Performance Analysis of Human Activity

This project aims to develop an AI-powered gym assistant using Jupyter Notebook and MediaPipe, a popular computer vision library, count the repetitions of three joint exercises: curls, squats, sit-ups. The system will provide real-time feedback monitoring, allowing users track their progress improve performance. proposed method utilizes which offers pre-trained machine-learning models for human pose estimation hand tracking. These accurately detect critical body joints movements during exercises. then analyze detected poses identify each activity based on predefined movement patterns thresholds. be used as development environment coding testing system. Python programming language MediaPipe’s API employed implement repetition counting algorithms. also incorporate user-friendly interface, interact with receive exercise completed that can sit-ups in real-time. Additionally, this contribute advancement field fitness technology by showcasing potential combining artificial intelligence techniques monitoring performance results have benefit enthusiasts, trainers, researchers alike, providing contributions technology.