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GaitFM: A Cross-Modal Foundation Model for Wearable Gait Assessment — arXiv:2611.01684

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01684
License (as archived): CC0-1.0
Archived: 2026-11-03T18:54:36.207Z
SHA-256 of the archived text: 5763df8b3b93db28a7bf86f12a7bf37ec97b9cf2f770301f11b87716b4541665
Stable URL of this page: https://metraipolis.com/documento/dc_f4e9571deff7

How to cite

GaitFM: A Cross-Modal Foundation Model for Wearable Gait Assessment — arXiv:2611.01684 — arXiv (en), https://arxiv.org/abs/2611.01684 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f4e9571deff7 (Metraipolis archive dc_f4e9571deff7, sha256 5763df8b3b93db28a7bf86f12a7bf37ec97b9cf2f770301f11b87716b4541665)

BibTeX

@misc{dcf4e9571deff7,
  title = {GaitFM: A Cross-Modal Foundation Model for Wearable Gait Assessment — arXiv:2611.01684},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01684},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f4e9571deff7; sha256 5763df8b3b93db28a7bf86f12a7bf37ec97b9cf2f770301f11b87716b4541665},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01684. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Gait assessment supports the diagnosis, monitoring, and prognosis of diverse movement-related conditions, such as neurological and musculoskeletal disorders. Recent advances in deep learning using everyday wearable inertial measurement units (IMUs) offer an accessible and low-cost approach to automated gait assessment. However, IMU measurements vary substantially across individuals, body locations, and device configurations, while existing deep learning models are typically developed using small, homogeneous datasets for specific clinical tasks, limiting their robustness and generalizability in diverse real-world contexts. Here we present GaitFM, a cross-modal foundation model for gait assessment using IMU sensors. GaitFM improves the generalizability of gait assessment by transferring knowledge from large vision models and pretraining on unlabeled IMU sensor data. We pretrain GaitFM on a large curated dataset comprising about 4,000 participants and 1.23 million gait windows across diverse clinical and daily conditions, sensor modalities, and body locations. GaitFM demonstrates strong performance across 18 gait-related tasks, including 3 gait parameter estimation tasks, 14 disease and gait abnormality detection tasks, and 1 clinical outcome prediction task. Furthermore, GaitFM generalizes across different sensor configurations and body locations, including those commonly used in clinical settings and everyday wearable devices. These findings demonstrate the effectiveness of transferring visual knowledge in improving sensor-based gait assessment performance, offering a new promising pathway for addressing data scarcity in the sensor and medical domains.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.01684 — 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 2026-11-03T18:16:52.946Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).