Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1756
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dc.contributor.authorJayaweera, K. N-
dc.contributor.authorKallora, K. M. C-
dc.contributor.authorSubasinghe, N. A. C. K-
dc.contributor.authorRupasinghe, L-
dc.contributor.authorLiyanapathirana, C-
dc.date.accessioned2022-03-23T04:59:55Z-
dc.date.available2022-03-23T04:59:55Z-
dc.date.issued2020-12-10-
dc.identifier.citationK. N. Jayaweera, K. M. C Kallora, N. A. C K Subasinghe, L. Rupasinghe and C. Liyanapathirana, "An Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learning," 2020 2nd International Conference on Advancements in Computing (ICAC), 2020, pp. 43-48, doi: 10.1109/ICAC51239.2020.9357134.en_US
dc.identifier.isbn978-1-7281-8412-8-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1756-
dc.description.abstractAccording to recent studies, the majority of the world's population shows a lack of concern in their health. As a consequence, the non-communicable disease rate has increased dramatically. Amongst these diseases, heart diseases have caused the most catastrophic situations. Apart from the busy lifestyle, studies also show that stress is another factor that causes these diseases. Therefore, the focus of our research is to provide a user-friendly health monitoring system that causes minimum disturbance to its users. However, many studies have focused on predicting health; very few have focused on its usability. The objective of our research is to predict the possibility of cardiac arrests and the presence of stress in real-time using a wearable device prototype. The system uses biometric signals obtained from the photoplethysmogram sensor embedded in the wearable device to perform real-time predictions. We trained three models using random forest, k-nearest neighbor, and logistic regression classification algorithms to predict sudden cardiac arrests with accuracies 99.93%, 99.10%, and 94.47%, respectively. Further, we trained three additional models to predict stress using the same algorithms with accuracies 99.87%, 96.83%, and 65.00%, respectively. Thus, the results of this study show that an integrated framework, capable of predicting different health-related conditions, through sensor data collected from wearable sensors, is feasible.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2020 2nd International Conference on Advancements in Computing (ICAC);Volume 1 Pages 43-48-
dc.subjectIntegrated Frameworken_US
dc.subjectPredicting Healthen_US
dc.subjectHealth Baseden_US
dc.subjectSensor Dataen_US
dc.subjectMachine Learningen_US
dc.titleAn Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learningen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC51239.2020.9357134en_US
Appears in Collections:Research Papers - SLIIT Staff Publications

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