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.”

这项研究的目的是调查一类支持向量机（OCSVM）的异常检测方法对评估汽车驾驶过程中的精神工作量（MWL）的有用性。十二名学生（六男六女）参加了这次活动。参与者使用驾驶模拟器（DS）和用于控制其MWL的N后卫任务执行驾驶任务。N后卫任务有五个难度级别，从“无”到“ 3后卫”。在DS驾驶过程中测量了眼睛和头部的运动。结果表明，注视角度的标准偏差（SD），眼球旋转角度的标准差（SD），头部运动的分享率和眨眼频率与任务难度有显着相关性。OCSVM的决策边界可以检测到95％的高MWL状态（即“ 3-back”状态）。此外，