Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1741
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dc.contributor.authorRishan, F-
dc.contributor.authorDe Silva, B-
dc.contributor.authorAlawathugoda, S-
dc.contributor.authorNijabdeen, S-
dc.contributor.authorRupasinghe, L-
dc.contributor.authorLiyanapathirana, C-
dc.date.accessioned2022-03-22T05:59:27Z-
dc.date.available2022-03-22T05:59:27Z-
dc.date.issued2020-12-02-
dc.identifier.citationF. Rishan, B. De Silva, S. Alawathugoda, S. Nijabdeen, L. Rupasinghe and C. Liyanapathirana, "Infinity Yoga Tutor: Yoga Posture Detection and Correction System," 2020 5th International Conference on Information Technology Research (ICITR), 2020, pp. 1-6, doi: 10.1109/ICITR51448.2020.9310832.en_US
dc.identifier.isbn978-1-6654-1475-3-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1741-
dc.description.abstractPopularity of yoga is increasing daily. The reason for this is the physical, mental and spiritual benefits that could be obtained by practicing yoga. Many are following this trend and practicing yoga without the training of an expert practitioner. However, following yoga in an improper way or without a proper guidance will lead to bad health issues such as strokes, nerve damage etc. So, following proper yoga postures is an important factor to be considered. In this proposed system, the system is able to identify poses performed by the user and also guide the user visually. This process is required to be completed in real-time in order to be more interactive with the user. In this paper, the yoga posture detection was done in a vision-based approach. The Infinity Yoga Tutor application is able to capture user movements using the mobile camera, which is then streamed at a resolution of 1280 × 720 at 30 frames per second to the detection system. The system consists of two main modules, a pose estimation module which uses OpenPose to identify 25 keypoints in the human body, using the BODY_25 dataset, and a pose detection module which consists of a Deep Learning model, that uses time-distributed Convolutional Neural Networks, Long Short Term Memory and SoftMax regression in order to analyze and predict user pose or asana using a sequence of frames. This module was trained to classify 6 different asanas and the selected model which uses OpenPose for pose estimation has an accuracy of 99.91%. Finally, the system notifies the users on their performance visually in the user interface of the Mobile application.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2020 5th International Conference on Information Technology Research (ICITR);Pages 1-6-
dc.subjectInfinity Yoga Tutoren_US
dc.subjectYoga Postureen_US
dc.subjectDetectionen_US
dc.subjectCorrection Systemen_US
dc.titleInfinity yoga tutor: Yoga posture detection and correction systemen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICITR51448.2020.9310832en_US
Appears in Collections:Department of Computer Systems Engineering-Scopes
Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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