TY - GEN
T1 - The mental machine
T2 - 2nd International Conference on Adaptive Instructional Systems, AIS 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
AU - Hillege, Roderic H.L.
AU - Lo, Julia C.
AU - Janssen, Christian P.
AU - Romeijn, Nico
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Adaptive Instructional Systems
KW - Machine learning
KW - Mental workload classification
KW - Remote photoplethysmography
UR - http://www.scopus.com/inward/record.url?scp=85088750211&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50788-6_24
DO - 10.1007/978-3-030-50788-6_24
M3 - Conference contribution
AN - SCOPUS:85088750211
SN - 9783030507879
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 330
EP - 349
BT - Adaptive Instructional Systems - 2nd International Conference, AIS 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
A2 - Sottilare, Robert A.
A2 - Schwarz, Jessica
PB - SpringerOpen
Y2 - 19 July 2020 through 24 July 2020
ER -