Detection of forearm movements using wavelets and Adaptive Neuro-Fuzzy Inference System (ANFIS)

Author

Guvenc, Seyit Ahmet ; Demir, MenguÌˆ ; Ulutas, Mustafa

Author_Institution

Dept. of Comput. Technol., Suleyman Demirel Univ., Isparta, Turkey

fYear

2014

fDate

23-25 June 2014

Firstpage

192

Lastpage

196

Abstract

In this paper, a technique to classify seven different forearm movements using surface electromyography (sEMG) data which were received from 8 able bodied subjects was proposed. A 2-channel sEMG system was used for data acquisition and recording, then this raw electromyography (EMG) signals were applied to the wavelet denoising. In the next step, time-frequency feature is extracted calculating wavelet packet transform (WPT) coefficients for the offline classification. Feature vector of EMG signals were formed using only node energy of the WPT coefficients. In conclusion, seven forearm movements were separated by Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier and 92% success ratios over 500 samples were obtained.

Keywords

data acquisition; electromyography; fuzzy neural nets; fuzzy reasoning; medical signal processing; signal classification; signal denoising; wavelet transforms; ANFIS; WPT; able bodied subjects; adaptive neuro-fuzzy inference system; data acquisition; feature vector; forearm movements detection; offline classification; raw electromyography signals; sEMG system; surface electromyography data; wavelet denoising; wavelet packet transform coefficients; Accuracy; Electromyography; Feature extraction; Noise; Noise reduction; Wavelet transforms; Adaptive Neuro-Fuzzy Inference System (ANFIS); EMG signals; Wavelet; myoelectric;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on

Conference_Location

Alberobello

Print_ISBN

978-1-4799-3019-7

Type

conf

DOI

10.1109/INISTA.2014.6873617

Filename

6873617