  Sleep is among the most important factors affecting one's daily performance,
well-being, and life quality. Nevertheless, it became possible to measure it in
daily life in an unobtrusive manner with wearable devices. Rather than camera
recordings and extraction of the state from the images, wrist-worn devices can
measure directly via accelerometer, heart rate, and heart rate variability
sensors. Some measured features can be as follows: time to bed, time out of
bed, bedtime duration, minutes to fall asleep, and minutes after wake-up. There
are several studies in the literature regarding sleep quality and stage
prediction. However, they use only wearable data to predict or focus on the
sleep stage. In this study, we use the NetHealth dataset, which is collected
from 698 college students' via wearables, as well as surveys. Recently, there
has been an advancement in deep learning algorithms, and they generally perform
better than conventional machine learning techniques. Among them, Convolutional
Neural Networks (CNN) have high performances. Thus, in this study, we apply
different CNN architectures that have already performed well in the human
activity recognition domain and compare their results. We also apply Random
Forest (RF) since it performs best among the conventional methods. In future
studies, we will compare them with other deep learning algorithms.
