Objective-Analytical Measures of Workload – the Third Pillar of Workload Triangulation?
The ability to assess operator workload is important for dynamically allocating tasks in a way that allows efficient and effective goal completion. For over fifty years, human factors professionals have relied upon self-reported measures of workload. However, these subjective-empirical measures have limited use for real-time applications because they are often collected only at the completion of the activity. In contrast, objective-empirical measurements of workload, such as physiological data, can be recorded continuously, and provide frequently-updated information over the course of a trial. Linking the low-sample-rate subjective-empirical measurement to the high-sample-rate objective-empirical measurements poses a significant challenge. While the series of objective-empirical measurements could be down–sampled or averaged over a longer time period to match the subjective-empirical sample rate, this process discards potentially relevant information, and may produce meaningless values for certain types of physiological data. This paper demonstrates the technique of using an objective-analytical measurement produced by mathematical models of workload to bridge the gap between subjective-empirical and objective-empirical measures. As a proof of concept, we predicted operator workload from physiological data using VACP, an objective-analytical measure, which was validated against NASA-TLX scores. Strong predictive results pave the way to use the objective-empirical measures in real-time augmentation (such as dynamic task allocation) to improve operator performance.
KeywordsWorkload measurement Machine learning VACP IMPRINT
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