2026-05-05T18:27:01Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/484752025-02-20T09:27:18Zcom_10324_966com_10324_952com_10324_894col_10324_967

2021-09-02T08:23:25Z urn:hdl:10324/48475 Daily human activity recognition using non-intrusive sensors Gómez Ramos, Raúl Duque Domingo, Jaime Zalama Casanova, Eduardo Gómez García-Bermejo, Jaime Producción Científica 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. 2021-09-02T08:23:25Z 2021-09-02T08:23:25Z 2021 info:eu-repo/semantics/article Sensors, 2021, vol. 21, n. 16, 5270 1424-8220 https://uvadoc.uva.es/handle/10324/48475 10.3390/s21165270 eng https://www.mdpi.com/1424-8220/21/16/5270 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2021 The Authors Attribution-NonCommercial-NoDerivatives 4.0 Internacional MDPI