Real-time recognition of arm motion using artificial neural network multi-perceptron with arduino one microcontroller and EKG/EMG shield sensor

Luis A. Caro, Camilo Silva, Billy Peralta, Oriel A. Herrera, Sergio Barrientos

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

Resumen

Currently, human-computer interfaces have a number of useful applications for people. The use of electromyographic signals (EMG) has shown to be effective for human-computer interfaces. The classification of patterns based on EMG signals has been successfully applied in various tasks such as motion detection to control of video games. An alternative to increasing access to these applications is the use of low-cost hardware to sample the EMG signals considering a real-time response. This paper presents a methodology for recognizing patterns of EMG signals given by arm movements in real time. Our proposal is based on an artificial Neural Network, Multilayer Perceptron, where the EMG signals are processed by a set of signal processing techniques. The hardware used for obtaining the signal is based on Ag/AgCl connected to the EKG/EMG-Shield plate mounted on a Arduino One R3 card which is used to control a video game. The implemented application achieves an accuracy above 90% using less than 0.2 s for recognition of actions in time of testing. Our methodology is shown to predict different movements of the human arm reliably, at a low cost and in real time.

Idioma originalInglés
Título de la publicación alojadaAmbient Intelligence for Health - 1st International Conference, AmIHEALTH 2015, Proceedings
EditoresVladimir Villarreal, José Bravo, Ramón Hervás
EditorialSpringer Verlag
Páginas3-14
Número de páginas12
ISBN (versión impresa)9783319265070
DOI
EstadoPublicada - 1 ene 2015
Evento1st International Conference on Ambient Intelligence for Health, AmIHEALTH 2015 - Puerto Varas, Chile
Duración: 1 dic 20154 dic 2015

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen9456
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia1st International Conference on Ambient Intelligence for Health, AmIHEALTH 2015
PaísChile
CiudadPuerto Varas
Período1/12/154/12/15

Áreas temáticas de ASJC Scopus

  • Ciencia computacional teórica
  • Informática (todo)

Huella Profundice en los temas de investigación de 'Real-time recognition of arm motion using artificial neural network multi-perceptron with arduino one microcontroller and EKG/EMG shield sensor'. En conjunto forman una huella única.

  • Citar esto

    Caro, L. A., Silva, C., Peralta, B., Herrera, O. A., & Barrientos, S. (2015). Real-time recognition of arm motion using artificial neural network multi-perceptron with arduino one microcontroller and EKG/EMG shield sensor. En V. Villarreal, J. Bravo, & R. Hervás (Eds.), Ambient Intelligence for Health - 1st International Conference, AmIHEALTH 2015, Proceedings (pp. 3-14). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9456). Springer Verlag. https://doi.org/10.1007/978-3-319-26508-7_1