Yoga Trainer Using Artificial Intelligence and Deep Learning

Artificial Intelligence (AI) and deep learning have revolutionized human pose estimation and posture correction systems. This research introduces an AI-based Yoga Trainer system that leverages deep learning models to recognize and evaluate yoga postures while providing real-time feedback and corrections. The system employs Media-Pipe Pose for real-time human pose detection and extraction of body landmarks, focusing on key points such as the head, shoulders, elbows, hips, and knees. These extracted landmarks are processed using a Support Vector Classifier (SVC), implemented with scikit-learn’s Support Vector Machine (SVM), to classify yoga poses accurately. Predictive modeling is applied using the predict prob function to determine pose probabilities, enhancing classification precision. OpenCV facilitates video capture, frame processing, and visualization of annotated images, ensuring seamless real-time interaction. Additionally, NumPy is used for efficient array manipulation in landmark processing. Once a pose is classified, the system compares the user’s posture with the correct form for each asana. If misalignment is detected, it provides real-time guidance to help users correct their poses. This feature is particularly beneficial for home practitioners who may not have access to a yoga instructor. Future improvements may include enhancing the system’s ability to recognize dynamic yoga sequences and optimizing real-time processing for mobile and wearable devices.