Health monitoring IoT device with risk prediction using cloud computing and machine learning

dc.contributor.advisorRhaman, Md.Khalilur
dc.contributor.authorDas, Anindya
dc.contributor.authorNayeem, Zannatun
dc.contributor.authorFaysal, Abu Saleh
dc.contributor.authorHimu, Fardoush Hassan
dc.date.accessioned2021-05-29T08:37:03Z
dc.date.available2021-05-29T08:37:03Z
dc.date.issued2020-04
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 55-57).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
dc.description.abstractHealth issues often stay hidden due to not having regular health checkups. Sometimes these issues build-up to a signi cant health hazard which stays hidden until it's often too late. So we came up with a series of ideas that can deal with the abovestated problems and to some extent solve them. Our proposed device can actively check body vitals, send data through the cloud to designated doctors, and give patients noti cation of hazard from doctors. To carry out the above-stated solutions, we are designing an IoT device that interfaces multiple sensors to a microcomputer and sends the collected data to a cloud server for further manipulation which will be done by Machine Learning. After analysis, if the doctor feels there is any risk of health hazard to the patient, he/she can send in the noti cation of hazard through our proposed device.
dc.identifier.otherID 16101032
dc.identifier.otherID 16301021
dc.identifier.otherID 17301190
dc.identifier.otherID 17301212
dc.identifier.otherhttps://dspace.bracu.ac.bd/server/api/core/items/8f6e8ce5-46c6-4631-bc89-3aea5846c3b6
dc.identifier.urihttp://dspace.bracu.ac.bd/xmlui/handle/10361/14439
dc.language.isoen
dc.publisherBRAC University
dc.sourceBRAC University Institutional Repository
dc.subjectHealth Issue
dc.subjectIoT
dc.subjectCloud server
dc.subjectMachine Learning
dc.titleHealth monitoring IoT device with risk prediction using cloud computing and machine learning
dc.typeThesis

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