Enhancing Yoga Pose Estimation Accuracy Using Optimized Mask R-CNN Model
- DOI
- 10.2991/978-94-6463-716-8_30How to use a DOI?
- Keywords
- Yoga Pose Estimation; Optimized Mask R-CNN; Key point Detection; Feature Aggregation; Human Pose Segmentation; Real-Time Fitness Tracking
- Abstract
Yoga pose estimation is important for fitness, healthcare, and rehabilitation applications, existing models such as AlexNet, VGG, and ResNet cannot accurately recognize detailed key points or handle complex postures. To tackle these issues, this paper presents an improved mask R-CNN with better feature aggregation and segmentation and introduces a key point detection branch. Performance analysis demonstrates the effectiveness of our proposed model by improved values of mAP, AP@0. 5, and PCKh@0. 5 metrics. This approach has been experimentally shown to be used for real-time recovery from yoga poses. This work pushes forward the accuracy and scalability of pose estimation for widespread fitness and healthcare applications.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Deepak Shukla AU - Maya Rathore PY - 2025 DA - 2025/05/26 TI - Enhancing Yoga Pose Estimation Accuracy Using Optimized Mask R-CNN Model BT - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025) PB - Atlantis Press SP - 373 EP - 384 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-716-8_30 DO - 10.2991/978-94-6463-716-8_30 ID - Shukla2025 ER -