Integrating Skeleton Based Representations for Robust Yoga Pose Classification Using Deep Learning Models
PositiveArtificial Intelligence
- A new study has introduced a curated dataset named 'Yoga-16' aimed at improving automated yoga pose classification through deep learning models. The research systematically evaluates three architectures—VGG16, ResNet50, and Xception—using various input modalities, including skeleton-based representations, which have shown to outperform raw image inputs with an accuracy of 96.09%.
- This development is significant as it addresses the limitations of existing datasets and enhances the reliability of yoga pose classification, potentially reducing the risk of injuries associated with incorrect postures during practice.
- The integration of skeleton-based representations in yoga pose classification reflects a broader trend in artificial intelligence, where deep learning models are increasingly utilized for various applications, including medical image classification. This shift underscores the growing importance of transfer learning techniques in achieving high accuracy across diverse fields.
— via World Pulse Now AI Editorial System
