Machine Learning Models for the Classification of Sleep Deprivation Induced Performance Impairment During a Psychomotor Vigilance Task Using Indices of Eye and Face Tracking

Matthew S Daley; David Gever; Hugo F Posada-Quintero; Youngsun Kong; Ki Chon; Jeffrey B Bolkhovsky

doi:10.3389/frai.2020.00017

Machine Learning Models for the Classification of Sleep Deprivation Induced Performance Impairment During a Psychomotor Vigilance Task Using Indices of Eye and Face Tracking

Front Artif Intell. 2020 Apr 7:3:17. doi: 10.3389/frai.2020.00017. eCollection 2020.

Authors

Matthew S Daley¹, David Gever¹, Hugo F Posada-Quintero², Youngsun Kong², Ki Chon², Jeffrey B Bolkhovsky¹

Affiliations

¹ Naval Submarine Medical Research Laboratory, Groton, CT, United States.
² Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States.

Abstract

High risk professions, such as pilots, police officers, and TSA agents, require sustained vigilance over long periods of time and/or under conditions of little sleep. This can lead to performance impairment in occupational tasks. Predicting impaired states before performance decrement manifests is critical to prevent costly and damaging mistakes. We hypothesize that machine learning models developed to analyze indices of eye and face tracking technologies can accurately predict impaired states. To test this we trained 12 types of machine learning algorithms using five methods of feature selection with indices of eye and face tracking to predict the performance of individual subjects during a psychomotor vigilance task completed at 2-h intervals during a 25-h sleep deprivation protocol. Our results show that (1) indices of eye and face tracking are sensitive to physiological and behavioral changes concomitant with impairment; (2) methods of feature selection heavily influence classification performance of machine learning algorithms; and (3) machine learning models using indices of eye and face tracking can correctly predict whether an individual's performance is "normal" or "impaired" with an accuracy up to 81.6%. These methods can be used to develop machine learning based systems intended to prevent operational mishaps due to sleep deprivation by predicting operator impairment, using indices of eye and face tracking.

Keywords: feature selection; genetic algorithm; machine learning; performance impairment; psychomotor vigilance task; sequential forward selection; sleep deprivation.