面向复杂飞行任务的脑力负荷多维综合评估模型

卫宗敏

doi:10.13700/j.bh.1001-5965.2019.0452

面向复杂飞行任务的脑力负荷多维综合评估模型

doi: 10.13700/j.bh.1001-5965.2019.0452

卫宗敏^,

中国民航管理干部学院航空安全管理系, 北京 100102

基金项目:

国家“973”计划 2010CB734104

国家自然科学基金 61672076

国防科技创新特区项目 18-H863-01-ZT-005-033-01

民航安全能力建设项目 DFS20190402

详细信息

作者简介:
卫宗敏男, 博士, 讲师。主要研究方向:航空安全、适航技术、脑科学与人工智能、舱室设计与评估

通讯作者:
卫宗敏. E-mail:weizongmin8411@163.com

中图分类号: V857.1;B842.1
计量
- 文章访问数: 787
- HTML全文浏览量: 31
- PDF下载量: 181
- 被引次数: 0
出版历程
- 收稿日期: 2019-08-22
- 录用日期: 2019-08-30
- 网络出版日期: 2020-07-20

A multi-dimensional comprehensive evaluation model of mental workload for complex flight missions

WEI Zongmin^,

Department of Aviation Safety Management, Civil Aviation Management Institute of China, Beijing 100102, China

Funds:

National Basic Research Program of China 2010CB734104

National Natural Science Foundation of China 61672076

National High-tech Research and Development Program of China 18-H863-01-ZT-005-033-01

Civil Aviation Safety Capacity Building Project DFS20190402

More Information

Corresponding author: WEI Zongmin. E-mail:weizongmin8411@163.com

摘要

摘要:
针对多显示界面多飞行任务状态下的脑力负荷评价问题，设计了飞行仪表监控、飞行数字计算及飞行雷达探测3种不同类型的飞行任务，综合采用多种不同测评方法在飞行试验平台上开展脑力负荷实验测量和综合评价模型研究。实验结果表明：随着飞行任务类型的增多，NASA任务负荷指数（NASA-TLX）的主观评价分值显著增高；飞行正确探测率逐渐下降，反应时间显著延长；事件相关电位（ERP）测量技术中的P3a的峰值（在Fz电极处）逐步降低，心电（ECG）测量技术中的SDNN指标的数值逐步降低，眼电（EOG）测量技术中的眨眼次数没有显著变化。在此基础上，基于贝叶斯判别分析方法，建立了面向复杂飞行任务的脑力负荷多维综合评估模型，并将该综合评估模型与基于单一指标、双指标、三指标、四指标的模型进行了比较，结果显示：所提多维综合评估模型对于多显示界面多飞行任务中的显示界面脑力任务设计的等级分类预测准确率最高，其平均分类预测准确率为82.22%。所提多维综合评估模型为大型复杂系统中显示界面脑力任务设计提供了有效的量化方法和科学依据，有助于歼击机和运输类飞机设计人员优化显示界面脑力任务设计，并为相关型号飞机显示系统的适航审定工作提供新的验证工具。
- 显示界面 /
- 脑力负荷 /
- 多飞行任务 /
- 综合评估模型 /
- 适航审定
Abstract:
To solve the problems of mental workload assessment in multiple flight tasks of the aircraft cockpit multi-display interfaces, we design three different types of flight missions of the multi-display interfaces, i.e. flight monitor, flight calculation, and radar detection, to systematically develop the experimental measurement and the theoretical modeling of the mental workload via the conjunctive use of many kinds of measuring technique. Our experimental results reveal that, with increasing flight mission modes, the changes are:the subjective assessment scores of NASA-Task Load Index (NASA-TLX) increase significantly, the accuracy rate of the flight operation decreases gradually, and the response time becomes obviously longer; the value of the P3a component index in the Event-Related Potential(ERP) measurement technique at Fz electrode reduces gradually, the value of SDNN index in the Electrocardiogram (ECG) measurement also decreases gradually, and no obvious change in the number of blinks in the Electrooculogram (EOG) measurement is further confirmed. Based on the Bayesian discriminant analysis method, a multi-dimensional comprehensive evaluation model of mental workload for complex flight tasks was established, and the comprehensive evaluation model was compared with models based on a single indicator, dual indicators, three indicators, and four indicators. The results showed that the five-index model founded by the Bayesian-Fisher discrimination and classification method shows a much higher accuracy rate for the level discrimination and prediction results of mental workload in comparison with other index models. Its average discrimination accuracy rate is 82.22%. Obviously, This model provides an effective quantitative method and scientific basis for the display interface mental task design in large and complex systems, and helps fighter and transportation aircraft designers to optimize the display interface mental task design, but also provide a unique compliance verification tool for the airworthiness certification of flight deck display interface.
- display interface /
- mental workload /
- multi-flight missions /
- comprehensive evaluation model /
- airworthiness certification

HTML全文

图 1 飞行仪表监控任务界面

Figure 1. Fight instrument monitoring task interface

下载: 全尺寸图片幻灯片

图 2 飞行数字计算任务界面

Figure 2. Fight data calculation task interface

下载: 全尺寸图片幻灯片

图 3 飞行雷达探测任务界面

Figure 3. Fight radar information detection interface

下载: 全尺寸图片幻灯片

表 1 绩效指标的测量值

Table 1. Measured values of performance indexes

任务复杂水平	正确探测率/%	反应时间/ms
单任务	97.90±1.15	842.55±52.64
双任务	79.87±9.45	965.14±97.51
多任务	80.25±10.39	1 008.09±66.58

下载: 导出CSV

表 2 主观指标的测量值

Table 2. Measured values of subjective indexes

任务复杂水平	主观评价分值
单任务	61.60±10.18
双任务	71.88±7.77
多任务	78.96±7.42

下载: 导出CSV

表 3 生理指标的测量值

Table 3. Measured values of physiological indexes

任务复杂水平	MMN/μV (偏差刺激)	P3a/μV (偏差刺激)	MMN/μV (新异刺激)	P3a/μV (新异刺激)	SDNN	眨眼次数
单任务	-2.00±2.10	2.74±3.30	-4.73±2.05	3.64±2.76	49.33±14.28	107.93±56.64
双任务	-2.19±2.63	2.14±4.66	-2.71±3.81	5.53±2.68	45.07±12.02	105.06±39.25
多任务	-2.58±2.84	3.09±2.75	-2.89±3.39	6.82±3.62	39.53±10.64	101.66±46.77

下载: 导出CSV

表 4 基于回代检验法的检验结果

Table 4. Validation results based on original validation method

被试编号	实际类别	预测类别	被试编号	实际类别	预测类别	被试编号	实际类别	预测类别
1	1	1	1	2	2	1	3	3
2	1	1	2	2	2	2	3	3
3	1	1	3	2	3	3	3	3
4	1	1	4	2	2	4	3	3
5	1	1	5	2	3	5	3	3
6	1	1	6	2	2	6	3	3
7	1	1	7	2	3	7	3	3
8	1	1	8	2	2	8	3	2
9	1	1	9	2	2	9	3	3
10	1	1	10	2	2	10	3	3
11	1	1	11	2	1	11	3	2
12	1	1	12	2	2	12	3	3
13	1	1	13	2	2	13	3	3
14	1	1	14	2	3	14	3	3
15	1	1	15	2	2	15	3	2
注：“1”表示低脑力负荷水平；“2”表示中脑力负荷水平；“3”表示高脑力负荷水平。

下载: 导出CSV

表 5 基于交叉检验法的检验结果

Table 5. Validation results based on cross validation method

被试编号	实际类别	预测类别	被试编号	实际类别	预测类别	被试编号	实际类别	预测类别
1	1	1	1	2	3	1	3	3
2	1	1	2	2	2	2	3	3
3	1	1	3	2	3	3	3	3
4	1	1	4	2	2	4	3	3
5	1	1	5	2	3	5	3	3
6	1	1	6	2	2	6	3	2
7	1	1	7	2	3	7	3	3
8	1	1	8	2	2	8	3	2
9	1	1	9	2	2	9	3	3
10	1	1	10	2	1	10	3	2
11	1	1	11	2	1	11	3	2
12	1	1	12	2	2	12	3	3
13	1	1	13	2	2	13	3	3
14	1	1	14	2	3	14	3	3
15	1	1	15	2	2	15	3	2
注：“1”表示低脑力负荷水平；“2”表示中脑力负荷水平；“3”表示高脑力负荷水平

下载: 导出CSV

表 6 单一测量指标评估与各类综合评估结果的比较

Table 6. Result comparison of single index assessment and multi-dimensional synthetic assessment

指标组合	评估指标	分类预测准确率/%
指标组合	评估指标	低	中	高	平均
单指标	NASA-TLX	66.67	53.33	66.67	62.22
	正确探测率	100.00	46.67	40.00	62.22
	反应时间	86.67	46.67	66.67	66.67
	SDNN	46.67	13.33	66.67	42.22
	P3a	53.33	26.67	53.33	44.44
双指标组合	NASA-TLX，正确探测率	100.00	66.67	66.67	77.78
	NASA-TLX，反应时间	86.67	53.33	80.00	73.33
	NASA-TLX，SDNN	80.00	40.00	66.67	62.22
	NASA-TLX，P3a	66.67	53.33	66.67	62.22
	正确探测率，反应时间	100.00	40.00	66.67	68.89
	正确探测率，SDNN	100.00	33.33	46.67	60.00
	正确探测率，P3a	100.00	53.33	53.33	68.89
	反应时间，SDNN	86.67	46.67	80.00	71.11
	反应时间，P3a	93.33	26.67	46.67	55.56
	SDNN，P3a	60.00	46.67	40.00	48.89
三指标组合	NASA-TLX，正确探测率，反应时间	100.00	66.67	66.67	77.78
	NASA-TLX，正确探测率，SDNN	100.00	66.67	66.67	77.78
	NASA-TLX，正确探测率，P3a	100.00	66.67	66.67	77.78
	NASA-TLX，反应时间，SDNN	80.00	53.33	73.33	68.89
	NASA-TLX，反应时间，P3a	93.33	46.67	66.67	68.89
	NASA-TLX，SDNN，P3a	80.00	40.00	66.67	62.22
	正确探测率，反应时间，SDNN	100.00	46.67	66.67	71.11
	正确探测率，反应时间，P3a	100.00	40.00	60.00	66.67
	正确探测率，SDNN，P3a	100.00	53.33	40.00	64.44
	反应时间，SDNN，P3a	93.33	46.67	53.33	64.44
四指标组合	NASA-TLX，正确探测率，SDNN，P3a	100.00	66.67	66.67	77.78
	NASA-TLX，正确探测率，反应时间，SDNN	100.00	60.00	73.33	77.78
	NASA-TLX，正确探测率，反应时间，P3a	100.00	66.67	66.67	77.78
	NASA-TLX，反应时间，SDNN，P3a	86.67	60.00	73.33	73.33
	正确探测率，反应时间，SDNN，P3a	100.00	53.33	60.00	71.11
五指标组合	NASA-TLX，正确探测率，反应时间，SDNN，P3a	100.00	66.67	80.00	82.22

下载: 导出CSV

参考文献(16)

[1]	GUO X C, XIONG D Q, LIU Q F, et al. Experimental study on display format of target range for HUD of aircraft[C]//International Conference on Man-Machine-Environment System Engineering.Berlin: Springer, 2018: 509-514.
[2]	许为.自动化飞机驾驶舱中人-自动化系统交互作用的心理学研究[J].心理科学, 2003, 26(3):523-524. doi: 10.3969/j.issn.1671-6981.2003.03.040 XU W.The psychological research of the interfaces between flight crews and modern flight deck systems[J].Journal of Psychological Science, 2003, 26(3):523-524(in Chinese). doi: 10.3969/j.issn.1671-6981.2003.03.040
[3]	许为, 陈勇.从驾驶舱设计和适航来减少由设计引发的飞行员人为差错的挑战和途径[J].民用飞机设计与研究, 2014(3):5-11. doi: 10.3969/j.issn.1674-9804.2014.03.002 XU W, CHEN Y.Approaches to reducing design-induced pilot error based on flight deck design and certification[J].Civil Aircraft Design & Research, 2014(3):5-11(in Chinese). doi: 10.3969/j.issn.1674-9804.2014.03.002
[4]	KANTOWITZ B H, CAMPBELL J L.Pilot workload and flight deck automation[M]//PARASURAMAN R, MOULOUA M.Automation and human performance:Theory and applications.Boca Raton:CRC Press, 2018:137-156.
[5]	BORGHINI G, ASTOLFI L, VECCHIATO G, et al.Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness[J].Neuroscience & Biobehavioral Reviews, 2014, 44:58-75. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=a5ebcc8557b7c9314a29b208c448591f
[6]	ORLANDI L, BROOKS B.Measuring mental workload and physiological reactions in marine pilots:Building bridges towards redlines of performance[J].Applied Ergonomics, 2018, 69:74-92. doi: 10.1016/j.apergo.2018.01.005
[7]	YOUNG M S, BROOKHUIS K A, WICKENS C D, et al.State of science:Mental workload in ergonomics[J].Ergonomics, 2015, 58(1):1-17. http://europepmc.org/abstract/med/25442818
[8]	卫宗敏, 完颜笑如, 庄达民.飞机座舱显示界面脑力负荷测量与评价[J].北京航空航天大学学报, 2014, 40(1):86-91. https://bhxb.buaa.edu.cn/CN/Y2014/V40/I1/86 WEI Z M, WANYAN X R, ZHUANG D M.Measurement and evaluation of mental workload for aircraft cockpit display interface[J].Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(1):86-91(in Chinese). https://bhxb.buaa.edu.cn/CN/Y2014/V40/I1/86
[9]	WEI Z M, ZHUANG D M, WANYAN X R, et al.A model for discrimination and prediction of mental workload of aircraft cockpit display interface[J].Chinese Journal of Aeronautics, 2014, 27(5):1070-1077. doi: 10.1016/j.cja.2014.09.002
[10]	陆崑, 卫宗敏, 庄达民, 等.飞机驾驶舱显示界面脑力负荷判别预测生理模型[J].北京航空航天大学学报, 2016, 42(4):685-693. doi: 10.13700/j.bh.1001-5965.2015.0254 LU K, WEI Z M, ZHUANG D M, et al.Integrated physiological model for mental workload assessment and prediction of aircraft flight deck display interface[J].Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(4):685-693(in Chinese). doi: 10.13700/j.bh.1001-5965.2015.0254
[11]	RYU K, MYUNG R.Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic[J].International Journal of Industrial Ergonomics, 2005, 35(11):991-1009. doi: 10.1016/j.ergon.2005.04.005
[12]	BORGHINI G, ARICÒ P, GRAZIANI I, et al.Quantitative assessment of the training improvement in a motor-cognitive task by using EEG, ECG and EOG signals[J].Brain Topography, 2016, 29(1):149-161. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=723fe7dfaea3e8ca59ca6268733e923b
[13]	HOGERVORST M A, BROUWER A M, VAN ERP J B F.Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload[J].Frontiers in Neuroscience, 2014, 8:322. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=Doaj000004597804
[14]	薛薇.SPSS统计分析方法及应用[M].3版.北京:电子工业出版社, 2013:298-299. XUE W.SPSS statistical analysis and application[M].3rd ed.Beijing:Publishing House of Electronics Industry, 2013:298-299(in Chinese).
[15]	HART S G, STAVELAND L E.Development of NASA-TLX (task load index):Results of empirical and theoretical research[J].Advances in Psychology, 1988, 52:139-183. doi: 10.1016/S0166-4115(08)62386-9
[16]	卫宗敏.飞机驾驶舱显示界面脑力负荷实验测量与数学建模[D].北京: 北京航空航天大学, 2014: 7-8. WEI Z M.The experimental measurement and mathematical modeling of mental workload of aircraft cockpit display interface[D].Beijing: Beihang University, 2014: 7-8(in Chinese).