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MotionDecipher: LLM-Assisted Clinical Interpretation of Human Motion Signals for Efficient Gait Analysis — arXiv:2611.08795

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08795
License (as archived): CC0-1.0
Archived: 2026-11-09T22:34:37.653Z
SHA-256 of the archived text: 1e9110b73c9add18d8691d436c0bd4f9d09ed3816d003ebd3ff5d5a3273b34f3
Stable URL of this page: https://metraipolis.com/documento/dc_c8aeb5cc3477

How to cite

MotionDecipher: LLM-Assisted Clinical Interpretation of Human Motion Signals for Efficient Gait Analysis — arXiv:2611.08795 — arXiv (en), https://arxiv.org/abs/2611.08795 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c8aeb5cc3477 (Metraipolis archive dc_c8aeb5cc3477, sha256 1e9110b73c9add18d8691d436c0bd4f9d09ed3816d003ebd3ff5d5a3273b34f3)

BibTeX

@misc{dcc8aeb5cc3477,
  title = {MotionDecipher: LLM-Assisted Clinical Interpretation of Human Motion Signals for Efficient Gait Analysis — arXiv:2611.08795},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08795},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c8aeb5cc3477; sha256 1e9110b73c9add18d8691d436c0bd4f9d09ed3816d003ebd3ff5d5a3273b34f3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08795. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Clinical gait and motion analysis is widely used to guide targeted interventions for patients with neurological disorders, the outcome of which is typically a report with descriptions of joint movements and forces. Current practices rely on experts' manual effort, which is labor-intensive and time-consuming. Recent advancements in Large Language Models (LLMs) provide opportunities to address this problem, but translating human motion into clinical gait descriptions remains challenging because the quality of the context directly affects LLMs' reasoning capability over continuous motion signals. To tackle this, we introduce MotionDecipher, a novel framework bridging the semantic gap between motion data and clinical interpretations. Specifically, we made 3 key advancements: 1) a Human Motion Tokenizer that converts motion signals into domain-specific tokens for time- and frequency-informed gait interpretation; 2) a Meta Feature Selector that transforms tokens into Structured Representations; and 3) a Hybrid Context Constructor to determine the optimal demonstration-based prompt for In-Context Learning. We evaluated MotionDecipher on data collected at an accredited gait lab from 122 patients and 46 controls. Results show that MotionDecipher achieves 0.91 BERTScore (13% over GPT-5), while expert evaluation ranks MotionDecipher highest in Correctness, Completeness, and Conciseness compared to baselines, highlighting its promise for efficient gait analysis.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.08795 — 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 2026-11-09T22:30:47.309Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).