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基于时间线分析的飞行员心理工作量预测。

Pilots' mental workload prediction based on timeline analysis.

机构信息

School of Aeronautics Science and Engineering, Beihang University, Beijing, 100191, China.

China Academy of Electronics and Information Technology, Beijing, 100041, China.

出版信息

Technol Health Care. 2020;28(S1):207-216. doi: 10.3233/THC-209021.

Abstract

BACKGROUND

The aircraft cockpit is a highly intensive human-computer interaction system, and its design directly affects flight safety.

OBJECTIVE

To optimize the display interface design in complex flight tasks, the present study aimed to propose a dynamic conceptual framework and a timeline task analysis method for the quantization of the dynamic time effect of mental workload and the influencing factors of task types in the mental workload prediction model.

METHODS

The multi-factor mental workload prediction model based on attention resource allocation was integrated to establish the dynamic prediction model of mental workload. The ergonomics simulation experiment was carried out by recording the data on the performance of embedded subtasks, National Aeronautics and Space Administration-Task Load Index (NASA-TLX) subjective evaluation, and eye tracking.

RESULTS

The results indicated that the prediction model had a good prediction accuracy and effectiveness under different simulated interfaces and complex tasks, and the real-time monitoring of pilots' mental workload state was realized.

CONCLUSION

In conclusion, the prediction model and the experimental method could be applied to avoid the overload of the pilot throughout the flight phase by optimizing the display interface and adjusting the flight task.

摘要

背景

飞机驾驶舱是一个高度集中的人机交互系统,其设计直接影响飞行安全。

目的

为了优化复杂飞行任务中的显示界面设计,本研究旨在提出一种动态概念框架和时间线任务分析方法,用于量化心理工作量的动态时间效应,并对心理工作量预测模型中的任务类型影响因素进行量化。

方法

通过整合基于注意资源分配的多因素心理工作量预测模型,建立心理工作量的动态预测模型。通过记录嵌入式子任务的性能、国家航空航天局任务负荷指数(NASA-TLX)主观评估和眼动追踪数据,进行工效学模拟实验。

结果

结果表明,在不同的模拟界面和复杂任务下,预测模型具有良好的预测精度和效果,实现了对飞行员心理工作量状态的实时监测。

结论

总之,该预测模型和实验方法可以通过优化显示界面和调整飞行任务来避免飞行员在整个飞行阶段的过载。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1b1/7369057/f700e7a42685/thc-28-thc209021-g001.jpg

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