Mobile Application for Optimizing Exercise Posture Through Machine Learning and Computer Vision in Gyms

This paper introduces a mobile application that aims to improve exercise posture analysis in gym environments using machine learning and computer vision. The solution processes user-uploaded videos to detect posture errors, utilizing Long Short-Term Memory (LSTM) networks and MediaPipe for precise pose estimation. The trained model achieved high accuracy in classifying exercise postures, demonstrating reliable performance across different user scenarios. Traditional posture correction methods, such as personal trainers and wearable devices, often lack accessibility and precision. In contrast, our application offers a scalable, user-friendly tool that delivers actionable feedback, helping users optimize their workouts and reduce injury risks. The study highlights the potential of combining machine learning with mobile technology to enhance exercise safety and performance, setting a foundation for future improvements.