Evaluation of mental workload during automobile driving using one-class support vector machine with eye movement data
Evaluation of mental workload during automobile driving using one-class support vector machine with eye movement data
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DOI：
10.1016/j.apergo.2020.103201
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发表时间：
2020-11-01
影响因子：
3.2
通讯作者：
Sakamoto, Jiro
中科院分区：
文献类型：
--
作者：
Chihara, Takanori;Kobayashi, Fumihiro;Sakamoto, Jiro
研究一类支持向量机(OCSVM)异常检测方法在汽车驾驶脑力负荷(MWL)评估中的应用。12名学生(6名男性和6名女性)参加了测试。参与者使用驾驶模拟器(DS)和用于控制他们的MWL的N-back任务执行驾驶任务。N-Back任务有五个难度等级，从“无”到“3个Back”。在DS驾驶过程中测量眼睛和头部的运动。结果表明，凝视角的标准差、眼球旋转角的标准差、头部运动的共享率和眨眼频率与任务难度显著相关。OCSVM的决策边界可以检测出95%的高MWL状态(即3-back状态)。另外，随着任务难度从0-back到3-back，决策边界距离的绝对值也随之增加。
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."