Detecting Driver Drowsiness Based on Sensors: A Review <s> Hybrid Measures <s> The present study aimed to provide subject-specific estimates of the relation between subjective sleepiness measured with the Karolinska Sleepiness Scale (KSS) and blink duration (BLINKD) and lane drifting calculated as the standard deviation of the lateral position (SDLAT) in a high-fidelity moving base driving simulator. Five male and five female shift workers were recruited to participate in a 2-h drive (08:00-10:00 hours) after a normal night sleep and after working a night shift. Subjective sleepiness was rated on the KSS in 5-min intervals during the drive, electro-occulogram (EOG) was measured continuously to calculate BLINKD, and SDLAT was collected from the simulator. A mixed model anova showed a significant (P Language: en <s> BIB001 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Hybrid Measures <s> The electrocardiogram (ECG) provides important information about hear rate and autonomic activity. This report describes a nonrestrictive ECG method using a neckband to position activity. This report describes a nonrestrictive ECG method using a neckband to position activity electrodes on the skin of the neck. The system consists of a dry-type activity electrode, an instrumentation circuit, and a neckband. The dry-type activity electrode is connected to the instrumentation circuit with high input impedance in order to prevent noise contamination. Moreover, this method avoids skin rash since it does not use paste to reduce the impedance between the skin and the electrode. <s> BIB002 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Hybrid Measures <s> We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver. <s> BIB003 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Hybrid Measures <s> This paper presents a research developed in order to achieve a multidimensional measurement of drowsiness in drivers, and to find patterns in biomedical and biomechanical variables that allow to characterize the drowsiness cycle. Biomedical signals, eye closure, pressures on the seat, and longitudinal and lateral control of the vehicle have been recorded in a driving simulator, during a test in an environment that induced drowsiness, while subjects were motivated to struggle against sleep. Drowsiness was successfully induced in 80% of the subjects, and related changes in the measured signals was analyzed, as a scientific basis for future drowsiness detection systems. <s> BIB004 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Hybrid Measures <s> This study presents a novel approach to detect driver's drowsiness by applying two distinct methods in computer vision and image processing. The objective of this study is to combine both methods under one single profile instead of relied solely on a detection method to enhance the driver's drowsiness detection resolution. Therefore a non-intrusive drowsy-monitoring system is developed to alert the driver if driver falls into low arousal state. In physiological part, photoplethysmography (PPG) is analysed for its changes in signals waveform from awake to drowsy state. Meanwhile, eyes pattern or motion in image processing is addressed to detect driver fatigue. Genetic algorithm with template-matching approach is designed to detect eye region and estimate the drowsiness in different metric standard based on eyes behaviour. Moreover, PPG drowsy signals are integrated with eyes motion to derive the final probability model for delivering valid and reliable drowsiness detection system. Indeed, the proposed system provides high competitive edge over existing arbitrary drowsiness detection system where the driver's health and mental states can be monitored in real-time without constraints. <s> BIB005 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Hybrid Measures <s> For the past decade, it is well defined in the literature that fatigue is one of the most prospective factor in affecting the driver behavior. This paper presents a novel evaluation of driver fatigue condition based on multi-classifier technique and fusion of attributes approach. The process involved fusion of attributes including image of eye movement and photoplethysmography (PPG) signals that are given as inputs to multi-classifier. In order to develop the best inference classifiers, artificial neural network (ANN), dynamic bayesian network (DBN), support vector machine (SVM), independent component analysis (ICA) and genetic algorithm (GA) were tested in our study. The output from each inference classifier are scaled and product in an intervention module to indicate driver aptitude in real-time. Implementation of monitoring system is practically designed in Android-based smartphone device where it can received all the sensory information from the dedicated sensors installed at the steering wheel via a ... <s> BIB006 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Hybrid Measures <s> Driver drowsiness is one of the major causes of on-road accidents. Abnormal eye behavior, steering wheel activity, and vehicle trajectory during different drowsiness stages were studied in detail to overcome the limitations of single-sensor approaches. Some measures, such as percentage of eyelid closure, maximum close duration, and percentage of nonsteering were analyzed using analysis of variance ANOVA methods. Moreover, a two-stage data fusion framework was developed for the modeling combination of information from different sources. Fisher's linear discriminant was implied as the feature-level fusion method, and Dempster-Shafer evidence theory was introduced in the decision-level fusion process. The results suggest that the recognition system proposed here provided 90.7% accuracy. The reliability and accuracy of the fusion method were significantly higher than those of single sensors. © 2012 Wiley Periodicals, Inc. <s> BIB007
Each method used for detecting drowsiness has its own advantages and limitations. Vehicle-based measures are useful in measuring drowsiness when a lack of vigilance affects vehicle control or deviation. However, in some cases, there was no impact on vehicle-based parameters when the driver was drowsy BIB001 , which makes a vehicle-based drowsiness detection system unreliable. Behavioral measures are an efficient way to detect drowsiness and some real-time products have been developed  . However, when evaluating the available real-time detection systems, Lawrence et al. observed that different illumination conditions affect the reliability and accuracy of the measurements  . Physiological measures are reliable and accurate because they provide the true internal state of the driver; however, their intrusive nature has to be resolved. Among all physiological parameters investigated, ECG can be measured in a less intrusive manner. EEG signals require a number of electrodes to be placed on the scalp and the electrodes used for measuring EoG signals are placed near the eye which can hinder driving. Non-obtrusive physiological sensors to estimate the drowsiness of drivers are expected to become feasible in the near future BIB006 BIB002 . The advantages of physiological measures and the increasing availability of non-intrusive measurement equipment make it beneficial to combine physiological signals with behavioral and vehicle-based measures. A sample drowsiness detection system developed by combining ECG signals, standard deviation of lane position and facial images is shown in Figure 2 . Few research studies are attempting to detect driver drowsiness by the fusion of different methods BIB003 BIB005 BIB004 BIB007 . Cheng et al. combined behavioral measures and vehicle based measures and concluded that the reliability and accuracy of the hybrid method was significantly higher than those using single sensors BIB007 . Guosheng et al. used a mixture of subjective, behavioral (PERCLOS) and physiological measures (ECG, EEG) to detect drowsiness and found that this combination resulted in a significantly higher success rate than any individual metric. The average square error while removing physiological features were 1.2629, while the average square error for fusion was 0.5269 BIB003 . Although hybrid systems using different sensors have not been tested in a real environment, it would be interesting to investigate the ability to detect drowsiness using a combination of physiological signals with other measurements.