Predict The Caloric Expenditure and Pose Estimation Through the Assistance of The Virtual Gym Coach

Abstract

This research paper introduces a novel virtual gym assistant leveraging Google's Mediapipe library, designed for diverse multimodal machine learning and deep learning pipelines. The system offers real-time guidance by analyzing user movements during specific exercises using posture estimation algorithms. Developed with Mediapipe's deep algorithms and pose estimation module, the system captures user movements through the identification of body landmarks, facilitating rep counting for each exercise. Angles and landmarks are then processed and transmitted to various machine learning models, enabling the classification of correct postures and reps. Through machine learning algorithms, the system evaluates the accuracy of user-performed reps. Utilizing diverse fitness datasets and custom data, the results demonstrate that the proposed system delivers precise and personalized feedback, enhancing exercise performance and minimizing the risk of injury. Additionally, the system calculates caloric expenditure, providing comprehensive support for users through the Virtual Gym Coach.