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      Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface

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          Abstract

          Everyday, people interact with different types of human machine interfaces, and the use of them is increasing, thus, it is necessary to design interfaces which are capable of responding in an intelligent, natural, inexpensive, and accessible way, regardless of social, cultural, economic, or physical features of a user. In this sense, it has been sought out the development of small interfaces to avoid any type of user annoyance. In this paper, bioelectric signals have been analyzed and characterized in order to propose a more natural human-machine interaction system. The proposed scheme is controlled by electromyographic signals that a person can create through arm movements. Such arm signals have been analyzed and characterized by a back-propagation neural network, and by a wavelet analysis, in this way control commands were obtained from such arm electromyographic signals. The developed interface, uses Extensible Messaging and Presence Protocol (XMPP) to send control commands remotely. In the experiment, it manipulated a vehicle that was approximately 52 km away from the user, with which it can be showed that a characterized electromyographic signal can be sufficient for controlling embedded devices such as a Raspberri Pi, and in this way we can use the neural network and the wavelet analysis to generate control words which can be used inside the Internet of Things too. A Tiva-C board has been used to acquire data instead of more popular development boards, with an adequate response. One of the most important aspects related to the proposed interface is that it can be used by almost anyone, including people with different abilities and even illiterate people. Due to the existence of individual efforts to characterize different types of bioelectric signals, we propose the generation of free access Bioelectric Control Dictionary, to define and consult each characterized biosignal.

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          Most cited references 37

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          A review of classification algorithms for EEG-based brain-computer interfaces.

          In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.
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            Visual interpretation of hand gestures for human-computer interaction: a review

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              Vision based hand gesture recognition for human computer interaction: a survey

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                24 April 2019
                April 2019
                : 19
                : 8
                Affiliations
                [1 ]Department of Engineering, ITSE, FESC, UNAM, Cuautitlán Izcalli 54714, Edo. de Mex, Mexico; fernando.gudino@ 123456comunidad.unam.mx
                [2 ]Department of Engineering, Technology Bachelor’s Degree, FESC, UNAM, Cuautitlán Izcalli 54714, Edo. de Mex, Mexico; caro408@ 123456comunidad.unam.mx
                Author notes
                [* ]Correspondence: dativa19@ 123456comunidad.unam.mx ; Tel.: +55-397-059-42
                Article
                sensors-19-01923
                10.3390/s19081923
                6515184
                31022847
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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