dc.contributor.advisor | Rhaman, Md.Khalilur | |
dc.contributor.author | Das, Anindya | |
dc.contributor.author | Nayeem, Zannatun | |
dc.contributor.author | Faysal, Abu Saleh | |
dc.contributor.author | Himu, Fardoush Hassan | |
dc.date.accessioned | 2021-05-29T08:37:03Z | |
dc.date.available | 2021-05-29T08:37:03Z | |
dc.date.copyright | 2020 | |
dc.date.issued | 2020-04 | |
dc.identifier.other | ID 16101032 | |
dc.identifier.other | ID 16301021 | |
dc.identifier.other | ID 17301190 | |
dc.identifier.other | ID 17301212 | |
dc.identifier.uri | http://dspace.bracu.ac.bd/xmlui/handle/10361/14439 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 55-57). | |
dc.description.abstract | Health 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. | en_US |
dc.description.statementofresponsibility | Anindya Das | |
dc.description.statementofresponsibility | Zannatun Nayeem | |
dc.description.statementofresponsibility | Abu Saleh Faysal | |
dc.description.statementofresponsibility | Fardoush Hassan Himu | |
dc.format.extent | 57 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | Health Issue | en_US |
dc.subject | IoT | en_US |
dc.subject | Cloud server | en_US |
dc.subject | Machine Learning | en_US |
dc.subject.lcsh | Machine learning | |
dc.subject.lcsh | Cloud computing | |
dc.title | Health monitoring IoT device with risk prediction using cloud computing and machine learning | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |