Abstract
The world population is aging at a rapid pace. According to the WHO (World Health Organization), from 2015 to 2050, the proportion of elderly people will practically double, from 12 to 22%, representing 2.1 billion people. From the individual’s point of view, aging brings a series of challenges, mainly related to health conditions. Although, seniors can experience opposing health profiles. With advancing age, cognitive functions tend to degrade, and conditions that affect the physical and mental health of the elderly are disabilities or deficiencies that affect Activities of Daily Living (ADL). The difficulty of carrying out these activities within the domestic context prevents the individual from living independently in their home. Abnormal behaviors in these activities may represent a decline in health status and the need for intervention by family members or caregivers. This work proposes the identification of anomalies in the ADL of the elderly in the domestic context through Machine Learning algorithms using the Novelty Detection method. The focus is on using available ADL data to create a baseline of behavior and using new data to classify them as normal or abnormal daily. The results obtained using the E-Health Monitoring database, using different Novelty Detection algorithms, have an accuracy of 91% and an F1-Score of 90%.
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Acknowledgments
We would like to thank the Fundação de Amparo a Pesquisa e Inovação do Estado de Santa Catarina - FAPESC, for supporting the projects promoted: Public Notice 15/2021 TO 2021TR001236 and Public Notice 29/2021 TO 2021TR001758.
This work is funded by FCT/MEC through national funds and co-funded by FEDER—PT2020 partnership agreement under the project UIDB/50008/2020.
This work is also supported by national funds through the Foundation for Science and Technology, I.P. (Portuguese Foundation for Science and Technology) by the project UIDB/05064/2020 (VALORIZA—Research Center for Endogenous Resource Valorization), and Project UIDB/04111/2020, ILIND—Lusophone Institute of Investigation and Development, under project COFAC/ILIND/COPELABS/3/2020.
This article is based upon work from COST Action IC1303-AAPELE— Architectures, Algorithms, and Protocols for Enhanced Living Environments and COST Action CA16226–SHELD-ON—Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). COST is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. It boosts their research, career, and innovation. More information on www.cost.eu.
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Freitas, M. et al. (2023). Identification of Abnormal Behavior in Activities of Daily Life Using Novelty Detection. In: Longfei, S., Bodhi, P. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-34776-4_29
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