Expert committee classifier for hand motions recognition from EMG signals

ABSTRACT This paper presents the design and implementation of a novel technique for the recognition of four hand motions for real time response (flexion (FL), extension (EX), opening (OP) and closure (CL)) from electromyographic (EMG) signals generated from two forearm muscles: palmaris longus and e...

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Autores principales:	Reyes López,David A., Loaiza Correa,Humberto, Arias López,Mauricio, Duarte Sánchez,Jorge E.
Lenguaje:	English
Publicado:	Universidad de Tarapacá. 2018
Materias:	Neural networks and support vector machines EMG signals discriminant function real time response
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052018000100062
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spelling	oai:scielo:S0718-330520180001000622018-02-27Expert committee classifier for hand motions recognition from EMG signalsReyes López,David A.Loaiza Correa,HumbertoArias López,MauricioDuarte Sánchez,Jorge E. Neural networks and support vector machines EMG signals discriminant function real time response ABSTRACT This paper presents the design and implementation of a novel technique for the recognition of four hand motions for real time response (flexion (FL), extension (EX), opening (OP) and closure (CL)) from electromyographic (EMG) signals generated from two forearm muscles: palmaris longus and extensor digitorum. The development of the work had two main stages: the low cost hardware for acquisition and conditioning of the EMG analog signals and the processing system for the identification and classification of the movement performed for real time response; the entire system was integrated in a hardware-software application using MATLAB and processing techniques for the discriminant analysis were performed. Three methods were evaluated for pattern recognition getting 98% recognition rates with the method proposed which had the best performance.info:eu-repo/semantics/openAccessUniversidad de Tarapacá.Ingeniare. Revista chilena de ingeniería v.26 n.1 20182018-03-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052018000100062en10.4067/S0718-33052018000100062
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Neural networks and support vector machines EMG signals discriminant function real time response
spellingShingle	Neural networks and support vector machines EMG signals discriminant function real time response Reyes López,David A. Loaiza Correa,Humberto Arias López,Mauricio Duarte Sánchez,Jorge E. Expert committee classifier for hand motions recognition from EMG signals
description	ABSTRACT This paper presents the design and implementation of a novel technique for the recognition of four hand motions for real time response (flexion (FL), extension (EX), opening (OP) and closure (CL)) from electromyographic (EMG) signals generated from two forearm muscles: palmaris longus and extensor digitorum. The development of the work had two main stages: the low cost hardware for acquisition and conditioning of the EMG analog signals and the processing system for the identification and classification of the movement performed for real time response; the entire system was integrated in a hardware-software application using MATLAB and processing techniques for the discriminant analysis were performed. Three methods were evaluated for pattern recognition getting 98% recognition rates with the method proposed which had the best performance.
author	Reyes López,David A. Loaiza Correa,Humberto Arias López,Mauricio Duarte Sánchez,Jorge E.
author_facet	Reyes López,David A. Loaiza Correa,Humberto Arias López,Mauricio Duarte Sánchez,Jorge E.
author_sort	Reyes López,David A.
title	Expert committee classifier for hand motions recognition from EMG signals
title_short	Expert committee classifier for hand motions recognition from EMG signals
title_full	Expert committee classifier for hand motions recognition from EMG signals
title_fullStr	Expert committee classifier for hand motions recognition from EMG signals
title_full_unstemmed	Expert committee classifier for hand motions recognition from EMG signals
title_sort	expert committee classifier for hand motions recognition from emg signals
publisher	Universidad de Tarapacá.
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052018000100062
work_keys_str_mv	AT reyeslopezdavida expertcommitteeclassifierforhandmotionsrecognitionfromemgsignals AT loaizacorreahumberto expertcommitteeclassifierforhandmotionsrecognitionfromemgsignals AT ariaslopezmauricio expertcommitteeclassifierforhandmotionsrecognitionfromemgsignals AT duartesanchezjorgee expertcommitteeclassifierforhandmotionsrecognitionfromemgsignals
_version_	1714203453671079936

Expert committee classifier for hand motions recognition from EMG signals

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