Pattern recognition methods: a novel analysis for the pupillographic sleepiness test

Abstract

The aim of this paper is to improve the information gained by the most commonly applied fit-for-duty sleepiness test (Pupillographic Sleepiness test, PST) by using pattern recognition approaches. The pupil diameter based sleepiness detection is enriched by several new features and machine learning methods. Using all newly computed pupil diameter features we achieved on the two-class detection problem (moderate sleepiness vs. high sleepiness) an accuracy of 83.03% on participant-dependent data with a Random Forest classifier. This result suggested that the PST-standard feature set should be enriched by the here proposed enlarged feature set.