Deep neural network for EMG signal classification of wrist position: Preliminary results

Alvaro David Orjuela-Cañón, Andrés F. Ruíz-Olaya, Leonardo Forero

Producción científica: Capítulo en Libro/ReporteContribución a la conferencia

25 Citas (Scopus)

Resumen

Physically impaired people may use Surface Electromyography (SEMG) signals to control rehabilitation and assistive devices. SEMG is the electrical manifestation of the neuromuscular activation associated with a contracting muscle. SEMG directly reflects the human motion intention; thus, they can be used as input information for human-robot interaction. This paper proposes an EMG-based pattern recognition algorithm for classification of joint wrist angular position during flexion-extension movements from EMG signals. The algorithm uses a feature extraction stage based on a combination of time and frequency domain. The pattern recognition stage uses an artificial neural network (NN) as classifier. Also, using an autoencoder, deep NN architecture was tested. It was carried out a set of experiment with 10 subjects. Experiments included five recorded SEMG channels from forearm executing wrist flexion and extension movements, as well as the use of a commercial electrogoniometer to acquire joint angle. Results show that shallow NN had better performance that architectures with more layers based on autoencoders.

Idioma originalInglés estadounidense
Título de la publicación alojada2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1-5
Número de páginas5
ISBN (versión digital)9781538637340
DOI
EstadoPublicada - jul. 2 2017
Publicado de forma externa
Evento2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Arequipa, Perú
Duración: nov. 8 2017nov. 10 2017

Serie de la publicación

Nombre2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
Volumen2017-November

Conferencia

Conferencia2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017
País/TerritorioPerú
CiudadArequipa
Período11/8/1711/10/17

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial
  • Redes de ordenadores y comunicaciones
  • Gestión y sistemas de información

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