Citation

Chihara T, Kobayashi F, Sakamoto J. Appl. Ergon. 2020; 89: e103201.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.apergo.2020.103201

PMID

32658775

Abstract

The aim of this study is to investigate the usefulness of the anomaly detection method by one-class support vector machine (OCSVM) for the evaluation of mental workload (MWL) during automobile driving. Twelve students (six males and six females) participated. The participants performed driving tasks with a driving simulator (DS) and the N-back task that was used to control their MWL. The N-back task had five difficulty levels from "none" to "3-back." Eye and head movements were measured during the DS driving.

RESULTS showed that the standard deviation (SD) of the gaze angle, SD of eyeball rotation angle, share rate of head movement, and blink frequency had significant correlations with the task difficulty. The decision boundary of OCSVM could detect 95% of high MWL state (i.e., "3-back" state). In addition, the absolute value of the distance from the decision boundary increased with the task difficulty from "0-back" to "3-back."

Language: en

Keywords

Machine learning; Distracted driving; Cognitive capacity