向馗  武汉理工大学自动化学院教授.2006年获得浙江大学控制理论与控制工程博士学位.主要研究方向为人体行走机理, 仿生智能关节.E-mail: xkarcher@126.com

    通讯作者:

    苏本跃  安庆师范大学计算机与信息学院、安徽省智能感知与计算高校重点实验室教授. 2007年获得合肥工业大学计算机与信息学院博士学位.主要研究方向为模式识别与机器学习, 图形图像处理.本文通信作者.E-mail: bysu@aqnu.edu.cn

An Improved Motion Intent Recognition Method for Intelligent Lower Limb Prosthesis Driven by Inertial Motion Capture Data

Funds: 

National Nature Science Foundation of China 61603003

National Nature Science Foundation of China 11471093

Funds of Integration of Cloud Computing and Big Data, Innovation of Science and Technology of Ministry of Education of China 2017A09116

Anhui Provincial Department of Education Outstanding Top-notch Talent-funded Projects gxbjZD26

More Information
    Author Bio:

    WANG Jie Master student at the School of Computer and Information, Anqing Normal University and the University Key Laboratory of Intelligent Perception and Computing of Anhui Province. Her research interest covers machine learning and pattern recognition

    LIU Shuang-Qing Master student at the School of Mathematics and Computational Science, Anqing Normal University and the University Key Laboratory of Intelligent Perception and Computing of Anhui Province. His research interest covers machine learning and pattern recognition

    SHENG Min Professor at the School of Mathematics and Computational Science, Anqing Normal University and the University Key Laboratory of Intelligent Perception and Computing of Anhui Province. She received her Ph. D. degree from the School of Computer and Information, Hefei University of Technology in 2009. Her research interest covers pattern recognition and image & video processing

    XIANG Kui Professor at the School of Automation, Wuhan University of Technology. He received his Ph. D. degree from control theory and control engineering, Zhejiang University in 2006. His research interest covers human walking mechanism and bio-inspired intelligent joint

    Corresponding author: SU Ben-Yue Professor at the School of Computer and Information, Anqing Normal University and the University Key Laboratory of Intelligent Perception and Computing of Anhui Province. He received his Ph. D. degree from the School of Computer and Information, Hefei University of Technology in 2007. His research interest covers pattern recognition, machine learning, image processing, and computer graphics. Corresponding author of this paper
  • 摘要: 为了解决传统意图识别方法使用多模态传感器信号所带来的复杂性以及识别转换模式一般具有滞后性等问题, 本文提出了基于惯性传感器的智能下肢假肢的运动意图实时识别方法.从模式识别的角度看, 在对象空间到模式空间的转换中, 对运动模式尤其是运动转换模式进行了重定义; 在模式采集中, 采用在患侧的运动模式进行转换之前, 采集绑定在健侧的传感器于摆动相前期所产生的时序运动数据, 选择均值、方差等特征统计量和支持向量机分类器对其进行特征选择提取与特征分类的策略, 实现对残疾人运动意图准确、实时地识别.实验结果表明, 本文所提出的方法可以识别出单肢截肢患者在不同地形下的运动意图, 包括平地行走、上楼、下楼、上坡、下坡5种稳态模式, 识别率可达到97.52 %, 并且加入在5种模式之间相互转换的转换模式之后, 识别率可达到95.12 %.本文方法可以极大提高智能下肢假肢的控制性能, 实现智能假肢能根据人的运动意图在多种运动模式之间进行自然、无缝的状态切换.
    Recommended by Associate Editor CHEN Ji-Ming
    1)  本文责任编委 陈积明
  • 图  1  智能下肢假肢中的分层策略

    Fig.  1  Hierarchical control strategy of robotic lower-limb prostheses

    图  2  基于机械传感器的人体运动意图识别方法

    Fig.  2  Method of motion intent recognition based on mechanical sensors

    图  3  一个步态周期

    Fig.  3  Gait cycle

    图  4  支撑相与摆动相

    Fig.  4  Stance phase and swing phase

    图  5  稳态模式与稳态步

    Fig.  5  Steady state and steady step

    图  6  转换模式与转换步(平地行走—上坡)

    Fig.  6  Transitional state and transitional step (level ground to up ramp)

    图  7  转换模式与转换步(上楼—平地行走)

    Fig.  7  Transitional state and transitional step (up stair to level ground)

    图  8  加速度信号(平地行走到上楼)

    Fig.  8  Signals of accelerometer (level ground to stair ascent)

    图  9  触地状态

    Fig.  9  Signals of contact state with ground

    图  10  本文所提取窗口数据

    Fig.  10  Data extracted by the window method in this paper

    图  11  转换模式样本的加速度信号(大腿处传感器数据)

    Fig.  11  Acceleration signals of transitional state (data of sensor placed on thigh)

    图  12  实验设备与实验环境

    Fig.  12  equipment and environment

    图  13  惯性传感器穿戴示意

    Fig.  13  Position of inertial sensors

    图  14  5种稳态模式的混淆矩阵

    Fig.  14  Confusion matrix of five steady states

    图  15  13种运动模式的混淆矩阵

    Fig.  15  Confusion matrix of 13 motion states classes

    图  16  传感器置于患侧时5种稳态模式的混淆矩阵

    Fig.  16  Confusion matrix of steady states when sensors placed at affected side

    图  17  传感器置于患侧时13种运动模式的混淆矩阵

    Fig.  17  Confusion matrix of 13 motion states classes when sensors placed at affected side

    图  18  正常组13种运动模式的混淆矩阵

    Fig.  18  Confusion matrix of 13 motion states classes with simulation of normal subjects

    表  1  基于机械传感器的人体运动意图识别研究

    Table  1  Research on motion intent recognition based on mechanical sensors

    文献 机械传感器类型 机械信号特征 分类器 模式种类 识别精度
    Liu等[10] (2017) 加速度计、陀螺仪、压力传感器 组内相关系数 HMM 5 95.8%
    Young等[11] (2014) 加速度计、陀螺仪、压力传感器 均值、标准差、最大值、最小值 LDA 13 93.9%
    Young等[12] (2014) 惯性测量单元、压力传感器等 均值、标准差、最大值、最小值 DBN 5 94.7%
    Zheng等[14] (2017) 加速度计、陀螺仪、压力传感器 均值、标准差、绝对值等 SVM+QDA 8 94.9%
    Young等[16] (2016) 惯性测量单元、压力传感器 均值、标准差、最大值、最小值 DBN 13 90.0%
    Chen等[17] (2015) 惯性测量单元、压力传感器 均值、标准差、最大值等 LDA+QDA 6 90.0%
    下载: 导出CSV

    表  2  13种不同运动模式

    Table  2  13 classes of motion states

    编号 行为模式 行为模式描述
    1 走—上楼 平地行走到上楼转换
    2 走—下楼 平地行走到下楼转换
    3 走—上坡 平地行走到上坡转换
    4 走—下坡 平地行走到下坡转换
    5 上坡—走 上坡到平地行走转换
    6 下坡—走 下坡到平地行走转换
    7 上楼—走 上楼到平地行走转换
    8 下楼—走 下楼到平地行走转换
    9 行走 平地行走
    10 上楼 稳步上台阶
    11 下楼 稳步下台阶
    12 上坡 稳步上坡
    13 下坡 稳步下坡
    下载: 导出CSV

    表  3  运动模式及迈步顺序

    Table  3  Motions and step sequence

    运动模式 迈步顺序具体描述
    5种稳态模式 健侧—模拟患侧—健侧—模拟患侧
    走—上楼 健侧(平地行走)—模拟患侧(平地行走)—健侧(平地行走向上楼转换)—模拟患侧(平地行走向上楼转换)
    走—下楼 健侧(平地行走)—模拟患侧(平地行走)—健侧(平地行走向下楼转换)—模拟患侧(平地行走向下楼转换)
    走—上坡 健侧(平地行走)—模拟患侧(平地行走)—健侧(平地行走向上坡转换)—模拟患侧(平地行走向上坡转换)
    走—下坡 健侧(平地行走)—模拟患侧(平地行走)—健侧(平地行走向下坡转换)—模拟患侧(平地行走向下坡转换)
    上楼—走 模拟患侧(上楼)—健侧(上楼)—模拟患侧(上楼向平地行走转换)—健侧(上楼向平地行走转换)
    下楼—走 模拟患侧(下楼)—健侧(下楼)—模拟患侧(下楼向平地行走转换)—健侧(下楼向平地行走转换)
    上坡—走 健侧(上坡)—模拟患侧(上坡)—健侧(上坡向平地行走转换)—模拟患侧(上坡向平地行走转换)
    下坡—走 健侧(下坡)—模拟患侧(下坡)—健侧(下坡向平地行走转换)—模拟患侧下坡向平地行走转换)
    下载: 导出CSV

    表  4  传感器置于患侧时的迈步顺序

    Table  4  Step sequence of the situation where sensors placed at affected side

    运动模式 迈步顺序具体描述
    走—上楼 模拟患侧(平地行走)—健侧(平地行走)—模拟患侧(平地行走向上楼转换)—健侧(平地行走向上楼转换)
    走—下楼 模拟患侧(平地行走)—健侧(平地行走)—模拟患侧(平地行走向下楼转换)—健侧(平地行走向下楼转换)
    走—上坡 模拟患侧(平地行走)—健侧(平地行走)—模拟患侧(平地行走向上坡转换)—健侧(平地行走向上坡转换)
    走—下坡 模拟患侧(平地行走)—健侧(平地行走)—模拟患侧(平地行走向下坡转换)—健侧(平地行走向下坡转换)
    上楼—走 健侧(上楼)—模拟患侧(上楼)—健侧(上楼向平地行走转换)—模拟患侧(上楼向平地行走转换)
    下楼—走 健侧(下楼)—模拟患侧(下楼)—健侧(下楼向平地行走转换)—模拟患侧(下楼向平地行走转换)
    上坡—走 模拟患侧(上坡)—健侧(上坡)—模拟患侧(上坡向平地行走转换)—健侧(上坡向平地行走转换)
    下坡—走 模拟患侧(下坡)—健侧(下坡)—模拟患侧(下坡向平地行走转换)—健侧(下坡向平地行走转换)
    下载: 导出CSV

    表  5  正常组的迈步顺序

    Table  5  Step sequence of the situation with simulation of normal subjects

    运动模式 迈步顺序具体描述
    5种稳态模式 健侧(左)—健侧(右)—健侧(左)—健侧(右)
    走—上楼左侧 (平地行走)—右侧(平地行走)—左侧(平地行走向上楼转换)—右侧(平地行走向上楼转换)
    走—下楼左侧 (平地行走)—右侧(平地行走)—左侧(平地行走向下楼转换)—右侧(平地行走向下楼转换)
    走—上坡左侧 (平地行走)—右侧(平地行走)—左侧(平地行走向上坡转换)—右侧(平地行走向上坡转换)
    走—下坡左侧 (平地行走)—右侧(平地行走)—左侧(平地行走向下坡转换)—右侧(平地行走向下坡转换)
    上楼—走右侧 (上楼)—左侧(上楼)—右侧(上楼向平地行走转换)—左侧(上楼向平地行走转换)
    下楼—走右侧 (下楼)—左侧(下楼)—右侧(下楼向平地行走转换)—左侧(下楼向平地行走转换)
    上坡—走左侧 (上坡)—右侧(上坡)—左侧(上坡向平地行走转换)—右侧(上坡向平地行走转换)
    下坡—走左侧 (下坡)—右侧(下坡)—左侧(下坡向平地行走转换)—右侧(下坡向平地行走转换)
    下载: 导出CSV

    表  6  方法与实验结果对比

    Table  6  Comparison of the methods and experimental results

    文献 传感器类型/数量 传感器位置 机械信号特征 分类器 运动模式种类 识别精度
    稳态 转换
    Liu等[10] (2017) 1个加速度计、1个陀螺仪、2个压力传感器 患侧 组内相关系数 HMM 5 / 95.8%
    Young等[11] (2014) 3个加速度计、3个陀螺仪、1个压力传感器 患侧 均值、标准差、最大值、最小值 LDA 5 8 (下一模式已发生, 有滞后性) 93.9%
    Young等[12] (2014) 1个惯性测量单元、1个压力传感器等 患侧 均值、标准差、最大值、最小值 DBN 5 / 94.7%
    Zheng等[14] (2017) 2个加速度计、2个陀螺仪、1个压力传感器 患侧 均值、标准差、绝对值等 SVM+QDA / 8 (下一模式已发生, 有滞后性) 94.9%
    Young等[16] (2016) 1个惯性测量单元、1个压力传感器 患侧 均值、标准差、最大值、最小值 DBN 5 8 (下一模式已发生, 有滞后性) 90.0%
    Chen等[17] (2015) 2个惯性测量单元、1个压力传感器 患侧 均值、标准差、最大值等 LDA+QDA 6 / 90.0%
    本文方法 3个惯性测量单元 健侧 均值、方差、 SVM 5 / 97.52%
    最大值与最小值 5 8 (下一模式未发生, 无滞后性) 95.12%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-01-27
  • 录用日期:  2018-04-16
  • 刊出日期:  2020-07-24

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