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基于渐进无迹卡尔曼滤波网络的人体肢体运动估计

杨旭升 王雪儿 汪鹏君 张文安

杨旭升, 王雪儿, 汪鹏君, 张文安. 基于渐进无迹卡尔曼滤波网络的人体肢体运动估计. 自动化学报, 2023, 49(8): 1723−1731 doi: 10.16383/j.aas.c220523
引用本文: 杨旭升, 王雪儿, 汪鹏君, 张文安. 基于渐进无迹卡尔曼滤波网络的人体肢体运动估计. 自动化学报, 2023, 49(8): 1723−1731 doi: 10.16383/j.aas.c220523
Yang Xu-Sheng, Wang Xue-Er, Wang Peng-Jun, Zhang Wen-An. Estimation of human limb motion based on progressive unscented Kalman filter network. Acta Automatica Sinica, 2023, 49(8): 1723−1731 doi: 10.16383/j.aas.c220523
Citation: Yang Xu-Sheng, Wang Xue-Er, Wang Peng-Jun, Zhang Wen-An. Estimation of human limb motion based on progressive unscented Kalman filter network. Acta Automatica Sinica, 2023, 49(8): 1723−1731 doi: 10.16383/j.aas.c220523

基于渐进无迹卡尔曼滤波网络的人体肢体运动估计

doi: 10.16383/j.aas.c220523
基金项目: 浙江省 “尖兵” “领雁” 研发攻关计划 (2022C03114), 国家自然科学基金 (62173305), 浙江省自然科学基金 (LD21F030002) 资助
详细信息
    作者简介:

    杨旭升:浙江工业大学信息工程学院副研究员. 主要研究方向为信息融合估计, 人体运动估计和目标定位. E-mail: xsyang@zjut.edu.cn

    王雪儿:浙江工业大学信息工程学院硕士研究生. 主要研究方向为人体运动估计, 信息融合估计. E-mail: wangxueer@zjut.edu.cn

    汪鹏君:温州大学电气与电子工程学院教授. 主要研究方向为人工智能, 信息安全. E-mail: wangpengjun@wzu.edu.cn

    张文安:浙江工业大学信息工程学院教授. 主要研究方向为多源信息融合估计及应用. 本文通信作者. E-mail: wazhang@zjut.edu.cn

Estimation of Human Limb Motion Based on Progressive Unscented Kalman Filter Network

Funds: Supported by Zhejiang Province “Jianbing” “Lingyan” Research and Development Project (2022C03114), National Natural Science Foundation of China (62173305), and Natural Science Foundation of Zhejiang Province (LD21F030002)
More Information
    Author Bio:

    YANG Xu-Sheng Associate researcher at the College of Information Engineering, Zhejiang University of Technology. His research interest covers information fusion estimation, human motion estimation, and target positioning

    WANG Xue-Er Master student at the College of Information Engineering, Zhejiang University of Technology. Her research interest covers human motion estimation and information fusion estimation

    WANG Peng-Jun Professor at the College of Electrical and Electronic Engineering, Wenzhou University. His research interest covers artificial intelligence and information security

    ZHANG Wen-An Professor at the College of Information Engineering, Zhejiang University of Technology. His research interest covers multi-sensor information fusion estimation and its applications. Corresponding author of this paper

  • 摘要: 针对基于表面肌电信号 (Surface electromyography, sEMG) 的人体肢体运动估计建模困难的问题, 提出一种渐进无迹卡尔曼滤波网络 (Progressive unscented Kalman filter network, PUKF-net), 来实现降低肢体运动与sEMG量测的建模难度以及提高肢体运动估计精度的目的. 首先, 设计深度神经网络从sEMG数据中学习肢体运动状态与sEMG量测之间的映射关系和噪声统计特性. 其次, 采用渐进量测更新方法对先验状态估计进行修正, 减小运动估计的线性化误差, 提高PUKF-net模型的稳定性. 通过结合深度神经网络和渐进卡尔曼滤波的优势, 使得PUKF-net具有良好的模型适应性和抗噪能力. 最后, 设计基于sEMG的人体肢体运动估计实验, 验证了PUKF-net模型的有效性. 相较于长短期记忆网络 (Long short-term memory, LSTM) 和其他卡尔曼滤波网络, PUKF-net在肢体运动估计中的均方根误差 (Root mean square error, RMSE) 下降了14.9%, 相关系数R2提高了5.1%.
  • 图  1  PUKF-net结构

    Fig.  1  Structure of PUKF-net

    图  2  $ \text{LSTM}_Q$, $ \text{LSTM}_R$网络结构

    Fig.  2  Network structure of $ \text{LSTM}_Q$ and $ \text{LSTM}_R$

    图  3  $ \text{LSTM}_h$计算流程

    Fig.  3  Calculation process including $ \text{LSTM}_h$

    图  4  实验设计 ((a) 传感器布局; (b) 关节角度坐标; (c) 轨迹规划; (d) Optitrack采集到手腕关节点轨迹)

    Fig.  4  Experiment design ((a) Sensor layout; (b) Joint angle coordinates; (c) Trajectory planning; (d) Track of wrist joint collected by Optitrack)

    图  5  sEMG分析 ((a) 人体大臂肌肉分布; (b) Myo位置肌肉横截面; (c)协同矩阵$ W$; (d) sEMG原始信号)

    Fig.  5  sEMG analysis ((a) Muscle distribution of human upper arm; (b) Cross-section of Myo wearing position; (c) Non-negative matrix factorization comatrix $ W$; (d) Original signal of sEMG)

    图  6  关节角度估计曲线

    Fig.  6  Joint angle estimation curve

    表  1  测试者身体参数

    Table  1  Physiological information of subjects

    测试者年龄身高 (cm)体重 (kg)性别
    S13115565
    S22416153
    S32918285
    S42017761
    S52517375
    S62817565
    S73016047
    S82517172
    S92217570
    S102416250
    S113215954
    S122917078
    下载: 导出CSV

    表  2  LSTM、LSTM-KF、PUKF-net在测试集上的RMSE和$ \text{R}^2 $

    Table  2  RMSE and $ \text{R}^2 $ of LSTM, LSTM-KF, PUKF-net

    测试者RMSE $ \text{R}^2$
    LSTMLSTM-KFPUKF-net LSTMLSTM-KFPUKF-net
    S115.91312.66811.9400.8230.8960.906
    S224.56818.67715.4730.6220.7480.829
    S319.73616.99614.0440.7370.8250.872
    S420.65313.31512.6680.6790.8630.876
    S526.74620.67516.4480.6290.7610.824
    S616.79313.66411.5880.8030.8800.905
    S722.19317.16414.1870.6990.8520.868
    S817.98415.24112.2940.7480.8270.880
    S922.53718.46415.6240.7100.8170.861
    S1024.14218.55516.1650.6550.8090.848
    S1114.60111.27110.5450.6820.7920.844
    S1219.19616.13713.0440.7210.8040.865
    平均值20.42216.06913.6680.7090.8230.865
    下载: 导出CSV
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  • 收稿日期:  2022-06-24
  • 录用日期:  2023-01-11
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  • 刊出日期:  2023-08-21

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