Smart alarm based on sleep stages prediction

A sleep inertia after waking up strongly affects the overall mental recovery after sleep. The sleep inertia depends not as much on overall sleep quality but also on the sleep stage in the waking up moment. The fix-time alarming system results in waking up at random sleep stage, which results in frequent sleep inertia. The widely used flow-time alarming systems based on motion detection (actigraphy) reduce but do not eliminate sleep inertia. Such systems do not wake up users in Deep sleep stage, but may instead wake them up in Wake, Light, or REM (Rapid Eyes Movement) stages. Moreover, frequent waking up in the REM stage results in serious psychological issues. We present a smartwatch alarm system that predicts sleep stages and thus produces an alarm call at an easy waking up moment with minimal sleep inertia effect. The sleep stages are predicted using an Encoder-Decoder Recurrent Neural Network model. The rationale of the prediction is that each sleep stages cycling pattern is a continuous quasi-periodic process. Experimental results from over 138 nocturnal sleep periods from 92 respondents show that our system provides 66-70% accuracy for Deep, Light, Wake, REM sleep stages and 71-77% accuracy for 2-classes (Deep/REM vs. Light/Wake stages) prediction classifications. The proposed alarm system wakes up the user at the moment when Easy Wake (Wake/Light) stage is the most probable.