Real-Time Bodybuilding Pose Estimation Using YOLO26-Pose

Bradika Almandin Wisesa (Politeknik Manufaktur Negeri Bangka Belitung)
Vivin Mahat Putri (Politeknik Manufaktur Negeri Bangka Belitung)
Evvin Faristasari (Politeknik Manufaktur Negeri Bangka Belitung)
Sirlus Andreanto Jasman Duli (Politeknik Manufaktur Negeri Bangka Belitung)
Satria Agus Darma (Politeknik Manufaktur Negeri Bangka Belitung)

Journal Of Artificial Intelligence And Software Engineering
Vol 6, No 2 (2026): Juni (OnProgress)

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 (mAP@0.5) 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.