# Wrist Photoplethysmography Predicts Dietary Information

arXiv: https://arxiv.org/abs/2511.19260 · HTML: https://mlsanity.com/paper/2511.19260/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Large-scale Training of Foundation Models for Wearable Biosignals](https://mlsanity.com/paper/2312.05409/) (2023)
- [Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions](https://mlsanity.com/paper/2507.00191/) (2025)
- [NutriBench: A Dataset for Evaluating Large Language Models on Nutrition Estimation from Meal Descriptions](https://mlsanity.com/paper/2407.12843/) (2024)

## 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.