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dc.contributor.authorEscobar Valencia, David
dc.contributor.authorRegino Ubarnes, Fernando Jesús
dc.contributor.authorGómez Camperos, July Andrea
dc.coverage.spatialColombiaen_US
dc.date.accessioned2025-10-27T20:44:48Z
dc.date.available2025-10-27T20:44:48Z
dc.date.issued2022-06-01
dc.identifier.issn1735-188Xen_US
dc.identifier.urihttps://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5956
dc.description.abstractLas señales electrooculográficas o EOG son ampliamente utilizadas para desarrollar equipos robóticos para atender a personas que han perdido parcial o totalmente la funcionalidad de uno o varios miembros de su cuerpo debido a enfermedades neurodegenerativas como la Esclerosis Lateral Amiotrófica o accidentes graves. Estas señales se aplican en el control de brazos robóticos, juegos serios, sillas de ruedas robóticas, entre otros. Asimismo, estas señales requieren circuitos electrónicos especializados para su amplificación y filtrado, además de requerir un microcontrolador para su procesamiento. En este trabajo se presentan los resultados del desarrollo de un prototipo de un electrooculógrafo para el movimiento de una báscula de silla de ruedas, el diseño de adquisición de datos, el sistema de transmisión vía Bluetooth y el control de la silla de ruedas con el movimiento ocular. La adquisición de datos se realiza mediante electrodos de gel para EOG, un sistema de acondicionamiento de señal y la caracterización de este, también se presenta el sistema de transmisión de datos vía Bluetooth y la etapa de potencia.en_US
dc.language.isoen_USen_US
dc.relationhttps://www.webology.org/abstract.php?id=4201en_US
dc.titleSilla de ruedas eléctrica a escala controlada por señales electrooculográficasen_US
dc.title.translatedScaled Electric Wheelchair Controlled By Electro-Oculographic Signalsen_US
dc.contributor.cvlacDavid Escobar Valencia [0000154045]en_US
dc.contributor.cvlacFernando Jesús Regino Ubarnes [0001508915]en_US
dc.contributor.cvlacJuly Andrea Gómez Camperos [0001164287]en_US
dc.contributor.googlescholarFernando Jesús Regino Ubarnes [l6g9NY8AAAAJ]en_US
dc.contributor.orcidDavid Escobar Valencia [0000-0002-1076-1597]en_US
dc.contributor.orcidFernando Jesús Regino Ubarnes [0000-0003-0414-8654]en_US
dc.contributor.orcidJuly Andrea Gómez Camperos [0000-0003-3894-144X]en_US
dc.contributor.researchgateFernando Jesús Regino Ubarnes [Fernando-Regino]en_US
dc.contributor.scopusFernando Jesús Regino Ubarnes [57212414222]en_US
dc.contributor.scopusJuly Andrea Gómez Camperos [57220550902]en_US
dc.description.abstractenglishThe electrooculographic signals or EOG are widely used to develop robotic equipment to serve people who have partially or totally lost the functionality of one or several members of their body due to neurodegenerative diseases such as Amyotrophic Lateral Sclerosis or serious accidents. These signals are applied in the control of robotic arms, serious games, robotic wheelchairs, among others. Also, these signals require specialized electronic circuits for amplification and filtering, in addition to requiring a microcontroller for processing them. This paper presents the results of the development of a prototype of an elctrooculography for the movement of a wheelchair scale, the design of data acquisition, the transmission system via Bluetooth and control of the wheelchair with eye movement is presented. The data acquisition is performed using gel electrodes for EOG, a signal conditioning system and the characterization of this, also presents the data transmission system via Bluetooth and the power stage.en_US
dc.subject.proposalElectrooculografíaen_US
dc.subject.proposalSeñales biológicasen_US
dc.subject.proposalDipoloen_US
dc.subject.proposalControlen_US
dc.subject.keywordsElectro oculographyen_US
dc.subject.keywordsBio signalsen_US
dc.subject.keywordsDipoleen_US
dc.subject.keywordsControlen_US
dc.publisher.facultyFacultad ingenieríasen_US
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTen_US
dc.relation.referencesAlvarez-Estevez, D., van Velzen, I., Ottolini-Capellen, T., & Kemp, B. (2017). Derivation and modeling of two new features for the characterization of rapid and slow eye movements in electrooculographic sleep recordings. Biomedical Signal Processing and Control, 35, 87–99. https://doi.org/10.1016/j.bspc.2017.02.014en_US
dc.relation.referencesAungsakul, S., Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). Evaluating Feature Extraction Methods of Electrooculography (EOG) Signal for Human-Computer Interface. Procedia Engineering, 32, 246–252. https://doi.org/10.1016/j.proeng.2012.01.1264en_US
dc.relation.referencesBarea, R., Boquete, L., Ortega, S., López, E., & Rodríguez-Ascariz, J. M. (2012). EOG-based eye movements codification for human computer interaction. Expert Systems with Applications, 39(3), 2677–2683. https://doi.org/10.1016/j.eswa.2011.08.123en_US
dc.relation.referencesBertomeu-Motos, A., Ezquerro, S., Barios, J. A., Lledó, L. D., Domingo, S., Nann, M., Martin, S., Soekadar, S. R., & Garcia-Aracil, N. (2019). User activity recognition system to improve the performance of environmental control interfaces: A pilot study with patients. Journal of NeuroEngineering and Rehabilitation, 16(1), 1–9. https://doi.org/10.1186/s12984-018-0477-5en_US
dc.relation.referencesHoracio, A. (2014). Interfaz humano-computadora basada en señales de electrooculografía para personas con discapacidad motriz. ReCIBE. Revista Electrónica de Computación, Informática, Biomédica y Electrónica, 3(2).en_US
dc.relation.referencesLi, Y., He, S., Huang, Q., Gu, Z., & Yu, Z. L. (2018). A EOG-based switch and its application for “start/stop” control of a wheelchair. Neurocomputing, 275, 1350–1357. https://doi.org/10.1016/j.neucom.2017.09.085en_US
dc.relation.referencesLv, Z., Wang, Y., Zhang, C., Gao, X., & Wu, X. (2018). An ICA-based spatial filtering approach to saccadic EOG signal recognition. Biomedical Signal Processing and Control, 43, 9–17. https://doi.org/10.1016/j.bspc.2018.01.003en_US
dc.relation.referencesMehra, D., Tiwari, A., & Joshi, D. (2021). Investigating neural correlates of locomotion transition via temporal relation of EEG and EOG-recorded eye movements. Computers in Biology and Medicine, 132, 104350. https://doi.org/10.1016/j.compbiomed.2021.104350en_US
dc.relation.referencesMohammadi Fathabad, S., & Shahri, F. (2021). BH hysteresis measurement system for thin soft magnetic materials. Measurement, 172, 108896. https://doi.org/10.1016/j.measurement.2020.108896en_US
dc.relation.referencesRamli, R., Arof, H., Ibrahim, F., Mokhtar, N., & Idris, M. Y. I. (2015). Using finite state machine and a hybrid of EEG signal and EOG artifacts for an asynchronous wheelchair navigation. Expert Systems with Applications, 42(5), 2451–2463. https://doi.org/10.1016/j.eswa.2014.10.052en_US
dc.relation.referencesSharma, K., Jain, N., & Pal, P. K. (2020). Detection of eye closing/opening from EOG and its application in robotic arm control. Biocybernetics and Biomedical Engineering, 40(1), 173–186. https://doi.org/10.1016/j.bbe.2019.10.004en_US
dc.relation.referencesTang, W., Wang, A., Ramkumar, S., & Nair, R. K. R. (2020). Signal identification system for developing rehabilitative device using deep learning algorithms. Artificial Intelligence in Medicine, 102, 101755. https://doi.org/10.1016/j.artmed.2019.101755en_US
dc.relation.referencesWangmo, P., Ramyavani, G., Iyer, K. S., & Karthik Raj, V. (2019). Two electrode ECG and EOG system for monitoring applications. International Journal of Innovative Technology and Exploring Engineering, 8(11), 262–265. https://doi.org/10.35940/ijitee.K1315.0981119en_US


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