Driver drowsiness detection with eyelid related parameters by Support Vector Machine

By Shuyan Hu, Gangtie Zheng. Expert systems with applications, 2009

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Driver Fatigue Detection Systems: A Review (2019, methodology): “However, KSS has been used as a benchmark to check the accuracy of other systems [57], [69].”
Detecting Driver Drowsiness Based on Sensors: A Review (2012, methodology): “measured the KSS ratings of drivers every 5 min and used it as a reference to the EoG signal collected [28].”
Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm (2011, methodology): “Specif cally, many of these studies suggested that the change in the cognitive state can be associated with signif cant changes in the EEG frequency bands such as delta (δ: 0-4Hz), theta (θ: 48Hz), alpha (α: 8-13Hz), and beta (β: 13-20Hz) [14], [15] or their combinations [16], [17], and with changes”
Driver Inattention Monitoring System for Intelligent Vehicles: A Review (2009, methodology): “Hu and Zheng [69] employed an SVM to perform drowsiness prediction with 11 eyelid-related features extracted from EOG.”