Yoga Pose Detection and Correction Using 3D Pose Estimation and Machine Learning

Yoga is an ancient Indian practice that nurtures and enhances both physical and mental well-being. However, performing yoga poses incorrectly can lead to adverse health effects. Many practitioners lack sufficient knowledge of the poses or fail to follow proper instructions, creating a need for a system that can accurately classify yoga poses and offer corrective guidance. This research aims to integrate technology with yoga to assist individuals in performing poses correctly, thus reducing the risk of injury and enhancing the effectiveness of their practice. Specifically, the objective is to detect live, performed yoga poses and provide real-time assistance using the Mediapipe library and deep learning techniques, such as the multilayer perceptron model. Recent advancements in computer vision and deep learning, including pose estimation algorithms and convolutional neural networks (CNNs), have shown promise in yoga pose recognition. However, challenges persist, such as accurately identifying complex and dynamic poses, dealing with variations in body shapes and postures, and ensuring real-time feedback. The proposed system seeks to overcome these challenges by leveraging robust pose detection methods and providing corrective suggestions to improve performance. This research marks a significant advancement in the fusion of technology with wellness, offering innovative solutions in the evolving field of mind-body fitness.