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

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dc.contributor.authorFreitas, Mauricio
dc.contributor.authorde Aquino Piai, Vinicius
dc.contributor.authorDazzi, Rudimar
dc.contributor.authorTeive, Raimundo
dc.contributor.authorParreira, Wemerson
dc.contributor.authorFernandes, Anita
dc.contributor.authorPires, Ivan Miguel
dc.contributor.authorLeithardt, Valderi Reis Quietinho
dc.date.accessioned2025-01-14T14:38:49Z
dc.date.available2025-01-14T14:38:49Z
dc.date.issued2023
dc.date.updated2023-06-27T18:42:12Z
dc.description.abstractThe 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%.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-031-34776-4_29pt_PT
dc.identifier.isbn9783031347757
dc.identifier.isbn9783031347764
dc.identifier.issn1867-8211
dc.identifier.issn1867-822X
dc.identifier.slugcv-prod-3293890
dc.identifier.urihttp://hdl.handle.net/10400.26/53809
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Nature Switzerlandpt_PT
dc.subjectNovelty Detectionpt_PT
dc.subjectAnomaly Detectionpt_PT
dc.subjectActivities of Daily Livingpt_PT
dc.subjectMachine Learningpt_PT
dc.subjectOne-Class Support Vector Machine (OC-SVM)pt_PT
dc.subjectLocal Outlier Factor (LOF)pt_PT
dc.titleIdentification of Abnormal Behavior in Activities of Daily Life Using Novelty Detectionpt_PT
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage570pt_PT
oaire.citation.startPage559pt_PT
oaire.citation.titleLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineeringpt_PT
rcaap.cv.cienciaid0614-5834-E7F3 | Valderi Reis Quietinho Leithardt
rcaap.rightsrestrictedAccesspt_PT
rcaap.typebookPartpt_PT

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