High-quality, medically validated labels exist for clinical actigraphy data but not for ubiquitous consumer wearables like the Apple Watch. Manually labeling wearables data is expensive and doesn't scale. This paper offers a novel framework that transfers valuable labels from a source domain (e.g., actigraphy) to a target domain (e.g., Apple Watch) without requiring paired data. Instead of working with raw time-series signals, we project both domains into a shared latent embedding space using time-series foundation models (TSFMs) and develop a new framework to align the cross-device representations. Our method, Adversarial Alignment of TSFM Embeddings forces the distributions of source and target embeddings to align within this space, facilitating label transfer across device type.
@article{arxiv.2509.06966,
title = {Cross-device Zero-shot Label Transfer via Alignment of Time Series Foundation Model Embeddings},
author = {Neal G. Ravindra and Arijit Sehanobish},
journal= {arXiv preprint arXiv:2509.06966},
year = {2025}
}
Comments
5 pages, 3 figures, 1 table. tl;dr: Adversarial alignment of Time-Series Foundation Model (TSFM) embeddings enables transfer of high-quality clinical labels from medical-grade to consumer-grade wearables, enabling zero-shot prediction of gestational age without requiring paired data