David Kaber
Professor | University of Florida
Gainesville, FL, UNITED STATES
David Kaber uses empirical methods to describe human performance and applies heuristic and mathematical methods to model responses.
Biography
David Kaber is currently the dean’s leadership professor. David’s primary area of research interest is human-systems engineering with a focus on human-automaton interaction, including design and analysis for situation awareness in complex human in-the-loop systems. Domains of study for his research have included physical work systems, industrial safety systems, robotic systems, transportation systems and healthcare. David is a fellow of HFES, IEEE, and IISE.
Areas of Expertise (8)
System Safety Analysis Methods
Measuring and Modeling Situation Awareness and Cognitive Workload
Aviation Human Factors and Cockpit Display Design
Adaptive Automation
Human-in-the-loop Systems
Human-Robot Interaction
Virtual Reality Training Simulation Design and Testing
Ergonomics-Related Risk Factor Identification and Measurement in Physical Work Tasks
Media Appearances (2)
The ISE Journey at UF
UF Department of Industrial & Systems Engineering tv
2023-04-05
Over the past five years, our department has realized tremendous growth, with 19 new faculty in various methods and applications of ISE. Our objectives have been to create a technically broad department to promote the skill set and competitiveness of our students in the job market, and to elevate our research enterprise for a greater contribution to ISE science, as well as the impact on human work and society.
UF Industrial & Systems Engineering's Undergraduate Program
UF Department of Industrial & Systems Engineering tv
2018-11-01
Explore the Department of Industrial & Systems Engineering's undergraduate program at the University of Florida and hear first-hand testimonials from current students.
Articles (3)
Assessing workload in using electromyography (EMG)-based prostheses
ErgonomicsJunho Park, et. al
2023-06-12
Using prosthetic devices requires a substantial cognitive workload. This study investigated classification models for assessing cognitive workload in electromyography (EMG)-based prosthetic devices with various types of input features including eye-tracking measures, task performance, and cognitive performance model (CPM) outcomes. Features selection algorithm, hyperparameter tuning with grid search, and k-fold cross-validation were applied to select the most important features and find the optimal models.
Age differences in driver visual behavior and vehicle control when driving with in-vehicle and on-road deliveries of service logo signs
International Journal of Industrial ErgonomicsJing Feng, et. al
2023-01-07
With the advances in vehicle technologies, more information is communicated in real-time to the driver via an in-vehicle interface. In-vehicle messaging may deliver safety-related information such as warnings as well as non-safety-related information such as an upcoming lodging place. While much research has focused on the design of messaging safety-related information, little is known about the best practice in in-vehicle messaging of non-safety-related information.
Effect of levels of automation and vehicle control format on driver performance and attention allocation
International Journal of Industrial ErgonomicsYulin Deng, David B. Kaber
2022-11-02
Recent technology advancements have enabled the development of vehicle automation and novel control formats for accessing vehicle functionality. However, the implications of these developments on driver performance remains unclear. Using a driving simulation experiment, this study compared driver hazard negotiation performance under adaptive cruise control (ACC) and manual driving. The study also investigated the effect of different vehicle control interfaces.
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