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

arXiv: https://arxiv.org/abs/2509.17805 · HTML: https://mlsanity.com/paper/2509.17805/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation](https://mlsanity.com/paper/2303.12787/) (2023)
- [It Takes Two: Accurate Gait Recognition in the Wild via Cross-granularity Alignment](https://mlsanity.com/paper/2411.10742/) (2024)

## Abstract (arXiv)

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.