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Título
SDHAR-HOME: A sensor dataset for human activity recognition at home
Autor
Año del Documento
2022
Editorial
MDPI
Descripción
Producción Científica
Documento Fuente
Sensors, 2022, Vol. 22, Nº. 21, 8109
Zusammenfassung
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
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
Propietario de los Derechos
© 2022 The Authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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