Dynamic Motion Metrics for Objective Evaluation of Laparoscopic Camera Navigation Skill
Quantifying surgical expertise is essential in surgical training programs and often requires capturing both the task outcome and the underlying dynamic motion of the system. This study presents a framework for objectively evaluating laparoscopic camera navigation skills based on dynamic metrics derived from trajectory analysis. The position and orientation of a 30-degree laparoscope was recorded while 59 participants performed a standardized navigation task on a 3D-printed maze. Motion metrics for the assessment included idle time, total time, dimensionless jerk, rotation smoothness, space coverage, backtracking percentage, sample power entropy, directional changes, and speed entropy. Of these metrics, five were statistically significant: 1) Idle time (p-value = 3.7e-05), 2) total time (p-value = 3.7e-05), 3) dimensionless jerk (p-value = 0.0004), rotation smoothness (p-value = 0.0007), and space coverage (p-value = 0.0024). Results highlighted how experts demonstrate controlled variability in their movements and smoother motion patterns, while novices tend toward more unpredictable and exploratory movements. These findings help establish that analyzing dynamic metrics provides quantitative insight into camera control behavior between surgeons at different skill levels. As prior research has focused on traditional surgical skills rather than laparoscopic camera navigation skills, this work is a novel complement to more conventional time-based measures, proving it is possible to use dynamic metrics to automate feedback in surgical training programs.
