Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables

El proyecto de tesis, muestra el diseño, implementación y desarrollo de un algoritmo de aprendizaje profundo que permite realizar detección de caídas en especial en personal mayores que se encuentran viviendo solas, en espacios médicos o en centros de cuidados geriátricos. Se busca que este proyecto...

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Autores:
Saa Beltrán, María Paula
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2023
Institución:
Universidad Santo Tomás
Repositorio:
Repositorio Institucional USTA
Idioma:
spa
OAI Identifier:
oai:repository.usta.edu.co:11634/50378
Acceso en línea:
http://hdl.handle.net/11634/50378
Palabra clave:
Neural Networks
Falls
Long-Short Term Memory
Deep Learning
Ingeniería Electrónica
Personas Vulnerables
Diseño-Algoritmos
Redes Neuronales
Caídas
Long-Short Term Memory (LSTM)
Aprendizaje Profundo
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 2.5 Colombia
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network_name_str Repositorio Institucional USTA
repository_id_str
dc.title.spa.fl_str_mv Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables
title Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables
spellingShingle Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables
Neural Networks
Falls
Long-Short Term Memory
Deep Learning
Ingeniería Electrónica
Personas Vulnerables
Diseño-Algoritmos
Redes Neuronales
Caídas
Long-Short Term Memory (LSTM)
Aprendizaje Profundo
title_short Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables
title_full Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables
title_fullStr Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables
title_full_unstemmed Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables
title_sort Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables
dc.creator.fl_str_mv Saa Beltrán, María Paula
dc.contributor.advisor.none.fl_str_mv Cruz Capador, Gerson David
Guarnizo Marín, José Guillermo
dc.contributor.author.none.fl_str_mv Saa Beltrán, María Paula
dc.contributor.orcid.spa.fl_str_mv https://orcid.org/0000-0002-3723-7509
https://orcid.org/0000-0002-8401-4949
https://orcid.org/0000-0001-8509-2378
dc.contributor.googlescholar.spa.fl_str_mv https://scholar.google.com/citations?hl=es&user=fVo6U9MAAAAJ
https://scholar.google.com/citations?hl=es&user=3JSJ0C4AAAAJ
dc.contributor.cvlac.spa.fl_str_mv https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001334709
https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000855847
https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001767650
dc.contributor.corporatename.spa.fl_str_mv Universidad Santo Tomás
dc.subject.keyword.spa.fl_str_mv Neural Networks
Falls
Long-Short Term Memory
Deep Learning
topic Neural Networks
Falls
Long-Short Term Memory
Deep Learning
Ingeniería Electrónica
Personas Vulnerables
Diseño-Algoritmos
Redes Neuronales
Caídas
Long-Short Term Memory (LSTM)
Aprendizaje Profundo
dc.subject.lemb.spa.fl_str_mv Ingeniería Electrónica
Personas Vulnerables
Diseño-Algoritmos
dc.subject.proposal.spa.fl_str_mv Redes Neuronales
Caídas
Long-Short Term Memory (LSTM)
Aprendizaje Profundo
description El proyecto de tesis, muestra el diseño, implementación y desarrollo de un algoritmo de aprendizaje profundo que permite realizar detección de caídas en especial en personal mayores que se encuentran viviendo solas, en espacios médicos o en centros de cuidados geriátricos. Se busca que este proyecto pueda realizar esta detección, sin la necesidad de emplear dispositivos corporales que pueden generar inconvenientes en los pacientes, razón por la cual se opta por emplear un algoritmo basado en redes neuronales recurrentes de tipo LSTM (Memoria prolongada de corto plazo), que tienen la capacidad de recordar información relevante en secuencias y preservarlo por varios instantes de tiempo. Se realizan las pruebas en ambientes controlados, junto con personas que emulen las caídas y que no cuenten con ningún inconveniente de salud, la evaluación del proyectó se realiza a través de distintas métricas y pruebas en tiempo real.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-04-19T16:31:33Z
dc.date.available.none.fl_str_mv 2023-04-19T16:31:33Z
dc.date.issued.none.fl_str_mv 2023-04-17
dc.type.local.spa.fl_str_mv Trabajo de grado
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.drive.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
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dc.identifier.citation.spa.fl_str_mv Saa Beltrán, M. P. (2023). Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11634/50378
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad Santo Tomás
dc.identifier.instname.spa.fl_str_mv instname:Universidad Santo Tomás
dc.identifier.repourl.spa.fl_str_mv repourl:https://repository.usta.edu.co
identifier_str_mv Saa Beltrán, M. P. (2023). Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.
reponame:Repositorio Institucional Universidad Santo Tomás
instname:Universidad Santo Tomás
repourl:https://repository.usta.edu.co
url http://hdl.handle.net/11634/50378
dc.language.iso.spa.fl_str_mv spa
language spa
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Charu C Aggarwal et al. Neural networks and deep learning. Springer, 10:978–3, 2018.
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spelling Cruz Capador, Gerson DavidGuarnizo Marín, José GuillermoSaa Beltrán, María Paulahttps://orcid.org/0000-0002-3723-7509https://orcid.org/0000-0002-8401-4949https://orcid.org/0000-0001-8509-2378https://scholar.google.com/citations?hl=es&user=fVo6U9MAAAAJhttps://scholar.google.com/citations?hl=es&user=3JSJ0C4AAAAJhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001334709https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000855847https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001767650Universidad Santo Tomás2023-04-19T16:31:33Z2023-04-19T16:31:33Z2023-04-17Saa Beltrán, M. P. (2023). Aprendizaje Profundo para la Detección de Caídas en Personas Vulnerables. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.http://hdl.handle.net/11634/50378reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coEl proyecto de tesis, muestra el diseño, implementación y desarrollo de un algoritmo de aprendizaje profundo que permite realizar detección de caídas en especial en personal mayores que se encuentran viviendo solas, en espacios médicos o en centros de cuidados geriátricos. Se busca que este proyecto pueda realizar esta detección, sin la necesidad de emplear dispositivos corporales que pueden generar inconvenientes en los pacientes, razón por la cual se opta por emplear un algoritmo basado en redes neuronales recurrentes de tipo LSTM (Memoria prolongada de corto plazo), que tienen la capacidad de recordar información relevante en secuencias y preservarlo por varios instantes de tiempo. Se realizan las pruebas en ambientes controlados, junto con personas que emulen las caídas y que no cuenten con ningún inconveniente de salud, la evaluación del proyectó se realiza a través de distintas métricas y pruebas en tiempo real.The undergraduate project shows the design, implementation and development of a deep learning algorithm that allows fall detection, especially in older people who are living alone, in medical centres or in geriatric care centres. It is sought that this project can carry out this detection, without the need to use body devices that can generate inconveniences in patients, that is why it is chosen to use an algorithm based on recurrent neural networks of the LSTM type (Long short-term memory), that have the ability to remember relevant information in sequences and preserve it for several instants of time. The tests are carried out in controlled environments, with people who emulate the falls and do not have any health issues, the evaluation of the project is carried out through different metrics and tests in real time.Ingeniero ElectronicoPregradoapplication/pdfspaUniversidad Santo TomásPregrado Ingeniería ElectrónicaFacultad de Ingeniería ElectrónicaAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Aprendizaje Profundo para la Detección de Caídas en Personas VulnerablesNeural NetworksFallsLong-Short Term MemoryDeep LearningIngeniería ElectrónicaPersonas VulnerablesDiseño-AlgoritmosRedes NeuronalesCaídasLong-Short Term Memory (LSTM)Aprendizaje ProfundoTrabajo de gradoinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisCRAI-USTA BogotáAhmed Abobakr, Mohammed Hossny, Hala Abdelkader, and Saeid Nahavandi. Rgb-d fall detection via deep residual convolutional lstm networks. In 2018 Digital Image Computing: Techniques and Applications (DICTA), pages 1–7, 2018.Charu C Aggarwal et al. Neural networks and deep learning. Springer, 10:978–3, 2018.Xavier Basogain. Redes neuronales artificiales y sus aplicaciones. Dpto. Ingeniería de Sistemas y Automática, Escuela Superior de Ingeniería Bilbao, howpublished=http://ocw.ehu.es/ensenanzas-tecnicas/ redes-neuronales-artificiales-y-sus-aplicaciones/Course_ listing., year=2008.Valentin Bazarevsky and Google Research Ivan Grishchenko, Research Engineers. On-device, real-time body pose tracking with mediapipe blazepose. https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html, aug 2020.Avijeet Biswal. Convolutional neural network tutorial. https://www.simplilearn.com/tutorials/deep-learning-tutorial/convolutional-neural-network, jul 2022.Avijeet Biswal. 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