Sleep-stage classification is where a lot of consumer sleep tech quietly cheats. A quick tour of the honest-vs-hand-wavy spectrum, from someone who has stared at too many hypnograms.
The ground truth nobody ships
The clinical gold standard is polysomnography: EEG, EOG, EMG, scored in 30-second epochs by a human. No wrist wearable or phone reproduces that. Anything claiming "REM detected" from a single accelerometer is estimating, and the honest ones say so.
What each signal can and cannot do
- Actigraphy (motion): decent at sleep-vs-wake, weak at staging. It infers deep sleep from stillness, which fails for anyone who lies still while awake.
- Heart rate / HRV: adds real signal - autonomic tone shifts across stages - but the mapping is probabilistic, not deterministic.
- Audio: the underused one. It cannot stage sleep on its own, but it directly captures events other sensors only infer: snoring intensity, breathing pauses, movement, environmental disturbance. Events, not stages.
The design lesson
Do not oversell staging. Report what you actually measured (an audio event at 03:14, a long quiet stretch, a cluster of arousals) and be honest about what you inferred. Users trust "here is what happened" more than "your REM was 18%" - especially when the second number is a model's guess.
The clinical background on why the audible events matter, particularly for apnea screening, is in this overview of snoring vs. sleep apnea.
Curious how others handle the honesty-vs-simplicity tradeoff in sleep UIs - what does your app claim vs. measure?
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