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

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.