Implementation of an Intelligent Real-Time Squat Detection and Performance Evaluation System Using Computer Vision and Pose Estimation Techniques
Abstract
The growing integration of artificial intelligence (AI) and computer vision in fitness and healthcare has enabled new possibilities for real-time movement tracking and automated performance evaluation. This research introduces AI Squat Sense, an intelligent, real-time squat detection and assessment system designed to monitor and enhance exercise performance. The system employs OpenCV for video acquisition and image processing, and MediaPipe Pose for extracting skeletal landmarks. By analyzing joint angles at the hip, knee, and ankle, AI Squat Sense accurately determines squat depth, counts repetitions, and evaluates posture quality. A graphical user interface (GUI) developed using Tkinter and ttkbootstrap ensures ease of use, offering real-time visual and auditory feedback through optional modules like pyttsx3 and simpleaudio. Experimental results demonstrate over 95% detection accuracy under normal lighting with minimal computational delay. The proposed system provides a cost-effective, accessible, and robust solution for fitness tracking, rehabilitation, and sports performance optimization, bridging the gap between personal fitness tools and intelligent motion analysis technologies.
Keywords
Artificial Intelligence, Computer Vision, Pose Estimation, Exercise Monitoring, Fitness Tracking, Human?Computer Interaction, Real-Time Analysis, OpenCV, MediaPipe.
Citations
IRE Journals:
S. Ranjithkumar, Dr. G. Nallavan, S. Sugumar "Implementation of an Intelligent Real-Time Squat Detection and Performance Evaluation System Using Computer Vision and Pose Estimation Techniques" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 1107-1112 https://doi.org/10.64388/IREV9I6-1712786
IEEE:
S. Ranjithkumar, Dr. G. Nallavan, S. Sugumar
"Implementation of an Intelligent Real-Time Squat Detection and Performance Evaluation System Using Computer Vision and Pose Estimation Techniques" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712786
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