Non-intrusive measurement of workload in real-time
Markus Guhe, Wenhui Liao, Zhiwei Zhu, Qiang Ji, Wayne D. Gray, Michael J. Schoelles · PsycEXTRA Dataset · 2005
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 us-ing such measures does not suffice to measure workload. Fifth, by using a Bayesian Net-work to assess the workload from the various manifesting measures the model adapts it-self to the individual user as well as to a particular task. Sixth, we use a cognitive compu-tational model to explain the cognitive mechanisms that cause the differences in work-load and performance.