Dedicated training for \(30^\circ\) laparoscopic camera navigation remains limited by the cost of high-fidelity simulators and the absence of continuous objective assessment. We present SECMA, a portable virtual reality platform combining a mechanically constrained interface that reproduces the trocar pivot, decoupled horizon stabilisation, and optical-redirection channels specific to \(30^\circ\) camera handling with high-frequency 6-DoF telemetry. To establish construct-related validity evidence, a six-target navigation task was administered to 20 surgeons and 18 medical students across four practice sessions. Three analytical layers were applied. First, under repeated nested cross-validation (\(5 \times 50\) outer folds), Logistic Regression was the best classifier (\(F_1 = 0.818\), \(\textrm{AUC} = 0.904\)); execution time, path length, and depth-axis velocity were primary discriminating features. Second, Hidden Markov Models (\(K = 6\)) identified an expert-enriched coordination regime occupied 12.9 percentage points more by experts (Cohen’s \(d = 0.707\)), characterised by purposeful pitch-axis exploration versus diffuse yaw–roll noise in novices. Third, linear mixed-effects models confirmed robust expert–novice differences across four kinematic outcomes (all \(p \le 0.018\)) and practice-related improvement in temporal and spatial efficiency, with no significant between-group difference in improvement rate. These convergent findings support SECMA as a scalable and analytically rigorous platform for \(30^\circ\) camera navigation training and proficiency-based assessment.
