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Estado del arte de señales fisiológicas aplicadas en el control de sillas de ruedas
dc.contributor.advisor | Sarmiento Rojas, Jefferson | |
dc.contributor.advisor | Quevedo Silva, Rodrigo | |
dc.contributor.author | Tello Urrea, Andrés Felipe | |
dc.date.accessioned | 2022-01-25T02:48:25Z | |
dc.date.available | 2022-01-25T02:48:25Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | https://repositorio.escuelaing.edu.co/handle/001/1963 | |
dc.description | Trabajo de grado: Estado del arte de señales fisiológicas aplicadas en el control de sillas de ruedas inteligentes | spa |
dc.description.abstract | Introducción: Un 1 % de la población mundial requiere del uso de sillas de ruedas, las cuales usualmente no están adecuadamente diseñadas para sus necesidades. Dentro de esta población, aquellas personas que no poseen la capacidad de mover sus extremidades inferiores e incluso que han perdido la movilidad adecuada del cuello (e.g. etapas tardías de la esclerosis lateral amiotrófica) poseen un nivel de autonomía muy limitado. Objetivo: El objetivo de este estado del arte es establecer los métodos de adquisición, procesamiento, filtrado, extracción de características y modelos usados en señales fisiológicas empleadas para la generación de órdenes en una silla de ruedas inteligente. Por medio de este, se busca establecer el modelado de las respectivas señales que permitan ayudar a una mayor cantidad de población en condición de discapacidad a mejorar su calidad de vida. Metodología: El estado del arte se desarrolló con base en el formato PICO, por medio de este se definió la población objetivo, interacción, enfoque del análisis y lo que conforma los resultados. Con este propósito, una primera búsqueda que permitiera observar cuales son las señales fisiológicas más adecuadas para la caracterización y solución del objetivo fue realizada. Posterior a la identificación de dichas señales, se procedió a establecer el estado del arte de estas en la generación de órdenes para sillas de ruedas. La aplicación de estas señales en otros dispositivos de interfaz humanomaquina fue incluida de ser necesaria. Resultados: Por medio de la primera búsqueda cinco artículos de revisión permitieron establecer las señales de electroencefalografia y electrooculografia como las más usadas, así como aquellas con que una mayor cantidad de población podría generar señales de control para una silla de ruedas. Basado en estas señales, de la segunda búsqueda veinte artículos fueron incluidos, con base en ellos se obtuvo ocho artículos que implementan SSVEP, cuatro de MI, uno de P300, tres Hibridos (combina EEG con EOG), dos de EOG y dos que implementan otro tipo de modelo basado en estas señales. Los métodos de adquisición, procesamiento, filtrado, extracción de características y modelos usados fueron comparados y complementados con implementaciones pertinentes observadas en otros tipos de dispositivos IHM. Conclusiones: En este estado del arte se observaron diferentes paradigmas implementados para la generación de órdenes en sillas de ruedas, dependiendo del paradigma el modelado de la señal varia. No obstante, se puede concluir que modelos basados en redes neuronales con el paradigma de MI es la opción que más se acopla a un mayor número de la población, esto en el contexto de EEG. Por otro lado, las señales de EOG se basan en modelos más sencillos de umbralización. La combinación de estas dos señales para la generación de órdenes en sillas de ruedas posee un amplio campo por investigar dentro de esta problemática. A pesar de que los métodos de SSVEP posee buenos resultados, se llegó a la conclusión de que el uso de pantallas que desvíen la vista del usuario debe ser evitado en lo posible. | spa |
dc.description.abstract | Introduction: 1% of the world population requires the use of wheelchairs, which are usually not adequately designed for their needs. Within this population, those people who do not have the ability to move their lower limbs and even who have lost adequate neck mobility (e.g. late stages of amyotrophic lateral sclerosis) have a very limited level of autonomy. Objective: The objective of this state of the art is to establish the methods of acquisition, processing, filtering, extraction of characteristics and models used in physiological signals used for the generation of orders in an intelligent wheelchair. Through this, it is sought to establish the modeling of the respective signals that allow helping a greater number of the population with disabilities to improve their quality of life. Methodology: The state of the art was developed based on the PICO format, through which the target population, interaction, analysis approach and what makes up the results were defined. With this purpose, a first search that would allow observing which are the most adequate physiological signals for the characterization and solution of the objective was carried out. After the identification of these signals, the state of the art of these in the generation of orders for wheelchairs was established. The application of these signals in other human-machine interface devices was included if necessary. Results: Through the first search, five review articles allowed us to establish the electroencephalography and electrooculography signals as the most used, as well as those with which a greater number of the population could generate control signals for a wheelchair. Based on these signals, from the second search twenty articles were included, based on them eight articles were obtained that implement SSVEP, four from MI, one from P300, three Hybrids (combines EEG with EOG), two from EOG and two that implement another type of model based on these signals. The methods of acquisition, processing, filtering, feature extraction and models used were compared and complemented with relevant implementations observed in other types of HMI devices. Conclusions: In this state of the art, different paradigms implemented for the generation of orders in wheelchairs were observed, depending on the paradigm, the modeling of the signal varies. However, it can be concluded that models based on neural networks with the MI paradigm is the option that best suits a larger number of the population, this in the context of EEG. On the other hand, EOG signals are based on simpler thresholding models. The combination of these two signals for the generation of orders in wheelchairs has a wide field to investigate within this problem. Although the SSVEP methods have good results, it was concluded that the use of screens that divert the user's view should be avoided as much as possible. | eng |
dc.format.extent | 37 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | spa | spa |
dc.rights | Derechos Reservados - Autor | spa |
dc.title | Estado del arte de señales fisiológicas aplicadas en el control de sillas de ruedas | spa |
dc.type | Trabajo de grado - Pregrado | spa |
dcterms.audience | Validadores | spa |
dc.type.version | info:eu-repo/semantics/publishedVersion | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_16ec | spa |
oaire.awardtitle | Estado del arte de señales fisiológicas aplicadas en el control de sillas de ruedas inteligentes | spa |
oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
dc.description.degreelevel | Pregrado | spa |
dc.description.degreename | Ingeniero(a) Biomédico(a) | spa |
dc.identifier.url | https://catalogo.escuelaing.edu.co/cgi-bin/koha/opac-detail.pl?biblionumber=22860 | |
dc.publisher.faculty | Ciencias de la salud | spa |
dc.publisher.program | Ingeniería Biomédica | spa |
dc.relation.indexed | N/A | spa |
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dc.rights.accessrights | info:eu-repo/semantics/closedAccess | spa |
dc.subject.armarc | Tecnología Biomédica | |
dc.subject.armarc | Señales fisiológicas -- órdenes -- silla de ruedas inteligente | |
dc.subject.proposal | Tecnología Biomédica | spa |
dc.subject.proposal | Biomedical Technology | eng |
dc.subject.proposal | Señales fisiológicas -- órdenes -- silla de ruedas inteligente | spa |
dc.subject.proposal | Physiological signals -- commands -- smart wheelchair | eng |
dc.type.coar | http://purl.org/coar/resource_type/c_7a1f | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/bachelorThesis | spa |
dc.type.redcol | https://purl.org/redcol/resource_type/TP | spa |
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