A model-driven approach to the a priori estimation of operator workload

Kbaier Ben Ismail, Dhouha and Grivard, Olivier (2015). A model-driven approach to the a priori estimation of operator workload. In: 2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision, IEEE pp. 1–7.

DOI: https://doi.org/10.1109/COGSIMA.2015.7107967

Abstract

The measurement, or at least the estimation, of the operators' workload is an important aspect of usage-oriented design of professional systems. Various approaches to the a priori measurement of workload have been proposed. They can be classified into three categories: performance measures, physiological measures and subjective measures. Subjective methods have many advantages such as high `face validity', ease of application and low cost. However, they have failed to take into account some important parameters that can heavily impact the workload estimation: experience, skills, level of training, etc. This paper addresses a new method for the estimation of workload, based on the following parameters: task complexity, time load, experience, knowledge and abilities compared to task requirements. Although these parameters have been identified in the literature as being important, they have not been deeply analyzed. The authors describe their approach and propose to use mental representations of human entities, human roles, tasks, knowledge and abilities. The approach is illustrated on an airborne maritime surveillance usecase, in the context of the French Medusa project.

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