基于肌電?慣性融合的人體運動(dòng)估計: 高斯濾波網(wǎng)絡(luò )方法

楊旭升; 李福祥; 胡佛; 張文安

doi:10.16383/j.aas.c230581

基于肌電?慣性融合的人體運動(dòng)估計: 高斯濾波網(wǎng)絡(luò )方法

doi: 10.16383/j.aas.c230581

楊旭升^{1, 2,},
李福祥^{1, 2,},
胡佛^{1, 2,},
張文安^{1, 2,}

1.
浙江工業(yè)大學(xué)信息工程學(xué)院杭州 310023
2.
浙江省嵌入式系統聯(lián)合重點(diǎn)實(shí)驗室杭州 310023

基金項目: 浙江省“尖兵”“領(lǐng)雁”研發(fā)攻關(guān)計劃(2022C03114), 浙江省自然科學(xué)基金(LY23F030006), 浙江省科技計劃項目(2023C04032)資助

詳細信息

作者簡(jiǎn)介:
楊旭升：浙江工業(yè)大學(xué)信息工程學(xué)院副教授. 主要研究方向為信息融合估計, 人體運動(dòng)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(yè)大學(xué)信息工程學(xué)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(dòng)估計. E-mail: fuxiangli@zjut.edu.cn

胡佛：浙江工業(yè)大學(xué)信息工程學(xué)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(yè)大學(xué)信息工程學(xué)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

計量
- 文章訪(fǎng)問(wèn)數: 432
- HTML全文瀏覽量: 146
- PDF下載量: 126
- 被引次數: 0
出版歷程
- 收稿日期: 2023-09-18
- 錄用日期: 2023-12-21
- 網(wǎng)絡(luò )出版日期: 2024-02-28
- 刊出日期: 2024-05-29

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

YANG Xu-Sheng^{1, 2
,},
LI Fu-Xiang^{1, 2
,},
HU Fo^{1, 2
,},
ZHANG Wen-An^{1, 2
,}

1.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023
2.
Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023

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

摘要: 本文研究了基于肌電(Electromyography, EMG)?慣性融合的人體運動(dòng)估計問(wèn)題, 提出了一種序貫漸進(jìn)高斯濾波網(wǎng)絡(luò )(Sequential progressive Gaussian filtering network, SPGF-net)估計方法來(lái)形成肌電和慣性的互補性?xún)?yōu)勢, 以提高人體運動(dòng)估計精度和穩定性. 首先, 利用卷積神經(jīng)網(wǎng)絡(luò )對觀(guān)測數據進(jìn)行特征提取, 以及利用長(cháng)短期記憶(Long short-term memory, LSTM)網(wǎng)絡(luò )模型來(lái)學(xué)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(xué)習相結合的網(wǎng)絡(luò )模型來(lái)實(shí)現人體運動(dòng)估計. 特別地, 引入漸進(jìn)量測更新對網(wǎng)絡(luò )量測特征的不確定性進(jìn)行補償. 最后, 通過(guò)實(shí)驗結果表明, 相比于現有的卡爾曼濾波網(wǎng)絡(luò ), 該融合方法在上肢關(guān)節角度估計中的均方根誤差(Root mean square error, RMSE)下降了13.8%, 相關(guān)系數(R²)提高了4.36%.
- 高斯濾波網(wǎng)絡(luò ) /
- 多傳感器融合 /
- 人體運動(dòng)估計 /
- 非線(xiàn)性卡爾曼濾波
Abstract: This paper investigates the issue of human motion estimation based on the fusion of electromyography (EMG) and inertial data. A sequential progressive Gaussian filtering network (SPGF-net) is proposed to leverage the complementary advantages of EMG and inertial data for enhancing the accuracy and stability of human motion estimation. First, a convolutional neural network is employed to extract features from the observed data and a long short-term memory (LSTM) network model is utilized to learn the statistical properties of noise and the measurement model. Second, a sequential fusion method is adopted to fuse the measurement features from heterogeneous sensors, thus a combined network model that integrates Gaussian filtering with deep learning techniques is formed for human motion estimation. Moreover, a progressive measurement update is introduced to compensate for the uncertainty in the network＇s measurement features. Finally, experimental results indicate that, compared with existing Kalman networks, the proposed fusion method has a 13.8% reduction in root mean square error (RMSE) and a 4.36% increase in the coefficient of determination (R²) for upper limb joint angle estimation.
- Gaussian filtering network /
- multi-sensor fusion /
- human motion estimation /
- nonlinear Kalman filtering

HTML全文

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

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圖 2 SPGF-net結構

Fig. 2 Structure of SPGF-net

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圖 3 各學(xué)習模塊

Fig. 3 Various learning modules

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圖 4 序貫漸進(jìn)量測更新

Fig. 4 Sequential progressive measurement update

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圖 5 數據采集

Fig. 5 Data collection

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圖 6 S1 ~ S4角度估計和誤差曲線(xiàn)

Fig. 6 S1 ~ S4 angle estimation and error curves

下載: 全尺寸圖片幻燈片

表 1 五種模型性能評價(jià)

Table 1 The performance evaluation of five models

測試者	均方根誤差 (RMSE)					相關(guān)系數(R²)
測試者	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

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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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參考文獻(29)

[1]	丁其川, 熊安斌, 趙新剛, 韓建達. 基于表面肌電的運動(dòng)意圖識別方法研究及應用綜述. 自動(dòng)化學(xué)報, 2016, 42(1): 13?25 Ding Qi-Chuan, Xiong An-Bin, Zhao Xin-Gang, Han Jian-Da. A review on researches and applications of sEMG-based motion intent recognition methods. Acta Automatica Sinica, 2016, 42(1): 13?25
[2]	Wen Y, Kim S J, Avrillon S, Levinel J T, Hug F, Pons J L. A deep CNN framework for neural drive estimation from HD-EMG across contraction intensities and joint angles. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 2950?2959 doi: 10.1109/TNSRE.2022.3215246
[3]	Huang H, Kuiken T A, Lipschutz R D. A strategy for identifying locomotion modes using surface electromyography. IEEE Transactions on Biomedical Engineering, 2009, 56(1): 65?73 doi: 10.1109/TBME.2008.2003293
[4]	Zhuang Y, Leng Y, Zhou J, Song R, Li L, Su S W. Voluntary control of an ankle joint exoskeleton by able-bodied individuals and stroke survivors using EMG-based admittance control scheme. IEEE Transactions on Biomedical Engineering, 2021, 68(2): 695?705 doi: 10.1109/TBME.2020.3012296
[5]	胡旭暉, 宋愛(ài)國, 李會(huì )軍. 基于表面肌電圖像的靈巧假手控制系統. 控制理論與應用, 2018, 35(12): 1707?1714 Hu Xu-Hui, Song Ai-Guo, Li Hui-Jun. A dexterous robot hand control system based on surface electromyography. Control Theory & Applications, 2018, 35(12): 1707?1714
[6]	張弼, 姚杰, 趙新剛, 談笑偉. 一種基于肌電信號的自適應人機交互控制方法. 控制理論與應用, 2020, 37(12): 2560?2570 Zhang Bi, Yao Jie, Zhao Xin-Gang, Tan Xiao-Wei. An adaptive human-robot interaction control method based on electromyography signals. Control Theory & Applications, 2020, 37(12): 2560?2570
[7]	He J Y, Jiang N. Biometric from surface electromyogram (sEMG): Feasibility of user verification and identification based on gesture recognition. Frontiers in Bioengineering and Biotechnology, 2020, 8: Article No. 58 doi: 10.3389/fbioe.2020.00058
[8]	趙新剛, 談曉偉, 張弼. 柔性下肢外骨骼機器人研究進(jìn)展及關(guān)鍵技術(shù)分析. 機器人, 2020, 42(3): 365?384 Zhao Xin-Gang, Tan Xiao-Wei, Zhang Bi. Development of soft lower extremity exoskeleton and its key technologies: A survey. Robot, 2020, 42(3): 365?384
[9]	李自由, 趙新剛, 張弼, 丁其川, 張道輝, 韓建達. 基于表面肌電的意圖識別方法在非理想條件下的研究進(jìn)展. 自動(dòng)化學(xué)報, 2021, 47(5): 955?969 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, 2021, 47(5): 955?969
[10]	Ding Q, Han J, Zhao X. Continuous estimation of human multi-joint angles from sEMG using a state-space model. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(9): 1518?1528 doi: 10.1109/TNSRE.2016.2639527
[11]	Zhao Y, Zhang Z, Li Z, Yang Z, Dehghani-Sanij A A, Xie S. An EMG-driven musculoskeletal model for estimating continuous wrist motion. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(12): 3113?3120 doi: 10.1109/TNSRE.2020.3038051
[12]	Zhang T, Sun H, Zou Y. An electromyography signals-based human-robot collaboration system for human motion intention recognition and realization. Robot Computer-Integrated Manufacturing, 2022, 77: Article No. 102359 doi: 10.1016/j.rcim.2022.102359
[13]	Xu H, Xiong A. Advances and disturbances in sEMG-based intentions and movements recognition: A review. IEEE Sensors Journal, 2021, 21(12): 13019?13028 doi: 10.1109/JSEN.2021.3068521
[14]	Xiong D, Zhang D, Zhao X, Zhao Y. Deep learning for EMG-based human-machine interaction: A review. IEEE/CAA Journal of Automatica Sinica, 2021, 8(3): 512?533 doi: 10.1109/JAS.2021.1003865
[15]	Stival F, Michieletto S, De Agnoi A, Pagello E. Toward a better robotic hand prosthesis control: Using EMG and IMU features for a subject independent multi joint regression model. In: Proceedings of 2018 IEEE 7th International Conference on Biomedical Robotics and Biomechatronics. Enschede, Netherlands: IEEE, 2018. 185?192
[16]	Sakamoto S I, Hutabarat Y, Owaki D, Hayashibe M. Ground reaction force and moment estimation through EMG sensing using long short-term memory network during posture coordination. Cyborg Bionic Syst, 2023, 4: Article No. 0016 doi: 10.34133/cbsystems.0016
[17]	Hollinger D, Schall M, Chen H, Bass S, Zabala M. The influence of gait phase on predicting lower-limb joint angles. IEEE Transactions on Medical Robotics and Bionics, 2023, 5(2): 343?352 doi: 10.1109/TMRB.2023.3260261
[18]	Xu L, Chen X, Cao S, Zhang X, Chen X. Feasibility study of advanced neural networks applied to sEMG-based force estimation. Sensors, 2018, 18(10): Article No. 3226 doi: 10.3390/s18103226
[19]	Han J, Ding Q, Xiong A, Zhao X. A state-space EMG model for the estimation of continuous joint movements. IEEE Transactions on Industrial Electronics, 2015, 62(7): 4267?4275 doi: 10.1109/TIE.2014.2387337
[20]	Coskun H, Achilles F, DiPietro R, Navab N, Tombari F. Long short-term memory Kalman filters: Recurrent neural estimators for pose regularization. In: Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017. 5524?5532
[21]	Revach G, Shlezinger N, Ni X, Escoriza A L, Van Sloun R J, Eldar Y C. KalmanNet: Neural network aided Kalman filtering for partially known dynamics. IEEE Transactions on Signal Processing, 2022, 70: 1532?1547 doi: 10.1109/TSP.2022.3158588
[22]	Bao T, Zhao Y, Zaidi S A R, Xie S, Yang P, Zhang Z. A deep Kalman filter network for hand kinematics estimation using sEMG. Pattern Recognition Letters, 2021, 143: 88?94 doi: 10.1016/j.patrec.2021.01.001
[23]	楊旭升, 王雪兒, 汪鵬君, 張文安. 基于漸進(jìn)無(wú)跡卡爾曼濾波網(wǎng)絡(luò )的人體肢體運動(dòng)估計. 自動(dòng)化學(xué)報, 2023, 49(8): 1723?1731 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
[24]	Ke A, Huang J, Chen L, Gao Z, He J. An ultra-sensitive modular hybrid EMG-FMG sensor with floating electrodes. Sensors, 2020, 20(17): Article No. 4775 doi: 10.3390/s20174775
[25]	Pasquina P F, Evangelista M, Carvalho A J, Lockhart J, Griffin S, Nanos G, et al. First-in-man demonstration of a fully implanted myoelectric sensors system to control an advanced electromechanical prosthetic hand. Journal of Neuroscience Methods, 2015, 244: 85?93 doi: 10.1016/j.jneumeth.2014.07.016
[26]	Zheng Z, Wu Z, Zhao R, Ni Y, Jing X, Gao S. A review of EMG-, FMG-, and EIT-based biosensors and relevant human-machine interactivities and biomedical applications. Biosensors, 2022, 12(7): Article No. 516 doi: 10.3390/bios12070516
[27]	張鋆豪, 何百岳, 楊旭升, 張文安. 基于可穿戴式慣性傳感器的人體運動(dòng)跟蹤方法綜述. 自動(dòng)化學(xué)報, 2019, 45(8): 1439?1454 Zhang Jun-Hao, He Bai-Yue, Yang Xu-Sheng, Zhang Wen-An. A review on wearable inertial sensor based human motion tracking. Acta Automatica Sinica, 2019, 45(8): 1439?1454
[28]	周穗華, 張宏欣, 馮士民. 高斯漸進(jìn)貝葉斯濾波器. 控制理論與應用, 2015, 32(8): 1023?1031 Zhou Sui-Hua, Zhang Hong-Xin, Feng Shi-Min. Gaussian progressive Bayesian filter. Control Theory & Applications, 2015, 32(8): 1023?1031
[29]	鄭婷婷, 楊旭升, 張文安, 俞立. 一種高斯漸進(jìn)濾波框架下的目標跟蹤方法. 自動(dòng)化學(xué)報, 2018, 44(12): 2250?2258 Zheng Ting-Ting, Yang Xu-Sheng, Zhang Wen-An, Yu Li. A target tracking method in Gaussian progressive filtering framework. Acta Automatica Sinica, 2018, 44(12): 2250?2258