SBIR-STTR Award

This project will produce a low-cost, high-performance, minimally invasive eyetracker. This instrument will be relatively inexpensive, easy to use, produce very precise data, and be minimally invasive. It will work on subjects that wear glasses or contact lenses, and will be supported on conventional PC platforms. It is intended for use in developing advanced training methods which use the cognitive insight provided from eye tracking to construct training methods and procedures that measure student performance and prescribe optimal training experience. It is also intended for use in systems that adapt to user workload and cognitive awareness. Phase I of this project will demonstrate the feasibility of the approach through extensive modeling and analysis of the system. Experiments will be performed to quantify these relationships and to demonstrate key concepts. The results of these experiments will be used to produce a formal design for the eyetracker. Phase II of this project will produce a fully developed prototype. Phase III will integrate the eyetracker into mainstream training products. The result of this project will significantly benefit both military and commercial applications. For the military it will enable the development of routine training techniques that use the cognitive glimpses provided through eye tracking to systematically and optimally provide training which assures cognitive readiness. In the commercial market it will support the development of products in such areas as advanced training, telemedicine, remote machine operation, and video teleconferencing.

Keywords:
Eyetracking, Foveal Region, Student State, Human Visual System, Perception, Image Processing

This project will produce a Multitask Training-Game Engine Component (MT-GEC) that can be used for developing Vision Centered-Multitask Training Environments (VC-MTEs). The game engine component will support routine development of scalable, 3D training applications that use metrics based on workload, interaction events, and simulation state to focus training activities at measured deficiencies. The computed measures will capture the subtle strategies that are used by expert performers in multitasking applications and enable the development of high precision training strategies. During the final year of this project, a training application will be developed using the game engine component to establish the applicability of this technology to military training. Phase III is expected to further extend this technology and apply it to mainstream training applications. During this commercialization phase, the technology will be licensed to game and training system developers that are building applications that require cognitive feedback. This phase is also expected to develop training applications for cognitive researchers that can be used to explore the methods that experts use to meet the computational demands of complex, information-intense, multitask decision-making.

Keywords:
DECISION-MAKING, GAME ENGINES, TRAINING, MULTITASKING, SITUATION AWARENESS, WORKLOAD, INTERRUPTIONS