Integrating Yoga Pose Estimation with Neural Networks: Bridging Tradition and Technology

Yoga pose estimation is a challenging task in computer vision due to the variability and complexity of yoga poses. In this study, we propose a novel approach for yoga pose estimation using neural networks and YOLOv8, a state-of-the-art object detection model. Our method accurately detects and classifies yoga poses in images or videos, providing valuable feedback for practitioners to improve their yoga practice. Initially, we preprocess the input data to enhance quality and reduce noise. YOLOv8 is then utilized to detect and localize key points corresponding to different body joints in the images. Subsequently, neural network architecture classifies the detected poses into various yoga poses. To ensure robustness and generalization capability, we train our model on a large dataset of annotated yoga pose images. We evaluate the performance using standard metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate the effectiveness and accuracy of our proposed approach, achieving competitive performance compared to existing methods. This study advances the field of yoga pose estimation, offering a promising solution for practitioners, instructors, and researchers interested in leveraging computer vision techniques to enhance yoga practice.