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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
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    References listed on IDEAS

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    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.
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    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.

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