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The Driving-based Adaptive Research Testbed (DART) allows for live connection with various physiological sensors to create a driving scenario that adapts in real time based on a multitude of variables.
The Driving-based Adaptive Research Testbed (DART) allows for live connection with various physiological sensors to create a driving scenario that adapts in real time based on a multitude of variables.
Summary
The Driving-based Adaptive Research Testbed (DART) utilizes physiological signals, in addition to users behavioral responses, to develop a rule-based augmentation protocol that adapts task difficulty in real time in an effort to facilitate and enhance cognitive performance.
Features and Design
Software
Unreal Engine 5
Lab streaming layer
Python
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Hardware
Logitech driving wheel and pedals
Numeric keypad
Contributions
Team:
Ramisha Knight, Ph.D.
Quintin Oliver
Summer Rebensky, Ph.D.
Shawn Turk
Outcomes and Additional Information
DART uses physiological sensors including heart rate and fNIRS.
The rule-based augmentation protocol adapts task difficulty in real time, based upon: (a) auditory task performance, (b) driving task performance, and (c) physiological stress states.
As an adaptive training support, visual indicators are added or removed based on performance and physiological metrics. Survey metrics can also be integrated in the absence of sensor data.
Research with the testbed could lead to flow-based training which can consistently challenge trainees, expedite training timelines, and improve training.
Publications related to this effort
Rebensky, S., Knight, R., Fussell, S., & Stalker, L. (in development). Dynamic cognitive workload for military training: A series of simulated experiments towards adaptive training. Cognitive research and principles, special issue in military applications.
Stalker, W., Rebensky, S., Turk, S., Perry, S., & McGee, S., (2024). From lab to battlefield: Exploring the relationship between military and basic science tasks for measuring competencies. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) Orlando, FL
Stalker, W., Rebensky, S., Knight, R., Perry, S., & Bennett, W. (2024). The power of performance and physiological state: Approaches and considerations in adaptive game-based simulations. HCII International 2024 Adaptive Instructional Systems Conference.
Rebensky, S., Stalker, W., Knight, R., & Perry, S. (2023). Using physiological and performance metrics for adaptive game-based simulation: Approaches for military research. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) Orlando, FL
Rebensky, S., Perry, S., & Bennett, W. (2022). How, when, and what to adapt: Effective adaptive training through game-based development technology. 2022 Interservice/Industry Training, Simulation, and Education Conference. Education Subcommittee Best Paper Award.
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