Real time modeling of the cognitive load of an Urban Search And Rescue robot operator
conference paper
Urban Search And Rescue (USAR) robots are used to find and save victims in the wake of disasters such as earthquakes or terrorist attacks. The operators of these robots are affected by high cognitive load; this hinders effective robot usage. This paper presents a cognitive task load model for real-time monitoring and, subsequently, balancing of workload on three factors that affect operator performance and cognitive load: time occupied, level of information processing and number of task switches. To test an implementation of the model, five participants drove a shape-shifting USAR robot, accumulating over 16 hours of driving time in the course of 485 USAR missions with varying objectives and difficulty. An accuracy of69% was obtained for discrimination between low and high cognitive load; higher accuracy was measured for discrimination between extreme cognitive loads. This demonstrates that such a model can contribute, in a non-invasive manner, to estimating an operator’s cognitive state. Several ways to further improve accuracy are discussed, based on additional experimental results.
Topics
TNO Identifier
521646
Publisher
IEEE
Source title
2014 RO-MAN: The 23rd IEEE International Symposium on Robot and Human Interactive Communication
Editor(s)
Loureiro, R.
Nagai, Y.
Sabanovic, S.
Tanaka, F.
Alissandrakis, A.
Tapus, A
Nagai, Y.
Sabanovic, S.
Tanaka, F.
Alissandrakis, A.
Tapus, A
Pages
874-879
Files
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