Abstract
Daytime sleepiness is not only the cause of productivity decline and accidents, but also an important metric of health risks. Despite its importance, the long-term quantitative analysis of sleepiness in daily living has hardly been done due to
time and effort required for the continuous tracking
of sleepiness. Although a number of sleepiness detection technologies have been proposed, most of
them focused only on driver’s drowsiness. In this
paper, we present the first step towards the continuous sleepiness tracking in daily living situations.
We explore a methodology for predicting subjective
sleepiness levels utilizing respiration and acceleration data obtained from a novel wearable sensor.
A class imbalance handling technique and hidden
Markov model are combined with supervised classifiers to overcome the difficulties in learning from
an imbalanced and time series dataset. We evaluate
the performance of our models through a comprehensive experiment