Selecting Optimal Camera Views for Gait Analysis: A Multi-Metric Assessment of 2D Projections

Objective: To systematically quantify the effect of the camera view (frontal vs. lateral) on the accuracy of 2D markerless gait analysis relative to 3D motion capture ground truth. Methods: Gait data from 18 subjects were recorded simultaneously using frontal, lateral and 3D motion capture systems. Pose estimation used YOLOv8. Four metrics were assessed to evaluate agreement: Dynamic Time Warping (DTW) for temporal alignment, Maximum Cross-Correlation (MCC) for signal similarity, Kullback-Leibler Divergence (KLD) for distribution differences, and Information Entropy (IE) for complexity. Wilcoxon signed-rank tests (significance: $p < 0.05$) and Cliff's delta ($δ$) were used to measure statistical differences and effect sizes. Results: Lateral views significantly outperformed frontal views for sagittal plane kinematics: step length (DTW: 53.08 ± 24.50 vs. 69.87 ± 25.36, $p = 0.005$) and knee rotation (DTW: 106.46 ± 38.57 vs. 155.41 ± 41.77, $p = 0.004$). Frontal views were superior for symmetry parameters: trunk rotation (KLD: 0.09 ± 0.06 vs. 0.30 ± 0.19, $p < 0.001$) and wrist-to-hipmid distance (MCC: 105.77 ± 29.72 vs. 75.20 ± 20.38, $p = 0.003$). Effect sizes were medium-to-large ($δ: 0.34$--$0.76$). Conclusion: Camera view critically impacts gait parameter accuracy. Lateral views are optimal for sagittal kinematics; frontal views excel for trunk symmetry. Significance: This first systematic evidence enables data-driven camera deployment in 2D gait analysis, enhancing clinical utility. Future implementations should leverage both views via disease-oriented setups.