Human mental workload (MWL) has gained importance, in the last few decades, as a fundamental design concept in human--‐computer interaction (HCI). At an early system design phase, designers require some explicit model to predict the mental workload imposed by their technologies on end--‐users so that alternative system designs can be evaluated. MWL can be intuitively defined as the amount of mental work necessary for a person to complete a task over a given period of time. However, this is a simplistic view because MWL is a multifaceted and complex construct with a plethora of ad--‐hoc definitions. Although measuring MWL has advantages in interaction/interface design, its formalisation as an operational/computational construct has not sufficiently been addressed. Many ad--‐hoc models are present in the literature and these have been subjectively employed by researchers thereby limiting their application in different contexts and making comparison across different models difficult. This project is focused on the formalisation of the construct of human mental workload (MWL) employing knowledge discovery, data mining (KDD) and machine learning (ML) a subfield of Artificial Intelligence (AI). The research assumption is that these approaches may have a positive impact in MWL representation and assessment. In turn, MWL could be captured, analysed and measured in ways that increase its understanding allowing and promoting its use for practical activities and for designing the interaction of human--‐computer and technological devices.
First class in computer science
Scholarship not available. Fees & Materials to be paid by the student.
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