Understanding patient activity patterns in smart homes with process mining

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dc.authoridDogan, Onur/0000-0003-3543-4012
dc.authorwosidDogan, Onur/HPC-1959-2023
dc.contributor.authorDoğan, Onur
dc.contributor.authorAkkol, Ekin
dc.contributor.authorOluçoğlu, Müge
dc.date.accessioned2023-03-22T19:47:17Z
dc.date.available2023-03-22T19:47:17Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description4th Iberoamerican Conference and 3rd Indo-American Conference Knowledge Graphs and Semantic Web Conference (KGSWC) -- NOV 21-23, 2022 -- Madrid, SPAINen_US
dc.description.abstractEspecially in people over 50 years of age, sedentary lifestyle can cause muscle loss called sarcopenia. Inactivity causes undesirable outcomes such as excessive weight gain and muscle loss. Weight gain can lead to a variety of problems, including deteriorating of the musculoskeletal system, joint problems, and sleep problems. In order to provide better service, it can be beneficial to understand human behavior in terms of health services. Process mining, which can be considered a part of knowledge graphs, is a crucial methodology for process improvement since it offers a model of the process that can be analyzed and optimized. This study uses process mining approaches to examine data from three patient that were collected using indoor location sensors, allowing the collection of flows of human behavior in the home. The analyses indicated how much time was spent by the patients of the house in each room during the day as well as how frequently they occurred. The movement of patients from room to room was observed daily and subjected to a variety of analyses. With the help of user pathways, lengths of stay in the rooms, and frequency of presence, it has been possible to reveal the details of daily human behavior. Inferences about the habits of the participants were revealed day by day.en_US
dc.description.sponsorshipVLIR-UOS Network Univ Cooperat Programme-Cubaen_US
dc.identifier.doi10.1007/978-3-031-21422-6_22
dc.identifier.endpage311en_US
dc.identifier.isbn978-3-031-21421-9
dc.identifier.isbn978-3-031-21422-6
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.scopus2-s2.0-85142736036en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage298en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-21422-6_22
dc.identifier.urihttps://hdl.handle.net/20.500.14034/582
dc.identifier.volume1686en_US
dc.identifier.wosWOS:000921164700022en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.journalKnowledge Graphs And Semantic Web, Kgswc 2022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectProcess miningen_US
dc.subjectIndoor location systemen_US
dc.subjectSmart homesen_US
dc.subjectSensorsen_US
dc.subjectBehavioren_US
dc.subjectTrackingen_US
dc.subjectPredictionen_US
dc.titleUnderstanding patient activity patterns in smart homes with process miningen_US
dc.typeConference Objecten_US

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