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Complementing real datasets with simulated data: a regression-based approach
Department of Industrial Management, Agroindustry and Operations, Universidad de la Costa CUC, Barranquilla, Colombia.ORCID iD: 0000-0001-6890-7547
Convergia Consulting, Halmstad, Sweden.
School of Computing, Computer Science Research Institute, Ulster University, Belfast, United Kingdom.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. (SA3L, HMC2, CyRev, SmartSafe)ORCID iD: 0000-0001-9307-9421
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2020 (English)In: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721Article in journal (Refereed) Epub ahead of print
Abstract [en]

Activity recognition in smart environments is essential for ensuring the wellbeing of older residents. By tracking activities of daily living (ADLs), a person’s health status can be monitored over time. Nonetheless, accurate activity classification must overcome the fact that each person performs ADLs in different ways and in homes with different layouts. One possible solution is to obtain large amounts of data to train a supervised classifier. Data collection in real environments, however, is very expensive and cannot contain every possible variation of how different ADLs are performed. A more cost-effective solution is to generate a variety of simulated scenarios and synthesize large amounts of data. Nonetheless, simulated data can be considerably different from real data. Therefore, this paper proposes the use of regression models to better approximate real observations based on simulated data. To achieve this, ADL data from a smart home were first compared with equivalent ADLs performed in a simulator. Such comparison was undertaken considering the number of events per activity, number of events per type of sensor per activity, and activity duration. Then, different regression models were assessed for calculating real data based on simulated data. The results evidenced that simulated data can be transformed with a prediction accuracy of R2 = 97.03%.

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Place, publisher, year, edition, pages
New York, NY: Springer, 2020.
Keywords [en]
Activity recognition, Activity duration, Regression analysis, Non-linear models, Determination coefficient, Quantile-quantile plots
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Other Computer and Information Science
Identifiers
URN: urn:nbn:se:hh:diva-41728DOI: 10.1007/s11042-019-08368-5ISI: 000507701400004Scopus ID: 2-s2.0-85078616730OAI: oai:DiVA.org:hh-41728DiVA, id: diva2:1407531
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REMIND
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EU, Horizon 2020, 734355Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2020-03-03

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Järpe, EricPinheiro Sant'Anna, Anita

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CAISR - Center for Applied Intelligent Systems ResearchHalmstad Embedded and Intelligent Systems Research (EIS)
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