R. Govindaraj, S. Karmani, S. Varambally, and B. Gangadhar, “Yoga and physical exercise – a review and comparison,” International Review of Psychiatry, vol. 28, no. 3, pp. 242–253, Apr. 2016. https://doi.org/10.3109/09540261.2016.1160878
T. Cartwright, H. Mason, A. Porter, and K. Pilkington, “Yoga practice in the UK: A cross-sectional survey of motivation, health benefits and behaviours,” BMJ open, vol. 10, no. 1, 2020, Art. no. e031848. https://doi.org/10.1136/bmjopen-2019-031848
S. Goel, S. Mohanty, and S. Markanday, “Classification of yoga pose using pre-trained CNN models and machine learning classifiers,” in 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET), Bhopal, India, Dec. 2022, pp. 1–6. https://doi.org/10.1109/CCET56606.2022.10080048
F. B. Ashraf, M. U. Islam, M. R. Kabir, and J. Uddin, “YoNet: A neural network for yoga pose classification,” SN Computer Science, vol. 4, Feb. 2023, Art. no. 198. https://doi.org/10.1007/s42979-022-01618-8
C. Nagalakshmi and S. Mukherjee, “Classification of yoga asanas from a single image by learning the 3D view of human poses,” in Digital Techniques for Heritage Presentation and Preservation, Springer, Mar. 2021, pp. 37–49. https://doi.org/10.1007/978-3-030-57907-4_3
S. Jain, A. Rustagi, S. Saurav, R. Saini, and S. Singh, “Three- dimensional CNN-inspired deep learning architecture for yoga pose recognition in the real-world environment,” Neural Computing and Applications, vol. 33, pp. 6427–6441, 2021. https://doi.org/10.1007/s00521-020-05405-5
R. P. Srivastava, L. S. Umrao, and R. S. Yadav, “Real-time yoga pose classification with 3-D pose estimation model with LSTM,” Multimedia Tools and Applications, vol. 83, pp. 33019–33030, Sep. 2023. https://doi.org/10.1007/s11042-023-17036-8
C. Long, E. Jo, and Y. Nam, “Development of a yoga posture coaching system using an interactive display based on transfer learning,” The Journal of Supercomputing, vol. 78, pp. 5269–5284, Mar. 2022. https://doi.org/10.1007/s11227-021-04076-w
Y. Agrawal, Y. Shah, and A. Sharma, “Implementation of machine learning technique for identification of yoga poses,” in 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT), Gwalior, India, Apr. 2020, pp. 40–43. https://doi.org/10.1109/CSNT48778.2020.9115758
V. Bhosale, P. Nandeshwar, A. Bale, and J. Sankhe, “Yoga pose detection and correction using Posenet and KNN,” International Research Journal of Engineering and Technology, vol. 9, no. 4, pp. 1290–1293, 2022.
S. Garg, A. Saxena, and R. Gupta, “Yoga pose classification: a CNN and MediaPipe inspired deep learning approach for real-world application,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 16551–16562, 2022. https://doi.org/10.1007/s12652-022-03910-0
S. Abarna, V. Rathikarani, and P. Dhanalakshmi, “Skeleton pose estimation features based classification of yoga asana using deep learning techniques,” International Journal of Mechanical Engineering, 2022.
J. Z. Tan, C. P. Lee, K. M. Lim, and J. Y. Lim, “Yoga pose estimation with machine learning,” in 2023 11th International Conference on Information and Communication Technology (ICoICT), Melaka, Malaysia, Aug. 2023, pp. 260–265. https://doi.org/10.1109/ICoICT58202.2023.10262445
U. Bahukhandi and S. Gupta, “Yoga pose detection and classification using machine learning techniques,” Int. Re.s J. Mod. Eng. Technol. Sci., vol. 3, no. 12, pp. 13–15, 2021.
J. Palanimeera and K. Ponmozhi, “Classification of yoga pose using machine learning techniques,” Materials Today: Proceedings, vol. 37, pp. 2930–2933, 2021. https://doi.org/10.1016/j.matpr.2020.08.700
M. Verma, S. Kumawat, Y. Nakashima, and S. Raman, “Yoga-82: A new dataset for fine-grained classification of human poses,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, Jun. 2020, pp. 4472–4479, https://doi.org/10.1109/CVPRW50498.2020.00527
B. Jo and S. Kim, “Comparative analysis of OpenPose, PoseNet, and MoveNet models for pose estimation in mobile devices,” Traitement du Signal, vol. 39, pp. 119–124, Feb. 2022. https://doi.org/10.18280/ts.390111
G. Arulampalam and A. Bouzerdoum, “A generalized feedforward neural network architecture for classification and regression,” Neural Networks, vol. 16, no. 5–6, pp. 561–568, June–July 2003. https://doi.org/10.1016/S0893-6080(03)00116-3
D. Bhatt, C. Patel, H. Talsania, J. Patel, R. Vaghela, S. Pandya, K. Modi, and H. Ghayvat, “CNN variants for computer vision: History, architecture, application, challenges and future scope,” Electronics, vol. 10, no. 20, Oct. 2021, Art. no. 2470. https://doi.org/10.3390/electronics10202470
M. Verma, S. Kumawat, Y. Nakashima, and S. Raman, “Yoga-82: a new dataset for fine-grained classification of human poses,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA, Jun. 2020, pp. 1038–1039. https://doi.org/10.1109/CVPRW50498.2020.00527