연락처 : PO 이메일 : jason.ledesma@yahoo.com

Modern sleep tracking rings utilize an integrated system of physiological detectors and AI-driven analysis to distinguish between the three primary sleep stages—deep, REM, and light—by capturing dynamic biological signals that shift systematically throughout your sleep cycles. Compared to clinical sleep labs, which require multiple wired sensors and professional supervision, these rings rely on comfortable, unobtrusive hardware to gather continuous data while you sleep—enabling accurate, at-home sleep analysis without disrupting your natural rhythm.

The foundational sensor system in these devices is PPG (photoplethysmographic) sensing, which applies infrared and green light diodes to measure changes in blood volume beneath the skin. As your body transitions between sleep stages, your cardiovascular dynamics shift in recognizable ways: deep sleep is marked by a steady, low heart rate, while REM sleep resembles wakefulness in heart rate variability. The ring analyzes these micro-variations over time to estimate your current sleep phase.

Additionally, a 3D motion sensor tracks micro-movements and restlessness throughout the night. Deep sleep is characterized by minimal motor activity, whereas light sleep involves frequent repositioning. REM sleep often manifests as brief muscle twitches, even though skeletal muscle atonia is active. By integrating motion metrics with PPG trends, and sometimes adding thermal sensing, the ring’s adaptive AI model makes statistically grounded predictions of your sleep phase.

This detection framework is grounded in extensive clinical sleep studies that have mapped physiological signatures to each sleep ring stage. Researchers have calibrated wearable outputs to gold-standard sleep metrics, enabling manufacturers to train deep learning models that extract sleep-stage features from imperfect signals. These models are refined through massive global datasets, leading to ongoing optimization of stage classification.

While sleep rings cannot match the clinical fidelity of polysomnography, they provide reliable trend data over weeks and months. Users can identify how habits influence their rest—such as how caffeine delays REM onset—and make informed behavioral changes. The real value proposition lies not in a precise snapshot of one sleep cycle, but in the long-term patterns they reveal, helping users take control of their sleep wellness.