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Detecting Brain Cancer Progression Through Signatures of Wearable Accelerometry Data — arXiv:2610.38021

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.38021
License (as archived): CC0-1.0
Archived: 2026-11-01T02:23:21.732Z
SHA-256 of the archived text: 113426d2d2b4bcbfb72ba1e76d696b46a8ceee3ee08425e52e5890cd80326d73
Stable URL of this page: https://metraipolis.com/documento/dc_0890a00a7527

How to cite

Detecting Brain Cancer Progression Through Signatures of Wearable Accelerometry Data — arXiv:2610.38021 — arXiv (en), https://arxiv.org/abs/2610.38021 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0890a00a7527 (Metraipolis archive dc_0890a00a7527, sha256 113426d2d2b4bcbfb72ba1e76d696b46a8ceee3ee08425e52e5890cd80326d73)

BibTeX

@misc{dc0890a00a7527,
  title = {Detecting Brain Cancer Progression Through Signatures of Wearable Accelerometry Data — arXiv:2610.38021},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.38021},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0890a00a7527; sha256 113426d2d2b4bcbfb72ba1e76d696b46a8ceee3ee08425e52e5890cd80326d73},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.38021. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Glioblastoma and other high-grade gliomas (HGG) have a poor prognosis and are associated with progressive functional decline. The standard of care to assess progressive disease is contrast-enhanced MRI, which is expensive, claustrophobic, and performed only once every 2-3 months. Wearable accelerometers offer a non-invasive alternative for continuously monitoring patient function between clinic visits. However, accelerometry data streams are typically noisy and highly patient-specific, making conventional time series methods and summary statistics poorly suited to detecting clinically meaningful change. This work aims to classify stable versus progressive disease states by applying rough path signatures for feature extraction from wrist-worn accelerometer data. We convert accelerometer recordings from HGG patients enrolled in the BrainWear study into walking-path segments and use signature kernel maximum mean discrepancy (SigMMD) to quantify change in patients' walking behaviour. We overcome the challenge of inter-patient heterogeneity by adjusting for the natural variation in a patient's walking during stable tumour periods. This patient-specific normalisation substantially improves separation, yielding a candidate biomarker of tumour progression. Our results provide proof-of-concept evidence that signature-based analysis of wearable accelerometry can distinguish stable from progressive disease states, motivating larger longitudinal studies of continuous wearable monitoring as a digital biomarker in neuro-oncology.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.38021 — 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 2026-11-01T02:12:44.097Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).