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Driver's Mental State Monitoring System Using CBR Based on Heart Rate Variability Analysis
Mälardalen University, School of Innovation, Design and Engineering. (IS)ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering. (IS)ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering. (IS)ORCID iD: 0000-0002-5562-1424
2012 (English)In:  , 2012Conference paper, Published paper (Refereed)
Abstract [en]

The consequences of tiredness, drowsiness, stress and lack of concentration caused by a variety of different factors such as illness, sleep depletion, drugs and alcohol is a serious problem in traffic and when operating industrial equipment. This is especially important for professional drivers since both expensive equipment and lives may be at stake, e.g. in mining, construction and personal transportation, reduced concentration, stress or tiredness are known to be the cause of many accidents. A system which recognizes the state of the driver and e.g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. Today different sensors enable clinician to determine a driver’s status with high accuracy. The aim of the paper is to develop an intelligent system that can monitor drivers’ stress depending on psychological and behavioral conditions/status using heart rate variability. An experienced clinician is able to diagnose a person’s stress level based on sensor readings. Here, we propose a solution using case-based reasoning to diagnose individual driver’s stress. During calibration a number of individual parameters are established. The system also considers the feedback from the driver’s on how well the test was performed The validation of the approach is based on close collaboration with experts and measurements from 18 driver’s from Volvo Construction Equipment are used as reference.

Place, publisher, year, edition, pages
2012.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-17321OAI: oai:DiVA.org:mdh-17321DiVA, id: diva2:579652
Conference
7th International Symposium Advances in Artificial Intelligence and Applications (AAIA'12)
Available from: 2012-12-20 Created: 2012-12-20 Last updated: 2017-01-25Bibliographically approved

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Begum, ShahinaAhmed, Mobyen UddinFunk, Peter

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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