Abstract
We present a new method to measure workload that offers several advantages. First, it uses non-intrusive means: cameras and a mouse. Second, the workload is measured in real-time. Third, the setup is comparably cheap: the cameras and sensors are off-the-shelf components. Fourth, we go beyond measuring performance and demonstrate that just using such measures does not suffice to measure workload. Fifth, by using a Bayesian Network to assess the workload from the various manifesting measures the model adapts itself to the individual user as well as to a particular task. Sixth, we use a cognitive computational model to explain the cognitive mechanisms that cause the differences in workload and performance.
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