A novel driver fatigue monitoring using optical imaging of face on safe driving system

Tayibnapis, Iman Rahmansyah; Koo, Dongyoung; Choi, Min-Kook; Kwon, Soon

doi:10.1109/iccerec.2016.7814994

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…Therefore, we selected the features that could be measured by the PPG sensor as the feature set. Conventional visualization and physiological features were mixed in [16].…”

Section: ) Estimation Resultsmentioning

confidence: 99%

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Vision-Based Instant Measurement System for Driver Fatigue Monitoring

et al. 2020

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In this paper, a vision-based physiological signal measurement system is proposed to instantly measure driver fatigue. A remote photoplethysmography (rPPG) signal is a type of physiological signal measured by a camera without any contact device, and it also retains the characteristics of the PPG, which is useful to evaluate fatigue. To solve the inconvenience caused by the traditional contact-based physiological fatigue detection system and to improve the accuracy, the system measures both the motional and physiological information by using one image sensor. In a practical application, the environmental noise would affect the measured signal, and therefore, we performed a preprocessing step on the signal to extract a clear signal. The experiment was designed in collaboration with Taipei Medical University, and a questionnaire-based method was used to define fatigue. The questionnaire that could directly react to the feeling of the subject was treated as our ground truth. The evaluated correlation was 0.89 and the root mean square error was 0.65 for ten-fold cross-validation on the dataset. The trend of driver fatigue could be evaluated without a contact device by the proposed system. This advantage ensures the safety of the driver and reliability of the system.INDEX TERMS Fatigue monitoring, remote photoplethysmography, biomedical monitoring, image sequence analysis.

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“…Therefore, we selected the features that could be measured by the PPG sensor as the feature set. Conventional visualization and physiological features were mixed in [16].…”

Section: ) Estimation Resultsmentioning

confidence: 99%

“…Qi and Wang applied rPPG to calculate PRV and analyzed the relationship between fatigue and PRV [15]. Tayibnapis combined facial features, rPPG signals, and the SVM method to classify the fatigue state [16].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Instant Measurement System for Driver Fatigue Monitoring

et al. 2020

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“…In 2016, Tayibnapis and coworkers [ 115 ] presented their results on the fatigue analysis of eyes, mouth, and head-pose using a NIR camera. For face analysis, the Viola–Jones algorithm [ 132 ] was employed.…”

Section: Optical Methodsmentioning

confidence: 99%

“…In addition, heart rate and heart rate variability were extracted using no-contact PPG imaging. Hence, their proposed system features two major contributions, namely “ (1) integrating various facial feature methods using an NIR camera, and (2) measuring the driver’s physiological state with a non-contactable method ” [ 115 ].…”

Section: Optical Methodsmentioning

confidence: 99%

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

Leonhardt

Leicht

Teichmann

2018

Sensors

103

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This review provides an overview of unobtrusive monitoring techniques that could be used to monitor some of the human vital signs (i.e., heart activity, breathing activity, temperature and potentially oxygen saturation) in a car seat. It will be shown that many techniques actually measure mechanical displacement, either on the body surface and/or inside the body. However, there are also techniques like capacitive electrocardiogram or bioimpedance that reflect electrical activity or passive electrical properties or thermal properties (infrared thermography). In addition, photopleythysmographic methods depend on optical properties (like scattering and absorption) of biological tissues and—mainly—blood. As all unobtrusive sensing modalities are always fragile and at risk of being contaminated by disturbances (like motion, rapidly changing environmental conditions, triboelectricity), the scope of the paper includes a survey on redundant sensor arrangements. Finally, this review also provides an overview of automotive demonstrators for vital sign monitoring.

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“…2. Contact sensor based detection approaches, which require the driver to physically wear some sensors to measure his/her physiological signals, e.g., electroencephalogram (EEG) [26,34,35,37], electrocardiography [20,26], electromyography [2,19], respiration [30,32], galvanic skin response [5,15], etc. Theoretically, physiological signals are more accurate and reliable drowsiness indicators, as they originate directly from the human body.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Driver Drowsiness Estimation Using Convolutional Neural Networks

Cui

2017

Neural Information Processing

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Deep learning, including convolutional neural networks (CNNs), has started finding applications in brain-computer interfaces (BCIs). However, so far most such approaches focused on BCI classification problems. This paper extends EEGNet, a 3-layer CNN model for BCI classification, to BCI regression, and also utilizes a novel spectral meta-learner for regression (SMLR) approach to aggregate multiple EEGNets for improved performance. Our model uses the power spectral density (PSD) of EEG signals as the input. Compared with raw EEG inputs, the PSD inputs can reduce the computational cost significantly, yet achieve much better regression performance. Experiments on driver drowsiness estimation from EEG signals demonstrate the outstanding performance of our approach.

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A novel driver fatigue monitoring using optical imaging of face on safe driving system

Cited by 15 publications

References 30 publications

Vision-Based Instant Measurement System for Driver Fatigue Monitoring

Vision-Based Instant Measurement System for Driver Fatigue Monitoring

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

EEG-Based Driver Drowsiness Estimation Using Convolutional Neural Networks

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