So while participating in a distributed team training exercise, a human operator is suddenly challenged by increasing task demands. As the workload rapidly escalates, so does the operator's heart rate and blood pressure. Can these and other body signals be leveraged by complex control systems to respond and adapt to operators' state to help improve performance?

Aptima, which applies expertise in how humans, think, learn, and behave, is addressing this challenge by integrating physiological measures into its A-Measure performance measurement software.

A-Measure, the industry's first full-performance measurement solution, is used in live and simulated environments to create, collect, and visualise individual and team performance in real time.

With the addition of sensor data for neural activity, heart rate, galvanic skin response, and other body signals, A-Measure can assess the internal states of human operators, such as cognitive workload and decision-making ability, producing a richer picture of performance.

"If we want machines to improve the function of humans engaged in complex missions, the systems need to better intuit or sense their state and what they need to accomplish," said Dr. Alexandra Geyer, a senior cognitive scientist and expert in neurophysiology at Aptima.  "By themselves, raw heart rate, brain activity, or eye movement data don't tell much. But by applying algorithms to these data, we can compute meaningful measures that can be used to optimise the operator's performance."

As data from sensors monitoring an operator's body signals, such as brain, heart, muscle-activity, eye movement, respiration, or galvanic skin response, are fed into A-Measure, they're computed into meaningful measures in real time by an automatic performance measurement application, the PM Engine. When combined with A-Measure's system- and observer-based measures, this results in a comprehensive assessment of an operator's state and mission performance.

"Besides mitigating performance bottlenecks in live operational systems, integrating the physiologicalmeasures with other measures holds great promise for increasing training effectiveness and learning efficiency, " added Emily Wiese, Aptima's A-Measure product manager. 

"Training system developers will be able to use these measurements to devise training environments that are tailored to each trainee's individual learning needs, resulting in better comprehension and retention of learned material."

Additional enhancements to A-Measure will also be demonstrated at I/ITSEC. These include optimisation of its SPOTLITE observer-based data collection software as a mobile app for use on an Android Tablet, and measurement cueing capabilities.

The measurement cueing engine allows a user to define criteria, such as ratings in the PM Engine, to cue the user to collect additional data, and then direct those measure cues to occur in real-time as the training exercise is occurring. For example, if the PM Engine receives a certain measure result, it might cue a SPOTLITE observer-instructor to collect a set of pre-defined measures by automatically adding them to the SPOTLITE measurement tree and notifying the user of their addition.