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.

Download full-text


Available from: Xianghong Sun, Dec 18, 2013
  • Source
    • "Finally, the software generated the HRV results in excel format. The HRV processing software is widely used among Chinese researchers and its reliability and validity have been documented [23] [24] [25]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Objectives: The present study aimed to investigate the effects of guided imagery training on heart rate variability in individuals while performing spaceflight emergency tasks. Materials and methods: Twenty-one student subjects were recruited for the experiment and randomly divided into two groups: imagery group (n = 11) and control group (n = 10). The imagery group received instructor-guided imagery (session 1) and self-guided imagery training (session 2) consecutively, while the control group only received conventional training. Electrocardiograms of the subjects were recorded during their performance of nine spaceflight emergency tasks after imagery training. Results: In both of the sessions, the root mean square of successive differences (RMSSD), the standard deviation of all normal NN (SDNN), the proportion of NN50 divided by the total number of NNs (PNN50), the very low frequency (VLF), the low frequency (LF), the high frequency (HF), and the total power (TP) in the imagery group were significantly higher than those in the control group. Moreover, LF/HF of the subjects after instructor-guided imagery training was lower than that after self-guided imagery training. Conclusions: Guided imagery was an effective regulator for HRV indices and could be a potential stress countermeasure in performing spaceflight tasks.
    Full-text · Article · Jul 2015
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    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.
    Full-text · Conference Paper · Jun 2012
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this work, the focus is on developing a system that can detect hypovigilance, which includes both drowsiness and inattention, using Electrocardiogram (ECG) and Electromyogram (EMG) signals. Drowsiness has been manipulated by allowing the driver to drive monotonously at a limited speed for long hours and inattention was manipulated by asking the driver to respond to phone calls and short messaging services. ECG and EMG signals along with the video recording have been collected throughout the experiment. The gathered physiological signals were preprocessed to remove noise and artifacts. The hypovigilance features were extracted from the preprocessed signals using higher order spectral features. The features were classified using k Nearest Neighbor, Linear Discriminant Analysis and Quadratic Discriminant Analysis. The bispectral features gave an overall maximum accuracy of 96.75% and 92.31% for ECG and EMG signals, respectively using k fold validation. The features of ECG and EMG signals were fused using principal component analysis to obtain the optimally combined features and the classification accuracy was 96%. A number of road accidents can be avoided if an alert is sent to a driver who is drowsy or inattentive.
    Full-text · Article · Dec 2015 · Expert Systems with Applications