Decoding arm movements by myoeletric signals and artificial neural networks

Author

Favieiro, Gabriela W. ; Balbinot, Alexandre ; Barreto, Mara M G

Author_Institution

Dept. of Electr. Eng. - PPGEE - IEE, Fed. Univ. of Rio Grande do Sul - UFRGS, Porto Alegre, Brazil

fYear

2011

fDate

6-8 Jan. 2011

Firstpage

1

Lastpage

6

Abstract

The scientific researches in the field of rehabilitation engineering are increasingly providing mechanisms in order to help people with disability to perform simple tasks of day-to-day. Several studies have been carried out highlighting the advantages of using muscle signal in order to control rehabilitation devices, such as experimental prostheses. This paper presents a study investigating the use of forearm surface electromyography (EMG) signals for classification of several movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an artificial neural network to process signal features to recognize performed movements. The average accuracy reached for the classification of six different movements was 68-88%.

Keywords

biomechanics; biomedical electrodes; electromyography; handicapped aids; medical signal processing; neural nets; patient rehabilitation; prosthetics; signal classification; EMG; arm movement decoding; artificial neural networks; disability; experimental prostheses; forearm surface electromyography signals; muscle signal; myoeletric signals; rehabilitation device control; rehabilitation engineering; signal classification; signal process; surface electrodes; Artificial neural networks; Electrodes; Electromyography; Muscles; Prosthetics; Training; Wrist; EMG; assistive technology; bio-robotics; biomedical instrumentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Biosignals and Biorobotics Conference (BRC), 2011 ISSNIP

Conference_Location

Vitoria

Print_ISBN

978-1-4244-8212-2

Type

conf

DOI

10.1109/BRC.2011.5740677

Filename

5740677