DrGait: Biomechanically Grounded Visual Reasoning for Interpretable Clinical Gait Analysis

Researchers introduced DrGait, a training-free agentic framework that shifts vision-language models from direct visual reasoners to clinical planners for interpretable gait analysis, per arXiv paper 2609.28796v1. DrGait decouples semantic reasoning from geometric perception through a Triage-Verification-Synthesis workflow that calls deterministic biomechanical tools operating on reconstructed 3D mesh trajectories, segmented 2D pose tracks, and event-centered video evidence. The closed-loop mechanism recursively updates the agent's reasoning context from tool feedback, reducing hallucinations while producing transparent, audit-ready clinical reports at competitive diagnostic accuracy.

arXiv:2609.28796v1 Announce Type: new Abstract: 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 agent first performs a heuristic triage to propose diagnostic hypotheses, which are then verified by autonomously calling deterministic biomechanical tools that operate on reconstructed 3D mesh trajectories, segmented 2D pose tracks, and event-centered video evidence. Finally, a closed-loop mechanism recursively updates the agent's reasoning context based on the feedback. By anchoring VLM's reasoning in verifiable geometric and temporal measurements, DrGait reduces hallucinations, achieving competitive diagnostic accuracy while generating transparent and audit-ready clinical reports.