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

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Title: A Real-time Assessment System for Cognitive Performances of Armored Vehicle Crews Based upon the Multi-source Information Fusion.
Authors: Huang, Qingyang (AUTHOR), Guo, Mingyang (AUTHOR), Wei, Yuning (AUTHOR), Sun, Houjie (AUTHOR), Zhang, Jingyuan (AUTHOR), Xie, Fang (AUTHOR), Jin, Xiaoping (AUTHOR)
Source: International Journal of Human-Computer Interaction. Jan2025, Vol. 41 Issue 2, p932-950. 19p.
Subjects: Das-Naglieri Cognitive Assessment System, Fisher discriminant analysis, Armored vehicles, Cognitive ability, Human-machine systems
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. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Label: Title
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  Data: A Real-time Assessment System for Cognitive Performances of Armored Vehicle Crews Based upon the Multi-source Information Fusion.
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  Data: <searchLink fieldCode="AR" term="%22Huang%2C+Qingyang%22">Huang, Qingyang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Mingyang%22">Guo, Mingyang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Yuning%22">Wei, Yuning</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Houjie%22">Sun, Houjie</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jingyuan%22">Zhang, Jingyuan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Fang%22">Xie, Fang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jin%2C+Xiaoping%22">Jin, Xiaoping</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Jan2025, Vol. 41 Issue 2, p932-950. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Das-Naglieri+Cognitive+Assessment+System%22">Das-Naglieri Cognitive Assessment System</searchLink><br /><searchLink fieldCode="DE" term="%22Fisher+discriminant+analysis%22">Fisher discriminant analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Armored+vehicles%22">Armored vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+ability%22">Cognitive ability</searchLink><br /><searchLink fieldCode="DE" term="%22Human-machine+systems%22">Human-machine systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10447318.2024.2306439
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      – SubjectFull: Fisher discriminant analysis
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              Text: Jan2025
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