Wrist Photoplethysmography Predicts Dietary Information
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866918255171469312 |
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| author | Verrier, Kyle Nazaret, Achille Futoma, Joseph Miller, Andrew C. Sapiro, Guillermo |
| author_facet | Verrier, Kyle Nazaret, Achille Futoma, Joseph Miller, Andrew C. Sapiro, Guillermo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19260 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wrist Photoplethysmography Predicts Dietary Information Verrier, Kyle Nazaret, Achille Futoma, Joseph Miller, Andrew C. Sapiro, Guillermo Machine Learning Artificial Intelligence Computation and Language 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. |
| title | Wrist Photoplethysmography Predicts Dietary Information |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2511.19260 |