Fatigue Monitoring Based on Yawning and Head Movement

Raman, Kirbana Jai; Azman, Azreen; Arumugam, Venosha; Ibrahim, Siti Zainab; Yogarayan, Sumendra; Abdullah, Mohd Fikri Azli; Razak, Siti Fatimah Abdul; Amin, Anang Hudaya Muhamad; Sonaimuthu, Kalaiarasi

doi:10.1109/icoict.2018.8528759

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…However, this method is almost inevitable to use intrusive approaches to acquire physiological signals of drivers, which result in many end users unwilling to accept it [6]. With the widespread application of machine vision technology, the facial expressions and driving behaviours of the driver can be used to detect fatigue driving with eye‐blink and mouth‐yawn analysis [7], and head pose estimation [8]. In addition, the steering wheel angle (SWA) can also be used to establish fatigue driving method [9].…”

Section: Introductionmentioning

confidence: 99%

Fatigue driving detection model based on multi‐feature fusion and semi‐supervised active learning

Lin

Wang

et al. 2019

IET Intelligent Transport Systems

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Section: Introductionmentioning

confidence: 99%

Fatigue driving detection model based on multi‐feature fusion and semi‐supervised active learning

Lin

Wang

et al. 2019

IET Intelligent Transport Systems

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“…Jin et al [30] and Guo et al [31] fused the data on the eye movements of drivers and those on vehicle running status, the index system was established to evaluate secondary task driving based on the fused data, and analyzed the importance of the system to driving safety. To improve the accuracy of drowsy driving detection, many other methods have been developed based on the fusion of yawn and head movement [32], or the fusion of multiple types of features [33]- [36], e.g. the facial features of the driver and the steering features of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Drowsy Driving Detection Based on Fused Data and Information Granulation

Wang

Jin

et al. 2019

IEEE Access

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To detect drowsy driving accurately, this paper collects the characteristic parameters of driver's operating behavior and vehicle's running state through simulation experiments. Then, the factor analysis was adopted to reduce the dimensionality of the characteristic parameters, and the composite factor scores was computed under both normal and drowsy states, forming a time series. Next, the time series of composite factor scores was divided into information granules and the particle swarm optimization (PSO) was implemented to optimize the dynamic time window for the characteristic parameters. After that, the mean and standard deviation were computed for each composite factor score in each sub-time window, and fused into one data. Finally, a drowsy driving model was established with LIBSVM based on the fused data. The experimental results demonstrate that our model achieved an accuracy of 86.47% in detecting drowsy driving. The research finding shed new lights on the detection of drowsy driving.

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