Continuous Motion Estimation of Elbow Joint Based on Multi-Modal Information Fusion
10.16156/j.1004-7220.2023.02.18
- VernacularTitle:基于多模态信息融合的肘关节连续运动估计
- Author:
Sujiao LI
1
,
2
,
3
;
Yue ZHU
1
,
2
;
Kun WU
1
,
2
;
Chunyu ZHU
1
,
2
;
Hongliu YU
1
,
2
,
3
Author Information
1. Institute of Rehabilitative Engineering & Technology, University of Shanghai for Science and Technology
2. Shanghai Engineering Research Center of Assistive Devices
3. Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs
- Publication Type:Journal Article
- Keywords:
feature fusion;
neural networks;
particle swarm optimization;
continuous motion estimation;
surface electromyography (sEMG)
- From:
Journal of Medical Biomechanics
2023;38(2):E324-E330
- CountryChina
- Language:Chinese
-
Abstract:
Objective Aiming at the problems of lacking initiative in upper limb rehabilitation training equipment, single training mode, and low active participation of patients, an upper limb continuous motion estimation algorithm model based on multi-modal information fusion was proposed, so to realize accurate estimation of elbow joint torque. Methods Firstly, the surface electromyography (sEMG) signal and posture signal of participants were collected at four angular velocities, and the time domain characteristics of the signal were extracted. The principal component analysis was adopted to multi-feature fusion. The back propagation neural network (BPNN) was optimized through the additional momentum and the adaptive learning rate method. The particle swarm optimization (PSO) algorithm was used to optimize the neural network and a continuous motion estimation model based on PSO-BPNN was constructed. Finally, the joint torque calculated by the second type of Lagrangian equation was used as the accurate value to train the model. The performance of the model was compared with the traditional BP neural network model. Results The root mean square error (RMSE) of the traditional BP neural network model was 558.9 N·m, and the R2 coefficient was 77.19%, Whereas the RMSE and the R2 coefficient of the optimized model were 113.6 mN·m and 99.12%, respectively.Thereby, the accuracy of torque estimation was improved apparently. Conclusions The method for continuous motion estimation of the elbow joint proposed in this study can estimate the motion intention accurately, and provide a practical scheme for the active control of upper exoskeleton rehabilitation robot.
