2016
DOI: 10.1109/jsen.2016.2625323
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Stress Events Detection of Driver by Wearable Glove System

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Cited by 58 publications
(48 citation statements)
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“…The result indicated that the combination of ECG, EDA, and respiration signals to distinguish drivers' rest from stress has the optimal performance (accuracy: 87.3% ± 2.8) among 88:19 different signal combinations. In another study, SFFS was applied on 20 features extracted from drivers' PPG signals and steering-wheel motion data (Lee et al 2017). The features were separately fed to an SVM-Gaussian kernel function classifier to evaluate their performance.…”
Section: Wrapper Methodsmentioning
confidence: 99%
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“…The result indicated that the combination of ECG, EDA, and respiration signals to distinguish drivers' rest from stress has the optimal performance (accuracy: 87.3% ± 2.8) among 88:19 different signal combinations. In another study, SFFS was applied on 20 features extracted from drivers' PPG signals and steering-wheel motion data (Lee et al 2017). The features were separately fed to an SVM-Gaussian kernel function classifier to evaluate their performance.…”
Section: Wrapper Methodsmentioning
confidence: 99%
“…The effects of stress on a driver can be measured indirectly through dynamic data obtained from the vehicle. This data can be collected through different systems such as CAN-Bus (Bořil et al 2012;Rigas et al 2012;Rodrigues et al 2015), GPS tracking 88:11 (Rigas et al 2012), driving simulators (Cantin et al 2009;Tango and Botta 2013), mobile phones (Tchankue et al 2013), and orientation sensors (Lee et al 2017).…”
Section: Vehicle Dynamic Datamentioning
confidence: 99%
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