Continuous Motion Estimation of Knee Joint Based on a Parameter Self-Updating Mechanism Model

Jiayi Li; Kexiang Li; Jianhua Zhang; Jian Cao

doi:10.3390/bioengineering10091028

Continuous Motion Estimation of Knee Joint Based on a Parameter Self-Updating Mechanism Model

Bioengineering (Basel). 2023 Aug 31;10(9):1028. doi: 10.3390/bioengineering10091028.

Authors

Jiayi Li¹, Kexiang Li², Jianhua Zhang³, Jian Cao¹

Affiliations

¹ School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China.
² School of Mechanical and Materials Engineering, North China University of Technology, Beijing 100144, China.
³ School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Abstract

Estimation of continuous motion of human joints using surface electromyography (sEMG) signals has a critical part to play in intelligent rehabilitation. Traditional methods always use sEMG signals as inputs to build regression or biomechanical models to estimate continuous joint motion variables. However, it is challenging to accurately estimate continuous joint motion in new subjects due to the non-stationarity and individual differences in sEMG signals, which greatly limits the generalisability of the method. In this paper, a continuous motion estimation model for the human knee joint with a parameter self-updating mechanism based on the fusion of particle swarm optimization (PSO) and deep belief network (DBN) is proposed. According to the original sEMG signals of different subjects, the method adaptively optimized the parameters of the DBN model and completed the optimal reconstruction of signal feature structure in high-dimensional space to achieve the optimal estimation of continuous joint motion. Extensive experiments were conducted on knee joint motions. The results suggested that the average root mean square errors (RMSEs) of the proposed method were 9.42° and 7.36°, respectively, which was better than the results obtained by common neural networks. This finding lays a foundation for the human-robot interaction (HRI) of the exoskeleton robots based on the sEMG signals.

Keywords: continuous joint motion estimation; deep belief network (DBN); deep learning; feature extraction; particle swarm optimization (PSO); surface electromyography (sEMG).

Grants and funding

This research was funded by the Tianjin Outstanding Youth Fund Project (Zhang, J.), grant number No. 19JCJQJC61600, the Natural Science Foundation of Hebei Province (Zhang, J.), grant number No. F2020202051, the National Natural Science Foundation of China (Li, K.), grant number No. 62203149, and the Hebei Postdoctoral Science Foundation (Li, K.), grant number No. B20220030.