IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v28y2019i1d10.1007_s10260-018-0423-5.html
   My bibliography  Save this article

Random forest-based approach for physiological functional variable selection for driver’s stress level classification

Author

Listed:
  • Neska Haouij

    (University of Tunis El Manar
    Paris-Sud University
    CEA-Tech (CEA-LinkLab), CEA)

  • Jean-Michel Poggi

    (Paris-Sud University
    Paris Descartes University)

  • Raja Ghozi

    (University of Tunis El Manar)

  • Sylvie Sevestre-Ghalila

    (CEA-Tech (CEA-LinkLab), CEA)

  • Mériem Jaïdane

    (University of Tunis El Manar)

Abstract

This paper deals with physiological functional variables selection for driver’s stress level classification using random forests. Our analysis is performed on experimental data extracted from the drivedb open database available on PhysioNet website. The physiological measurements of interest are: electrodermal activity captured on the driver’s left hand and foot, electromyogram, respiration, and heart rate, collected from ten driving experiments carried out in three types of routes (rest area, city, and highway). The contributions of this work touch on the method as well as the application aspects. From a methodological viewpoint, the physiological signals are considered as functional variables, decomposed on a wavelet basis and then analyzed in search of most relevant variables. On the application side, the proposed approach provides a “blind” procedure for driver’s stress level classification, giving close performances to those resulting from the expert-based approach, when applied to the drivedb database. It also suggests new physiological features based on the wavelet levels corresponding to the functional variables wavelet decomposition. Finally, the proposed approach provides a ranking of physiological variables according to their importance in stress level classification. For the case under study, results suggest that the electromyogram and the heart rate signals are less relevant compared to the electrodermal and the respiration signals. Furthermore, the electrodermal activity measured on the driver’s foot was found more relevant than the one captured on the hand. Finally, the proposed approach also provided an order of relevance of the wavelet features.

Suggested Citation

  • Neska Haouij & Jean-Michel Poggi & Raja Ghozi & Sylvie Sevestre-Ghalila & Mériem Jaïdane, 2019. "Random forest-based approach for physiological functional variable selection for driver’s stress level classification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(1), pages 157-185, March.
  • Handle: RePEc:spr:stmapp:v:28:y:2019:i:1:d:10.1007_s10260-018-0423-5
    DOI: 10.1007/s10260-018-0423-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-018-0423-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10260-018-0423-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gregorutti, Baptiste & Michel, Bertrand & Saint-Pierre, Philippe, 2015. "Grouped variable importance with random forests and application to multiple functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 15-35.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Antonis Kostopoulos & Thodoris Garefalakis & Eva Michelaraki & Christos Katrakazas & George Yannis, 2024. "Modeling and Sustainability Implications of Harsh Driving Events: A Predictive Machine Learning Approach," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
    2. Huiqin Chen & Hao Liu & Hailong Chen & Jing Huang, 2023. "Towards Sustainable Safe Driving: A Multimodal Fusion Method for Risk Level Recognition in Distracted Driving Status," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    3. Fabrizio Maturo & Rosanna Verde, 2023. "Supervised classification of curves via a combined use of functional data analysis and tree-based methods," Computational Statistics, Springer, vol. 38(1), pages 419-459, March.
    4. Susrutha Babu Sukhavasi & Suparshya Babu Sukhavasi & Khaled Elleithy & Ahmed El-Sayed & Abdelrahman Elleithy, 2022. "Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection," IJERPH, MDPI, vol. 19(4), pages 1-23, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pedro Delicado & Daniel Peña, 2023. "Understanding complex predictive models with ghost variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 107-145, March.
    2. Fabrizio Maturo & Rosanna Verde, 2023. "Supervised classification of curves via a combined use of functional data analysis and tree-based methods," Computational Statistics, Springer, vol. 38(1), pages 419-459, March.
    3. A. Poterie & J.-F. Dupuy & V. Monbet & L. Rouvière, 2019. "Classification tree algorithm for grouped variables," Computational Statistics, Springer, vol. 34(4), pages 1613-1648, December.
    4. T. Górecki & Ł. Smaga, 2017. "Multivariate analysis of variance for functional data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2172-2189, September.
    5. Patrick J. Comer & Jon C. Hak & Marion S. Reid & Stephanie L. Auer & Keith A. Schulz & Healy H. Hamilton & Regan L. Smyth & Matthew M. Kling, 2019. "Habitat Climate Change Vulnerability Index Applied to Major Vegetation Types of the Western Interior United States," Land, MDPI, vol. 8(7), pages 1-27, July.
    6. Epifanio, Irene, 2016. "Functional archetype and archetypoid analysis," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 24-34.
    7. Pedro Delicado, 2019. "Comments on: Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 334-337, June.
    8. Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    9. Simon Valentin & Maximilian Harkotte & Tzvetan Popov, 2020. "Interpreting neural decoding models using grouped model reliance," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-17, January.
    10. Christophe Denis & Charlotte Dion & Miguel Martinez, 2020. "Consistent procedures for multiclass classification of discrete diffusion paths," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(2), pages 516-554, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stmapp:v:28:y:2019:i:1:d:10.1007_s10260-018-0423-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.