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Driver sleepiness detection in real driving situations
Chalmers University of Technology.
Chalmers University of Technology.
Autoliv .
Chalmers University of Technology.
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2016 (English)In: Traffic Injury Prevention, ISSN 1538-9588, E-ISSN 1538-957X, Vol. 17, p. 222-223Article in journal (Refereed) Published
Abstract [en]

Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives:

  1. to evaluate the suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment,
  2. relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.

The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5 min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student's t test.

The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.

Place, publisher, year, edition, pages
2016. Vol. 17, p. 222-223
Keywords [en]
Fatigue (human), Driver, Heart beat, Detection, Variability, ECG, Perception, Analysis (math)
National Category
Applied Psychology
Research subject
80 Road: Traffic safety and accidents, 84 Road: Road users
Identifiers
URN: urn:nbn:se:vti:diva-14601ISI: 000383897600032OAI: oai:DiVA.org:vti-14601DiVA, id: diva2:1375351
Available from: 2019-12-04 Created: 2019-12-04 Last updated: 2020-04-15Bibliographically approved

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Anund, Anna

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