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Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0001-9489-8330
University of Cádiz, Cádiz, Spain.ORCID iD: 0000-0001-9221-7351
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-2859-6155
University of Jaén, Jaén, Spain.ORCID iD: 0000-0003-1118-7782
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2020 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 24, no 2, p. 387-395Article in journal (Refereed) Published
Abstract [en]

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. © Copyright 2020 IEEE

Place, publisher, year, edition, pages
Piscataway: Institute of Electrical and Electronics Engineers (IEEE), 2020. Vol. 24, no 2, p. 387-395
Keywords [en]
Activity recognition, fuzzy temporal windows, deep learning, temporal evaluation
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-41633DOI: 10.1109/JBHI.2019.2918412Scopus ID: 2-s2.0-85079094027OAI: oai:DiVA.org:hh-41633DiVA, id: diva2:1392777
Funder
EU, Horizon 2020
Note

Other funding: Marie Sklodowska-Curie EU Framework for Research

Available from: 2020-02-10 Created: 2020-02-10 Last updated: 2020-03-04Bibliographically approved

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Ali Hamad, RebeenBouguelia, Mohamed-Rafik

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