Repository logo

VALORIZA - Artigos em Revistas Científicas

Permanent URI for this collection

http://hdl.handle.net/10400.26/38831

Browse

Browsing VALORIZA - Artigos em Revistas Científicas by Subject "Anomaly Detection"

Now showing 1 - 1 of 1
  • Identification of Abnormal Behavior in Activities of Daily Life Using Novelty Detection
    Publication . Freitas, Mauricio; de Aquino Piai, Vinicius; Dazzi, Rudimar; Teive, Raimundo; Parreira, Wemerson; Fernandes, Anita; Pires, Ivan Miguel; Leithardt, Valderi Reis Quietinho
    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%.
    2023Book part Restricted access Show more