A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Cognition Digital Twins for Personalized Information Systems of Smart Cities: Proof of Concept
Amid the rapid development of information communication technologies (ICTs), residents of future smart cities are expected to be exposed to unprecedented amounts of real-time information on a daily basis. The cognitive overload driven by an excess of complex information has become a potential issue. Nonetheless, standardized information systems are still widely used, despite individual differences in information intake. To set a foundation for the intelligent information systems of smart cities, this paper introduces methods and tools for a cognition-driven, personalized information system, which acknowledges individual differences in information preference and helps reduce the cognitive load in daily lives and at work. The proposed method includes the use of virtual reality (VR) to simulate complex tasks paired with the digital twin modeling of workers’ cognitive reactions to different information formats and contents in VR simulation. Collected data are then used to build a personal digital twins model of information-driven cognition, or Cog-DT. A human subject experiment was performed with a simulated industrial facility shutdown maintenance task as a proof of concept of Cog-DT. The latest neuroimaging technology and analysis methods were applied to model unique cognitive processes pertaining to information processing. Results indicate that cognitive activities driven by different information stimuli in the work context are distinguishable and modelable with Cog-DT methods and tools. This study is expected to contribute to digital twin literature by testing a human-centered, individual-level digital twin modeling method of cognitive activities. It also sets a preliminary foundation for developing personalized information systems for the smart cities of the future.
Cognition Digital Twins for Personalized Information Systems of Smart Cities: Proof of Concept
Amid the rapid development of information communication technologies (ICTs), residents of future smart cities are expected to be exposed to unprecedented amounts of real-time information on a daily basis. The cognitive overload driven by an excess of complex information has become a potential issue. Nonetheless, standardized information systems are still widely used, despite individual differences in information intake. To set a foundation for the intelligent information systems of smart cities, this paper introduces methods and tools for a cognition-driven, personalized information system, which acknowledges individual differences in information preference and helps reduce the cognitive load in daily lives and at work. The proposed method includes the use of virtual reality (VR) to simulate complex tasks paired with the digital twin modeling of workers’ cognitive reactions to different information formats and contents in VR simulation. Collected data are then used to build a personal digital twins model of information-driven cognition, or Cog-DT. A human subject experiment was performed with a simulated industrial facility shutdown maintenance task as a proof of concept of Cog-DT. The latest neuroimaging technology and analysis methods were applied to model unique cognitive processes pertaining to information processing. Results indicate that cognitive activities driven by different information stimuli in the work context are distinguishable and modelable with Cog-DT methods and tools. This study is expected to contribute to digital twin literature by testing a human-centered, individual-level digital twin modeling method of cognitive activities. It also sets a preliminary foundation for developing personalized information systems for the smart cities of the future.
Cognition Digital Twins for Personalized Information Systems of Smart Cities: Proof of Concept
Du, Jing (author) / Zhu, Qi (author) / Shi, Yangming (author) / Wang, Qi (author) / Lin, Yingzi (author) / Zhao, Daniel (author)
2019-12-10
Article (Journal)
Electronic Resource
Unknown
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