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dc.contributor.authorKovacevic, Ana
dc.contributor.authorUrosevic, Vladimir
dc.contributor.authorKaddachi, Firas
dc.date.accessioned2021-06-02T10:11:13Z
dc.date.available2021-06-02T10:11:13Z
dc.date.issued2018
dc.identifierONIX_20210602_10.5772/intechopen.75203_392
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/49278
dc.description.abstractIn this chapter, we propose a methodology for behavior variation and anomaly detection from acquired sensory data, based on temporal clustering models. Data are collected from five prominent European smart cities, and Singapore, that aim to become fully “elderly-friendly,” with the development and deployment of ubiquitous systems for assessment and prediction of early risks of elderly Mild Cognitive Impairments (MCI) and frailty, and for supporting generation and delivery of optimal personalized preventive interventions that mitigate those risks, utilizing smart city datasets and IoT infrastructure. Low level data collected from IoT devices are preprocessed as sequences of activities, with temporal and causal variations in sequences classified as normal or anomalous behavior. The goals of proposed methodology are to (1) recognize significant behavioral variation patterns and (2) support early identification of pattern changes. Temporal clustering models are applied in detection and prediction of the following variation types: intra-activity (single activity, single citizen) and inter-activity (multiple-activities, single citizen). Identified behavioral variations and anomalies are further mapped to MCI/frailty onset behavior and risk factors, following the developed geriatric expert model.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UN Databases::UNF Data mining
dc.subject.othertemporal clustering, IoT, smart cities, behavior recognition, anomaly detection
dc.titleChapter Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities
dc.typechapter
oapen.identifier.doi10.5772/intechopen.75203
oapen.relation.isPublishedBy09f6769d-48ed-467d-b150-4cf2680656a1
oapen.relation.isFundedByH2020-PHC-2015-single-stage
oapen.grant.number689731
oapen.grant.acronymCity4Age


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