DC FieldValueLanguage
dc.contributor.authorAnagnostopoulos, Theodoros-
dc.contributor.authorNtanos, Stamatios-
dc.contributor.authorSalmon, Ioannis-
dc.contributor.authorNtalianis, Klimis-
dc.contributor.authorTsotsolas, Nikos-
dc.contributor.authorKyriakopoulos, Grigorios-
dc.date.accessioned2024-03-22T14:48:09Z-
dc.date.available2024-03-22T14:48:09Z-
dc.date.issued2020-01-02-
dc.identifierscopus-85077908904-
dc.identifier.issn1660-4601-
dc.identifier.issn1661-7827-
dc.identifier.other85077908904-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/1562-
dc.description.abstractEveryday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.en_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Environmental Research and Public Healthen_US
dc.subjectElderly and impaireden_US
dc.subjectFall verificationen_US
dc.subjectHealthcareen_US
dc.subjectInternet of Things (IoT)en_US
dc.subjectSmart homesen_US
dc.subjectTemporal inference modelen_US
dc.titleInternet of things (IoT)-enabled elderly fall verification, exploiting temporal inference models in smart homesen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/ijerph17020408en_US
dc.identifier.scopus2-s2.0-85077908904-
dcterms.accessRights1en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume17en_US
dc.relation.issue2en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.grantfulltextnone-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.languageiso639-1en-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-5587-2848-
crisitem.author.orcid0009-0006-9089-8898-
crisitem.author.orcid0000-0003-4173-3780-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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