Surgical work-related musculoskeletal disorders (WRMSDs) has mostly been investigated using qualitative observational assessments and questionnaires, as well as quantitative marker-based motion analysis. The main limitations of these methods are the subjectivity of questionnaires and the impracticality of markers in operating room. This study investigates the feasibility of predicting ergonomic level and surgical expertise level using quantitative postural metrics.

Sixty orthopedic surgeons with three expertise levels were recorded without markers using two cameras, frontal and sagittal views. Human pose was extracted from the 120 videos using OpenPose combined with a custom post-processing pipeline. The ergonomic level was assessed by two expert surgeons using a 5-point Likert scale. A large set of quantitative postural metrics, designed with expert surgeons, was computed and used to predict both ergonomic level and surgical expertise level using Random Forest and XGBoost Tree.

The best performance for surgical expertise prediction was achieved using Random Forest model, with a balanced accuracy of 48%. For ergonomic level prediction, the lowest error was obtained with the Random Forest model, with a RMSE of 0.77. The most informative postural metrics for predictions were primarily related to upper-limb joints.

The feasibility of predicting ergonomic and surgical expertise level using markerless postural metrics was demonstrated. The identified influential body regions were consistent with anatomical locations commonly associated with WRMSDs reported in the literature.