Exercise Pose Estimation and AI Feedback System

The Exercise Pose Estimation System successfully demonstrates how artificial intelligence and computer vision can be applied to improve fitness training. The system is capable of detecting human body posture in real time and analyzing it using pose estimation techniques. By calculating joint angles and comparing them with standard values, it accurately determines whether an exercise is performed correctly. One of the key achievements of this project is the ability to provide immediate feedback to users. This real-time guidance helps individuals correct their posture instantly, improving the effectiveness of their workouts and reducing the risk of injuries. The system acts as a virtual trainer, making fitness training more accessible and convenient, especially for people who prefer exercising at home. The use of technologies like MediaPipe and OpenCV ensures accurate detection of body movements and efficient processing of video input. The system is simple, cost-effective, and easy to use, making it suitable for users of all fitness levels. Overall, this project highlights the potential of AI in the fitness and healthcare domain. It provides a smart and efficient solution for posture correction and promotes safe exercise practices. The results obtained show that the system is reliable and can be further enhanced for real-world applications.

1.      Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., and Sheikh, Y. (2019). OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2.      Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.L., Yong, M.G., Lee, J., and others (2019). MediaPipe: A Framework for Building Perception Pipelines. arXiv:1906.08172. 3.      Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools. 4.      Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv:1804.02767. 5.      He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6.      Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. 7.      Toshev, A., and Szegedy, C. (2014). DeepPose: Human Pose Estimation via Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).