  Sleep is a fundamental physiological process that is essential for sustaining
a healthy body and mind. The gold standard for clinical sleep monitoring is
polysomnography(PSG), based on which sleep can be categorized into five stages,
including wake/rapid eye movement sleep (REM sleep)/Non-REM sleep 1
(N1)/Non-REM sleep 2 (N2)/Non-REM sleep 3 (N3). However, PSG is expensive,
burdensome, and not suitable for daily use. For long-term sleep monitoring,
ubiquitous sensing may be a solution. Most recently, cardiac and movement
sensing has become popular in classifying three-stage sleep, since both
modalities can be easily acquired from research-grade or consumer-grade devices
(e.g., Apple Watch). However, how best to fuse the data for the greatest
accuracy remains an open question. In this work, we comprehensively studied
deep learning (DL)-based advanced fusion techniques consisting of three fusion
strategies alongside three fusion methods for three-stage sleep classification
based on two publicly available datasets. Experimental results demonstrate
important evidence that three-stage sleep can be reliably classified by fusing
cardiac/movement sensing modalities, which may potentially become a practical
tool to conduct large-scale sleep stage assessment studies or long-term
self-tracking on sleep. To accelerate the progression of sleep research in the
ubiquitous/wearable computing community, we made this project open source, and
the code can be found at: https://github.com/bzhai/Ubi-SleepNet.
