The mental machine: Classifying mental workload state from unobtrusive heart rate-measures using machine learning

Roderic H.L. Hillege, Julia C. Lo, Christian P. Janssen, Nico Romeijn

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Downloads (Pure)

Abstract

This paper investigates whether mental workload can be classified in an operator setting using unobtrusive psychophysiological measures. Having reliable predictions of workload using unobtrusive sensors can be useful for adaptive instructional systems, as knowledge of a trainee’s workload can then be used to provide appropriate training level (not too hard, not too easy). Previous work has investigated automatic mental workload prediction using biophysical measures and machine learning, however less attention has been given to the level of physical obtrusiveness of the used measures. We therefore explore the use of color-, and infrared-spectrum cameras for remote photoplethysmography (rPPG) as physically unobtrusive measures. Sixteen expert train traffic operators participated in a railway human-in-the-loop simulator. We used two machine learning models (AdaBoost and Random Forests) to predict low-, medium- and high-mental workload levels based on heart rate features in a leave-one-out cross-validated design. Results show above chance classification for low- and high-mental workload states. Based on infrared-spectrum rPPG derived features, the AdaBoost machine learning model yielded the highest classification performance.

Original languageEnglish
Title of host publicationAdaptive Instructional Systems - 2nd International Conference, AIS 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
EditorsRobert A. Sottilare, Jessica Schwarz
PublisherSpringer Open
Pages330-349
Number of pages20
ISBN (Print)9783030507879
DOIs
Publication statusPublished - 2020
Event2nd International Conference on Adaptive Instructional Systems, AIS 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020 - Copenhagen, Denmark
Duration: 19 Jul 202024 Jul 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12214 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Adaptive Instructional Systems, AIS 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
CountryDenmark
CityCopenhagen
Period19/07/2024/07/20

Keywords

  • Adaptive Instructional Systems
  • Machine learning
  • Mental workload classification
  • Remote photoplethysmography

Fingerprint Dive into the research topics of 'The mental machine: Classifying mental workload state from unobtrusive heart rate-measures using machine learning'. Together they form a unique fingerprint.

  • Cite this

    Hillege, R. H. L., Lo, J. C., Janssen, C. P., & Romeijn, N. (2020). The mental machine: Classifying mental workload state from unobtrusive heart rate-measures using machine learning. In R. A. Sottilare, & J. Schwarz (Eds.), Adaptive Instructional Systems - 2nd International Conference, AIS 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings (pp. 330-349). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12214 LNCS). Springer Open. https://doi.org/10.1007/978-3-030-50788-6_24