Cross-device Zero-shot Label Transfer via Alignment of Time Series Foundation Model Embeddings
This addresses the scalability issue of labeling wearable data for health monitoring, though it is incremental as it builds on existing foundation model and alignment techniques.
The paper tackles the problem of transferring medically validated labels from clinical actigraphy data to consumer wearables like the Apple Watch without paired data, achieving this by aligning embeddings from time-series foundation models to enable cross-device label transfer.
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.