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Decoding Arm Movements by Myoelectric Signal and Artificial Neural Networks

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DOI: 10.4236/ica.2013.41012    5,575 Downloads   7,421 Views   Citations

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 (sEMG) 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%.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

A. Balbinot, A. Júnior and G. Favieiro, "Decoding Arm Movements by Myoelectric Signal and Artificial Neural Networks," Intelligent Control and Automation, Vol. 4 No. 1, 2013, pp. 87-93. doi: 10.4236/ica.2013.41012.

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