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dc.contributor.advisorMoreno Corzo, Feisar Enrique
dc.contributor.authorTigreros Niño, Jhenner Sneyder
dc.coverage.spatialColombiaspa
dc.date.accessioned2021-08-19T15:24:12Z
dc.date.available2021-08-19T15:24:12Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/20.500.12749/13898
dc.description.abstractLas dificultades físicas y desplazamiento a las que se ven sometidas personas con algún tipo de disminución en las capacidades motoras de su cuerpo, personas con parálisis o cuadripléjicos por algún accidente sufrido, es un problema al cual la sociedad ha estado integrándose lentamente, convirtiendo la infraestructura física de las grandes ciudades y las edificaciones de estas, con el fin de tener un ambiente más incluyente con esta población vulnerable. Actualmente con la implementación de nuevas tecnologías como la Inteligencia Artificial, principalmente el Aprendizaje de Maquina y el Aprendizaje Profundo, para la detección y clasificación de las acciones que desea hacer una persona en situación de discapacidad mediante el procesamiento de las señales cerebrales capturadas por un dispositivo de Interfaz Cerebro-Maquina, ha generado una nueva posibilidad y oportunidad a estas personas de usar modernas prótesis para sus extremidades inferiores y superiores. En este proyecto se desarrollará un algoritmo para la clasificación de estas ondas cerebrales enfocado en el movimiento de miembros inferior mediante la Inteligencia Artificial, para ser la base de nuevos productos de apoyo para estas poblaciones vulnerables.spa
dc.description.tableofcontents1. PLANTEAMIENTO DEL PROBLEMA Y JUSTIFICACIÓN 13 1.1 PLANTEAMIENTO DEL PROBLEMA 13 1.2 JUSTIFICACIÓN 14 1.3 LINEA DE INVESTIGACIÓN 15 2. OBJETIVOS Y PRODUCTOS 16 2.1. OBJETIVO GENERAL 16 2.2. OBJETIVOS ESPECÍFICOS 16 2.3. PRODUCTOS 16 3. ANTECEDENTES Y ESTADO DEL ARTE 18 3.1. ANTECEDENTES 18 3.2. ESTADO DEL ARTE 19 4. MARCO CONCEPTUAL 22 4.1. INTERFAZ CEREBRO-COMPUTADORA BASADA EN ELECTROENCEFALOGRAFÍA (EEG) 22 4.1.1. Ritmos motores sensoriales e imágenes motores 22 4.2. PREPROCESAMIENTO DE SEÑALES 23 4.2.1. Filtro de Band-Stop 23 4.2.2. Filtro High Pass 24 4.2.3. Normalización 25 4.3. REDES NEURONALES ARTIFICIALES 25 4.3.1. Neurona 26 4.3.1.1. Estructura 26 4.3.2. Función de activación 27 4.3.2.1. ReLU 27 4.3.2.2. Tanh 28 4.3.3. Función de costos 28 4.3.3.1. Linear 29 4.3.3.2. Sigmoide 29 4.3.3.3. Softmax 31 4.4. APRENDIZAJE PROFUNDO 31 4.4.1. Red Neuronal Recurrente Clásica. 31 4.4.2. Red Long Short-Term Memory (LSTM) 33 5. MARCO METODOLÓGICO 37 5.1 DEFINICIÓN DE STACK TECNOLOGICO 40 5.1.2 Python 40 5.1.3 Tensorflow 40 5.1.4 Google Cloud Platform 40 5.1.5 Scipy 41 5.1.6 Scikit-Learn 41 5.2 DEFINICIÓN DE CASOS DE USO 41 5.3 DESCRIPCIÓN DE CASOS DE USO 43 5.3.1 Carga de información en batch 43 Actores 43 Precondiciones 43 Flujo Básico 43 Flujo Alternativo 1 44 Flujo Alternativo 2 44 Descripción de Objetos e Interacciones 44 Implementación 45 5.3.2 Carga de información en tiempo real 45 Actores 46 Precondiciones 46 Flujo Básico 46 Flujo Alternativo 1 46 Flujo Alternativo 2 46 Descripción de Objetos e Interacciones 47 Implementación 48 5.3.3 Visualizar predicción 48 Actores 49 Precondiciones 49 Flujo Básico 49 Descripción de Objetos e Interacciones 49 Implementación 50 5.3.4 Preprocesamiento de datos 51 Actores 51 Precondiciones 51 Flujo Básico 51 Flujo Alternativo 51 Descripción de Objetos e Interacciones 52 Implementación 53 5.3.5 Visualizar señales 53 Actores 54 Precondiciones 54 Flujo Básico 54 Flujo Alternativo 54 Descripción de Objetos e Interacciones 54 Implementación 55 5.3.6 Guardar información en la nube 55 Actores 56 Precondiciones 56 Flujo Básico 56 Flujo Alternativo 56 Descripción de Objetos e Interacciones 57 Implementación 57 5.4 LEVANTAMIENTO DE DATOS 58 5.4.1 Recolección de datos 58 5.4.2 Muestra de datos 59 5.4.3 Método experimental 59 5.4.4 Datos 60 5.4.5 Validación técnica 60 5.5 PROCESAMIENTO DE SEÑALES 60 5.5.1 Filtro Band-Stop 60 5.5.2 Filtro High-Pass 62 5.5.3 Normalización 63 6. ARQUITECTURA DE RED NEURONAL 64 6.1 Capas 64 6.2 Funciones de activación 65 6.3 Modelo 65 7. RESULTADOS OBTENIDOS 67 7.1 CONJUNTO DE DATOS EN BRUTO Y PREPROCESAMIENTO DE INFORMACIÓN. 67 7.2 CARACTERIZACIÓN DE LAS ONDAS EEG 67 7.3 ARQUITECTURA DE APRENDIZAJE PRONFUNDO 68 7.4 IMPLEMENTACIÓN DE LA ARQUITECTURA DE APRENDIZAJE PRONFUNDO 68 7.5 ENTRENAMIENTO DE LA RED NEURONAL 68 7.6 PRUEBAS DE LA RED NEURONAL ENTRENADA 68 8. ANALISIS DE RESULTADOS 69 8.1 ENTRENAMIENTO DE LA RED NEURONAL 69 8.2 PRUEBAS EN EL CONJUNTO DE VALIDACIÓN 73 8.3 PROCESAMIENTO DE DATOS 74 8.4 CONJUNTO DE ENTRENAMIENTO 75 8.5 DIAGRAMA DE DESPLIEGUE 75 9. CONCLUSIONES 76 10. RECOMENDACIONES 77 BIBLIOGRAFÍA 79 ANEXOS 85spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.titleAlgoritmo para la clasificación de ondas cerebrales mediante técnicas de aprendizaje profundo enfocado en el movimiento de miembros inferiores haciendo uso de una interfaz cerebro-máquinaspa
dc.title.translatedAlgorithm for the classification of brain waves using deep learning techniques focused on the movement of the lower limbs using a brain-machine interfacespa
dc.degree.nameIngeniero de Sistemasspa
dc.publisher.grantorUniversidad Autónoma de Bucaramanga UNABspa
dc.rights.localAbierto (Texto Completo)spa
dc.publisher.facultyFacultad Ingenieríaspa
dc.publisher.programPregrado Ingeniería de Sistemasspa
dc.description.degreelevelPregradospa
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.localTrabajo de Gradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.subject.keywordsSystems engineerspa
dc.subject.keywordsTechnological innovationsspa
dc.subject.keywordsBrain-signalspa
dc.subject.keywordsArtificial intelligencespa
dc.subject.keywordsMovementspa
dc.subject.keywordsBrain-machine interfacespa
dc.subject.keywordsAdaptive control systemsspa
dc.subject.keywordsSimulation methodsspa
dc.subject.keywordsAlgorithmsspa
dc.subject.keywordsMachine theoryspa
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga - UNABspa
dc.identifier.reponamereponame:Repositorio Institucional UNABspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2spa
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dc.contributor.cvlacMoreno Corzo, Feisar Enrique [0001499008]spa
dc.contributor.googlescholarMoreno Corzo, Feisar Enrique [jz75nEcAAAAJ&hl=es&oi=ao]spa
dc.contributor.orcidMoreno Corzo, Feisar Enrique [0000-0002-5007-3422]spa
dc.contributor.researchgateMoreno Corzo, Feisar Enrique [Feisar-Enrique-Moreno-Corzo-2169498891]spa
dc.subject.lembIngeniería de sistemasspa
dc.subject.lembInnovaciones tecnológicasspa
dc.subject.lembSistemas de control adaptablespa
dc.subject.lembMétodos de simulaciónspa
dc.subject.lembAlgoritmosspa
dc.subject.lembTeoría de las máquinasspa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.description.abstractenglishThe physical difficulties and displacement to which people with some type of decrease in the motor capacities of their body are subjected, people with paralysis or quadriplegics due to an accident suffered, is a problem to which society has been slowly integrating, converting the infrastructure physics of large cities and their buildings, in order to have a more inclusive environment with this vulnerable population. Currently with the implementation of new technologies such as Artificial Intelligence, mainly Machine Learning and Deep Learning, for the detection and classification of the actions that a person in a situation of disability wishes to do through the processing of brain signals captured by a device. Brain-Machine Interface, has generated a new possibility and opportunity for these people to use modern prostheses for their lower and upper extremities. In this project, an algorithm will be developed for the classification of these brain waves focused on the movement of lower limbs through Artificial Intelligence, to be the basis of new support products for these vulnerable populations.spa
dc.subject.proposalSeñal cerebralspa
dc.subject.proposalInteligencia artificialspa
dc.subject.proposalMovimientospa
dc.subject.proposalInterfaz cerebro-máquinaspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TP
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*


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