Abstract
A cognitive systems engineering evaluation of an imagery analysis system was conducted to capture baseline performance and workload and compare it to performance with advanced filtering capabilities. Experienced Imagery Analysts searched for and annotated targets of interest in full-motion video in Army- relevant scenarios. Measures of performance included percent of primary targets found, time to find primary target, total targets found, and interactions with the system (via mouse clicks). Performance metrics were augmented with continuous physiological and behavioral measurements in order to capture more accurate cognitive state fluctuations during human-system interaction. The findings suggest that in time-pressured situations, analysts were able to identify more targets with the advanced filter capabilities than in the baseline condition. The findings were used to suggest specific design changes to address workflow deficiencies. The study also developed and implemented a multi-aspect approach to estimate operator functional state during system evaluation.
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