Pattern recognition of finger movement detection using Support Vector Machine

Author

Darmakusuma, Reza ; Prihatmanto, Ary S. ; Indrayanto, Adi ; Mengko, Tati L.

Author_Institution

Sch. of Electr. Eng. & Inf., Bandung Inst. of Technol., Bandung, Indonesia

fYear

2012

fDate

11-12 Sept. 2012

Firstpage

1

Lastpage

3

Abstract

This paper describes signal processing of surface electromyography (sEMG) for finger movement detection. Stoke survivors could use this application to retrain or helping them in their activities. This assistive technology will help them in order to improve the functional capabilities. The signal processing in this experiment is using 256Hz sampled data of sEMG signal. Three fingers of right hand is detected by using three channels of sEMG signal sources. System using Butterworth bandpass filter to eliminate noises. The filter using cut-off frequency 10Hz and 40Hz. Some features for the detection is built from statistical approach. System is using Support Vector Machine (SVM) to detect and classify fingers movement by using those features. From experiment, the accuracy of the sytem is about 98.3%.

Keywords

Butterworth filters; band-pass filters; electromyography; feature extraction; medical signal detection; signal classification; statistical analysis; support vector machines; Butterworth bandpass filter; SVM; feature detection; finger classification; finger movement detection; frequency 10 Hz; frequency 256 Hz; frequency 40 Hz; noise elimination; pattern recognition; sEMG signal sources; signal processing; statistical approach; stoke survivors; support vector machine; surface electromyography; Band pass filters; Electromyography; Humans; Impedance; Skin; Thumb; Butterworth; SVM; assistive technology; sEMG; stroke;

fLanguage

English

Publisher

ieee

Conference_Titel

System Engineering and Technology (ICSET), 2012 International Conference on

Conference_Location

Bandung

Print_ISBN

978-1-4673-2375-8

Type

conf

DOI

10.1109/ICSEngT.2012.6339335

Filename

6339335