RT info:eu-repo/semantics/article
T1 Daily human activity recognition using non-intrusive sensors
A1 Gómez Ramos, Raúl
A1 Duque Domingo, Jaime
A1 Zalama Casanova, Eduardo
A1 Gómez García-Bermejo, Jaime
K1 Recurrent neural networks
K1 Redes neuronales recurrentes
K1 Non-intrusive sensors
K1 Sensores no intrusivos
K1 Smart homes
K1 Hogares inteligentes
K1 1203.04 Inteligencia Artificial
AB 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.
PB MDPI
SN 1424-8220
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/48475
UL https://uvadoc.uva.es/handle/10324/48475
LA eng
NO Sensors, 2021, vol. 21, n. 16, 5270
NO Producción Científica
DS UVaDOC
RD 19-abr-2024