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A Neuro-Fuzzy System for Characterization of Arm Movements
Electrical Engineering, Instrumentation Laboratory, Federal University of Rio Grande do Sul, Avenue Osvaldo Aranha 103, Porto Alegre 90035-190, Brazil
* Author to whom correspondence should be addressed.
Received: 13 December 2012; in revised form: 15 February 2013 / Accepted: 16 February 2013 / Published: 21 February 2013
Abstract: The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours).
Keywords: biomedical instrumentation; surface electromyography (sEMG); arm movements; neuro-fuzzy system
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Cite This Article
MDPI and ACS Style
Balbinot, A.; Favieiro, G. A Neuro-Fuzzy System for Characterization of Arm Movements. Sensors 2013, 13, 2613-2630.
Balbinot A, Favieiro G. A Neuro-Fuzzy System for Characterization of Arm Movements. Sensors. 2013; 13(2):2613-2630.
Balbinot, Alexandre; Favieiro, Gabriela. 2013. "A Neuro-Fuzzy System for Characterization of Arm Movements." Sensors 13, no. 2: 2613-2630.