A performance-based mental workload identification method for special vehicle crews

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS

ACS Applied Bio Materials Pub Date : 2024-09-28 DOI:10.1016/j.physbeh.2024.114706

Mingyang Guo, Peiyan Duan, Xiaoping Jin, Qingyang Huang, Yuning Wei

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引用次数: 0

Abstract

Detecting the mental workload state of armored vehicle crews is of great significance for monitoring the driving state of the crew and improving comprehensive combat effectiveness. In this manuscript, we propose a performance-based mental workload identification method and carry out experimental validation to improve the accuracy of crew mental workload identification and realize the effective classification of mental workload. Based on the virtual simulation system of the special vehicle crew task, this manuscript selects 20 subjects for the mental workload experiment of special vehicle crews. The experiment collected NASA-TLX scale, EEG, eye-tracking data, and performance data. The results show that the mental workload of the crews fluctuates in the segmented tasks of complex operations in typical scenes of special vehicles. The method of determining mental workload using NASA-TLX generates label noise in classification, which is not suitable for special vehicle tasks. Performance-based mental workload identification method is able to recognize fluctuations in the crew's mental workload during segmented tasks. Performance-based and NASA-TXL-based methods were classified using linear discriminant analysis. The results show that the accuracy of the method based on performance is improved by 15.72 %. This manuscript found the NASA-TXL scale is not suitable for the complex tasks of special vehicles, and proposed a performance-based identification method that can help to categorize the mental workload states of special vehicle crews.

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基于性能的特种车辆乘员心理工作量识别方法。

检测装甲车辆乘员的脑力劳动负荷状态，对于监控乘员驾驶状态、提高综合战斗力具有重要意义。本稿提出了一种基于性能的心理工作量识别方法，并进行了实验验证，以提高乘员心理工作量识别的准确性，实现心理工作量的有效分类。基于特种车辆乘员任务虚拟仿真系统，本稿选取了20名受试者进行特种车辆乘员心理工作量实验。实验收集了 NASA-TLX 量表、脑电图、眼动跟踪数据和表现数据。结果表明，在特种车辆典型场景复杂操作的分段任务中，乘员的脑力劳动负荷是波动的。使用 NASA-TLX 确定心理工作量的方法在分类时会产生标签噪声，不适合特种车辆任务。基于性能的脑力劳动负荷识别方法能够识别乘员在分段任务中的脑力劳动负荷波动。利用线性判别分析对基于性能的方法和基于 NASA-TXL 的方法进行了分类。结果表明，基于性能的方法准确率提高了 15.72%。本手稿发现 NASA-TXL 量表不适合特种车辆的复杂任务，并提出了一种基于性能的识别方法，有助于对特种车辆乘员的心理工作量状态进行分类。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Bio Materials Chemistry-Chemistry (all)

CiteScore

9.40

自引率

2.10%

发文量

464