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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/48475

    Título
    Daily human activity recognition using non-intrusive sensors
    Autor
    Gómez Ramos, RaúlAutoridad UVA Orcid
    Duque Domingo, JaimeAutoridad UVA Orcid
    Zalama Casanova, EduardoAutoridad UVA Orcid
    Gómez García-Bermejo, JaimeAutoridad UVA Orcid
    Año del Documento
    2021
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Sensors, 2021, vol. 21, n. 16, 5270
    Abstract
    In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person’s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS.
    Materias Unesco
    1203.04 Inteligencia Artificial
    Palabras Clave
    Recurrent neural networks
    Redes neuronales recurrentes
    Non-intrusive sensors
    Sensores no intrusivos
    Smart homes
    Hogares inteligentes
    ISSN
    1424-8220
    Revisión por pares
    SI
    DOI
    10.3390/s21165270
    Patrocinador
    Ministerio de Ciencia, Innovación y Universidades (project RTI2018-096652-B-I00)
    Junta de Castilla y León - Fondo Europeo de Desarrollo Regional (project VA233P18)
    Version del Editor
    https://www.mdpi.com/1424-8220/21/16/5270
    Propietario de los Derechos
    © 2021 The Authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/48475
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • ITAP - Artículos de revista [53]
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    Attribution-NonCommercial-NoDerivatives 4.0 InternacionalLa licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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