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The paper proposes Synheart Capacity, a theory-driven multimodal learning framework that models cognitive capacity dynamics from wearable cardiac and electrodermal signals, enabling continuous estimation of mental effort and stress states.
Google researchers propose SensorFM, a foundation model trained on over 1 trillion minutes of unlabeled wearable data from 5 million people, which learns general physiological patterns and outperforms engineered features on 34 of 35 health prediction tasks.
Introduces Peak-Detector, a framework that uses instruction-tuned large language models for robust, cross-modal, and explainable peak detection in physiological signals like ECG, PPG, BCG, and BSG. The method transforms time-series data into a condensed 'peak-representation' format and is optimized via supervised fine-tuning followed by reinforcement learning with a multi-objective reward.
Introduces NormWear-2, a world model that encodes multivariate physiological signals and clinical interventions into a shared latent space, using chaos-theoretic balancing to improve long-horizon forecasting across daily life, point-of-care, and clinical settings.