Objective-analytical Measures of Workload - the Third Pillar of Workload Triangulation?
Document Type
Conference Proceeding
Publication Date
2015
Abstract
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.
Source Publication
Foundations of Augmented Cognition. AC 2015 (LNCS 9183)
Recommended Citation
Rusnock C., Borghetti B., McQuaid I. (2015) Objective-Analytical Measures of Workload – the Third Pillar of Workload Triangulation?. In: Schmorrow D.D., Fidopiastis C.M. (eds) Foundations of Augmented Cognition. AC 2015. Lecture Notes in Computer Science, vol 9183. Springer, Cham. https://doi.org/10.1007/978-3-319-20816-9_13
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