Adaptive Human–Computer Interface Design for Supervision Task Based on User Attention and System State

Abstract

The rapid development of computer information technology has increased the complexity of the human–computer interface, especially in supervision tasks. It is difficult for users to pay attention to multiple information and make decisions in real-time, which needs higher requirements for the design of a human–computer interface. The research designs a novel adaptive human–computer interface using the Adaptive Interface Design (AID) framework: Combining the real-time user attention and system state as the input layer of AID; Building a hybrid entropy attention allocation model as the decision layer of AID; Adjusting the real-time saliency of the information in the interface as the output layer of AID. The comparative experiments in supervision tasks are conducted to test the performance of the users in the designed adaptive interface and traditional interface. The results show that the designed adaptive human–computer interface can effectively improve the supervisors’ performance and reduce the risk of system failure.

Keywords:

• Human–computer interface
• adaptive interface design
• supervision task

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Our research was supported by Beijing Natural Science Foundation (L201024), National Natural Science Foundation of China (62203039) and The 2nd Batch of 2020 MOE of PRC Industry-University Collaborative Education Program (202002SJ08, Kingfar-CES “Human Factors and Ergonomics” Program).

Notes on contributors

Haifeng Bao

Haifeng Bao is an assistant professor at State Key Laboratory of Rail Traffic Control and Safety at Beijing Jiaotong University. He received BS and PhD degree from Beijing Jiaotong University, China. He focuses on rail traffic human factors and ergonomics.

Weining Fang

Weining Fang is a professor at State Key Laboratory of Rail Traffic Control and Safety at Beijing Jiaotong University. He received a BS in industrial design and an MS and PhD degree in mechanical engineering from Chongqing University, China. He focus on interaction design and modeling for rail transit.

Beiyuan Guo

Beiyuan Guo received a BS and an MS degree from Chongqing University, Chongqing, China and a PhD degree from Beijing Jiaotong University, China. He is currently a professor at State Key Laboratory of Rail Traffic Control and Safety at Beijing Jiaotong University.

Hanzhao Qiu

Hanzhao Qiu received BS from School of Business Jiangnan University, China and an MS and PhD degree in industrial engineering from Beijing Jiaotong University, China. He is currently an assistant professor at State Key Laboratory of Rail Traffic Control and Safety at Beijing Jiaotong University.