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基于气压肌动图和改进神经模糊推理系统的手势识别研究

汪雷 黄剑 段涛 伍冬睿 熊蔡华 崔雨琦

汪雷, 黄剑, 段涛, 伍冬睿, 熊蔡华, 崔雨琦. 基于气压肌动图和改进神经模糊推理系统的手势识别研究. 自动化学报, 2022, 48(5): 1220−1233 doi: 10.16383/j.aas.c200901
引用本文: 汪雷, 黄剑, 段涛, 伍冬睿, 熊蔡华, 崔雨琦. 基于气压肌动图和改进神经模糊推理系统的手势识别研究. 自动化学报, 2022, 48(5): 1220−1233 doi: 10.16383/j.aas.c200901
Wang Lei, Huang Jian, Duan Tao, Wu Dong-Rui, Xiong Cai-Hua, Cui Yu-Qi. Research on gesture recognition based on pressure-based mechanomyogram and improved neural fuzzy inference system. Acta Automatica Sinica, 2022, 48(5): 1220−1233 doi: 10.16383/j.aas.c200901
Citation: Wang Lei, Huang Jian, Duan Tao, Wu Dong-Rui, Xiong Cai-Hua, Cui Yu-Qi. Research on gesture recognition based on pressure-based mechanomyogram and improved neural fuzzy inference system. Acta Automatica Sinica, 2022, 48(5): 1220−1233 doi: 10.16383/j.aas.c200901

基于气压肌动图和改进神经模糊推理系统的手势识别研究

doi: 10.16383/j.aas.c200901
基金项目: 国家自然科学基金联合基金重点支持项目(U19132207), 湖北省技术创新专项(2019AEA171), 科技部政府间国际科技创新合作重点专项(2017YFE0128300)资助
详细信息
    作者简介:

    汪雷:华中科技大学人工智能与自动化学院硕士研究生. 2019年获得华中科技大学学士学位. 主要研究方向为机器学习, 手势识别. E-mail: wml0531@hust.edu.cn

    黄剑:华中科技大学人工智能与自动化学院教授. 2005年获得华中科技大学博士学位. 主要研究方向为康复机器人, 机器人装配, 网络控制系统和生物信息学. 本文通信作者. E-mail: huang_jan@mail.hust.edu.cn

    段涛:2020年获得华中科技大学硕士学位. 主要研究方向为智能机器人, 模式识别. E-mail: tao_duan@hust.edu.cn

    伍冬睿:华中科技大学人工智能与自动化学院教授. 主要研究方向为机器学习, 脑机接口, 计算智能和情感计算. E-mail: drwu@hust.edu.cn

    熊蔡华:华中科技大学机械科学与工程学院数字制造装备与技术国家重点实验室教授. 1998年获得华中理工大学(现华中科技大学)机械电子工程专业博士学位. 主要研究方向为机器人学, 生机电一体化和康复工程装备. E-mail: chxiong@hust.edu.cn

    崔雨琦:华中科技大学人工智能与自动化学院博士研究生. 2017年获得华中科技大学电子信息工程学士学位. 主要研究方向为模糊系统, 脑机接口和可穿戴设备. E-mail: yqcui@hust.edu.cn

Research on Gesture Recognition Based on Pressure-based Mechanomyogram and Improved Neural Fuzzy Inference System

Funds: Supported by National Natural Science Foundation of China (61873321, U1913207), Technology Innovation Project of Hubei Province of China (2019AEA171), and International Science and Technology Cooperation Program of China (2017YFE0128300)
More Information
    Author Bio:

    WANG Lei Master student at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. He received his bachelor degree from Huazhong University of Science and Technology in 2019. His research interest covers machine learning and gesture recognition

    HUANG Jian Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. He received his Ph.D. degree from Huazhong University of Science and Technology in 2005. His research interest covers rehabilitation robot, robotic assembly, networked control systems, and bioinformatics. Corresponding author of this paper

    DUAN Tao He received his master degree from Huazhong University of Science and Technology in 2020. His research interest covers intelligent robot and pattern recognition

    WU Dong-Rui Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers machine learning, brain-computer interfaces, computational intelligence, and affective computing

    XIONG Cai-Hua Professor at the State Key Laboratory of Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology. He received his Ph.D. degree in mechatronics from Huazhong University of Science and Technology in 1998. His research interest covers robotics, biomechatronics, and rehabilitation engineering equipment

    CUI Yu-Qi Ph.D. candidate at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. He received his bachelor degree in electronic information engineering from Huazhong University of Science and Technology in 2017. His research interest covers fuzzy systems, brain-computer interfaces, and wearable devices

  • 摘要: 手势识别是人机交互领域的重要研究内容, 为截肢患者控制智能假肢手提供基础. 当前主流方法之一是利用表面肌电图(Electromyogram, EMG)识别手部运动意图, 但肌电信号存在信号弱和易受噪声、汗液、疲劳影响等缺点. 同时肌电图在识别准确率方面, 尤其是截肢患者手势识别方面仍然具有较大的提升空间. 针对这些问题, 设计了基于气压肌动图(Pressure-based mechanomyogram, pMMG)的穿戴式信号采集装置, 为手势识别提供了优质的信号源. 结合深度神经网络中全连接层结构、典型抽样和标准正则化技术, 提出了一种改进多类神经模糊推理系统(Improved multicalss neural fuzzy inference system, IMNFIS), 与传统自适应神经模糊推理系统(Adaptive neural fuzzy inference system, ANFIS)相比, 泛化能力得到显著提升. 招募了7名健康受试者和1名截肢受试者, 并用8种算法开展离线实验. 所提方法在残疾人手势识别实验中取得了97.25%的最高平均准确率, 在健康人手势识别实验中取得了98.18%的最高平均准确率. 与近年公开报道的多种手势识别研究相比, 所提方法的综合性能更优.
    1)  收稿日期 2020-10-27 录用日期 2021-03-02 Manuscript received October 27, 2020; accepted March 2, 2021 国家自然科学基金联合基金重点支持项目 (U1913207), 湖北省技术创新专项 (2019AEA171), 科技部政府间国际科技创新合作重点专项 (2017YFE0128300) 资助 Supported by National Natural Science Foundation of China (U1913207), Technology Innovation Project of Hubei Province(2019AEA171), and International Science and Technology Cooperation Program of China (2017YFE0128300) 本文责任编委 郑伟诗 Recommended by Associate Editor ZHENG Wei-Shi 1. 华中科技大学人工智能与自动化学院图像信息处理与智能控制教育部重点实验室 武汉 430074 2. 华中科技大学机械科学与
    2)  工程学院数字制造装备与技术国家重点实验室 武汉 430074 1. Ministry of Education Key Laboratory on Image Information Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074 2. State Key Laboratory of Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074
  • 图  1  pMMG原理图

    Fig.  1  The schematic diagram of pMMG

    图  2  手势识别装置及其佩戴位置

    Fig.  2  The gesture recognition device and it's wearing position

    图  3  每一轮的手势数据采集过程

    Fig.  3  Each round of gesture data acquisition process

    图  4  采集的原始手势数据流

    Fig.  4  The collected raw gesture data stream

    图  5  手势数据处理过程

    Fig.  5  Gesture data processing

    图  6  MC_ANFIS结构图

    Fig.  6  Structure of MC_ANFIS

    图  7  本文研究的6种手势

    Fig.  7  Six gestures studied in this paper

    图  8  MC_TS_UR融合算法在每名受试者数据集上的混淆矩阵

    Fig.  8  The CM of the MC_TS_UR fusion algorithm applied to the datasets of every subject

    图  9  4种基于MC_ANFIS的算法在训练过程中的分类误差随时间变化曲线

    Fig.  9  The classification error changes curve of four MC_ANFIS based algorithms with time during the training process

    表  1  参与手势识别实验的受试者信息

    Table  1  Information of the subjects participating in the gesture recognition experiment

    受试者性别年龄身高 (cm)体重 (kg)腕围 (cm)健康状况
    Subject-125180.472.418.8健康
    Subject-224169.558.516.5健康
    Subject-356164.661.215.8手部截肢
    Subject-425172.362.817.9健康
    Subject-522177.557.016.8健康
    Subject-626166.665.718.4健康
    Subject-723170.173.319.1健康
    Subject-825175.566.917.1健康
    下载: 导出CSV

    表  2  6种手腕手势对应的肌肉信息

    Table  2  Muscles information of the corresponding six gestures

    手势肌肉作用
    屈腕尺侧腕屈肌手腕屈曲和尺侧偏移
    握拳指浅屈肌手指弯曲
    尺侧倾桡侧腕屈肌手腕弯曲和径向偏移
    伸腕尺侧腕伸肌手腕伸展和尺侧偏移
    伸掌指伸肌手指伸展
    桡侧倾桡侧腕伸肌手腕伸展和径向偏移
    下载: 导出CSV

    表  3  8种算法在健康人数据集上的离线实验结果

    Table  3  The offline experiment results of eight algorithms on datasets of the normal

    指标SVMGBDTLDATSK_GD_LSEMCMC_TSMC_URMC_TS_UR
    ${\rm{RER}}$6.07%7.82%5.15%5.26%3.16%2.52%2.30%1.82%
    ${\rm{BER}}$6.18%8.74%5.21%5.35%2.83%2.41%2.33%1.77%
    $\kappa$0.92580.90180.93750.93580.96600.97110.97200.9787
    $T_t$224.64.40.61121.9796.1886.5734.7310.2
    下载: 导出CSV

    表  4  8种算法在残疾人数据集上的离线实验结果

    Table  4  The offline experiment results of eight algorithms on datasets of the disabled

    指标SVMGBDTLDATSK_GD_LSEMCMC_TSMC_URMC_TS_UR
    ${\rm{RER}}$5.94%8.13%4.46%5.77%4.64%3.83%3.77%2.75%
    ${\rm{BER}}$6.10%8.27%4.48%6.11%4.72%3.98%3.65%2.73%
    $\kappa$0.92680.90080.94620.92670.94340.95220.95620.9672
    $T_t$173.05.30.71006.5766.8942.9768.6313.1
    下载: 导出CSV

    表  5  与近期同类研究工作文献的比较

    Table  5  Comparison with similar research work literature

    文献传感器实验对象是否为公共数据集分类算法手势类别数识别准确率
    [25]6 通道 pMMG6 名健康人Fuzzy logic695.30%
    [26]8 通道 FMG10 名健康人SVM693.00%
    [27]2 通道 sEMG7 名健康人SVM495.00%
    [28]4 通道 sEMG + 1 通道 IMU10 名健康人LDA892.60%
    [29]8 通道 sEMG21 名健康人LDA694.70%
    [30]8 通道 sEMG8 名健康人Hidden Markov model 694.20%
    Proposed6 通道 pMMG + 1 通道 IMU7 名健康人MC_TS_UR698.18%
    [31]8 通道 sEMG4 名残疾人LDA792.00%
    [32]7 通道 sEMG3 名残疾人SVM594.02%
    Proposed6 通道 pMMG + 1 通道 IMU1 名残疾人MC_TS_UR697.25%
    下载: 导出CSV
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  • 收稿日期:  2020-10-27
  • 网络出版日期:  2021-05-22
  • 刊出日期:  2022-05-13

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