Real-Time Personalized Fitness Coaching System Using Computer Vision and Pose Estimation

B Guru Naveen, Talakanti Sainath Reddy, Pinjari Shaikshavali and Ajay Sharma

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 3, 778-786

Abstract: With the increasing prevalence of sedentary lifestyles and the subsequent rise in home-based fitness routines, the risk of exercise-induced injuries due to improper form has become a significant concern. While professional personal trainers provide necessary guidance, they are often inaccessible due to cost or location. This paper presents a personalized fitness coaching system leveraging computer vision and deep learning techniques. Utilizing the MediaPipe framework for real-time pose estimation, the proposed system tracks 33 distinct skeletal landmarks to analyze user posture during exercise. Geometric analysis is applied to calculate joint angles, which are compared against heuristic thresholds derived from professional fitness standards. The system provides immediate visual feedback on the correctness of the movement. Experimental results demonstrate that the system achieves an average classification accuracy of 94.5% across three standard exercises (Squats, Bicep Curls, and Push-ups) while maintaining a high frame rate suitable for real-time deployment on standard consumer hardware.

Keywords: Pose Estimation; Computer Vision; Human-Computer Interaction; MediaPipe; Digital Health; Biomechanics (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i3:id:1666

DOI: 10.32628/IJSRST26133202

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