VISION-PF: A Computer Vision Approach to Injury Prevention in Physical Fitness

Ensuring accurate and effective exercise performance monitoring is essential for sports-focused injury prevention. Maintaining perfect form is crucial since incorrect training increases the chance of injuries dramatically. This article focuses on the popular push-up exercise and introduces a new approach that uses a carefully curated dataset for analysis. The proposed model can accurately discriminate between correct and wrong push-up forms by employing LSTM networks and sophisticated computer vision algorithms. This improved method enables focused interventions, tailored feedback, and the detection of form deviations, providing insights into typical errors. This research aims to improve athlete safety, maximize athletic performance, and establish a framework for using the concept in other significant sports-related injury prevention initiatives. The proposed model significantly improved performance, achieving F1 scores of 0.9 and 0.85 on the training and testing sets, respectively.