3D pose estimation for scalable remote gait kinematics assessment.

ABSTRACT: Marker-based gait analysis is a cornerstone of neurological rehabilitation, but its high cost and clinical setting dependency limit its use for continuous, real-world monitoring. This study presents a scalable, markerless approach for pathological gait analysis, addressing a critical need for remote monitoring in conditions such as spinal cord injury (SCI). To ensure the highest fidelity for this application, we first investigated and benchmarked state-of-the-art 3D pose estimation models, finding VideoPose3D to be the best-performing for pathological gait in SCI. Our automated pipeline utilizes this model with a single video to generate detailed kinematic data, extracting time-series data for key joint angles. Our approach automatically identifies distinct gait patterns in the largest SCI

SUBMITTER: Natraj S 

PROVIDER: S-EPMC12800279 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature