Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1501
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dc.contributor.authorMendis, L.-
dc.contributor.authorHathurusinghe, S.-
dc.contributor.authorEpa, H.-
dc.contributor.authorEdirisinghe, T.-
dc.contributor.authorWickramarathne, J.-
dc.contributor.authorRupasinghe, S.-
dc.date.accessioned2022-03-04T09:14:57Z-
dc.date.available2022-03-04T09:14:57Z-
dc.date.issued2020-12-10-
dc.identifier.isbn978-1-7281-8412-8-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1501-
dc.description.abstractPeople become unable to perform tasks that were done at the younger ages as they were when the ages pass with time. Falls play a major issue in the lives of elderly people as the physical and mental quality of life is dependable on the effects of falls. This research presents an e-Caretaker SURAKSHA which is an elder falling detection and alerting system based on Machine Learning concepts. Researchers that have been done in this area have produced different solutions to detect only the falls but not to automatically detect and notify them to the caretakers. This solution serves as a smart wearable device that is capable of automatically monitoring real-time postures, detecting sudden falls, possible arrhythmia conditions of the heart of the fallen person, and daily route deviations along with the fallen location which is finally notified to the caretakers through a mobile application. According to the performed studies, python model development was used to implement the system through Machine Learning concepts by referring to the Markov model, Prophet model, and Naïve Bayes algorithms. This solution provides the results of this research with an accuracy of around 89.9% leading to a successful product in the domain. Keywords—en_US
dc.language.isoenen_US
dc.publisher2020 2nd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.relation.ispartofseriesVol.1;-
dc.subjectFalling Detectionen_US
dc.subjectCurrent Posturesen_US
dc.subjectGeo fenceen_US
dc.subjectWearable deviceen_US
dc.subjectmobile applicationen_US
dc.subjectMachine Learningen_US
dc.titleSURAKSHA e-Caretaker: Elders Falling Detection and Alerting System using Machine Learningen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC51239.2020.9357305en_US
Appears in Collections:2nd International Conference on Advancements in Computing (ICAC) | 2020
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