A Real-Time Ergonomic Posture Analysis System for Surgeons in Operating Rooms Using YOLOv11

Abstract

Background: Surgical procedures involve prolonged static postures. There is, therefore, a great risk of musculoskeletal disorders since the high-risk factors are two: the procedure and the static posture. It is important to note that ergonomic posture analysis remains one of the measures toward the risk of MSDs. Unfortunately, existing methods of ergonomic assessment do not provide the surgeons with feedback in real-time.

Purpose: This study presents an Ergonomic Monitoring System based on artificial intelligence, which joins YOLO version 11 for real-time posture analysis and feedback with special reference to occlusion-heavy operating room scenarios. The system is designed to improve posture correction by means of automated, data-empowered ergonomic risk assessments.

Methods: The system detects key postural landmarks—neck, shoulders, back, and elbows—to calculate joint angles and classify postures using the RULA and REBA ergonomic models. A dataset of 700 annotated images was obtained in collaboration with the King Abdulaziz University Hospital and publicly available sources. Ground truth values were established using ergonomic risk models and expert validation. The system was evaluated against OpenPose and MediaPipe, with performance measured through standard pose estimation metrics.

Results: YOLOv11 outperformed both OpenPose and MediaPipe with respect to mean Average Precision (mAP), achieving 94.5% with a Precision of 95% and recall of 84.7%. Being able to give feedback in real-time through video and text will allow the surgeon to dynamically adjust his posture and reduce ergonomic risks effectively.

Conclusions : This study presents a real-time AI-based assessment tool for surgical ergonomics, which was not possible with manual evaluations. The real-time posture correction by the system would be a major step toward MSD minimization for surgeons. In the time to come, 3D pose estimation will be integrated, the dataset will be expanded, and fatigue tracking will be included to have a complete ergonomic assessment.