Doğan, OnurAkkol, EkinOluçoğlu, Müge2023-03-222023-03-222022978-3-031-21421-9978-3-031-21422-61865-09291865-0937https://doi.org/10.1007/978-3-031-21422-6_22https://hdl.handle.net/20.500.14034/5824th Iberoamerican Conference and 3rd Indo-American Conference Knowledge Graphs and Semantic Web Conference (KGSWC) -- NOV 21-23, 2022 -- Madrid, SPAINEspecially 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.eninfo:eu-repo/semantics/closedAccessProcess miningIndoor location systemSmart homesSensorsBehaviorTrackingPredictionConference Object10.1007/978-3-031-21422-6_221686298311N/AWOS:0009211647000222-s2.0-85142736036Q4