| Author Name | Affiliation | | Kun Yang | Key Laboratory of Civil Aircraft Airworthiness and Maintenance, Civil Aviation University of China, Tianjin 300300, China | | Cuanyan Feng | Key Laboratory of Civil Aircraft Airworthiness and Maintenance, Civil Aviation University of China, Tianjin 300300, China
School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China | | Jie Bai | Key Laboratory of Civil Aircraft Airworthiness and Maintenance, Civil Aviation University of China, Tianjin 300300, China |
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| Abstract: |
| Physiological measures indexed by fixation frequency and total fixation time, task performance based on n-back accuracy, subjective assessment based on NASA Task Load Index (NASA-TLX) were used to measure the Mental Workload (MW) in different levels which were induced by vision-related flight task combined with auditory cognitive load. 16 healthy novice pilots were recruited to complete a monitoring task based on Head-Up Display (HUD) and an auditory n-back task which was used to manipulate the Mental Workload Level (MWL) in flight simulation environment. In our experiment, fixation frequency, average saccade time, blink rate and average pupil diameter were sensitive to MW. What’s more, a comprehensive assessment method of pilot mental workload based on various measures was advocated. At last, a Fisher projection function based on the Fisher discrimination method and a three-level discriminate model established by the Bayes discrimination method were built up, the original validation and cross-validation methods of both models were 97.92% and 95.83% respectively, which could discriminate various Mental Workload Levels (MWLs) ideally. |
| Key words: mental workload fixation frequency bayes discrimination method |
| DOI：10.11916/j.issn.1005-9113.16124 |
| Clc Number:V328 |
| Document Code::A |
| Fund: |
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| Descriptions in Chinese: |
| 飞行员不同脑力负荷水平综合评估 杨坤1, 冯传宴1, 2, 白杰1 （1.中国民航大学 民用航空器适航与维修重点实验室，天津 300300； 2.北京航空航天大学 航空科学与工程学院，北京 100191） 创新点说明：提出了基于眼动、绩效、主观指标的飞行员脑力负荷综合评估模型。 研究目的： 通过呈现动态飞行模拟环境，记录并分析被试在不同脑力负荷水平下的主观、绩效及生理参数变化，选取多方面指标分析并进行评估建模。 研究方法： 选择16名中国民航大学新手模拟飞行员，基于飞行模拟仿真平台、眼动仪、飞行摇杆等开展实验研究。实验采用的眼动仪为Tobii TX300型，采样率300 Hz，记录注视、扫视、眨眼三方面的眼指标生理数据。实验开始前，每位实验对象均需在飞行模拟仿真平台上进行适应性飞行任务训练和N-back任务培训（要求达到一定的准确率）,整个培训时长约30 min。培训结束后，开始眼动校标，完成后开始正式实验。 被试均需要操纵飞行摇杆，依靠HUD完成包括起飞、爬升、巡航、进近、着陆在内的五边飞行任务，实验要求被试者在飞行模拟过程的巡航阶段完成监控HUD上的航向、高度、空速、飞行姿态等主要飞行参数的监视主任务，同时需要对呈现的N-back任务做出反应。对应的 N-back任务,依据N-back设置呈现出低、中、高三种脑力负荷水平，每次N-back任务时间为2 min，N-back任务间隔为30 s，整个实验过程在20 min左右。 结果： 实验结果表明，注视频率、注视总时间、眨眼率、平均瞳孔直径与脑力负荷变化联系紧密，N-back任务正确率和NASA-TLX得分能够很好地评估脑力负荷状态。 结论： 1） 针对飞行视觉相关任务结合听觉认知负荷，眼指标的平均注视时间、扫视频率、扫视总时间变化并不显著，注视频率、眨眼率和平均瞳孔直径与脑力负荷变化联系紧密； 2） 基于HUD的视觉相关任务结合听觉N-back认知任务可以很好地实现对高、低脑力负荷水平的划分，NASA-TLX得分、N-back任务正确率、注视频率、和瞳孔直径在高、低脑力负荷水平变化显著； 3） 相关性分析表明，随着脑力负荷的变化，眼指标的注视、扫视和眨眼指标之间具有一定的相关；其中，平均注视时间与扫视频率、扫视总时间高度负相关；扫视频率与扫视总时间高度正相关； 4）综合注视频率的生理评估、主观的NASA-TLX量表、任务绩效指标的N-back正确率进行分类建模，分别得到了基于Fisher判别的投影函数和分类结果散点图，分类结果比较理想，另外建立了基于Bayes判别的判别模型，其对飞行任务中视觉任务的脑力负荷水平的初始验证和交叉验证的判别和预测准确率分别为93.75%和87.50%，能够很好地实现对不同脑力负荷状态的判别。 关键词：脑力负荷；注视频率； Bayes判别法 |
