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Stress Map Based Information System for Increasing Road Safety

  • Patrick DatkoEmail author
  • Ralf Seepold
  • Natividad Martínez Madrid
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 392)

Abstract

Stress is becoming an important topic in modern life. The influence of stress results in a higher rate of health disorders such as burnout, heart problems, obesity, asthma, diabetes, depressions and many others. Furthermore individual’s behavior and capabilities could be directly affected leading to altered cognition, inappropriate decision making and problem solving skills. In a dynamic and unpredictable environment, such as automotive, this can result in a higher risk for accidents. Different papers faced the estimation as well as prediction of drivers’ stress level during driving. Another important question is not only the stress level of the driver himself, but also the influence on and of a group of other drivers in the near area. This paper proposes a system, which determines a group of drivers in a near area as clusters and it derives the individual stress level. This information will be analyzed to generate a stress map, which represents a graphical view about road section with a higher stress influence. Aggregated data can be used to generate navigation routes with a lower stress influence to decrease stress influenced driving as well as improve road safety.

Keywords

Stress Level Heart Rate Variability Road Safety Universal Mobile Telecommunication System Road Section 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Asbridge M, Smart RG, Mann RE (2006) Can we prevent road rage? Trauma Violence Abuse 7:109–121. doi: 10.1177/1524838006286689
  2. 2.
    Lisetti CL, Nasoz F (2004) Using noninvasive wearable computers to recognize human emotions from physiological signals. EURASIP J Appl Signal Process 2004:1672–1687Google Scholar
  3. 3.
    Fernandez JM, Ochoa EP, Madrid NM, Seepold R (2009) A distributed management platform to support trading decisions under panic behavior. In: 2009 seventh workshop on intelligent solutions in embedded systems, pp 141–147 (2009)Google Scholar
  4. 4.
    Yata T, Sannohe H, Nakasako M, Tao M (1993) How to cope with stress. YuhikakuGoogle Scholar
  5. 5.
    Yamakoshi T, Yamakoshi K, Tanaka S, Nogawa M, Park SB, Shibata M, Sawada Y, Rolfe P, Hirose Y (2008) Feasibility study on driver’s stress detection from differential skin temperature measurement. In: 30th annual international conference of the IEEE Engineering in Medicine and Biology Society. EMBS 2008, pp 1076–1079Google Scholar
  6. 6.
    Choi J, Gutierrez-Osuna R (2009) Using heart rate monitors to detect mental stress. In: Sixth international workshop on wearable and implantable body sensor networks. BSN 2009, pp 219–223Google Scholar
  7. 7.
    Salahuddin L, Jeong MG, Kim D, Lim SK, Won K, Woo JM (2007) Dependence of heart rate variability on stress factors of stress response inventory. In: 9th international conference on e-health networking, application and services, pp 236–239Google Scholar
  8. 8.
    Nakamura Y, Yamanaka K, Park MK, Kawakami M (2013) Basic study for promoting driving safety support systems among elderly drivers. In: 2013 international conference on Biometrics and Kansei Engineering (ICBAKE), pp 109–112Google Scholar
  9. 9.
    Eilebrecht B, Wolter S, Lem J, Lindner H, Vogt R, Walter M, Leonhardt S (2012) The relevance of HRV parameters for driver workload detection in real world driving. Comput Cardiol (CinC) 2012:409–412Google Scholar
  10. 10.
    Rigas G, Katsis CD, Bougia P, Fotiadis DI (2008) A reasoning-based framework for car driver’s stress prediction. In: 16th mediterranean conference on control and automation, pp 627–632Google Scholar
  11. 11.
    Yamaguchi M, Wakasugi J, Sakakima J (2006) Evaluation of driver stress using biomarker in motor-vehicle driving simulator. In: 28th annual international conference of the IEEE Engineering in Medicine and Biology Society. EMBS’06, pp 1834–1837Google Scholar
  12. 12.
    Yang Z, Zhang L, Wang J, Wang Y, Guan Q, Feng J (2006) Design of intelligent in-vehicle navigation systems for dynamic route guidance with real-time information. In: IEEE international conference on vehicular electronics and safety. ICVES 2006, pp 184, 188, 13–15 Dec 2006. doi: 10.1109/ICVES.2006.371579
  13. 13.
    Sato K, Otsu H, Madokoro H, Kadowaki S (2013) Analysis of psychological stress factors by using bayesian network. In: 2013 IEEE international conference on mechatronics and automation (ICMA), pp 811, 818, 4–7 Aug 2013. doi: 10.1109/ICMA.2013.6618020
  14. 14.
    IEEE Std 802.11p (2010) IEEE standard for information technology—local and metropolitan area networks—specific requirements—part 11: wireless lan medium access control (mac) and physical layer (phy) specifications amendment 6: wireless access in vehicular environments. IEEE Std 802.11p-2010 (Amendment to IEEE Std 802.11-2007 as amended by IEEE Std 802.11k-2008, IEEE Std 802.11r-2008, IEEE Std 802.11y-2008, IEEE Std 802.11n-2009, and IEEE Std 802.11w-2009), pp 1–51Google Scholar
  15. 15.
    Lee GY, Park HM, Cho HG, Choi SH, Park SH (2011) The implementation of the intelligent transport system for the real-time roadside environment information transfer. In: 2011 13th international conference on advanced communication technology (ICACT), pp 76–81Google Scholar
  16. 16.
    Kloiber B, Strang T, Spijker H, Heijenk G (2012) Improving information dissemination in sparse vehicular networks by adding satellite communication. In: Intelligent vehicles symposium (IV), 2012 IEEE, pp 611–617Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Patrick Datko
    • 1
    Email author
  • Ralf Seepold
    • 1
  • Natividad Martínez Madrid
    • 2
  1. 1.HTWG KonstanzKonstanzGermany
  2. 2.Reutlingen UniversityReutlingenGermany

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