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Feasibility analysis of gesture recognition based human wearable electronic accessories

bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorBhuian, Mohammed Belal Hossain
dc.contributor.authorTaki, Khan Sakib Nur
dc.contributor.authorTonny, Jannatul Ferdous
dc.contributor.authorNahin, S.H.M
dc.contributor.departmentDepartment of Electrical and Electronic Engineering
dc.date.accessioned2023-09-19T06:42:48Z
dc.date.available2023-09-19T06:42:48Z
dc.date.copyright2022
dc.date.issued2022-11
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 46-49).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2022.en_US
dc.description.abstractThe Oximeter is commonly used on the finger tips for better analysis of the oxygen saturation on the blood. Still there are some errors to be aware of during the scans. It has a tendency to be accuracy 90% of the time. Furthermore, if there are possibilities to get some errors from scanning the part of the body with less skin density such as fingers, scanning on the wrist will have far more errors than this. Yet, from comparing the two measurements we can find the difference between the error and add them on the taken analysis to get the actual result. Now it is as simple as adding the error of the analysis gap we get both the wrist and the fingertip. After comparing with the market best oximeter with the reading of different individuals the accuracy has been checked and corrected. This system can be efficient for monitoring the COVID-19 patients as they mostly face the falling oxygen saturation staen_US
dc.description.degreeBachelor of Science in Electrical and Electronic Engineering
dc.description.statementofresponsibilityKhan Sakib Nur Taki
dc.description.statementofresponsibilityJannatul Ferdous Tonny
dc.description.statementofresponsibilityS.H.M Nahin
dc.format.extent55 pages
dc.identifier.otherID 15321005
dc.identifier.otherID 15321011
dc.identifier.otherID 15321018
dc.identifier.urihttp://hdl.handle.net/10361/21017
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBrac 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.subjectHRMen_US
dc.subjectESP8266 wifi moduleen_US
dc.subjectMAX30100 sensoren_US
dc.subjectBpmen_US
dc.subjectMCUen_US
dc.subjectMAXHRen_US
dc.subjectLEDen_US
dc.subjectIoTen_US
dc.subject.lcshElectronic circuit design--Computer programs
dc.subject.lcshInternet of things
dc.titleFeasibility analysis of gesture recognition based human wearable electronic accessoriesen_US
dc.typeThesisen_US

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