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SleepFM-2 model learns transferable human physiology from 2M hours of sleep data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SleepFM-2, a novel sleep foundation model trained on over two million hours of multimodal physiological data from nearly 300,000 sleep recordings. This model demonstrates significant improvements in predicting diseases and scoring sleep compared to its predecessor, SleepFM. SleepFM-2 shows strong transferability across different sensors, including wearables, and can even capture subjective aspects of sleep quality not typically reflected in standard polysomnography summaries. AI

IMPACT This model could enhance disease prediction and health monitoring using readily available sleep data from wearables.

RANK_REASON The cluster describes a new research paper detailing a novel foundation model for sleep physiology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SleepFM-2 model learns transferable human physiology from 2M hours of sleep data

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The cluster describes a new research paper detailing a novel foundation model for sleep physiology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rahul Thapa, Christopher Sun, William Theodor Lehn-Schioler, Sophia Claire Kivelson, Umaer Hanif, Hyatt Moore IV, Harrison G. Zhang, Hafsa Ahmed, Marcus Dige, Niels R. Lorenzen, Elisabeth Roxane M. Heremans, Adrien Specht, Ulysse Gimenez, Robin Guillard,… ·

    Learning transferable human physiology from two million hours of sleep with SleepFM-2

    arXiv:2609.06849v1 Announce Type: new Abstract: Sleep provides a nightly window into health by capturing coordinated activity across the brain, heart, muscles and respiratory system. We introduce SleepFM-2, a sleep foundation model developed and evaluated on 282,511 polysomnograp…

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