To read the full version of this content please select one of the options below:

sEMG-based shoulder-elbow composite motion pattern recognition and control methods for upper limb rehabilitation robot

Xiufeng Zhang (National Research Center for Rehabilitation Technical Aids, Beijing, China)
Jitao Dai (Harbin Engineering University, Harbin, China)
Xia Li (Harbin Engineering University, Harbin, China)
Huizi Li (Harbin Engineering University, Harbin, China)
Huiqun Fu (Independent Researcher, Beijing, China)
Guoxin Pan (National Research Center for Rehabilitation Technical Aids, Beijing, China)
Ning Zhang (National Research Center for Rehabilitation Technical Aids, Beijing, China)
Rong Yang (National Research Center for Rehabilitation Technical Aids, Beijing, China)
Jianguang Xu (National Research Center for Rehabilitation Technical Aids, Beijing, China)

Assembly Automation

ISSN: 0144-5154

Publication date: 5 August 2019

Abstract

Purpose

This paper aims to develop a signal acquisition system of surface electromyography (sEMG) and use the characteristics of (sEMG) signal to interference action pattern.

Design/methodology/approach

This paper proposes a fusion method based on combining the coefficient of AR model and wavelet coefficient. It improves the recognition rate of the target action. To overcome the slow convergence speed and local optimum in standard BP network, the study presents a BP algorithm which combine with LM algorithm and PSO algorithm, and it improves the convergence speed and the recognition rate of the target action.

Findings

Experiments verify the effectiveness of the system from two aspects the target motion recognition rate and the corresponding reaction speed of the robotic system.

Originality/value

The study developed a signal acquisition system of sEMG and used the characteristics of (sEMG) signal to interference action pattern. The myoelectricity integral values are presented to determine the starting point and end point of target movement, which is more effective than using single sample point amplitude method.

Keywords

Citation

Zhang, X., Dai, J., Li, X., Li, H., Fu, H., Pan, G., Zhang, N., Yang, R. and Xu, J. (2019), "sEMG-based shoulder-elbow composite motion pattern recognition and control methods for upper limb rehabilitation robot", Assembly Automation, Vol. 39 No. 3, pp. 394-400. https://doi.org/10.1108/AA-11-2017-148

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited