KnotVision: Three-Dimensional Hand Motion Reconstruction and Quantitative Skill Assessment for Surgical Knot-Tying

Abstract

Assessment of open surgical knot-tying skill remains largely subjective, time-intensive,and dependent on expert observation, limiting the scalability and consistency of technical- skills evaluation in surgical training. This thesis develops a markerless dual-view video framework to quantify knot-tying performance from hand-motion data. Using a benchtop knot-tying task recorded with two camera views, the proposed pipeline performs hand land- mark extraction, temporal synchronization, three-dimensional triangulation, and knot-level segmentation to reconstruct hand kinematics. Translational and rotational motion metrics are then computed to characterize timing, movement behavior, and dexterity during task execution. The results demonstrate that markerless hand tracking can provide objective, process-based measures of surgical knot-tying performance in a controlled setting. These findings support the feasibility of scalable video-based surgical skill assessment and establish a foundation for future hand-centered applications in surgical education and robotics.