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A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application

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dc.citedby58
dc.contributor.authorMostafa S.A.en_US
dc.contributor.authorMustapha A.en_US
dc.contributor.authorMohammed M.A.en_US
dc.contributor.authorAhmad M.S.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorid37036085800en_US
dc.contributor.authorid57200530694en_US
dc.contributor.authorid57192089894en_US
dc.contributor.authorid56036880900en_US
dc.contributor.authorid55247787300en_US
dc.date.accessioned2023-05-29T06:52:30Z
dc.date.available2023-05-29T06:52:30Z
dc.date.issued2018
dc.descriptionAccident prevention; Assisted living; Computer circuits; Fuzzy logic; Multi agent systems; Adjustable autonomy; Ambient assisted living; Autonomous decision; Complex environments; Elderly remote care; Fall detection; Fuzzy logic control; Movement monitoring; Autonomous agents; aged; Article; automation; decision making; falling; fuzzy logic; human; movement (physiology); patient autonomy; patient monitoring; priority journal; algorithm; automated pattern recognition; computer simulation; movement (physiology); physiologic monitoring; procedures; theoretical model; Aged; Algorithms; Computer Simulation; Fuzzy Logic; Humans; Models, Theoretical; Monitoring, Physiologic; Movement; Pattern Recognition, Automateden_US
dc.description.abstractAutonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls. � 2018 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.ijmedinf.2018.02.001
dc.identifier.epage184
dc.identifier.scopus2-s2.0-85041918630
dc.identifier.spage173
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85041918630&doi=10.1016%2fj.ijmedinf.2018.02.001&partnerID=40&md5=f1f28d3faca6bfd1d93ef051d0261519
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23861
dc.identifier.volume112
dc.publisherElsevier Ireland Ltden_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Medical Informatics
dc.titleA fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring applicationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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