analyses are continuing to examine the relationships between sleep apneas, real-time physiology measures, and participant responses to workload transitions.
Increased computing power makes detailed physiological data collection possible, supporting capabilities for adaptive automation in complex HMI tasks. Therefore, the primary limitations of linking real- time physiological measures to operator performance capabilities are the complexities of the physiology- performance relationships themselves. Links between physiology and task activity are identifiable, but there are subtle multifactor relationships that depend heavily on the type of task performance and the dynamics of task and workload transitions. With sufficient measurement and event resolutions, SCAMPI studies were able to use as little as one minute of physiological data to predict significant variance in future task performance from I to 10 minutes later. Important concerns resulting from SCAMPI projects include the preeminence of workload transitions and dynamic cognitive resource allocations, rather than vigilance decrements, as issues for further study. Future system development must emphasize relating vectors of physiology data to vectors of component task performance, based on the goals and success criteria of the tasks themselves.
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