Date of Award
Fall 11-25-2025
Document Type
Thesis
Publication Status
Version of Record
Submission Date
December 2025
Department
Computer and Electrical Engineering and Computer Science
Degree Name
Master of Science (MS)
Thesis/Dissertation Advisor [Chair]
Behnaz Ghoraani
Abstract
This thesis presents a comparative evaluation of two deep learning-based methods for extracting 2D and 3D gait metrics from monocular video: a 2D-to-3D lifting approach combining AlphaPose for keypoint detection with MotionBERT for temporal 3D reconstruction, and a direct 3D estimation method using MeTRAbs. Videos of two healthy adults walking across a ProtoKinetics Zeno™ Walkway were captured laterally at 4K/60 fps using an iPhone 16. Ground-truth spatiotemporal gait parameters were obtained from the pressure-sensitive mat, enabling quantitative validation via Mean Absolute Error (MAE) and Pearson correlation. MeTRAbs consistently outperformed the lifting pipeline, achieving MAE below 2% for temporal metrics (e.g., 1.58% for step time, 1.09% for gait cycle time) and strong correlations (r > 0.90, p < 0.001). Spatial metrics showed MAE of 15.28% (step length) and 15.84% (stride length), with superior robustness to occlusions. AlphaPose+MotionBERT exhibited higher spatial errors (20.40% and 18.47%, respectively) and weaker correlations, though it remained viable for velocity (9.34% MAE) and low-resource settings. The results highlight direct 3D estimation as the preferred method for clinical precision and biomechanical fidelity, while 2D-to-3D lifting offers a lightweight alternative for scalable, non-intrusive monitoring. Future work should expand to pathological gaits, multi-view fusion, and edge-optimized models to broaden real-world applicability.
Recommended Citation
Kushwaha, Akhilesh Singh, "COMPARATIVE ANALYSIS OF 2D AND 3D GAIT METRICS FROM VIDEO USING DEEP LEARNING TECHNIQUES" (2025). Electronic Theses and Dissertations. 200.
https://digitalcommons.fau.edu/etd_general/200