RT info:eu-repo/semantics/article
T1 SDHAR-HOME: A sensor dataset for human activity recognition at home
A1 Gómez Ramos, Raúl
A1 Duque Domingo, Jaime
A1 Zalama Casanova, Eduardo
A1 Gómez García-Bermejo, Jaime
A1 López, Joaquín
K1 Dataset
K1 Electronic data processing - Data preparation
K1 Machine learning
K1 Biosensors
K1 Computer communication systems
K1 Ingeniería de sistemas
K1 Neural networks (Computer science)
K1 Redes neuronales (Informática)
K1 Home automation
K1 Automatización del hogar
K1 Domótica
K1 Artificial intelligence
K1 1203.04 Inteligencia Artificial
K1 1203.25 Diseño de Sistemas Sensores
AB 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.
PB MDPI
SN 1424-8220
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/61626
UL https://uvadoc.uva.es/handle/10324/61626
LA eng
NO Sensors, 2022, Vol. 22, Nº. 21, 8109
NO Producción Científica
DS UVaDOC
RD 12-sep-2024