Convolutional neural network proposal for wrist position classification from electromyography signals

Alvaro D. Orjuela-Canon; Oscar J. Perdomo-Charry; Cesar H. Valencia-Nino; Leonardo Forero

doi:10.1109/ColCACI50549.2020.9247924

Convolutional neural network proposal for wrist position classification from electromyography signals

Título traducido de la contribución: Propuesta de red neuronal convolucional para la clasificación de la posición de la muñeca a partir de señales electromiográficas

Alvaro D. Orjuela-Canon, Oscar J. Perdomo-Charry, Cesar H. Valencia-Nino, Leonardo Forero

Producción científica: Capítulo en Libro/Informe › Capítulo › Investigación

Resumen

Comúnmente, las señales de electromiografía (EMG) se han empleado para la clasificación de movimientos o patrones. Para ello, se aplican diferentes métodos de procesamiento digital de señales para extraer características, antes de una etapa de clasificación. El presente trabajo aborda una propuesta basada en el uso de clasificación de imágenes empleando técnicas de aprendizaje profundo. Las imágenes fueron obtenidas a partir de un análisis de espectrogramas como proceso previo al empleo de redes neuronales convolucionales. A continuación, se lleva a cabo una clasificación de cinco posiciones a partir de los movimientos de la muñeca del modelo. Los resultados mostraron que la precisión es comparable a técnicas similares empleadas con una red neuronal superficial y una red neuronal profunda aplicadas al mismo conjunto de datos.

Título traducido de la contribución	Propuesta de red neuronal convolucional para la clasificación de la posición de la muñeca a partir de señales electromiográficas
Idioma original	Inglés estadounidense
Título de la publicación alojada	2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings
Editores	Alvaro David Orjuela-Canon
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781728194066
DOI	https://doi.org/10.1109/ColCACI50549.2020.9247924
Estado	Publicada - ago. 7 2020
Evento	2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Virtual, Cali, Colombia Duración: ago. 7 2020 → ago. 9 2020

Serie de la publicación

Nombre	2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings

Conferencia

Conferencia	2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020
País/Territorio	Colombia
Ciudad	Virtual, Cali
Período	8/7/20 → 8/9/20

Áreas temáticas de ASJC Scopus

Inteligencia artificial
Informática aplicada
Visión artificial y reconocimiento de patrones
Teoría de la decisión (miscelánea)
Gestión y sistemas de información
Control y optimización

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1109/ColCACI50549.2020.9247924

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Orjuela-Canon, A. D., Perdomo-Charry, O. J., Valencia-Nino, C. H., & Forero, L. (2020). Convolutional neural network proposal for wrist position classification from electromyography signals. En A. D. Orjuela-Canon (Ed.), 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings Artículo 9247924 (2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ColCACI50549.2020.9247924

Orjuela-Canon, Alvaro D. ; Perdomo-Charry, Oscar J. ; Valencia-Nino, Cesar H. et al. / Convolutional neural network proposal for wrist position classification from electromyography signals. 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings. editor / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings).

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abstract = "Commonly, electromyography (EMG) signals have been employed for movements or pattern classification. For this, different digital signals processing methods are applied to extract features, before a classification stage. The present work deals with a proposal based on the use of image classification employing deep learning techniques. The images were obtained from a spectrogram analysis as a previous process of the convolutional neural network employment. Then, a classification of five positions from wrist movements is carried out the model. Results showed that the accuracy is comparable to similar techniques employed with a shallow neural network and a deep neural network applied to the same dataset. ",

author = "Orjuela-Canon, {Alvaro D.} and Perdomo-Charry, {Oscar J.} and Valencia-Nino, {Cesar H.} and Leonardo Forero",

note = "Funding Information: The authors thank to Universidad el Rosario, Universidad Santo Tomas and Universidade Estadual do Rio de Janeiro for the support in this work. In addition, special thanks to professor Andres Ruiz in Universidad Antonio Nari?o for his support in the acquisition signals employed to develop the present proposal. Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 ; Conference date: 07-08-2020 Through 09-08-2020",

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Orjuela-Canon, AD, Perdomo-Charry, OJ, Valencia-Nino, CH & Forero, L 2020, Convolutional neural network proposal for wrist position classification from electromyography signals. En AD Orjuela-Canon (ed.), 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings., 9247924, 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020, Virtual, Cali, Colombia, 8/7/20. https://doi.org/10.1109/ColCACI50549.2020.9247924

Convolutional neural network proposal for wrist position classification from electromyography signals. / Orjuela-Canon, Alvaro D.; Perdomo-Charry, Oscar J.; Valencia-Nino, Cesar H. et al.
2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings. ed. / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2020. 9247924 (2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings).

Producción científica: Capítulo en Libro/Informe › Capítulo › Investigación

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AU - Orjuela-Canon, Alvaro D.

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AU - Forero, Leonardo

N1 - Funding Information: The authors thank to Universidad el Rosario, Universidad Santo Tomas and Universidade Estadual do Rio de Janeiro for the support in this work. In addition, special thanks to professor Andres Ruiz in Universidad Antonio Nari?o for his support in the acquisition signals employed to develop the present proposal. Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/8/7

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N2 - Commonly, electromyography (EMG) signals have been employed for movements or pattern classification. For this, different digital signals processing methods are applied to extract features, before a classification stage. The present work deals with a proposal based on the use of image classification employing deep learning techniques. The images were obtained from a spectrogram analysis as a previous process of the convolutional neural network employment. Then, a classification of five positions from wrist movements is carried out the model. Results showed that the accuracy is comparable to similar techniques employed with a shallow neural network and a deep neural network applied to the same dataset.

AB - Commonly, electromyography (EMG) signals have been employed for movements or pattern classification. For this, different digital signals processing methods are applied to extract features, before a classification stage. The present work deals with a proposal based on the use of image classification employing deep learning techniques. The images were obtained from a spectrogram analysis as a previous process of the convolutional neural network employment. Then, a classification of five positions from wrist movements is carried out the model. Results showed that the accuracy is comparable to similar techniques employed with a shallow neural network and a deep neural network applied to the same dataset.

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Orjuela-Canon AD, Perdomo-Charry OJ, Valencia-Nino CH, Forero L. Convolutional neural network proposal for wrist position classification from electromyography signals. En Orjuela-Canon AD, editor, 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9247924. (2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings). doi: 10.1109/ColCACI50549.2020.9247924

Convolutional neural network proposal for wrist position classification from electromyography signals

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