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Self‐similarity analysis of vehicle driver's electrodermal activity
Author(s) -
El Haouij Neska,
Ghozi Raja,
Poggi JeanMichel,
SevestreGhalila Sylvie,
Jaïdane Mériem
Publication year - 2019
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2526
Subject(s) - hurst exponent , context (archaeology) , similarity (geometry) , self similarity , rescaled range , arousal , fractional brownian motion , computer science , stress (linguistics) , artificial intelligence , mathematics , detrended fluctuation analysis , simulation , pattern recognition (psychology) , psychology , statistics , brownian motion , social psychology , geography , linguistics , philosophy , geometry , archaeology , scaling , image (mathematics)
This paper characterizes stress levels via a self‐similarity analysis of the electrodermal activity (EDA) collected in a real‐world driving context. To characterize the EDA richness over scales, the fractional Brownian motion (FBM) process and its corresponding exponent H , estimated via a wavelet‐based approach, are used. Specifically, an automatic scale range selection is proposed in order to detect the linearity in a log scale diagram. The procedure is applied to the EDA signals, from the open database drivedb , originally captured on the foot and the hand of the drivers during a real‐world driving experiment, designed to evoke different levels of arousal and stress. The estimated Hurst exponent H offers a distinction in stress levels when driving in highway versus city, with a reference to restful state of minimal stress level. Specifically, the estimated H values tend to decrease when the driving environmental complexity increases. In addition, the estimated H values on the foot EDA signals allow a better characterization of the driving task than that of hand EDA. The self‐similarity analysis was applied to various physiological signals in literature but not to the EDA so far, a signal which was found to correlate most with human affect. The proposed analysis could be useful in real‐time monitoring of stress levels in urban driving spaces, among other applications.