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DrGait: Biomechanically Grounded Visual Reasoning for Interpretable Clinical Gait Analysis — arXiv:2609.28796

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.28796
License (as archived): CC0-1.0
Archived: 2026-10-08T04:27:07.791Z
SHA-256 of the archived text: 766b857b3098681b507070574a4370e147f7c33bf2f00147c65c69fd388295da
Stable URL of this page: https://metraipolis.com/documento/dc_2ecfe447ff1c

How to cite

DrGait: Biomechanically Grounded Visual Reasoning for Interpretable Clinical Gait Analysis — arXiv:2609.28796 — arXiv (en), https://arxiv.org/abs/2609.28796 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2ecfe447ff1c (Metraipolis archive dc_2ecfe447ff1c, sha256 766b857b3098681b507070574a4370e147f7c33bf2f00147c65c69fd388295da)

BibTeX

@misc{dc2ecfe447ff1c,
  title = {DrGait: Biomechanically Grounded Visual Reasoning for Interpretable Clinical Gait Analysis — arXiv:2609.28796},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.28796},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2ecfe447ff1c; sha256 766b857b3098681b507070574a4370e147f7c33bf2f00147c65c69fd388295da},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.28796. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-25 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.28796 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T03:50:57.956Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).