skip to main content
10.1145/3239092.3265970acmconferencesArticle/Chapter ViewAbstractPublication PagesautomotiveuiConference Proceedingsconference-collections
research-article

Biometric Interface for Driver's Stress Detection and Awareness

Published:23 September 2018Publication History

ABSTRACT

One of the factors for stress reduction among vehicle drivers is to be aware that stress is present. This project presents a biometric interface for stress detection in drivers, built with open source sensors and hardware. In two series of experiments, we induce stress in test subjects by making them drive progressively difficult scenarios in a simulator. Using the C4.5 classification algorithm, we classified the subjects' biometric data in order to determine whether the subject was stressed or not. In another series of experiments, we tested the efficacy of two driver feedback systems, a haptic one and a visual one. Identifying a stressful situation allows real-time feedback to drivers, so they can be aware of their stressed state, thus being able to take corrective actions on time, and avoid behavior leading to an accident.

References

  1. Adafruit. NeoPixel Ring. Retrieved July 13, 2018 from https://www.adafruit.com/product/1643Google ScholarGoogle Scholar
  2. Arduino. Arduino Uno Rev 3. Retrieved July 13, 2018 from https://store.arduino.cc/usa/arduino-uno-rev3Google ScholarGoogle Scholar
  3. J. Bakker, M. Pechenizkiy and N. Sidorova. What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data. 2011 IEEE 11th International Conference on Data Mining Workshops, Vancouver, BC, 2011, pp. 573--580 Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Grove. Finger-clip heart rate sensor with shell. Retrieved July 13, 2018 from https://www.seeedstudio.com/Grove-Finger-clip-Heart-Rate-Sensor-with-shell-p-2420.htmlGoogle ScholarGoogle Scholar
  5. Grove. GSR sensor. Retrieved July 13, 2018 from https://www.seeedstudio.com/Grove-GSR-sensor-p-1614.htmlGoogle ScholarGoogle Scholar
  6. Logitech. G-Force G920 Steering Wheel. Retrieved July 13, 2018 from https://www.logitechg.com/ensg/drivingGoogle ScholarGoogle Scholar
  7. Ministerio del Interior de España. Otros factores de riesgo: El estrés. 2014. Retrieved July 13, 2018 from http://www.dgt.es/PEVI/documentos/catalogo_recursos/didacticos/did_adultas/estres.pdfGoogle ScholarGoogle Scholar
  8. OpenDS. Retrieved November 11, 2017 from https://www.opends.eu/software/featuresGoogle ScholarGoogle Scholar
  9. S. Ruggieri. Efficient C4.5 {classification algorithm}. In IEEE Transactions on Knowledge and Data Engineering, 14, 2, pp. 438--444, Mar/Apr 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. Sathyadevan and R.R. Nair. Comparative Analysis of Decision Tree Algorithms: ID3, C4.5 and Random Forest. In Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi, 2015Google ScholarGoogle Scholar
  11. Sparkfun. LilyPad Vibe Board. Retrieved July 13, 2018 from https://www.sparkfun.com/products/11008Google ScholarGoogle Scholar
  12. Sparkfun. Load Sensor -- 50 kg. Retrieved July 13, 2018 from https://www.sparkfun.com/products/10245Google ScholarGoogle Scholar
  13. Alejandro P. Taddia, et al. Fortaleciendo el sector académico para reducir los siniestros de tránsito en América Latina: Investigaciones y casos de estudio en seguridad vial. 2014. Retrieved July 13, 2018 from https://publications.iadb.org/handle/11319/6476Google ScholarGoogle Scholar
  14. University of Waikato. WEKA 3: Data Mining Software in Java. Retrieved July 13, 2018 from https://www.cs.waikato.ac.nz/ml/weka/Google ScholarGoogle Scholar
  15. World Health Organization. World report on road traffic injury prevention. 2004. Retrieved August 10, 2018 from http://www.who.int/violence_injury_prevention/publications/road_traffic/world_report/enGoogle ScholarGoogle Scholar

Index Terms

  1. Biometric Interface for Driver's Stress Detection and Awareness

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          AutomotiveUI '18: Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
          September 2018
          282 pages
          ISBN:9781450359474
          DOI:10.1145/3239092

          Copyright © 2018 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 23 September 2018

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed limited

          Acceptance Rates

          Overall Acceptance Rate248of566submissions,44%

          Upcoming Conference

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader