A Real-time Assessment System for Cognitive Performances of Armored Vehicle Crews Based upon the Multi-source Information Fusion

Abstract

In the human-machine system of armored vehicles, the cognitive performance state of crews is crucial for the personnel security and combat efficiency. The purpose of this research was to establish a real-time assessment system for cognitive performances of armored vehicle crews, consisting of the data input module, data processing module, data visualization module, and scheduling module. Forty subjects were recruited to cooperate and execute the cross-platform strike task in a virtual simulation platform. The physiological data and operation behavior data was collected during the experiment process. To realize the accurate classification of different cognitive performance states, a multi-source information fusion algorithm was developed based on linear discriminant analysis (LDA) and D-S evidence theory, which included the information collection module, the feature extraction module, and the information fusion module. The results indicated that there existed a significant correlation between the extractive feature indicators (i.e., EOG, ECG, and task performance indicators) and the cognitive performance. The recognition accuracy and the data efficiency of the proposed assessment system were 91.25% and 96.69% respectively by using the complementarity of different evidences, which were higher than the others using partial information sources. This study can provide a reference for the comprehensive assessment of cognitive performance of human operators in military and industrial domains.

Keywords:

• Armored vehicle
• cognitive performance
• human-machine system
• information fusion
• real-time evaluation

Acknowledgements

The authors are grateful to the anonymous reviewers those offered constructive comments and suggestions on improving this article.

Consent form

Informed consent was obtained from all participants involved in this research.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Ethics statement

The study was conducted in accordance with the Declaration of Helsinki and approved by College of Engineering, China Agricultural University.

Data availability statement

The data are not publicly available due to confidential reasons.

Additional information

Funding

This work was supported by the China North Industries Group Corporation Limited under Grant 2020-JCJQ-JJ-046-01.

Notes on contributors

Qingyang Huang

Qingyang Huang is a PhD student majoring in automotive engineering in College of Engineering, China Agricultural University, Beijing, China. His research interests involve human factors, ergonomics, and human-computer interaction.

Mingyang Guo

Mingyang Guo is a master student majoring in automotive engineering in College of Engineering, China Agricultural University, Beijing, China. His research interests are ergonomics, and human-computer interaction.

Yuning Wei

Yuning Wei is a master student majoring in automotive engineering in College of Engineering, China Agricultural University, Beijing, China. Her research interests include industrial design and human-machine interface design and evaluation.

Houjie Sun

Houjie Sun is a master student majoring in mechanical engineering in College of Engineering, China Agricultural University, Beijing, China. His research interests are ergonomics and human-computer interaction.

Jingyuan Zhang

Jingyuan Zhang is a master student majoring in automotive engineering in College of Engineering, China Agricultural University, Beijing, China. Her research interests are industrial design and human-machine interface design and evaluation.

Fang Xie

Fang Xie is a researcher in China North Vehicle Research Institute, Beijing, China. Her research interests include human factors, and human-computer interaction.

Xiaoping Jin

Xiaoping Jin received her PhD from College of Engineering, China Agricultural University, Beijing, China. Her research fields involve ergonomics, automotive intelligent safety assistance technology, digital design and virtual simulation, and human-machine interface design and evaluation.