Explainable sleep quality evaluation model using machine learning approach

Abstract

This research presents a scheme for explainable sleep quality evaluation utilizing the heart rate based sleep index. In the proposed model, the global covering rule induction of LERS (Learning from Examples based on Rough Sets) is used to generate rules associated with sleep quality status, such as ‘Bad,’ ‘Normal,’ and ‘Good.’ These rules are used to interpret the three sleep statuses. To show the applicability of the proposed scheme, we construct a sleep quality evaluation model based on sleep intraday time-series data collected from 280 factory and office workers with Fitbit fitness trackers. An evaluation of the proposed model was provided through statistical cross validation experiments.