arXiv (All)
open access
Phenomenon-Graph JEPA: Label-Efficient Representation Learning for Contactless Cardiorespiratory Sensing
Millimeter-wave (mmWave) radar and RGB-D cameras can record cardiac and respiratory waveforms continuously and without contact, but labeled recordings remain scarce because every label requires a supervised acquisition session. Self-supervised pretraining can exploit the unlabeled signals, yet contr
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Pre- and Post-Exercise Monitoring of Physiological Skin States in Sportsmen Using 77 GHz FMCW Radar and Wavelet Scattering Transform#1048903
- An intelligent garment for long COVID-19 real-time monitoring#159229
- A Self-Paced Walk Test for Individual Calibration of Heart Rate to Energy Expenditure#234361
- DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals#1108975
- BreathSense: A Two-Stage Digital Framework for Student Stress Monitoring Using Personalized Breath-VOC Thresholding and In-the-Wild Validation.#315338
Record · ID 1117640
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive