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oai:riuma.uma.es:10630/353582026-02-03T10:54:53Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Rebeen, Hamad author Salguero-Hidalgo, Alberto Gabriel author Bouguelia, Mohamed-Rafik author Espinilla-Estévez, Macarena author Medina-Quero, Javier author 2019 Human activity recognition has become an active research field over the past few years due to its wide application in various fields such as health-care, smart home monitoring, and surveillance. Existing approaches for activity recognition in smart homes have achieved promising results. Most of these approaches evaluate real-time recognition of activities using only sensor activations that precede the evaluation time (where the decision is made). However, in several critical situations, such as diagnosing people with dementia, “preceding sensor activations” are not always sufficient to accurately recognize the inhabitant's daily activities in each evaluated time. To improve performance, we propose a method that delays the recognition process in order to include some sensor activations that occur after the point in time where the decision needs to be made. For this, the proposed method uses multiple incremental fuzzy temporal windows to extract features from both preceding and some oncoming sensor activations. The proposed method is evaluated with two temporal deep learning models (convolutional neural network and long short-term memory), on a binary sensor dataset of real daily living activities. The experimental evaluation shows that the proposed method achieves significantly better results than the real-time approach, and that the representation with fuzzy temporal windows enhances performance within deep learning models. Hamad, R. A., Hidalgo, A. S., Bouguelia, M. R., Estevez, M. E., & Quero, J. M. (2019). Efficient activity recognition in smart homes using delayed fuzzy temporal windows on binary sensors. IEEE journal of biomedical and health informatics, 24(2), 387-395. 2168-2194 https://hdl.handle.net/10630/35358 10.1109/JBHI.2019.2918412 Domótica Reconocimiento de formas Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors.