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

ABSTRACT 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.

1 Bookmark
 · 
120 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.
    Physical Review Letters 09/2002; 89(6):068102. · 7.73 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Sharing attention between two tasks requiring the same mental resources is supposed to increase the resulting strain. Phoning while driving may elicit cognitive interference between driving operations and conversation and consequently, may affect driving efficiency. The road scene cues may thus be perceived late or even omitted, increasing the probability to be involved in a critical situation. The aim of the experiment was to study how the additional strain elicited by a secondary task may change drivers' arousal with potential consequences on driving performance. Electrodermal activity, heart rate and reaction time (RT) were the dependent variables. Listening to the radio, holding an in-vehicle or a cell-phone conversation were the secondary communication tasks, performed by 10 participants during a driving sequence on a private circuit. Within nominal driving, each communication task was requested at random to prevent any habituation or anticipation. The cell-phone conversation made RT increase by about 20%, by comparison to the nominal driving condition. Nevertheless, the in-vehicle conversation impacted RT almost in the same proportion. Physiological data showed that arousal level increased as a function of dual-tasks requirements, the in-vehicle conversation eliciting the same strain as the remote conversation. With caution due to contextual differences between these two communication tasks, conversing with a passenger was thus as detrimental as using a cell-phone.
    Applied ergonomics 03/2009; 40(6):1041-6. · 1.11 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, however, well suited to analysis of the short and noisy data sets encountered in cardiovascular and other biological studies. Pincus introduced approximate entropy (ApEn), a set of measures of system complexity closely related to entropy, which is easily applied to clinical cardiovascular and other time series. ApEn statistics, however, lead to inconsistent results. We have developed a new and related complexity measure, sample entropy (SampEn), and have compared ApEn and SampEn by using them to analyze sets of random numbers with known probabilistic character. We have also evaluated cross-ApEn and cross-SampEn, which use cardiovascular data sets to measure the similarity of two distinct time series. SampEn agreed with theory much more closely than ApEn over a broad range of conditions. The improved accuracy of SampEn statistics should make them useful in the study of experimental clinical cardiovascular and other biological time series.
    AJP Heart and Circulatory Physiology 07/2000; 278(6):H2039-49. · 4.01 Impact Factor

Full-text (2 Sources)

Download
35 Downloads
Available from
May 29, 2014