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              基于肌電?慣性融合的人體運動估計: 高斯濾波網絡方法

              楊旭升 李福祥 胡佛 張文安

              楊旭升, 李福祥, 胡佛, 張文安. 基于肌電?慣性融合的人體運動估計: 高斯濾波網絡方法. 自動化學報, 2024, 50(5): 991?1000 doi: 10.16383/j.aas.c230581
              引用本文: 楊旭升, 李福祥, 胡佛, 張文安. 基于肌電?慣性融合的人體運動估計: 高斯濾波網絡方法. 自動化學報, 2024, 50(5): 991?1000 doi: 10.16383/j.aas.c230581
              Yang Xu-Sheng, Li Fu-Xiang, Hu Fo, Zhang Wen-An. Human motion estimation based on EMG-inertial fusion: A Gaussian filtering network approach. Acta Automatica Sinica, 2024, 50(5): 991?1000 doi: 10.16383/j.aas.c230581
              Citation: Yang Xu-Sheng, Li Fu-Xiang, Hu Fo, Zhang Wen-An. Human motion estimation based on EMG-inertial fusion: A Gaussian filtering network approach. Acta Automatica Sinica, 2024, 50(5): 991?1000 doi: 10.16383/j.aas.c230581

              基于肌電?慣性融合的人體運動估計: 高斯濾波網絡方法

              doi: 10.16383/j.aas.c230581
              基金項目: 浙江省“尖兵”“領雁”研發攻關計劃(2022C03114), 浙江省自然科學基金(LY23F030006), 浙江省科技計劃項目(2023C04032)資助
              詳細信息
                作者簡介:

                楊旭升:浙江工業大學信息工程學院副教授. 主要研究方向為信息融合估計, 人體運動估計和目標定位. 本文通信作者. E-mail: xsyang@zjut.edu.cn

                李福祥:浙江工業大學信息工程學院碩士研究生. 主要研究方向為多源信息融合估計, 人體運動估計. E-mail: fuxiangli@zjut.edu.cn

                胡佛:浙江工業大學信息工程學院助理研究員. 主要研究方向為人機交互, 情感計算和人工智能. E-mail: fohu@zjut.edu.cn

                張文安:浙江工業大學信息工程學院教授. 主要研究方向為多源信息融合估計及應用. E-mail: wazhang@zjut.edu.cn

              Human Motion Estimation Based on EMG-Inertial Fusion: A Gaussian Filtering Network Approach

              Funds: Supported by the “Pioneer” and “Leading Goose” Research and Development Program of Zhejiang Province (2022C03114), Natural Science Foundation of Zhejiang Province (LY23F030006), and the Key Technology Research and Development Program of Zhejiang Province (2023C04032)
              More Information
                Author Bio:

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

                LI Fu-Xiang Master student at the College of Information Engineering, Zhejiang University of Technology. His research interest covers multi-source information fusion estimation and human motion estimation

                HU Fo Assistant researcher at the College of Information Engineering, Zhejiang University of Technology. His research interest covers human machine interaction, emotional computing and artificial intelligence

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

              • 摘要: 本文研究了基于肌電(Electromyography, EMG)?慣性融合的人體運動估計問題, 提出了一種序貫漸進高斯濾波網絡(Sequential progressive Gaussian filtering network, SPGF-net)估計方法來形成肌電和慣性的互補性優勢, 以提高人體運動估計精度和穩定性. 首先, 利用卷積神經網絡對觀測數據進行特征提取, 以及利用長短期記憶(Long short-term memory, LSTM)網絡模型來學習噪聲統計特性和量測模型. 其次, 采用序貫融合的方式融合異構傳感器量測特征, 以建立高斯濾波與深度學習相結合的網絡模型來實現人體運動估計. 特別地, 引入漸進量測更新對網絡量測特征的不確定性進行補償. 最后, 通過實驗結果表明, 相比于現有的卡爾曼濾波網絡, 該融合方法在上肢關節角度估計中的均方根誤差(Root mean square error, RMSE)下降了13.8%, 相關系數(R2)提高了4.36%.
              • 圖  1  多傳感器融合的人體肢體估計示意圖

                Fig.  1  Multi-sensor fusion human body limb estimation schematic diagram

                圖  2  SPGF-net結構

                Fig.  2  Structure of SPGF-net

                圖  3  各學習模塊

                Fig.  3  Various learning modules

                圖  4  序貫漸進量測更新

                Fig.  4  Sequential progressive measurement update

                圖  5  數據采集

                Fig.  5  Data collection

                圖  6  S1 ~ S4角度估計和誤差曲線

                Fig.  6  S1 ~ S4 angle estimation and error curves

                表  1  五種模型性能評價

                Table  1  The performance evaluation of five models

                測試者 均方根誤差 (RMSE) 相關系數(R2)
                CNN
                (sEMG+IMU)
                PUKF
                (sEMG)
                PUKF
                (IMU)
                PUKF
                (sEMG+IMU)
                SPGF-net CNN
                (sEMG+IMU)
                PUKF
                (sEMG)
                PUKF
                (IMU)
                PUKF
                (sEMG+IMU)
                SPGF-net
                S1 9.75 11.91 12.48 9.56 9.27 0.922 0.884 0.872 0.925 0.930
                S2 11.65 12.18 13.25 10.89 9.78 0.917 0.913 0.893 0.923 0.941
                S3 16.18 15.90 16.42 15.63 14.15 0.864 0.868 0.859 0.876 0.896
                S4 15.66 16.18 16.95 14.57 13.45 0.825 0.822 0.816 0.832 0.847
                S5 24.24 23.30 23.79 22.74 18.98 0.594 0.624 0.609 0.651 0.751
                S6 10.15 11.43 11.65 9.96 8.91 0.937 0.920 0.917 0.941 0.949
                S7 16.31 16.62 17.19 16.13 15.90 0.856 0.851 0.847 0.860 0.869
                S8 16.84 16.37 16.53 16.30 16.23 0.807 0.809 0.805 0.813 0.821
                S9 9.23 9.95 10.86 8.82 7.73 0.930 0.918 0.903 0.938 0.951
                S10 14.97 15.74 16.17 14.53 14.00 0.849 0.831 0.821 0.853 0.866
                S11 16.86 17.19 17.66 16.62 15.78 0.852 0.846 0.838 0.857 0.864
                S12 12.46 14.09 14.83 12.13 11.74 0.905 0.885 0.870 0.909 0.924
                均值 14.52 15.07 15.64 13.99 12.99 0.854 0.847 0.838 0.865 0.884
                標準差 4.21 3.54 3.46 3.96 3.51 0.093 0.080 0.080 0.080 0.060
                下載: 導出CSV

                表  2  五種模型的復雜度

                Table  2  The complexity of five models

                CNN (sEMG+
                IMU)
                PUKF (sEMG) PUKF (IMU) PUKF (sEMG+
                IMU)
                SPGF-net
                FLOPs 1 237 714 719 448 619 828 1 328 864 1 419 176
                Params 442 337 256 511 255 971 473 970 505 614
                下載: 導出CSV
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                        • 收稿日期:  2023-09-18
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