Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/8/1923 |
| _version_ | 1828153714373820416 |
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| author | David Tinoco Varela Fernando Gudiño Peñaloza Carolina Jeanette Villaseñor Rodelas |
| author_facet | David Tinoco Varela Fernando Gudiño Peñaloza Carolina Jeanette Villaseñor Rodelas |
| author_sort | David Tinoco Varela |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:29:06Z |
| format | Article |
| id | doaj.art-260e09970cd84ebd960ae21dc723186e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:29:06Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-260e09970cd84ebd960ae21dc723186e2022-12-22T03:59:32ZengMDPI AGSensors1424-82202019-04-01198192310.3390/s19081923s19081923Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine InterfaceDavid Tinoco Varela0Fernando Gudiño Peñaloza1Carolina Jeanette Villaseñor Rodelas2Department of Engineering, ITSE, FESC, UNAM, Cuautitlán Izcalli 54714, Edo. de Mex, MexicoDepartment of Engineering, ITSE, FESC, UNAM, Cuautitlán Izcalli 54714, Edo. de Mex, MexicoDepartment of Engineering, Technology Bachelor’s Degree, FESC, UNAM, Cuautitlán Izcalli 54714, Edo. de Mex, MexicoEveryday, 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.https://www.mdpi.com/1424-8220/19/8/1923nano systemscommand and control systemshuman machine interfacesintegrated circuit interconnectionsintelligent controlbiosignalswavelets |
| spellingShingle | David Tinoco Varela Fernando Gudiño Peñaloza Carolina Jeanette Villaseñor Rodelas Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface Sensors nano systems command and control systems human machine interfaces integrated circuit interconnections intelligent control biosignals wavelets |
| title | Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface |
| title_full | Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface |
| title_fullStr | Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface |
| title_full_unstemmed | Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface |
| title_short | Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface |
| title_sort | characterized bioelectric signals by means of neural networks and wavelets to remotely control a human machine interface |
| topic | nano systems command and control systems human machine interfaces integrated circuit interconnections intelligent control biosignals wavelets |
| url | https://www.mdpi.com/1424-8220/19/8/1923 |
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