基于肌电−惯性融合的人体运动估计: 高斯滤波网络方法

杨旭升; 李福祥; 胡佛; 张文安

doi:10.16383/j.aas.c230581

基于肌电−惯性融合的人体运动估计: 高斯滤波网络方法

doi: 10.16383/j.aas.c230581

杨旭升^{1, 2,},
李福祥^{1, 2,},
胡佛^{1, 2,},
张文安^{1, 2,}

1.
浙江工业大学信息工程学院杭州 310023
2.
浙江省嵌入式系统联合重点实验室杭州 310023

基金项目: 浙江省“尖兵”“领雁”研发攻关计划(2022C03114), 浙江省自然科学基金(LY23F030006), 浙江省科技计划项目(2023C04032)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教授. 主要研究方向为信息融合估计, 人体运动估计和目标定位. 本文通信作者. E-mail: xsyang@zjut.edu.cn

李福祥：浙江工业大学信息工程学院硕士研究生. 主要研究方向为多源信息融合估计, 人体运动估计. E-mail: fuxiangli@zjut.edu.cn

胡佛：浙江工业大学信息工程学院助理研究员. 主要研究方向为人机交互, 情感计算和人工智能. E-mail: fohu@zjut.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. E-mail: wazhang@zjut.edu.cn

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出版历程
- 收稿日期: 2023-09-18
- 录用日期: 2023-12-21
- 网络出版日期: 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: 本文研究了基于肌电(Electromyography, EMG)−惯性融合的人体运动估计问题, 提出了一种序贯渐进高斯滤波网络(Sequential progressive Gaussian filtering network, SPGF-net)估计方法来形成肌电和惯性的互补性优势, 以提高人体运动估计精度和稳定性. 首先, 利用卷积神经网络对观测数据进行特征提取, 以及利用长短期记忆(Long short-term memory, LSTM)网络模型来学习噪声统计特性和量测模型. 其次, 采用序贯融合的方式融合异构传感器量测特征, 以建立高斯滤波与深度学习相结合的网络模型来实现人体运动估计. 特别地, 引入渐进量测更新对网络量测特征的不确定性进行补偿. 最后, 通过实验结果表明, 相比于现有的卡尔曼滤波网络, 该融合方法在上肢关节角度估计中的均方根误差(Root mean square error, RMSE)下降了13.8%, 相关系数(R²)提高了4.36%.
- 高斯滤波网络 /
- 多传感器融合 /
- 人体运动估计 /
- 非线性卡尔曼滤波
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

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图 1 多传感器融合的人体肢体估计示意图

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

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图 2 SPGF-net结构

Fig. 2 Structure of SPGF-net

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图 3 各学习模块

Fig. 3 Various learning modules

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图 4 序贯渐进量测更新

Fig. 4 Sequential progressive measurement update

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图 5 数据采集

Fig. 5 Data collection

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图 6 S1 ~ S4角度估计和误差曲线

Fig. 6 S1 ~ S4 angle estimation and error curves

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表 1 五种模型性能评价

Table 1 The performance evaluation of five models

测试者	均方根误差 (RMSE)					相关系数(R²)
测试者	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
标准差	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 五种模型的复杂度

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

下载: 导出CSV

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