International Journal of Science, Engineering and TechnologyAuthors: Dhruv Gajanan Lokhande, Abhishek Mukesh Agarwal, Shivraj Hanumant Akhade, Kailas Anil Yadav, Assistant Professor Chandani P. Lachake
Abstract: Physical activity is essential for maintaining health and fitness. Recent advancements in computer vision and machine learning have enabled highly accurate human pose estimation models that can track and analyze body movements in real time. This paper reviews state-of-the-art techniques in pose estimation and prediction, highlighting their applications in sports science, rehabilitation, and computer animation. It also presents the development of a virtual fitness trainer using MediaPipe, integrated with deep learning frameworks like TensorFlow and Keras. The system leverages posture estimation algorithms to detect key body landmarks, enabling accurate tracking of exercises and performance evaluation. By analyzing user movements, it provides feedback and predicts future poses to improve exercise quality.With the rapid evolution of deep learning techniques, pose estimation models continue to improve in accuracy and efficiency. These advancements hold strong potential to transform fitness and health monitoring by enabling smarter, real-time movement analysis and personalized training systems.
