AI-Enabled Personalized Yoga for Mental Health and Holistic Well-Being

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Dr. Sweta Kumari

TGT Yoga Teacher

Kendriya Vidyalaya

Sec 24, Noida

Orcid id: 0009-0002-7378-1613

Abstract— Mental health disorders have become one of the leading global public health concerns, necessitating innovative, accessible, and personalized intervention strategies that extend beyond conventional clinical care. Although yoga has been widely recognized as an effective non-pharmacological therapy for reducing stress, anxiety, depression, and emotional distress, existing yoga programs generally adopt standardized protocols that do not adequately address individual differences in psychological conditions, physiological responses, physical capabilities, or lifestyle patterns. Concurrently, advances in artificial intelligence (AI), wearable sensing technologies, and digital healthcare have created opportunities to develop intelligent wellness systems capable of delivering adaptive and data-driven interventions. This study proposes an AI-enabled personalized yoga framework that integrates machine learning, wearable sensor analytics, computer vision-based posture recognition, and continuous user feedback to recommend customized yoga practices for enhancing mental health and holistic well-being. The proposed framework utilizes multimodal health data, including physiological signals, behavioral indicators, mental health assessment scores, and user preferences, to generate individualized yoga recommendations that dynamically evolve according to changes in users’ psychological and physical conditions. Furthermore, the framework incorporates real-time posture correction and adaptive learning mechanisms to improve intervention accuracy, user engagement, and long-term adherence. A comprehensive review of recent literature demonstrates that while significant progress has been achieved in AI-assisted healthcare, intelligent mental health prediction, and yoga posture recognition, existing systems primarily focus on isolated functionalities such as pose estimation or stress detection. Limited research has explored integrated solutions that combine AI-driven mental health assessment, personalized yoga prescription, physiological monitoring, and continuous recommendation optimization within a unified digital ecosystem. To address this gap, the proposed framework emphasizes explainable AI, personalization, privacy preservation, and evidence-based wellness recommendations. The anticipated outcomes include improved stress management, enhanced emotional resilience, better sleep quality, increased user satisfaction, and overall holistic well-being through intelligent and adaptive yoga interventions. The proposed framework contributes to the growing field of AI-powered digital therapeutics by providing a scalable, preventive, and personalized approach that supports both clinical mental healthcare and everyday wellness management. The study offers valuable insights for researchers, healthcare professionals, yoga practitioners, and developers seeking to design next-generation intelligent wellness platforms that combine the therapeutic benefits of yoga with the predictive and adaptive capabilities of artificial intelligence.

Keywords— Artificial Intelligence (AI), Personalized Yoga, Mental Health, Holistic Well-Being, Machine Learning, Wearable Sensors

References

• World Mental Health Report: Transforming Mental Health for All. Geneva, Switzerland: World Health Organization, 2022.
• Pal, “Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions,” Sensors, vol. 23, no. 8, Art. 3962, 2023. https://doi.org/10.3390/s23083962.
• M. Cramer, K. Lauche, H. Haller, et al., “Yoga for Depression: A Systematic Review and Meta-analysis,” Depression and Anxiety, vol. 30, no. 11, pp. 1068–1083, 2013. https://doi.org/10.1002/da.22166.
• Hofmann, A. Asnaani, I. Vonk, et al., “The Efficacy of Cognitive Behavioral Therapy: A Review of Meta-analyses,” Cognitive Therapy and Research, vol. 36, pp. 427–440, 2012. https://doi.org/10.1007/s10608-012-9476-1.
• Kasneci et al., “ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education,” Learning and Individual Differences, vol. 103, 2023, Art. 102274. https://doi.org/10.1016/j.lindif.2023.102274.
• Lu, X. Liu, J. Sun, et al., “Bridging the Gap Between Artificial Intelligence and Mental Health,” Science Bulletin, vol. 68, no. 15, pp. 1586–1589, 2023. https://doi.org/10.1016/j.scib.2023.06.018.
• W. Jin, Q. Li, Y. Xie, and G. Xiao, “Artificial Intelligence in Mental Healthcare: An Overview and Future Perspectives,” British Journal of Radiology, vol. 96, no. 1148, 2023. https://doi.org/10.1259/bjr.20230120.
• Schuller, S. Steidl, and A. Batliner, The Interspeech 2013 Computational Paralinguistics Challenge: Social Signals, Conflict, Emotion, Autism. ISCA, 2013.
• -C. Yeh and C.-K. Yang, “Yoga Pose Recognition and Motion Analysis for a Home-Based Fitness Monitoring and Health Management System,” Signal, Image and Video Processing, vol. 19, Art. 841, 2025. https://doi.org/10.1007/s11760-025-04436-6.
• Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. https://doi.org/10.1109/CVPR.2017.143.
• Vaswani et al., “Attention Is All You Need,” in Advances in Neural Information Processing Systems (NeurIPS), 2017.
• Esteva, A. Robicquet, et al., “A Guide to Deep Learning in Healthcare,” Nature Medicine, vol. 25, pp. 24–29, 2019. https://doi.org/10.1038/s41591-018-0316-z.
• Topol, “High-Performance Medicine: The Convergence of Human and Artificial Intelligence,” Nature Medicine, vol. 25, pp. 44–56, 2019. https://doi.org/10.1038/s41591-018-0300-7.
• Torous, M. Bucci, et al., “The Growing Field of Digital Psychiatry: Current Evidence and the Future of Apps, Social Media, Chatbots, and Virtual Reality,” World Psychiatry, vol. 20, no. 3, pp. 318–335, 2021. https://doi.org/10.1002/wps.20883.
• Verma, R. Sharma, and N. S. Rajput, “Enhancing Yoga Practice Through Real-Time Posture Detection and Correction Using Artificial Intelligence: A Comprehensive Review,” NeuroQuantology, vol. 21, no. 6, pp. 1053–1059, 2023. https://doi.org/10.48047/NQ.2023.21.6.NQ23111.