DrGait: Biomechanically Grounded Visual Reasoning for Interpretable Clinical Gait Analysis
Current automated gait analysis for clinical applications relies on uninterpretable black-box classifiers. Although Vision-Language Models (VLMs) offer strong reasoning capabilities, applying them directly to gait videos often leads to hallucinations, because they struggle to measure subtle geometric deviations from raw visual contexts. To address this, we introduce DrGait, a training-free agentic framework that shifts the VLM's role from a direct visual reasoner to a clinical planner. DrGait decouples semantic reasoning from geometric perception through a structured Triage-Verification-Synthesis (TVS) workflow. Given an input video and a set of basic spatiotemporal metrics, the DrGait ag...
| Authors | Xiangyu Yin, Shiqi Wang, Abrar Alamri et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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