xMAE pretrains biosignal representations via masked cross-modal reconstruction of temporally ordered signals like ECG and PPG, outperforming baselines on 15 of 19 downstream tasks including cardiovascular prediction and sleep staging.
In The Thirteenth International Conference on Learning Representations, ICLR 2025
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A cross-modal masked autoencoder creates reusable biosignal fingerprints that match or exceed specialist models on seven cardiovascular tasks using only single-modality input.
Membership inference attacks can detect whether specific ECG data participated in pretraining self-supervised foundation encoders, with leakage strongest in small cohorts and contrastive models.
PRISM-CTG is the first large-scale foundation model for cardiotocography that uses multi-view self-supervised learning on unlabeled data to learn transferable representations, outperforming baselines on seven downstream tasks with external validation.
The survey organizes foundation models for sensor-based HAR into a lifecycle taxonomy and identifies three trajectories: HAR-specific models from scratch, adaptation of general time-series models, and integration with large language models.
PulseLM aggregates PPG data from 16 sources into 1M segments and 2.5M QA pairs for 12 tasks, providing a standardized benchmark for PPG-text multimodal learning.
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PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning
PulseLM aggregates PPG data from 16 sources into 1M segments and 2.5M QA pairs for 12 tasks, providing a standardized benchmark for PPG-text multimodal learning.