สำนักงานเทศบาลตำบลนครชุม l ยึดมั่นธรรมาภิบาล บริการเพื่อประชาชน

How Sleep Rings Detect Light, Deep, And REM Sleep

โดย : Selena เมื่อวันที่ : พฤหัสบดี ที่ 4 เดือน ธันวาคม พ.ศ.2568

Contemporary wearable <a href="https://jklyc.com/">sleep ring</a> monitors utilize a fusion of sensors and machine learning algorithms to track the progression of the three primary sleep stages_deep, REM, and light_by monitoring subtle physiological changes that occur predictably throughout your sleep cycles. Compared to clinical sleep labs, which require laboratory-grade instrumentation, these rings rely on comfortable, unobtrusive hardware to gather continuous data while you sleep_enabling reliable longitudinal sleep tracking without disrupting your natural rhythm. The primary detection method in these devices is photoplethysmography (PPG), which uses embedded LEDs and light sensors to track pulsatile blood flow through capillaries. As your body transitions between sleep stages, your heart rate and blood pressure shift in recognizable ways: during deep sleep, your pulse slows and stabilizes, while during REM sleep, heart rate becomes irregular and elevated. The ring analyzes these micro-variations over time to infer your sleep architecture. <img src="http://www.imageafter.com/image.php?image=b19nature_characters_humanoids043.jpg&dl=1" style="max-width:420px;float:left;padding:10px 10px 10px 0px;border:0px;"> Additionally, a 3D motion sensor tracks micro-movements and restlessness throughout the night. Deep sleep is characterized by minimal motor activity, whereas light sleep includes noticeable body adjustments. REM is accompanied by intermittent myoclonic movements, even though skeletal muscle atonia is active. By integrating motion metrics with PPG trends, and sometimes incorporating respiratory rate estimates, the ring_s adaptive AI model makes statistically grounded predictions of your sleep phase. This detection framework is grounded in decades of peer-reviewed sleep science that have mapped physiological signatures to each sleep stage. Researchers have aligned ring-derived signals with polysomnography data, enabling manufacturers to optimize classification algorithms that learn individual sleep profiles across populations. These models are continuously updated using anonymized user data, leading to incremental gains in precision. While sleep rings cannot match the clinical fidelity of polysomnography, they provide a consistent, longitudinal view of your sleep. Users can identify how habits influence their rest_such as how alcohol reduces deep sleep_and adjust routines for better rest. The real value proposition lies not in the exact percentages reported each night, but in the cumulative insights that guide lasting change, helping users build healthier sleep routines. 

เข้าชม : 3