A Study on ML-Based Sleep Score Model Using Lifelog Data

Abstract

The rate of people suffering from sleep disorders has been continuously increasing in recent years, such that interest in healthy sleep is also naturally increasing. Although there are many health-care industries and services related to sleep, specific and objective evaluation of sleep habits is still lacking. Most of the sleep scores presented in wearable-based sleep health services are calculated based only on the sleep stage ratio, which is not sufficient for studies considering the sleep dimension. In addition, most score generation techniques use weighted expert evaluation models, which are often selected based on experience instead of objective weights. Therefore, this study proposes an objective daily sleep habit score calculation method that considers various sleep factors based on user sleep data and gait data collected from wearable devices. A credit rating model built as a logistic regression model is adapted to generate sleep habit scores for good and bad sleep. Ensemble machine learning is designed to generate sleep habit scores for the intermediate sleep remainder. The sleep habit score and evaluation model of this study are expected to be in demand not only in health-care and health-service applications but also in the financial and insurance sectors.

Keywords

• sleep
• health
• sleep score
• sleep dimension
• lifelog data
• stacking ensemble regression