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Simulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling
Department of Industrial Management, Agroindustry and Operations, Universidad de la Costa CUC, Barranquilla, Colombia.ORCID-id: 0000-0001-6890-7547
School of Computing, Computer Science Research Institute, Ulster University, Belfast, United Kingdom.ORCID-id: 0000-0003-2368-7354
School of Computing, Computer Science Research Institute, Ulster University, Belfast, United Kingdom.ORCID-id: 0000-0001-6295-8669
Academic Division of Information Science and Technology, Juarez Autonomous University of Tabasco, Tabasco, Mexico.ORCID-id: 0000-0002-5482-6372
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2020 (engelsk)Inngår i: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 12, nr 5, artikkel-id 771Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Automatic detection and recognition of Activities of Daily Living (ADL) are crucial for providing effective care to frail older adults living alone. A step forward in addressing this challenge is the deployment of smart home sensors capturing the intrinsic nature of ADLs performed by these people. As the real-life scenario is characterized by a comprehensive range of ADLs and smart home layouts, deviations are expected in the number of sensor events per activity (SEPA), a variable often used for training activity recognition models. Such models, however, rely on the availability of suitable and representative data collection and is habitually expensive and resource-intensive. Simulation tools are an alternative for tackling these barriers; nonetheless, an ongoing challenge is their ability to generate synthetic data representing the real SEPA. Hence, this paper proposes the use of Poisson regression modelling for transforming simulated data in a better approximation of real SEPA. First, synthetic and real data were compared to verify the equivalence hypothesis. Then, several Poisson regression models were formulated for estimating real SEPA using simulated data. The outcomes revealed that real SEPA can be better approximated ( R2pred = 92.72 % ) if synthetic data is post-processed through Poisson regression incorporating dummy variables. © 2020 MDPI (Basel, Switzerland)

sted, utgiver, år, opplag, sider
Basel: MDPI, 2020. Vol. 12, nr 5, artikkel-id 771
Emneord [en]
activity recognition, Activities of Daily Living (ADL), digital simulation, poisson regression, large-scale datasets, sensor systems, smart homes
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Identifikatorer
URN: urn:nbn:se:hh:diva-41726DOI: 10.3390/rs12050771OAI: oai:DiVA.org:hh-41726DiVA, id: diva2:1407530
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REMIND
Forskningsfinansiär
EU, Horizon 2020, 734355Tilgjengelig fra: 2020-02-28 Laget: 2020-02-28 Sist oppdatert: 2020-03-24bibliografisk kontrollert

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