Real Time Yoga Pose Detection Using AI

Abstract—After the pandemic situation in the world, a new era started where everything is going online like education, training, shopping, work, etc. by taking the advantages of AI technology and the online trend of doing YOGA at home, Bringing the idea, there is a chance to shift offline yoga training to online. This study introduces a novel method to detect real-time yoga poses using artificial intelligence (AI) and camera input and provide corrective feedback accordingly. The method uses a real-time pose prediction model, using the advantage of Media Pipe to capture human body landmarks from camera frames. These key points are then analyzed to identify different yoga poses. The system provides real-time corrective feedback on posture by analyzing it to help users make sure that it works smoothly. By combining AI-based pose recognition and feedback, this solution aims to improve yoga practice, especially for beginners.This research presents a deep learning-based approach for yoga pose detection using the YOLOv5 object detection framework. The data set, named YOLO Yoga Dataset.v1i.yolov5, consists of 1,013 annotated images in five yoga poses: Bridge, Downward Dog, Plank, Shoulderstand and Tree Pose. The data set was sourced and preprocessed through Roboflow and trained on YOLOv5 with data augmentation to improve generalization.

Yoga Pose, OpenCV, Pose Estimation, Media Pipe, Real-Time Feedback Introduction

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