eHeart-BP: prototipo de Internet de las Cosas para monitorear la presión arterial utilizando algoritmos de Machine Learning

dc.contributor.advisorSalcedo Parra, Octavio Joséspa
dc.contributor.advisorCangrejo Aljure, Libia Denisespa
dc.contributor.authorBolívar Pulgarín, Néstor Germánspa
dc.date.accessioned2020-04-13T17:37:09Zspa
dc.date.available2020-04-13T17:37:09Zspa
dc.date.issued2019-12-01spa
dc.description.abstractEste trabajo se da en respuesta a las altas tasas de muerte en Colombia y en el mundo por hipertensión arterial, problemática que se puede abordar mediante la promoción de los hábitos saludables, el autocuidado y monitoreo sistemático. Hoy en día, se utilizan diversos dispositivos de medición para la supervisión de esta variable biométrica, sin embargo, es notoria la ausencia de un dispositivo practico que satisfaga los requerimientos de un sistema de monitoreo inteligente y cómodo de la presión arterial. El objetivo de esta investigación es el diseño y desarrollo de un prototipo de comunicación inalámbrica, sustentado en la utilización de un tensiómetro arterial de brazo. Este dispositivo fue modificado para posibilitar el envío de los datos de presión arterial y ritmo cardiaco a un servidor en la web. Si bien, en la búsqueda de literatura realizada se resaltan el desarrollo de múltiples sistemas de monitoreo de la presión arterial que involucran Tecnologías de la Información y Comunicación. Esta investigación enfatiza la utilización de herramientas del ecosistema digital actual que favorezcan una lectura cómoda y completa sobre los aspectos relevantes entorno a la presión arterial del usuario. También se destaca el diseño de algoritmos de aprendizaje automático que posibiliten el tratamiento de los datos proporcionados por el tensiómetro, de modo que se pueda obtener una probabilidad de riesgo de padecer un episodio de hipertensión mediante un análisis derivado de las medidas de presión arterial del usuario. En este documento se presenta el prototipo de monitoreo, la descripción y diseño de las capas estructurales que lo componen, tecnologías asociadas y los atributos técnicos que posibilitan su funcionamiento. Finalmente, se incluye un análisis comparativo de un sistema similar, donde se destaca en los resultados obtenidos, la predicción del modelo de aprendizaje de maquina en cerca del 67%. Adicional, es de resaltar la disponibilidad de las mediciones de PA en tiempo cercano al real, posibilitando notificaciones o alertas a eventualidades criticas.spa
dc.description.abstractThis work is given in response to the high death rates in Colombia and in the world due to arterial hypertension, problem that can be addressed by promoting healthy habits, self-care and systematic monitoring. Today, various measuring devices are used for the monitoring of this biometric variable, however, the absence of a practical device that meets the requirements of a smart and comfortable blood pressure monitoring system is noticeable. The objective of this research is the design and development of a wireless communication prototype, based on the use of an arm blood pressure monitor. This device was modified to enable the sending of blood pressure and heart rate data to a server on the web. Although, in the search for literature carried out, the development of multiple blood pressure monitoring systems that involve Information and Communication Technologies are highlighted, this research emphasizes the use of current digital ecosystem tools that favor a comfortable and complete reading about the relevant aspects around the user's blood pressure. It is also highlighted the design of machine learning algorithms that enable the treatment of the data provided by the tensiometer, so that a probability of risk of hypertension can be obtained through an analysis derived from the user's blood pressure measurements. This document presents the monitoring prototype, the description and design of the structural layers that compose it, associated technologies and the technical and design attributes that enable its operation. Finally, a comparative analysis of a similar system is included, where the prediction of the machine learning model stands out in about 67%. In addition, it is worth highlighting the availability of BP measurements in close to real time, allowing notifications or alerts to critical eventualities.spa
dc.description.additionalMagíster en Ingeniería - Telecomunicaciones. Línea de Investigación: Señales e informaciónspa
dc.description.degreelevelMaestríaspa
dc.format.extent83spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/77411
dc.language.isospaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
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dc.rightsDerechos reservados - Universidad Nacional de Colombiaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.spaAcceso abiertospa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.ddc600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionadosspa
dc.subject.proposalAprendizaje automáticospa
dc.subject.proposalInternet of Things (IoTspa
dc.subject.proposalPresión arterialspa
dc.subject.proposalTele-medicineeng
dc.titleeHeart-BP: prototipo de Internet de las Cosas para monitorear la presión arterial utilizando algoritmos de Machine Learningspa
dc.title.alternativeeHeart-BP, Prototype of the Internet of Things to Monitor Blood Pressurespa
dc.typeDocumento de trabajospa
dc.type.coarhttp://purl.org/coar/resource_type/c_8042spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/workingPaperspa
dc.type.redcolhttp://purl.org/redcol/resource_type/WPspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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