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    A review of abnormal behavior detection in activities of daily living

    Tay, Nian Chi, Connie, Tee, Ong, Thian Song, Teoh, Andrew Beng Jin and Teh, Pin Shen ORCID logoORCID: https://orcid.org/0000-0002-0607-2617 (2023) A review of abnormal behavior detection in activities of daily living. IEEE Access, 11. pp. 5069-5088. ISSN 2169-3536

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    Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend.

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