ENBIS-16 in Sheffield
11 – 15 September 2016; Sheffield
Abstract submission: 20 March – 4 July 2016
Random Forest-Based Approach for Physiological Functional Variable Selection for Driver's Stress Level Classification
14 September 2016, 10:50 – 11:10
- Submitted by
- Naska Haouij
- Naska Haouij (CEA-LinkLab, Telnet Innovation Labs), Raja Ghozi (U2S-ENIT, CEA-LinkLab), Jean-Michel Poggi (Univ. Paris Descartes et Univ. Paris Sud), Sylvie Sevestre Ghalila (CEA-LinkLab), Mériem Jaidane (U2S-ENIT, CEA-LinkLab)
- With the increasing urbanization and technological advances, urban driving is bound to be a complex task that requires higher levels of alertness. Thus, the driver’s mental workload should be optimal in order to manage critical situations in such challenging driving conditions. Past studies relied on driver’s performances used subjective measures. The new wearable and non-intrusive sensor technology, is not only providing real-time physiological monitoring, but also is enriching the tools for human affective and cognitive states monitoring.
This study focuses on a driver’s physiological changes using portable sensors in different urban routes. Specifically, the Electrodermal Activity (EDA) measured on two different locations: hand and foot, Electromyogram (EMG), Heart Rate (HR) and Respiration (RESP) of ten driving experiments in three types of routes are considered: rest area, city, and highway driving issued from physiological database, labelled "drivedb", available online on the PHYSIONET
Several studies have been done on driver's stress level recognition using physiological signals. Classically, researchers extract expert-based features from physiological signals and select the most relevant features in stress level recognition. This work aims to provide a random forest-based method for the selection of physiological functional variables in order to classify the stress level during real-world driving experience. The contribution of this study is twofold: on the methodological side, it considers physiological signals as functional variables and offers a procedure of data processing and variable selection. On the applied side, the proposed method provides a "blind" procedure of driver's stress level classification that do not depend on the expert-based studies of physiological signals.
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