Application of different CNN based models to predict sleep quality

What is it about?

Sleep is among the most important factors affecting daily performance, well-being, and quality of life. Nevertheless, it became possible to measure it in daily life unobtrusively with wearable devices. Rather than camera recordings and state extraction 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, collected from 698 college students via wearables, as well as surveys.

Featured Image

Photo by Bastien Jaillot on Unsplash

Photo by Bastien Jaillot on Unsplash

Why is it important?

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.