Real-Time Bodybuilding Pose Estimation Using YOLO26-Pose

Bradika Almandin Wisesa, Vivin Mahat Putri, Evvin Faristasari, Sirlus Andreanto Jasman Duli, Satria Agus Darma

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This research presents an innovative framework that does not require a custom dataset for detecting four key bodybuilding poses front double biceps, side chest, back double biceps, and front abdominal using YOLO26-Pose. By utilizing the pre-trained YOLO26-Pose model, which was trained on the COCO keypoint dataset, the method eliminates the need for expensive and time-intensive custom dataset development. It leverages keypoint detection to calculate joint angles and applies geometric constraints for real-time classification of poses, achieving a mean Average Precision ([email protected]) of 93%, an average angle error of 2.6°, and real-time processing at 43 frames per second (FPS). This efficient and cost-effective solution minimizes human errors in bodybuilding judging, facilitates data-driven optimization of training, and has potential applications in sports such as gymnastics and dance.

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DOI: http://dx.doi.org/10.30811/jaise.v6i2.9081

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