Prediction of drowsy driving using behavioral measures of drivers - Change of neck bending angle and sitting pressure distribution

Atsuo Murata, Taiga Koriyama, Takuya Endoh, Takehito Hayami

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, in Japan, the percentage of the death toll in traffic accidents due to drowsy driving is the most dominant in all death tolls in traffic accidents. Therefore, it is essential for automotive manufacturers to develop a warning system of drowsy driving. A lot of studies are conducted to prevent traffic accident due to drowsy driving, and make an attempt to assess drowsiness by physiological measures such as EEG. However, it is difficult to use such equipment for predicting drowsiness, because it is difficult to equip an automotive cockpit with such equipment due to expensiveness and measurement noise. As more convenient measure used to predict drowsiness, it was examined whether the neck bending angle and the sitting pressure distribution could be used to discriminate the arousal level. The effectiveness of these convenient measures was experimentally assessed. In order to prevent traffic accidents due to drowsy driving, an attempt was made to predict drowsiness (low arousal state) using the change of neck bending angle and sitting pressure distribution. As a result, these measures were found to be useful for evaluating arousal level and predicting arousal level in advance.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages78-87
Number of pages10
Volume8025 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2013
Event4th Int. Conf. on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management: Healthcare and Safety of the Environment and Transport, DHM 2013, Held as Part of 15th Int. Conf. Human-Computer Interaction, HCI 2013 - Las Vegas, NV, United States
Duration: Jul 21 2013Jul 26 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8025 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th Int. Conf. on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management: Healthcare and Safety of the Environment and Transport, DHM 2013, Held as Part of 15th Int. Conf. Human-Computer Interaction, HCI 2013
CountryUnited States
CityLas Vegas, NV
Period7/21/137/26/13

Fingerprint

Highway accidents
Pressure Distribution
Accidents
Pressure distribution
Driver
Traffic
Angle
Prediction
Predict
Alarm systems
Electroencephalography
Japan
Percentage

Keywords

  • COP (Center of Pressure)
  • ITS
  • neck bending angle
  • prediction of drowsiness
  • sitting pressure distribution

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Murata, A., Koriyama, T., Endoh, T., & Hayami, T. (2013). Prediction of drowsy driving using behavioral measures of drivers - Change of neck bending angle and sitting pressure distribution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 8025 LNCS, pp. 78-87). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8025 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-39173-6_10

Prediction of drowsy driving using behavioral measures of drivers - Change of neck bending angle and sitting pressure distribution. / Murata, Atsuo; Koriyama, Taiga; Endoh, Takuya; Hayami, Takehito.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8025 LNCS PART 1. ed. 2013. p. 78-87 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8025 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Murata, A, Koriyama, T, Endoh, T & Hayami, T 2013, Prediction of drowsy driving using behavioral measures of drivers - Change of neck bending angle and sitting pressure distribution. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 8025 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8025 LNCS, pp. 78-87, 4th Int. Conf. on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management: Healthcare and Safety of the Environment and Transport, DHM 2013, Held as Part of 15th Int. Conf. Human-Computer Interaction, HCI 2013, Las Vegas, NV, United States, 7/21/13. https://doi.org/10.1007/978-3-642-39173-6_10
Murata A, Koriyama T, Endoh T, Hayami T. Prediction of drowsy driving using behavioral measures of drivers - Change of neck bending angle and sitting pressure distribution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 8025 LNCS. 2013. p. 78-87. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-39173-6_10
Murata, Atsuo ; Koriyama, Taiga ; Endoh, Takuya ; Hayami, Takehito. / Prediction of drowsy driving using behavioral measures of drivers - Change of neck bending angle and sitting pressure distribution. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8025 LNCS PART 1. ed. 2013. pp. 78-87 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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