Real-Time Workload Estimation Using Eye Tracking: A Bayesian Inference Approach-Reference-Cited by-同舟云学术

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Real-Time Workload Estimation Using Eye Tracking: A Bayesian Inference Approach

Published:2023-05-04 Issue: Volume: Page:1-16
ISSN:1044-7318
Container-title:International Journal of Human–Computer Interaction
language:en
Short-container-title:International Journal of Human–Computer Interaction

Author:

Luo Ruikun¹,Weng Yifan²,Jayakumar Paramsothy³,Brudnak Mark J.³,Paul Victor³,Desaraju Vishnu R.⁴,Stein Jeffrey L.²,Ersal Tulga²,Yang X. Jessie⁵^ORCID

Affiliation:

1. Robotics Institute, University of Michigan, Ann Arbor, MI, USA

2. Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA

3. U.S. Army Ground Vehicles System Center, Warren, MI, USA

4. Toyota Research Institute, Ann Arbor, MI, USA

5. Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA

Funder

Automotive Research Center

U.S. Army Ground Vehicle Systems Center

Publisher

Informa UK Limited

Subject

Computer Science Applications,Human-Computer Interaction,Human Factors and Ergonomics

Link

https://www.tandfonline.com/doi/pdf/10.1080/10447318.2023.2205274

Reference81 articles.

1. Pupillary Responses During Information Processing Vary with Scholastic Aptitude Test Scores

2. Using eye movement activity as a correlate of cognitive workload

3. Eye movement and pupillary response indices of mental workload during visual search of symbolic displays

4. Driver workload and eye blink duration

5. Cognitive workload estimation due to vague visual stimuli using saccadic eye movements

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Towards Enhanced Autonomous Driving Takeovers: Fuzzy Logic Perspective for Predicting Situational Awareness;Applied Sciences;2024-06-29

2. Using Multimodal Methods and Machine Learning to Recognize Mental Workload: Distinguishing Between Underload, Moderate Load, and Overload;International Journal of Human–Computer Interaction;2024-05-20

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