|
| ORIGINAL ARTICLE |
 |
|
|
| Year : 2022 |
Volume
: 15 | Issue : 2 | Page
: 137-143 |
|
Estimation of yoga postures using machine learning techniques
D Mohan Kishore1, S Bindu2, Nandi Krishnamurthy Manjunath1
1 Division of Yoga and Life Sciences, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bengaluru, Karnataka, India
2 Department of Electronics and Communication Engineering, B N M Institute of Technology, Bengaluru, Karnataka, India
Correspondence Address:
D Mohan Kishore
Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Jigani, Bengaluru – 560105, Karnataka
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijoy.ijoy_97_22
|
|
|
Yoga is a traditional Indian way of keeping the mind and body fit, through physical postures (asanas), voluntarily regulated breathing (pranayama), meditation, and relaxation techniques. The recent pandemic has seen a huge surge in numbers of yoga practitioners, many practicing without proper guidance. This study was proposed to ease the work of such practitioners by implementing deep learning-based methods, which can estimate the correct pose performed by a practitioner. The study implemented this approach using four different deep learning architectures: EpipolarPose, OpenPose, PoseNet, and MediaPipe. These architectures were separately trained using the images obtained from S-VYASA Deemed to be University. This database had images for five commonly practiced yoga postures: tree pose, triangle pose, half-moon pose, mountain pose, and warrior pose. The use of this authentic database for training paved the way for the deployment of this model in real-time applications. The study also compared the estimation accuracy of all architectures and concluded that the MediaPipe architecture provides the best estimation accuracy.
|
|
|
|
| [FULL TEXT] [PDF]* |
|
 |
|
|
|
