MYPO-Net: A Robust Deep Learning Approach for Multi-Yoga Pose Detection and Occlusion Handling

Yoga has become a well-known holistic process worldwide and has been appreciated due to its physical, psychological, and injury-preventive effects. The swift development of online fitness applications has created a growing need for automated systems that can precisely identify and analyze yoga poses. The current methods, however, are limited in terms of the limited diversity of datasets, insufficient occlusion, low performance in multi-person settings, and a lack of feedback mechanisms to offer corrective feedback. In order to overcome these shortcomings, this study introduces MYPO-Net, an artificial intelligence (AI) based deep learning model that uses the efficiency of MobileNet and the classification performance of EfficientNetB0. The model is trained and tested on the Yoga82 data, with a detailed preprocessing pipeline, such as resizing, normalization, and data augmentation, to improve resilience to real-world variations. Experimental evidence shows a classification accuracy of 97.65, which is higher than a variety of baseline architectures (VGG16: 87%, InceptionV3: 82%, ResNet50: 58) and has high computational efficiency. Confusion matrix analysis shows that there is valid detection in 16 yoga position classes. The persisting issues in real-time implementation and poor image quality settings are distinguished as future work directions. MYPO-Net is a highly scalable, affordable, and open-source platform to support digital yoga teaching, fitness apps, and rehabilitative health care.

@article{Danial2026,
  title     = {MYPO-Net: A Robust Deep Learning Approach for Multi-Yoga Pose Detection and Occlusion Handling},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Rehana Danial and Nosheen Qamar and Nosheen Sabahat and Faria Nazir and Ali Salem Bin Sama and Lamia Hassan Rahamatalla and Osman Elwasila and Abdulrahman Alojail and Marwan Abu-Zanona},
  doi       = {10.14569/IJACSA.2026.0170637},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170637}
}