| Summary: | Yoga is a very popular form of exercise that originated in India that has numerous benefits for the mind and body. According to recent statistics, there are over 300 million yoga practitioners worldwide, with the number of yoga instructors increasing annually. However, incorrect yoga postures can lead to injuries and health complications. This highlights the importance of correct yoga posture and the need for a system that can detect and correct improper poses. This abstract presents a yoga posture detection and correction system designed using OpenCV for computer vision, and kinematic representation of the human body considering 17 points mapped on the human body, utilizing the tf-pose estimation algorithm for precise pose estimation. The system also includes a convolutional neural network (CNN) model developed using the Keras API and trained on the TensorFlow platform's MoveNet architecture for handling training of the model. The MoveNet pose estimation module has been used to detect keypoints of the human body which achieved an accuracy of 99.88%. The system works by live capturing of the yoga practitioner using a camera, extracting the key features of the pose, and comparing them with a trained data model of known yoga poses. If the pose is incorrect, the system provides real-time feedback to correct the pose.
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