Comparison of Multiple Physiological Sensors to Classify Operator State in Adaptive Automation Systems

Abstract

Automating tasks alleviates operator resources to be delegated to other demands, but the cost is often situation awareness. In contrast, complete manual control of a system opens the door for greater human error. Therefore, an ideal situation would require the development of an adaptive system in which automation can be triggered based on performance of a particular task, time spent on the task, or perhaps physiological response. The latter pertains to the goal for this particular study. Electroencephalogram (EEG), electrocardiogram (ECG), and eye tracking measures were recorded during six multi-tasking scenarios to assess if any one single measure is best suited for future implementation as an automation invocation. EEG showed the greatest potential for that purpose.

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