Wrist Photoplethysmography Predicts Dietary Information

2025/11/24 by Verrier, Kyle, Nazaret, Achille, Futoma, Joseph +2
#Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2511.19260

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.

Citations

• Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
• Gemma 3 Technical Report
• NutriBench: A Dataset for Evaluating Large Language Models on Nutrition Estimation from Meal Descriptions
• Large-scale Training of Foundation Models for Wearable Biosignals

Related

• DietGlance: Dietary Monitoring and Personalized Analysis at a Glance with Knowledge-Empowered AI Assistant
• MealMeter: Using Multimodal Sensing and Machine Learning for Automatically Estimating Nutrition Intake
• PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation
• LLMs for energy and macronutrients estimation using only text data from 24-hour dietary recalls: a parameter-efficient fine-tuning experiment using a 10-shot prompt
• PaPaGei: Open Foundation Models for Optical Physiological Signals