Bayesian Network classifiers inferring workload from physiological features: Compared performance

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 in...

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Bibliographic Details
Published in2012 IEEE Intelligent Vehicles Symposium pp. 282 - 287
Main Authors Besson, P., Dousset, E., Bourdin, C., Bringoux, L., Marqueste, T., Mestre, D. R., Vercher, J. L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2012
Subjects
Accuracy
Brain models
Data models
Entropy
Physiology
Trajectory
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ISBN9781467321198
1467321192
ISSN1931-0587
DOI10.1109/IVS.2012.6232134

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Summary: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.
ISBN:9781467321198
1467321192
ISSN:1931-0587
DOI:10.1109/IVS.2012.6232134