Abstract
The automation of spaces has become a recurrent theme in current affairs due to the need to improve comfort, quality of life and facilitate work for the human being. Thus, this article proposes an intelligent system that allows controlling devices wirelessly in a domestic environment in a simple and safe way. Our system is based on the recognition of different gestures that user makes with his arm, using the bracelet MYO of the company Thalmic Labs. The bracelet consists of 8 electrodes, an accelerometer and a gyroscope. The implementation of the system is done through a wireless data collection classification module. The communication system is made up with ZigBee modules, which control the electrical and electronic devices in the home. In order to perform the recognizing and classification of electromyography (EMG) signals, an artificial neural network model based on supervised learning has used. This work specifies the procedure that we have followed to extract the characteristics of received signals, the training phase of learning system, and an explanation of used algorithms.
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References
Bustinza, M.S., Ccopa, L.P., Maldonado, D.A., Perca, E.C.: Adquisición, parametrización y clasificación de patrones de señales mioeléctricas para el control de movimiento de un exoesqueleto que asiste la bipedestación. IEEE (2016)
Silva, E.C., Clua, E.W., Montenegro, A.A.: Sensor data fusion for full arm tracking using Myo Armband and leap motion. In: 14th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pp. 12–134 (2015)
Ploengpit, Y., Phienthrakul, T.: Rock-paper-scissors with Myo Armband pose detection. In: International Computer Science and Engineering Conference (ICSEC), pp. 1–5 (2016)
Villarejo, J.: Detección de la intención de movimiento durante la marcha a partir de señales electromiográficas (2011)
Betancourt, G., Suárez, E.G., Franco, J.F.: Reconocimiento de patrones de movimiento a partir de señales electromiográficas (2004)
Park, S.-H., Lee, S.-P.: EMG pattern recognition based on artificial intelligence techniques. IEEE Trans. Rehabil. Eng. 6, 400–405 (1998)
Mendoza, L.E., Peña, J., Muñoz-Bedoya, L.A., Velandia-Villamizar, H.J.: Procesamiento de señales provenientes del habla subvocal usando Wavelet Packet y Redes Neuronales. TecnoLógicas 655–667 (2013)
Chipana, M.A.: Clasificacion texturas mediante redes neuronales. Revista de Informacion, Tecnologia y Sociedad, p. 62 (2009)
Romo, H.A., Realpe, J.C., Jojoa, P.E.: Análisis de señales EMG superficiales y su aplicación en control de prótesis de mano. Avances en Sistemas e informatica, p. 4 (2007)
Frutos, D.T., Morales, N.R., Rodriguez, J.G., Zahira, J.F., Perez, L.R.: Clasificación de señales electromiograf\́ias mediante la configuración de una Red Neurona Artificial. CULCyT (2016)
Hargrove, L.J., Englehart, K., Hudgins, B.: A comparison of surface and intramuscular myoelectric signal classification. IEEE Trans. Biomed. Eng. 54, 847–853 (2007)
Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M.: Classification of the myoelectric signal using time-frequency based representations. Med. Eng. Phys. 21, 431–438 (1999)
Leite, E., Várela, L., Pires, V.F., Cardoso, F.D., Pires, A.J., Martins, J.F.: A ZigBee wireless domotic system with Bluetooth interface. In: IECON 2014—40th Annual Conference of the IEEE Industrial Electronics Society, pp. 2506–2511 (2014)
Alliance, Z.: ZigBee Specification. http://www.zigbee.org/Standards/ZigBeeSmartEnergy/Specification.aspx
Wei, L., Zhang, S., Chen, L., Song, J., Wang, X., Yin, L., He, M.: Wireless temperature monitoring system based on FBG and Zigbee. In: 2016 15th International Conference on Optical Communications and Networks (ICOCN), pp. 1–3 (2016)
Rodríguez-Gracia, D., Piedra-Fernández, J.A., Iribarne, L.: Adaptive Domotic System in Green Buildings. In: 2015 IIAI 4th International Congress on Advanced Applied Informatics, pp. 593–598 (2015)
Nieto, J.O.: Prototipo de Sistema Domótico Habilitado Para Interacción Cerebro-Máquina (2013)
Lozada, M.A., la Rosa R.F.D.: Simulation platform for domotic systems. In: 2014 IEEE Colombian Conference on Communications and Computing (COLCOM), pp. 1–6 (2014)
Contreras, J.C., Campoverde, R.S., Hidalgo, J.C., Tapia, P.E.: Mobile applications using TCP/IP-GSM protocols applied to domotic. In: 2015 XVI Workshop on Information Processing and Control (RPIC), pp. 1–4 (2015)
Choudhury, N., Matam, R., Deka, V.: Priority based ZigBee routing protocol for LR-WPAN. In: 2016 IEEE Students #8217, Technology Symposium (TechSym), pp. 19–201 (2016)
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Luna-Romero, S., Delgado-Espinoza, P., Rivera-Calle, F., Serpa-Andrade, L. (2018). A Domotics Control Tool Based on MYO Devices and Neural Networks. In: Duffy, V., Lightner, N. (eds) Advances in Human Factors and Ergonomics in Healthcare and Medical Devices. AHFE 2017. Advances in Intelligent Systems and Computing, vol 590. Springer, Cham. https://doi.org/10.1007/978-3-319-60483-1_56
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DOI: https://doi.org/10.1007/978-3-319-60483-1_56
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