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Using Learning Techniques to Observe Elderly’s Behavior Changes over Time in Smart Home

Abstract : Smart environments and technology used for elder care, increases independent living time and cuts long-term care costs. A key requirement for these systems consists in detecting and informing about abnormal behavior in users'routines. In this paper, our objective is to automatically observe the elderly behavior over time and detect anomalies that may occur on the long term. Therefore, we propose a learning method to formalize a normal behavior pattern for each elderly people related to his Activities of Daily Living (ADL). We also adopt a temporal similarity score between activities that allows to detect behavior changes over time. In change behavior period we focus on each activity to detect anomalies. A use case with real datasets are promising.
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Submitted on : Friday, June 17, 2022 - 6:22:02 PM
Last modification on : Saturday, June 18, 2022 - 4:06:31 AM


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Dorsaf Zekri, Thierry Delot, Mikael Desertot, Sylvain Lecomte, Marie Thilliez. Using Learning Techniques to Observe Elderly’s Behavior Changes over Time in Smart Home. Mohamed Jmaiel; Mounir Mokhtari; Bessam Abdulrazak; Hamdi Aloulou; Slim Kallel. The Impact of Digital Technologies on Public Health in Developed and Developing Countries. 18th International Conference, ICOST 2020, Hammamet, Tunisia, June 24–26, 2020, Proceedings, 12157, Springer International Publishing, pp.129-141, 2020, Lecture Notes in Computer Science, 978-3-030-51516-4. ⟨10.1007/978-3-030-51517-1_11⟩. ⟨hal-03698276⟩



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