• español
  • English
  • français
  • Deutsch
  • português (Brasil)
  • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Listar

    Todo UVaDOCComunidadesPor fecha de publicaciónAutoresMateriasTítulos

    Mi cuenta

    Acceder

    Estadísticas

    Ver Estadísticas de uso

    Compartir

    Ver ítem 
    •   UVaDOC Principal
    • PRODUCCIÓN CIENTÍFICA
    • Departamentos
    • Dpto. Ingeniería de Sistemas y Automática
    • DEP44 - Artículos de revista
    • Ver ítem
    •   UVaDOC Principal
    • PRODUCCIÓN CIENTÍFICA
    • Departamentos
    • Dpto. Ingeniería de Sistemas y Automática
    • DEP44 - Artículos de revista
    • Ver ítem
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano

    Exportar

    RISMendeleyRefworksZotero
    • edm
    • marc
    • xoai
    • qdc
    • ore
    • ese
    • dim
    • uketd_dc
    • oai_dc
    • etdms
    • rdf
    • mods
    • mets
    • didl
    • premis

    Citas

    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/61626

    Título
    SDHAR-HOME: A sensor dataset for human activity recognition at home
    Autor
    Gómez Ramos, RaúlAutoridad UVA Orcid
    Duque Domingo, JaimeAutoridad UVA Orcid
    Zalama Casanova, EduardoAutoridad UVA Orcid
    Gómez García-Bermejo, JaimeAutoridad UVA Orcid
    López, Joaquín
    Año del Documento
    2022
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Sensors, 2022, Vol. 22, Nº. 21, 8109
    Resumen
    Nowadays, one of the most important objectives in health research is the improvement of the living conditions and well-being of the elderly, especially those who live alone. These people may experience undesired or dangerous situations in their daily life at home due to physical, sensorial or cognitive limitations, such as forgetting their medication or wrong eating habits. This work focuses on the development of a database in a home, through non-intrusive technology, where several users are residing by combining: a set of non-intrusive sensors which captures events that occur in the house, a positioning system through triangulation using beacons and a system for monitoring the user’s state through activity wristbands. Two months of uninterrupted measurements were obtained on the daily habits of 2 people who live with a pet and receive sporadic visits, in which 18 different types of activities were labelled. In order to validate the data, a system for the real-time recognition of the activities carried out by these residents was developed using different current Deep Learning (DL) techniques based on neural networks, such as Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM) or Gated Recurrent Unit networks (GRU). A personalised prediction model was developed for each user, resulting in hit rates ranging from 88.29% to 90.91%. Finally, a data sharing algorithm has been developed to improve the generalisability of the model and to avoid overtraining the neural network.
    Materias (normalizadas)
    Dataset
    Electronic data processing - Data preparation
    Machine learning
    Biosensors
    Computer communication systems
    Ingeniería de sistemas
    Neural networks (Computer science)
    Redes neuronales (Informática)
    Home automation
    Automatización del hogar
    Domótica
    Artificial intelligence
    Materias Unesco
    1203.04 Inteligencia Artificial
    1203.25 Diseño de Sistemas Sensores
    ISSN
    1424-8220
    Revisión por pares
    SI
    DOI
    10.3390/s22218109
    Patrocinador
    Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación (AEI)/10.13039/501100011033, Fondo Europeo de Desarrollo Regional (FEDER) y Junta de Castilla y León, Consejería de Familia - (project PID2021-123020OB-I00)
    Version del Editor
    https://www.mdpi.com/1424-8220/22/21/8109
    Propietario de los Derechos
    © 2022 The Authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/61626
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP44 - Artículos de revista [78]
    Mostrar el registro completo del ítem
    Ficheros en el ítem
    Nombre:
    SDHAR-HOME.pdf
    Tamaño:
    9.681Mb
    Formato:
    Adobe PDF
    Thumbnail
    Visualizar/Abrir
    Atribución 4.0 InternacionalLa licencia del ítem se describe como Atribución 4.0 Internacional

    Universidad de Valladolid

    Powered by MIT's. DSpace software, Version 5.10