Abstract
The supervision tasks of complex dynamic systems are usually characterized by dynamics, suddenness, timeliness and uncertainty, so it is necessary to keep human drivers at an appropriate mental workload (MWL) level to ensure that they make the best decisions, judgments and actions in the dynamic environment of the real world. This paper proposes and validates a quantitative assessment model for studying MWL in time-critical multitasking scenarios. The Multi-Attribute Task Battery II (MATB-II) is used as a multi-task platform in the experiment. The results show that the multiple linear regression model, which comprehensively considers human performance data and eye movement data, has better prediction performance compared with single data alone, and can predict the MWL level of different task scenarios, which provides a reference for switching control authority of human-machine system and alarm design of the system, and has goods application prospects.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.