Statistical Methods & Applications

, Volume 28, Issue 1, pp 157–185 | Cite as

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

  • Neska El HaouijEmail author
  • Jean-Michel Poggi
  • Raja Ghozi
  • Sylvie Sevestre-Ghalila
  • Mériem Jaïdane
Original Paper


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.


Physiological signals Functional data Random forests Recursive feature elimination Wavelets Grouped variable importance 

Mathematics Subject Classification

62H30 62P30 



The authors gratefully acknowledge Dr. Chiraz Ben Abdelkader and Dr. Hassine Saidane for proofreading the paper. They also thank the anonymous referees for their useful suggestions and meaningful comments which led to a considerable improvement of this paper.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Engineering School of TunisUniversity of Tunis El ManarTunisTunisia
  2. 2.Paris-Sud UniversityOrsayFrance
  3. 3.CEA-Tech (CEA-LinkLab), CEATunisTunisia
  4. 4.Paris Descartes UniversityParisFrance

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