  This paper addresses the problem of the human operator cognitive workload
estimation while controlling a robot. Being capable of assessing, in real-time,
the operator's workload could help prevent calamitous events from occurring.
This workload estimation could enable an AI to make informed decisions to
assist or advise the operator, in an advanced human-robot interaction
framework. We propose a method, named Fessonia, for real-time cognitive
workload estimation from multiple parameters of an operator's driving behaviour
via the use of behavioural entropy. Fessonia is comprised of: a method to
calculate the entropy (i.e. unpredictability) of the operator driving behaviour
profile; the Driver Profile Update algorithm which adapts the entropy
calculations to the evolving driving profile of individual operators; and a
Warning And Indication System that uses workload estimations to issue advice to
the operator. Fessonia is evaluated in a robot teleoperation scenario that
incorporated cognitively demanding secondary tasks to induce varying degrees of
workload. The results demonstrate the ability of Fessonia to estimate different
levels of imposed workload. Additionally, it is demonstrated that our approach
is able to detect and adapt to the evolving driving profile of the different
operators. Lastly, based on data obtained, a decrease in entropy is observed
when a warning indication is issued, suggesting a more attentive approach
focused on the primary navigation task.
