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A method of motion recognition based on electromyographic signals

Luo, Jing; Liu, Chao; Feng, Ying; Yang, Chenguang

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Authors

Jing Luo

Chao Liu

Ying Feng



Abstract

In a robot-assisted surgery, a skillful surgeon can perform the operation excellently through flexible wrist motions and rich experience. However, there are little researches about the relationship between the wrist motion and electromyography (EMG) signal of surgeon. To this end, we introduce a classification framework of wrist motion to recognize the common wrist motion of the surgeon based on EMG signals. Generally, surface electromyogram (sEMG) signal has been widely used in prosthetic hand control and medical clinical application. Hence, in this paper, we utilize sEMG signals to evaluate the wrist motions. Eight channels of sEMG signals are captured through a MYO armband from the forearm of the subject. Different kinds of features based on EMG signal, root-mean-square, waveform length, and autoregressive are used to recognize wrist motion through linear discriminant analysis method. We test the impacts on recognition performance from the different sEMG features and different sampling moving window's length. Experimental results have verified the recognition performance of the presented approach. It is validated that the RMS feature can achieve best recognition performance with all different sampling moving window's length in comparison with the WL feature and AR feature.

Citation

Luo, J., Liu, C., Feng, Y., & Yang, C. (2020). A method of motion recognition based on electromyographic signals. Advanced Robotics, 34(15), 976-984. https://doi.org/10.1080/01691864.2020.1750480

Journal Article Type Article
Acceptance Date Mar 23, 2020
Online Publication Date Apr 7, 2020
Publication Date Aug 31, 2020
Deposit Date Apr 15, 2020
Publicly Available Date Apr 8, 2021
Journal Advanced Robotics
Print ISSN 0169-1864
Electronic ISSN 1568-5535
Publisher Taylor & Francis
Peer Reviewed Peer Reviewed
Volume 34
Issue 15
Pages 976-984
DOI https://doi.org/10.1080/01691864.2020.1750480
Keywords Control and Systems Engineering; Human-Computer Interaction; Hardware and Architecture; Software; Computer Science Applications
Public URL https://uwe-repository.worktribe.com/output/5872773
Additional Information Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=tadr20; Received: 2019-09-05; Revised: 2020-01-28; Accepted: 2020-03-23; Published: 2020-04-07

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