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Assessment of Mental Workload: A Comparison of Machine Learning Methods and Subjective Assessment Techniques

  • Karim Moustafa
  • Saturnino Luz
  • Luca Longo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 726)

Abstract

Mental workload (MWL) measurement is a complex multidisciplinary research field. In the last 50 years of research endeavour, MWL measurement has mainly produced theory-driven models. Some of the reasons for justifying this trend includes the omnipresent uncertainty about how to define the construct of MWL and the limited use of data-driven research methodologies. This work presents novel research focused on the investigation of the capability of a selection of supervised Machine Learning (ML) classification techniques to produce data-driven computational models of MWL for the prediction of objective performance. These are then compared to two state-of-the-art subjective techniques for the assessment of MWL, namely the NASA Task Load Index and the Workload Profile, through an analysis of their concurrent and convergent validity. Findings show that the data-driven models generally tend to outperform the two baseline selected techniques.

Keywords

Support Vector Machine Machine Learn Baseline Model Concurrent Validity Minority Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of ComputingDublin Institute of TechnologyDublinIreland
  2. 2.Usher Institute of Population Health Sciences and InformaticsThe University of EdinburghEdinburghScotland
  3. 3.The ADAPT CentreDublinIreland

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