Wrist Photoplethysmography Predicts Dietary Information
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 21 references that are in the map. First posted on arXiv 24 Nov 2025.
Builds on
4 of 21 references.
References · 4
- 2025Gemma 3 Technical Reportcited by 1,686
- 2023Large-scale Training of Foundation Models for Wearable Biosignalscited by 89
- 2025Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictionscited by 18
- 2024NutriBench: A Dataset for Evaluating Large Language Models on Nutrition Estimation from Meal Descriptionscited by 17
Built on by
Not cited yet.