STEM与日常科技·英语30篇（1）

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睡眠分期与智能手环如何估算

Human sleep consists of four distinct stages: N1, N2, N3 (deep sleep), and REM, each with unique brainwave and body patterns.
Smart bands estimate these stages using movement sensors, heart rate variability, and skin temperature changes—not direct brain scans.
During deep sleep, the body moves very little, heart rate slows, and breathing becomes steady—signals bands detect reliably.
REM sleep shows more micro-movements and faster heart rate fluctuations, which algorithms interpret as dreaming activity.
Because bands lack EEG electrodes, their accuracy is lower than clinical polysomnography but improves with long-term user calibration.
Wearing the band consistently helps it learn individual patterns, like how quickly you fall asleep or how long you stay in light sleep.
Some models now add blood oxygen monitoring to flag potential disruptions like sleep apnea events during the night.
Researchers validate band data against lab studies, finding best agreement for total sleep time and wake-after-sleep onset metrics.
Despite limitations, these devices raise awareness about sleep hygiene and encourage healthier bedtime routines.
Future versions may integrate ambient sound or breathing rhythm analysis to refine stage detection without added hardware.