Detecting finger movement through classification of electromyography signals for use in control of robots

Author

Soltanmoradi, Maryam Alimohammadi ; Azimirad, Vahid ; Hajibabazadeh, Mahdiyeh

Author_Institution

Dept. of Mechatron. Eng., Univ. of Tabriz Tabriz, Tabriz, Iran

fYear

2014

fDate

15-17 Oct. 2014

Firstpage

791

Lastpage

794

Abstract

This paper introduces a new method for surface electromyography (EMG) classification that it is used for controlling robot. EMG signals from individual´s muscles are important items for controlling the prosthesis movements. For this purpose, two EMG electrodes located on the human forearm are utilized to collect the EMG data. Time and frequency sets such as Number of Zero Crossings (ZC), Autoregressive (AR) and wavelet coefficients are considered as features. On the other hand, Support Vector Machine (SVM) is used as a classification method. Results show accuracy of proposed approach is ≅ 80%. Finally to show the effectiveness and applicability of results, outputs of classification system are implemented on a fixed robot named Tabriz-Puma.

Keywords

electromyography; prosthetics; robots; signal classification; AR; EMG classification; EMG data; EMG electrodes; EMG signals; SVM; ZC; autoregressive; controlling robot; electromyography signal classification; finger movement detection; fixed robot named Tabriz-Puma; frequency sets; human forearm; prosthesis movements; robot control; support vector machine; surface electromyography; wavelet coefficients; zero crossings; Accuracy; Electromyography; Feature extraction; Robots; Support vector machines; Thumb; Autoregressive; EMG; SVM; Wavelet coefficients; Zero Crossings;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Mechatronics (ICRoM), 2014 Second RSI/ISM International Conference on

Conference_Location

Tehran

Type

conf

DOI

10.1109/ICRoM.2014.6991000

Filename

6991000