FirsthandTech
arXiv — cs.AI preprintsInternational15 October 2026

HiPPT: Physiology-Grounded Wearable PPG Representation Learning via Multi-Scope Text Alignment

This is an official announcement record

Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.

arXiv:2610.14489v1 Announce Type: new Abstract: Reusable wearable PPG representations should capture local pulse morphology and broader physiological regularities. Existing pretraining objectives, however, usually apply supervision at the recording or pair level, overlooking that different concepts become reliable over different scopes of waveform evidence. We propose HiPPT, a physiology-grounded framework that matches each supervision source to its appropriate evidence scope. We construct a PPG-Text corpus of over 4 million wrist-worn recordings from 202K users, pairing recordings with wavefo
— arXiv — cs.AI preprints

More from arXiv — cs.AI preprints

This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.