an actual fighter jet training aircraft that is available at OPL. The goal of this physiological-based assessment of trainee performance is to enable improvements in training and to enhance training effectiveness, based on the real-time performance feedback provided by analysis of the recorded physiological data. “The purpose of QTEA is to understand tasks and skills attached to training device fidelity,” Melissa Walwanis-Nelson explained. She is a senior research psychologist in the Training & Usability in Simulation-based Tools Laboratory, a division of the NAWCTSD Training & Human Performance Research and Development Branch. “We want to understand whether or not an individual responds similarly to a specific task or stimulus that they are experiencing in a simulator as they are in an airplane. We have a jet instrumented up as well that we can make comparisons with across a range of tasks that one would perform. That way, we can make better decisions as to what we train in the simulator versus what gets trained in the jet.” Ultimately, the QTEA work will help researchers better understand how a person’s brain works in response to learning, Walwanis-Nelson said, Then training could be adapted real-time to what a person really needs to learn at any given point in time. The pace of training sessions could also be more geared to the individual’s needs as well, she added. Much analysis and validation work of the data recorded by the QTEA system will need to be accomplished before this
point in the project can be achieved, though, she cautioned. The first practical application of QTEA research will be when simulators at Pensacola Naval Air Station in Florida are outfitted with QTEA sensors and recorders towards the end of 2009 and early 2010. This facility is the primary center for Navy flight crew training. Here, the QTEA equipment will first be employed with the Navy Aviation Survival Training Program and its Reduced Oxygen Breathing Device (ROBD), which trains aviators to recognize the signs of hypoxia. The physiological data provided by QTEA equipment will help crews understand when they are becoming hypoxic, Walwanis-Nelson said. The data recorded will be used in after-action reviews for the aircrew trainees, she explained. Another early use of the QTEA equipment will also be used for training to recognize spatial disorientation while pilots are conducting simulator-based training. According to Tom Schnell, QTEA principal investigator for the OPL and research pilot, the basic idea of QTEA is to give the trainer a real-time picture of the performance of a trainee based on human physiological and cognitive data recorded via the system’s sensors. This additional information provides the instructor with another tool to objectively assess student performance. Typically, instructors don’t have the technology available to measure students’ performance physiologically, instead using observable measurements such as on speed or on time, he said.
“Now we can go beyond that by having additional metrics that are derived from the level of engagement at the cognitive level,” Schnell pointed out. “QTEA can indicate how close to perfect a pilot’s eye movement scan of the aircraft’s instruments are – looking at the right thing at the right time – for example.” Schnell described the OPL-developed CATS as the engine inside the QTEA system that connects with all of the physiological sensors, pulling in all of their data and generating a pilot workload estimate in real-time. “So as the pilot is getting more and more cognitively loaded, CATS provides a real number estimate of how hard the person is engaged in the sense of the cognitive workload, and this number is then available over HLA for use by the instructor or the instructor operating station software,” Schnell said. “What we want to do in Phase II is to use that workload number to adjust the training scenario in real time in such a way that the pilot is optimally stimulated throughout the mission exercise, with the hypothesis that learning is not as effective when you are being bored. When your workload estimate points out that you are 100 percent loaded, learning is probably not taking place as well. Somewhere in the upper higher range will be the optimum level for learning that occurs in maximum performance. And that is the point at which we want to drive the scenario to keep the pilot at that level.” The QTEA researchers also want to use the physiological data to establish benchmarks, or “gold standards,” to
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