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In\nrecent years, it has made many advances that have helped humanity in the fields of sports, surveillance,\nhealthcare, etc. Yoga is an ancient science intended to improve physical, mental and spiritual wellbeing.\nIt involves many kinds of asanas or postures that a practitioner can perform. Thus, the benefits\nof pose estimation can also be used for Yoga to help users assume Yoga postures with better accuracy.\nThe Yoga practitioner can detect their own current posture in real-time, and the pose estimation method\ncan provide them with corrective feedback if they commit mistakes. Yoga pose estimation can also help\nwith remote Yoga instruction by the expert teacher, which can be a boon during a pandemic. This paper\nreviews various Machine Learning, Artificial Intelligence-enabled techniques available for real-time\npose estimation and research pursued recently. We classify them based on the input they use for estimating\nthe individual's pose. We also discuss multiple Yoga posture estimation systems in detail. We\ndiscuss the most commonly used keypoint estimation techniques in the existing literature. In addition to\nthis, we discuss the real-time performance of the presented works. The paper further discusses the datasets\nand evaluation metrics available for pose estimation.<\/jats:p>\n<\/jats:sec>","DOI":"10.2174\/1574893618666230508105440","type":"journal-article","created":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T05:10:32Z","timestamp":1683609032000},"page":"264-280","update-policy":"https:\/\/doi.org\/10.2174\/bsp_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Advancements in Yoga Pose Estimation Using Artificial Intelligence: A\nSurvey"],"prefix":"10.2174","volume":"19","author":[{"given":"Vinay","family":"Chamola","sequence":"first","affiliation":[{"name":"Department of EEE &amp; APPCAIR, BITS Pilani, Pilani, Rajasthan, India"}]},{"given":"Egna Praneeth","family":"Gummana","sequence":"additional","affiliation":[{"name":"Department of Computer Science,\nBITS Pilani, Pilani, Rajasthan, India"}]},{"given":"Akshay","family":"Madan","sequence":"additional","affiliation":[{"name":"Department of Informatics and Networked Systems, School of Computing\nand Information, University of Pittsburgh, Pittsburgh, US"}]},{"given":"Bijay Kumar","family":"Rout","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, BITS\nPilani, Pilani, Rajasthan, India;"}]},{"given":"Joel Jos\u00e9 Puga","family":"Coelho Rodrigues","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum,\nEast China, Qingdao, 266555, China"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Covilh\u00e3, 6201-001, Portugal"}]}],"member":"965","reference":[{"key":"ref=1","doi-asserted-by":"publisher","first-page":"2750","DOI":"10.3390\/diagnostics12112750","volume":"12","author":"Mirza O.M.","year":"2022","unstructured":"Mirza O.M.; Mujlid H.; Manoharan H.; Selvarajan S.; Srivastava G.; Khan M.A.; Mathematical framework for wearable devices in the internet of things using deep learning. 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