Real-Time Prediction of Correct Yoga Asanas in Healthy Individuals With Artificial Intelligence Techniques: A Systematic Review for Nursing

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dc.authorid0000-0002-0566-5656
dc.authorid0000-0003-4352-1124
dc.contributor.authorOzsezer, Gozde
dc.contributor.authorMermer, Gulengul
dc.date.accessioned2026-02-03T12:03:12Z
dc.date.available2026-02-03T12:03:12Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractAimThis study aims to systematically review the real-time prediction of yoga asanas using artificial intelligence (AI) techniques to improve the quality of life in healthy individuals.DesignSystematic review.MethodsA comprehensive literature review was conducted in English using the keywords 'yoga', 'asana', 'pose', 'posture', 'machine learning', 'deep learning' and 'prediction' in the Web of Science, Google Scholar, PubMed and Scopus databases. The objective was to identify all relevant studies on the topic. Two independent researchers screened the titles and abstracts of the retrieved publications, applying the JBI Critical Appraisal Checklist for Diagnostic Test Accuracy Studies for quality assessment. The initial search yielded 3250 studies (Google Scholar: 3190, PubMed: 19, Scopus: 27, Web of Science: 14). After applying inclusion criteria, 15 studies were included in the final systematic review.ResultsAmong the included studies, nine employed deep learning (DL) models, three utilised machine learning (ML) and three applied a combination of both DL and ML techniques. The primary statistical evaluation method for real-time prediction was accuracy across all studies. The highest accuracy rates were observed in studies using DL models alone (min = 92.34%, max = 99.92%), followed by studies that combined DL and ML (min = 91.49%, max = 99.58%), and those using only ML (min = 90.9%, max = 98.51%). These findings indicate that integrating DL and ML models can enhance the accuracy of real-time yoga asana prediction.Patient or Public ContributionThe findings advocate for the implementation of DL and ML models in clinical and community settings to improve the real-time and precise prediction of yoga asanas, a well-established evidence-based nursing intervention for healthy individuals.
dc.identifier.doi10.1002/nop2.70278
dc.identifier.issn2054-1058
dc.identifier.issue8
dc.identifier.pmid40768382
dc.identifier.scopus2-s2.0-105012452832
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/nop2.70278
dc.identifier.urihttps://hdl.handle.net/20.500.12428/35000
dc.identifier.volume12
dc.identifier.wosWOS:001544678200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofNursing Open
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260130
dc.subjectartificial intelligence
dc.subjectnursing
dc.subjectposture
dc.subjectsystematic review
dc.subjectyoga
dc.titleReal-Time Prediction of Correct Yoga Asanas in Healthy Individuals With Artificial Intelligence Techniques: A Systematic Review for Nursing
dc.typeReview

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