Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 2; pp. 387 - 395
• Main Authors: Hamad, Rebeen Ali, Hidalgo, Alberto Salguero, Bouguelia, Mohamed-Rafik, Estevez, Macarena Espinilla, Quero, Javier Medina
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-02-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activity recognition; Artificial neural networks; Deep learning; Dementia disorders; Feature extraction; Fuzzy Logic; fuzzy temporal windows; Hidden Markov models; Human Activities; Human activity recognition; Humans; Intelligent sensors; Long short-term memory; Machine learning; Neural networks; Neural Networks, Computer; Performance enhancement; Real time; Real-time systems; Sensors; Smart buildings; Smart homes; Smart houses; temporal evaluation
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2019.2918412

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.