This paper presents an approach based on Bayesian Networks to estimate the workload of operators. The models take as inputs the entropy of different number of physiological features, as well as a cognitive feature (reaction time to a secondary task). They output the workload variation of subjects involved in successive tasks demanding different levels of cognitive resources. The performances of the classifiers are discussed in term of two criteria to be jointly optimized: the diversity, i.e. the ability of the model to perform on different subjects, and the accuracy, i.e., how close from the (subjectively estimated) workload level the model prediction is.


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    Titel :

    Bayesian Network classifiers inferring workload from physiological features: Compared performance


    Beteiligte:
    Besson, P. (Autor:in) / Dousset, E. (Autor:in) / Bourdin, C. (Autor:in) / Bringoux, L. (Autor:in) / Marqueste, T. (Autor:in) / Mestre, D. R. (Autor:in) / Vercher, J. L. (Autor:in)


    Erscheinungsdatum :

    2012-06-01


    Format / Umfang :

    553488 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Bayesian Network Classifiers Inferring Workload from Physiological Features: Compared Performance

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