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基于表面肌电的意图识别方法在非理想条件下的研究进展

李自由 赵新刚 张弼 丁其川 张道辉 韩建达

李自由, 赵新刚, 张弼, 丁其川, 张道辉, 韩建达. 基于表面肌电的意图识别方法在非理想条件下的研究进展. 自动化学报, 2020, x(x): 1−15 doi: 10.16383/j.aas.c200263
引用本文: 李自由, 赵新刚, 张弼, 丁其川, 张道辉, 韩建达. 基于表面肌电的意图识别方法在非理想条件下的研究进展. 自动化学报, 2020, x(x): 1−15 doi: 10.16383/j.aas.c200263
Li Zi-You, Zhao Xin-Gang, Zhang Bi, Ding Qi-Chuan, Zhang Dao-Hui, Han Jian-Da. Review of sEMG-based motion intent recognition methods in non-ideal conditions. Acta Automatica Sinica, 2020, x(x): 1−15 doi: 10.16383/j.aas.c200263
Citation: Li Zi-You, Zhao Xin-Gang, Zhang Bi, Ding Qi-Chuan, Zhang Dao-Hui, Han Jian-Da. Review of sEMG-based motion intent recognition methods in non-ideal conditions. Acta Automatica Sinica, 2020, x(x): 1−15 doi: 10.16383/j.aas.c200263

基于表面肌电的意图识别方法在非理想条件下的研究进展

doi: 10.16383/j.aas.c200263
基金项目: 国家自然科学基金(61773369, U1813214)、中国博士后科学基金项目(2019M661157)资助
详细信息
    作者简介:

    李自由:中国科学院沈阳自动化研究所博士研究生, 主要研究方向为生物电信号处理, 模式识别与机器学习

    赵新刚:中国科学院沈阳自动化研究所研究员. 主要研究方向为机器人控制, 智能系统与康复机器人

    张弼:中国科学院沈阳自动化研究所副研究员, 主要研究方向为先进控制理论及应用, 智能机器人交互

    丁其川:东北大学机器人科学与工程学院副教授. 主要研究方向为人体运动意图识别和智能机器人

    张道辉:中国科学院沈阳自动化研究所副研究员, 主要研究方向为机器人控制技术

    韩建达:南开大学人工智能学院教授.主要研究方向为智能系统, 移动机器人自主控制

Review of sEMG-based Motion Intent Recognition Methods in Non-ideal Conditions

Funds: Supported by National Science Foundation of China (61773369, U1813214), the China Postdoctoral Science Foundation (No. 2019M661157)
  • 摘要: 在基于表面肌电信号(surface electromyogram, sEMG)的意图识别研究领域, 目前大多数的研究主要集中在提高肌电识别的准确性方面. 然而, 在实际应用中, 基于sEMG识别的交互系统往往受到诸多非理想因素干扰, 肌电识别的准确性被大大降低. 本文主要关注在非理想条件下肌电识别的鲁棒性研究, 首先详细归纳了肌电识别方法受到的非理想干扰因素(如电极偏移、个体性差异、肌肉疲劳、肢体姿态或其他综合性干扰), 总结了当前研究的抗干扰方法; 随后讨论了非理想干扰因素研究现状中的主要问题; 最后在构建肌电数据集、探索深度学习和迁移学习, 以及肌电分解研究等方面, 对未来的关键技术进行了展望.
  • 图  1  基于监督学习的sEMG识别模型训练与测试框架

    Fig.  1  The training and testing framework of sEMG recognition model based on supervised learning

    图  2  (a)稀疏电极不同位置[13], (b)环形电极旋转偏移[15], 和(c)HD-sEMG电极中的纵向偏移和横向偏移[14]

    Fig.  2  (a) Different positions of sparse electrodes[13], (b) Ring electrode rotation offset[15], (c) Longitudinal and transverse offsets in HD-sEMG electrodes[14]

    图  3  基于用户依赖因子(User-dependent factor)和动作依赖因子(Motion-dependent factor)构建用户无关特征(User-independent feature)的双线性模型[45]

    Fig.  3  User-independent bilinear model based on user-dependent factor and motion-dependent factor[45]

    图  4  疲劳状态下肌电信号中值频率与肌肉输出力变化[52]

    Fig.  4  Changes of median frequency of EMG signal and muscle output force under fatigue condition[52]

    图  5  (a)不同的静态姿态, (b)模拟日常活动中的肢体姿态和(c)肢体姿态组合动态运动[70]

    Fig.  5  (a) Different static posture, (b) simulating the dynamic movement of body posture and (c) combination of body posture in daily activities[70]

    图  6  非理想肌电的关键技术展望

    Fig.  6  The key technology prospects of non-ideal EMG

    图  7  基于TCN网络结构的肌电信号时序、层级特征提取框架[96]

    Fig.  7  Sequential and hierarchical feature extraction framework of EMG signal based on TCN network[96]

    图  8  基于迁移学习的理想场景与非理想场景之间肌电识别模型的更新与适应

    Fig.  8  Update and adaptation of sEMG-based recognition model between ideal and non-ideal scenarios based on transfer learning

    图  9  sEMG分解与MUAP[107]

    Fig.  9  Surface EMG signal decomposition and MUAP[107]

    表  1  非理想因素及解决方案

    Table  1  Non-ideal factors and solutions

    干扰\方法数据扩增鲁棒特征模型更新
    电极偏移1.多位置数据扩增[13,16];
    2.多通道数据扩增[12];
    3.惯导等数据融合[17,18];
    1.AR、TDAR特征[16,19];
    2.倒频谱特征[20];
    3.Variogram特征[10];
    4.结构相似性特征[21];
    5.共空间模式特征[22];
    6.灰度共生矩阵特征[14];
    1.模型修正与协方差偏移适应[24,25]
    2.期望最大化迁移学习[15,26,27]
    3.自适应增量式混合分类器[28,29];
    4.偏移估计与模型更新[30];
    5.骨骼位置估计与校正[31];
    个体性差异1.多人数据扩增[36];
    2.惯导等数据融合[37];
    3.个体形态参数归一化[38];
    1.肌电分解特征[39];
    2.多分辨率肌肉协同特征[40];
    3.共同空间映射[41];
    1.SVM权重更新策略[42,43,90]
    2.典型相关分析低维共空间映射[44]
    3.用户、动作依赖的双线性模型[45]
    4.基于最大收缩力的模型泛化[46]
    5.基于卷积神经网络的模型迁移[47]
    肢体姿态1.多姿态下的数据扩增[7577];
    2.“动态训练”数据采集[70,78];
    3.惯导等数据融合[17,75,76,79];
    1. 谱矩等频域特征[8082];
    2. 稀疏表达特征[83,84];
    肌肉疲劳1.频域特征归一化(MDF、MNF[52,91]、STFT、WT[92,93]、iMDF、iMNF[56]、一维频谱-标准差[59]等);
    2.疲劳状态的分类识别[59,6264];
    下载: 导出CSV

    表  2  sEMG数据集

    Table  2  Surface EMG signal datasets

    数据集传感器参与人数动作类别
    NinaPro[4]DB11.Otto Bock MyoBock 13E200电极,
    (10通道, 肌电RMS特征)
    2.数据手套Cyberglove(22通道)
    27健康人52类手部动作重复10次
    DB2/31. Delsys Trigno Wireless 肌电系统,
    (12通道双差分EMG, 36通道ACC)
    2. 数据手套Cyberglove (22通道)
    3. 手指力传感器(6通道)4. 腕部倾
    角传感器(2通道)
    40健康人49个手部动作重复6次
    DB41. Cometa单差分无线肌电电极(12
    通道单差分)
    10名健康人52类手部动作重复6次
    DB51. Thalmic Myo肌电臂环(2套, 共计
    2×8通道单差分)
    2. 数据手套Cyberglove(22通道)
    10名健康人52类手部动作重复6次
    DB61. Delsys Trigno Wireless肌电系统
    (14通道, 42通道ACC)
    2. Tobii Pro Glasses II(追踪眼动和视野)
    10名健康人7个抓握动作重复12次
    重复5天
    DB71. Delsys Trigno Wireless 肌电系统
    (12通道EMG, 9轴IMU)
    2. 数据手套Cyberglove(22通道)
    20名健康人2名截肢者40个手部动作重复6次
    DB81. Delsys Trigno Wireless 肌电系统
    (16通道EMG, 9轴IMU, 采样至2 kHz)
    2. 数据手套Cyberglove (22通道)
    10名健康人2名截肢者9个手部动作
    CSL-HDEMG[31]1. HD-EMG(8×24=192通道)5名健康人27个手部/手指动作
    CapgMyo[95]DB-a1. HD-EMG(8x16=128通道)18名健康人8个手指动作
    8个手指动作(同上)
    不同时间段重复两次
    12个手指动作
    DB-b10名健康人
    DB-c10名健康人
    UCI等其他Myo[108]1. Thalmic Myo肌电臂环(8通道)36名健康人8个抓握动作
    Christos-Delsys[109]1. Delsys Trigno Wireless (2通道)6名健康人6个抓握动作
    下载: 导出CSV
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  • 收稿日期:  2020-04-29
  • 录用日期:  2020-12-14
  • 网络出版日期:  2021-01-19

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