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dc.contributor.advisorAshraf, Faisal Bin
dc.contributor.authorIslam, Abidul
dc.contributor.authorSadman, Zarif
dc.contributor.authorImran, Shah
dc.contributor.authorIslam, Sazzadul
dc.date.accessioned2023-03-22T06:17:35Z
dc.date.available2023-03-22T06:17:35Z
dc.date.copyright2022
dc.date.issued2022-05
dc.identifier.otherID 18301144
dc.identifier.otherID 18301008
dc.identifier.otherID 18326032
dc.identifier.otherID 18301162
dc.identifier.urihttp://hdl.handle.net/10361/18002
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 36-37).
dc.description.abstractYoga is one of the best activities from home to preserve our physical condition in the present epidemic. Yoga, on the other hand, is all about performing the 82 Yoga Asanas correctly over the course of six classes. Regrettably, not everyone have the knowledge or can perform yoga accurately. So to do yoga poses correctly we will have to find a yoga instructor, but it can be very hard and expensive to find yoga instructors considering all possible general situation and status. Using Deep Learning(DL), picture categorization and various machine learning approaches, we attempted to build a system or a model that will operate as a self-instructor of Yoga for the user to classify different poses of yoga to distinguish accurate pose in our thesis. It will assist the user in performing Yoga correctly by recognizing errors in their Yoga Asanas. In a nutshell, this section will cover several posture estimation, key point detection, and pose categorization techniques. Moreover, we tried ensemble modeling as a booster to improve the pose prediction accuracy as much as possible.en_US
dc.description.statementofresponsibilityAbidul Islam
dc.description.statementofresponsibilityZarif Sadman
dc.description.statementofresponsibilityShah Imran
dc.description.statementofresponsibilitySazzadul Islam
dc.format.extent37 pages
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.subjectYoga postureen_US
dc.subjectDeep learningen_US
dc.subjectLearning theoryen_US
dc.subjectArtificial intelligence (AI)en_US
dc.subject.lcshMachine learning
dc.subject.lcshCognitive learning theory
dc.titleYoga posture recognition using the deep learning processen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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