Mental workload prediction model based on information entropy. (16th December 2016)

• Record Type: Journal Article
• Authors: Li, Xiang; Fang, Weining; Zhou, Yingwei
• Is Part Of: Computer assisted surgery. Volume 21(2016)Supplement 1
• Page Start: 116
• Page End: 123
• Publication Date: 2016-12-16
• Subjects: Information entropy -- mental workload -- amount of information -- ergonomics; Computer-assisted surgery -- Periodicals
• DOI: 10.1080/24699322.2016.1240298
• Languages: English
• ISSNs: 2469-9322

Abstract

Abstract: This paper introduces the concept of information entropy in studying mental workloads to predict the mental workload of an urban railway dispatcher and thereby ensure safe rail system operation. This study combines factors that can influence mental workload, including visual behaviors required for dispatchers to obtain information, information display duration, and the amount of information in order to establish a comprehensive mental workload prediction model. Experimental monitoring tasks were carried out on a simulation dispatch interface platform to verify the model's validity. Three assessment methods (task performance assessment, subjective assessment, and physiological assessment) were adopted to measure the mental workload levels of dispatchers under different task conditions. The results demonstrate that the model's theoretical prediction value significantly correlates with the various experimental results, thereby verifying the model validity and indicating that it can be used to predict the mental workload for different dispatch tasks, to provide a reference for work performance evaluation, and in designing optimized dispatch display interfaces.