BCG-FM, the first foundation model for ambient BCG, achieves 3.26-year MAE on biological age estimation and discriminates 15 health conditions using frozen embeddings from participant-level contrastive pretraining on the largest raw biosignal corpus reported.
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Miller, and Ian Shapiro
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OpenWatch provides the first open multimodal smartwatch gesture dataset and benchmark, with MixToken and NormWear-Lora methods reaching 90% F1-score using 223k parameters versus 66% for 136M-parameter foundation models.
A PPG foundation model pretrained via multimodal ECG/respiratory contrastive sample selection on ICU data improves performance on 14 of 15 downstream tasks including field-like data while using 3x fewer subjects.
AURORA is a representation learning framework that uses contextual orthogonalization and relational alignment to create disentangled, geometrically interpretable latent spaces in healthcare foundation models.
Wearable accelerometry, EDA, and temperature data from 9 students with profound autism, processed with fine-tuned foundation models, enables prediction of challenging behavior episodes up to 10 minutes in advance at AUC-ROC 0.78 in actual classroom sessions.
WavesFM uses hierarchical SSL to pretrain a segment encoder on short waveforms followed by a temporal encoder on multi-day sequences, outperforming prior methods on 58 tasks after training on over 12 million hours of data from hundreds of thousands of people.
Event-centric waveform foundation models are learned via self-supervised consistency on latent event structures and interactions, yielding improved performance and label efficiency over sequence-based baselines on physiological tasks.
Health foundation model embeddings contain an interpretable symbolic organization shared across modalities that supports cross-domain transfer without joint training.
Sonata is a small hybrid world model pre-trained to predict future IMU states that outperforms autoregressive baselines on clinical discrimination, fall-risk prediction, and cross-cohort transfer while fitting on-device wearables.
The work introduces uncertainty-aware foundation models for clinical data by learning set-valued patient representations that enforce consistency across partial observations and integrate multimodal self-supervised objectives.
WISTERIA learns robust clinical representations from noisy EHR labels by enforcing consistency across multiple weak supervision views plus ontology regularization.
Large Sensor Models trained on large-scale multimodal wearable data can provide a scalable, general framework for wearable AI by learning transferable representations across modalities and tasks.
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