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

Abstract

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 ($\hat{I}´$) were used to measure statistical differences and effect sizes. Results: Lateral views significantly outperformed frontal views for sagittal plane kinematics: step length (DTW: $53.08\pm 24.50$ vs. $69.87\pm 25.36$, $p=0.005$) and knee rotation (DTW: $106.46\pm 38.57$ vs. $155.41\pm 41.77$, $p=0.004$). Frontal views were superior for symmetry parameters: trunk rotation (KLD: $0.09\pm 0.06$ vs. $0.30\pm 0.19$, $p<0.001$) and wrist-to-hipmid distance (MCC: $105.77\pm 29.72$ vs. $75.20\pm 20.38$, $p=0.003$). Effect sizes were medium-to-large ($\hat{I}´:0.34$--$0.76$). 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.