Abstract: For the previous years, thousands of gym goers are looking for the solution to having an efficient and customized workout. Currently, the majority of users go through a poor posture while doing some exercises that leads to pain or decreased result. To fight this issue, the "AI-Based Workout Guide" is a technology solution which utilizes computer vision and AI to provide real-time posture correction and rep counting.
The project utilizes AI and machine learning algorithms to determine users' body motion while performing a workout. Recorded by means of a camera or phone, the system will then compare this posture with the most common model ones for the optimal methods of performing the exercises. In case of a wrong posture, it will always provide immediate feedback regarding what one needs to change. Another aspect of the system is that the repetitions are automatically counted, and therefore no manual counting is needed and even greater focus on correct form by the user.
The model has been trained on the database of different exercise poses, like squats, push-ups, and lunges. OpenPose or Mediapipe computer vision libraries are utilized to detect important landmarks, i.e., joint angles and alignment. In real-time, the system checks these milestones in order to give a correct posture analysis and rep count. Eventually, this would assist the users in becoming more efficient in their workouts, minimizing the possibility of injury, and achieving fitness objectives better.
It's a readable, scalable AI-driven workout manual, from which it follows that it can be easily converted into any web or mobile application. Its usability reaches to beginners and intermediate fitness enthusiasts. This project demonstrates how technology can revolutionize personal training in fitness: the marriage of cutting-edge AI methods with a pragmatic solution for the implementation of fitness.
Real-Time Feedback for Ongoing Improvement: The real-time feedback allows users to correct immediately, and therefore maintain ongoing improvement in workout performance. This instant advice discourages poor habits from developing, important to ongoing fitness gains.
Increased Precision: The system takes advantage of leading machine learning methodologies, such as deep learning methodologies, to analyze and suggest precise posture adjustments against a huge dataset of correct postures during workouts. Precision is guaranteed while following complicated movements, and it's even able to modify the fit according to distinct users' variance in form.
Keywords: AI, Computer Vision, Workout Guide, Pose Estimation, MediaPipe, OpenCV, Exercise Form Correction.
|
DOI:
10.17148/IJARCCE.2025.145126
