{"ID":2837649,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19260","arxiv_id":"2511.19260","title":"Wrist Photoplethysmography Predicts Dietary Information","abstract":"Whether wearable photoplethysmography (PPG) contains dietary information remains unknown. We trained a language model on 1.1M meals to predict meal descriptions from PPG, aligning PPG to text. PPG nontrivially predicts meal content; predictability decreases for PPGs farther from meals. This transfers to dietary tasks: PPG increases AUC by 11% for intake and satiety across held-out and independent cohorts, with gains robust to text degradation. Wearable PPG may enable passive dietary monitoring.","short_abstract":"Whether wearable photoplethysmography (PPG) contains dietary information remains unknown. We trained a language model on 1.1M meals to predict meal descriptions from PPG, aligning PPG to text. PPG nontrivially predicts meal content; predictability decreases for PPGs farther from meals. This transfers to dietary tasks:...","url_abs":"https://arxiv.org/abs/2511.19260","url_pdf":"https://arxiv.org/pdf/2511.19260v2","authors":"[\"Kyle Verrier\",\"Achille Nazaret\",\"Joseph Futoma\",\"Andrew C. Miller\",\"Guillermo Sapiro\"]","published":"2025-11-24T16:12:03Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CL\"]","methods":"[\"Language Model\"]","has_code":false}