用手腕光体积描记信号预测饮食内容，效果显著且稳定
Wrist Photoplethysmography Predicts Dietary Information
- 用110万餐食数据训练语言模型，将手腕PPG信号与饮食文本对齐
- 远离进餐时间的PPG信号预测能力下降，但整体仍能提升饮食识别准确率11%
- 适用于被动饮食监测，对文本质量退化有强鲁棒性
可穿戴光体积描记（PPG）是否包含饮食信息尚不明确。我们利用110万餐食数据训练语言模型，从PPG信号中预测餐食描述，并实现PPG与文本的对齐。结果显示，PPG能够非平凡地预测餐食内容，且预测性能随距离进餐时间变远而降低。该方法可迁移至饮食任务：在独立队列中，PPG使摄入量与饱腹感预测的AUC提升11%，且在文本质量下降时仍保持稳健。可穿戴PPG有望实现被动式饮食监测。
原文摘要 · Abstract (English)
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.
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