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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 activeresearch field over the past few years due to its wide applicationin various fields such as health-care, smart homemonitoring, and surveillance. Existing approaches for activityrecognition in smart homes have achieved promisingresults. Most of these approaches evaluate real-timerecognition of activities using only sensor activations thatprecede the evaluation time (where the decision is made).However, in several critical situations, such as diagnosingpeople with dementia, “preceding sensor activations”are not always sufficient to accurately recognize theinhabitant’s daily activities in each evaluated time. Toimprove performance, we propose a method that delaysthe recognition process in order to include some sensoractivations that occur after the point in time where thedecision needs to be made. For this, the proposed methoduses multiple incremental fuzzy temporal windows toextract features from both preceding and some oncomingsensor activations. The proposed method is evaluated withtwo temporal deep learning models (convolutional neuralnetwork and long short-term memory), on a binary sensordataset of real daily living activities. The experimentalevaluation shows that the proposed method achievessignificantly better results than the real-time approach,and that the representation with fuzzy temporal windowsenhances 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.2019.2918412OAI: oai:DiVA.org:hh-41633DiVA, id: diva2:1392777
Funder
EU, Horizon 2020
Note

Other funder: Marie Sklodowska-Curie EU Framework for Research

Available from: 2020-02-10 Created: 2020-02-10 Last updated: 2020-02-10

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Ali Hamad, RebeenSalguero Hidalgo, AlbertoBouguelia, Mohamed-RafikEstevez, Macarena EspinillaQuero, Javier Medina
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