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Identification of Abnormal Behavior in Activities of Daily Life Using Novelty Detection

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Resumo: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 AQ1 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 AQ2 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%.
Autores principais:Freitas, Mauricio
Outros Autores:de Aquino Piai, Vinicius; Dazzi, Rudimar; Teive, Raimundo; Parreira, Wemerson; Fernandes, Anita; Pires, Ivan Miguel; Leithardt, Valderi Reis Quietinho
Assunto:Novelty Detection Anomaly Detection Activities of Daily Living Machine Learning One-Class Support Vector Machine (OC-SVM) Local Outlier Factor (LOF)
Ano:2023
País:Portugal
Tipo de documento:capítulo de livro
Tipo de acesso:acesso restrito
Instituição associada:Instituto Politécnico de Portalegre
Idioma:inglês
Origem:Instituto Politécnico de Portalegre

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