{"title":"An automatic tool for yoga pose grading using skeleton representation *","authors":"Thanh Nam Nguyen, Thanh-Hai Tran, Hai Vu","doi":"10.1109/NICS56915.2022.10013455","DOIUrl":null,"url":null,"abstract":"Automatic grading of yoga poses may help yoga self-practitioners to rectify their poses to follow correctly the teacher. Some existing wearable or depth sensor based methods require the user to equip additional devices. This paper presents a framework for automatically grading yoga poses from images taken from a conventional RGB camera/phone camera. Our framework consists of three main phases. First, we estimate human joints from RGB images using BlazePose model. Second, we investigate various deep models and select VGG-16 as a model for yoga pose recognition. Finally, we define a score that takes the difference of angles at important joints and yoga pose label into account. Our solution is low-cost, easy, and light to implement on mobile devices. It gives a confident score on the YogaPose dataset and our self-collected dataset.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NICS56915.2022.10013455","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Automatic grading of yoga poses may help yoga self-practitioners to rectify their poses to follow correctly the teacher. Some existing wearable or depth sensor based methods require the user to equip additional devices. This paper presents a framework for automatically grading yoga poses from images taken from a conventional RGB camera/phone camera. Our framework consists of three main phases. First, we estimate human joints from RGB images using BlazePose model. Second, we investigate various deep models and select VGG-16 as a model for yoga pose recognition. Finally, we define a score that takes the difference of angles at important joints and yoga pose label into account. Our solution is low-cost, easy, and light to implement on mobile devices. It gives a confident score on the YogaPose dataset and our self-collected dataset.