AI Based Personal Trainer
Amit Khan, Mudit Agrawal, Tushar Mohanty, Shraddha Kale, Dipali Junankar
Deep learning, Classification, Convolutional neural networks, Image segmentation, OpenCV, Artificial Intelligence Adaptive learning methods
Inactivity is one of the main causes of obesity which has affected many people worldwide. Studies show that fitness is an important goal for a healthy lifestyle and has been used as a measurement for health-related quality of life. A fitness trainer can motivate and teach users to do exercise daily and stay fit and healthy. However, to use a fitness trainer might involve a huge cost and sometimes is not suitable for a certain setting. Exercises are very beneficial for personal health, but they can also be ineffective and truly dangerous if performed in an incorrect method by the user. There are lot of mistakes made during a workout when user workout alone without supervision like wrong form which could result fatal for user as they can pull a hamstring or even fall due to it. In our project, we introduce AI Trainer, an application that detects the user’s exercise pose and provides personalized, detailed recommendations on how the user can improve their form. Pose Trainer uses the state of the art in pose estimation module known as “Blaze Pose” tool from “Media Pipe” to detect a user’s pose, then evaluates the pose of an exercise to provide useful feedback. An AI fitness trainer is a computer application that utilizes the capabilities of Python, OpenCV, and Media Pipe to guide users through physical fitness routines. The application uses computer vision techniques provided by OpenCV to track the user\'s movements and provide feedback on form and technique. Media Pipe is used to process video data and provide real-time analysis. The application also utilizes machine learning algorithms to provide personalized fitness recommendations and progress tracking. The combination of these technologies provides a highly interactive and effective way for users to improve their physical fitness.
Article Details
Unique Paper ID: 162438

Publication Volume & Issue: Volume 10, Issue 10

Page(s): 103 - 105
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