Conference Paper

Driving Distraction Analysis by ECG Signals: An Entropy Analysis

DOI: 10.1007/978-3-642-21660-2_29 Conference: Internationalization, Design and Global Development - 4th International Conference, IDGD 2011, Held as part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011. Proceedings
Source: DBLP


This paper presents a novel method in driving distraction analysis: entropy analysis of ECG signals. ECG signals were recorded
continuously while 15 drivers were driving with a simulator. Mental computation task was employed as driving distraction.
Sample entropy and power spectrum entropy of drivers. ECG signals while they were driving with and without distraction were
derived. The result indicated that entropy of drivers ECG signals was sensitive to driving distraction and were potential
significant metrics in driving distraction measurement.

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Available from: Xianghong Sun, Dec 18, 2013
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    • "We propose Bayesian Network (BN) models that take as inputs the entropy of these physiological measurements and output the change of the subject's workload while they are involved in task demanding different levels of cognitive resources. Entropy of ECG signal has been shown to be a good indicator of distraction [14] but, to the best of our knowledge, it has never been applied to other physiological signals in a computational model of workload. Different BN structures, built from expert knowledge, are tested, using in turn several combinations of physiological features. "
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    ABSTRACT: 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.
    Intelligent Vehicles Symposium, Alcalà de Henares, Madrid, Spain; 06/2012