AI Fitness Trainer: Dynamic Motion Correction and Real-Time Feedback for Enhancing Self-Managed Workouts

With the COVID-19 pandemic making self-managed exercise ever more popular, this project aims to create a reliable motion correction model through machine learning methods. The study tackles the urgent need for safe and efficient workout rou-tines that don't require medical supervision. The research process consists of three main stages: the model is integrated into a real-time 3D human position estimator for instant feedback during workouts; a graph convolutional network (GCN) architecture with dynamic temporal warping loss is implemented to align and optimize 3D posture sequences; and exercise videos, 2D/3D position data, and accuracy labels are carefully collected as the dataset. The results point to a significant improvement in exercise efficacy and safety, which may lower the risk of injury and boost workout results. In addition, the project promotes independence from professional supervision by following the trend toward independent exercise. The ramifications go beyond fitness and promise revolutionary changes in a variety of fields, including physical therapy, sports performance enhancement, healthcare monitoring, and rehabilitation.