The mental machine

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

Hillege, Roderic H.L. (ProRail; Ordina)
Lo, J.C. (TU Delft Organisation & Governance; ProRail)
Janssen, Christian P. (Universiteit Utrecht)
Romeijn, Nico (Universiteit Utrecht)

Sottilare, Robert A. (editor)
Schwarz, Jessica (editor)

2020

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.

Adaptive Instructional Systems
Machine learning
Mental workload classification
Remote photoplethysmography

http://resolver.tudelft.nl/uuid:83a1794c-52f8-420b-b588-eed019f78647

https://doi.org/10.1007/978-3-030-50788-6_24

SpringerOpen

9783030507879

Adaptive Instructional Systems - 2nd International Conference, AIS 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings

2nd International Conference on Adaptive Instructional Systems, AIS 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020, 2020-07-19 → 2020-07-24, Copenhagen, Denmark

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 12214 LNCS

Institutional Repository

conference paper

© 2020 Roderic H.L. Hillege, J.C. Lo, Christian P. Janssen, Nico Romeijn