Computer Science ›› 2016, Vol. 43 ›› Issue (7): 314-318.doi: 10.11896/j.issn.1002-137X.2016.07.058

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Driver Fatigue Detection Based on IMF Time-frequency Features of Pulse Signal and SVDD

JIANG Jian-chun, JIANG Li, TANG Hui, ZHANG Zhuo-peng and WU Xue-gang   

  • Online:2018-12-01 Published:2018-12-01

Abstract: To address the problems of traditional time-frequency features’ being hard to characterize the non-stationary signal (e.g.,pulse signal) and the fewer samples of driver fatigue pulses,an approach was proposed to detect driver’s fatigue based on the time-frequency features of intrinsic mode function (IMF) of pulse signal and support vector data description (SVDD).This approach makes full use of the advantages of the IMF’ being suitable for characterizing non-stationary signal and SVDD’ being good at addressing the classification with unbalanced samples.First,the pulse signals are decomposed by using empirical mode decomposition method to obtain multiple IMF components.Then,the time-frequency features of IMF are extracted,which consists of the normalized energy,the maximum instantaneous frequency and the average of instantaneous amplitude.Finally,the SVDD classifier is used to detect the fatigue status of drivers and give corresponding fatigue level.Comparison experiments suggest that this approach can effectively detect the fatigue status of drivers.

Key words: Fatigue driving,Pulse signal,Intrinsic mode function,Support vector data description

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