{
  "id": 12607,
  "url": "https://arxiv.org/abs/2607.18818v1",
  "title": "CITRUS: Candidate Inference and Temporal-tracking for Reliable, Unobtrusive Sensing of Wearable Heart Rate under Motion",
  "summary": "Wearable photoplethysmography (PPG) provides continuous heart-rate measurements, but its accuracy degrades under motion. In the ring-platform benchmark, the best supervised baseline reaches 5.33 BPM mean absolute error (MAE) on the overall heart-rate task. In the motion-focused ring-only audit, a supervised LSTM baseline reaches $14.39 \\pm 0.47$ BPM MAE on motion windows, and simple smoothing and ACC priors reduce this only to $13.00 \\pm 0.41$ BPM. This thesis addresses motion-corrupted HR estim",
  "authors": "Yi Wang",
  "category": "research",
  "topics": "privacy-surveillance,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-21T07:54:52.000Z",
  "fetched_at": "2026-07-22T05:10:49.469Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/12607",
  "original_url": "https://arxiv.org/abs/2607.18818v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}