Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> Driver fatigue is a major cause of road accidents and has implications for road safety. This review discusses the concepts of fatigue and provides a summary on psychophysiological associations with driver fatigue. A variety of psychophysiological parameters have been used in previous research as indicators of fatigue, with electroencephalography perhaps being the most promising. Most research found changes in theta and delta activity to be strongly linked to transition to fatigue. Therefore, monitoring electroencephalography during driver fatigue may be a promising variable for use in fatigue countermeasure devices. The review also identified anxiety and mood states as factors that may possibly affect driver fatigue. Furthermore, personality and temperament may also influence fatigue. Given the above, understanding the psychology of fatigue may lead to better fatigue management. The findings from this review are discussed in the light of directions for future studies and for the development of fatigue countermeasures. <s> BIB001 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> We developed a new method for estimation of vigilance level by using both EEG and EMG signals recorded during transition from wakefulness to sleep. Previous studies used only EEG signals for estimating the vigilance levels. In this study, it was aimed to estimate vigilance level by using both EEG and EMG signals for increasing the accuracy of the estimation rate. In our work, EEG and EMG signals were obtained from 30 subjects. In data preparation stage, EEG signals were separated to its subbands using wavelet transform for efficient discrimination, and chin EMG was used to verify and eliminate the movement artifacts. The changes in EEG and EMG were diagnosed while transition from wakefulness to sleep by using developed artificial neural network (ANN). Training and testing data sets consist of the subbanded components of EEG and power density of EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: awake, drowsy, and sleep. The accuracy of estimation was about 98–99% while the accuracy of the previous study, which uses only EEG, was 95–96%. <s> BIB002 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> Heart Rate Variability (HRV) reflects the balance between sympathetic and parasympathetic activity. Slower HRV rhythms (LF) indicate increased sympathetic and/or lower vagal activity, wakefulness characteristics, while faster HRV rhythms (HF) indicate lower sympathetic and/or increased parasympathetic and vagal activity, sleepy characteristics. In this work we demonstrate that power spectral analysis of drivers' heart rate can report driving errors caused by sleepiness. Furthermore, variation of Fractal Dimension (FD) can aid significant information for the assessment of the driving situation. ECG and EEG data were collected from sleep-deprived subjects exposed to real field driving conditions. A lower ratio of low frequency to high frequency components (LF/HF), and lower LF values were reported on the occurrence of driving errors. <s> BIB003 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> Various investigations show that drivers' drowsiness is one of the main causes of traffic accidents. Thus, countermeasure device is currently required in many fields for sleepiness related accident prevention. This paper intends to perform the drowsiness prediction by employing Support Vector Machine (SVM) with eyelid related parameters extracted from EOG data collected in a driving simulator provided by EU Project SENSATION. The dataset is firstly divided into three incremental drowsiness levels, and then a paired t-test is done to identify how the parameters are associated with drivers' sleepy condition. With all the features, a SVM drowsiness detection model is constructed. The validation results show that the drowsiness detection accuracy is quite high especially when the subjects are very sleepy. <s> BIB004 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> We have developed a new method for automatic estimation of vigilance level by using electroencephalogram (EEG), electromyogram (EMG) and eye movement (EOG) signals recorded during transition from wakefulness to sleep. In the previous studies, EEG signals and EEG signals with EMG signals were used for estimating vigilance levels. In the present study, it was aimed to estimate vigilance levels by using EEG, EMG and EOG signals. The changes in EEG, EMG and EOG were diagnosed while transiting from wakefulness to sleep by using wavelet transform and developed artificial neural network (ANN). EEG signals were separated to its subbands using wavelet transform, LEOG (Left EOG), REOG (Right EOG) and chin EMG was used in ANN process for increasing the accuracy of the estimation rate by evaluating their tonic levels and also used in data preparation stage to verify and eliminate the movement artifacts. Then, training and testing data sets consist of the EEG subbands (delta, theta, alpha and beta); LEOG, REOG and EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: Awake, drowsy, and sleep. The accuracy of estimation is about 97-98% while the accuracy of the previous study which used only EEG was 95-96% and the study which used EEG with EMG was 98-99%. The reason of decreasing the percentage of present study according to the last study is because of the increase of the input data. <s> BIB005 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> The study monitored physiological parameter changes after 120-min of simulated driving. Blood pressures, heart rate (HR), heart rate variability (HRV) and palm temperatures were measured using an ANSWatch® monitor. Subjects were divided into two groups (A & B). Both groups performed 2-hour driving, but group B additionally took a 15-min exercise break. Heart rate, systolic pressure, LF/HF, and palm temperature decreased for group A after driving; for group B only HR and palm temperatures decreased. HRV and parasympathetic indices HF(AU) and HF(NU) increased for group A, while HRV and sympathetic index LF(AU) increased in group B. Group A had higher fatigue scores than group B. ANS activation may overcome some fatigue symptoms, but the recovery is nonetheless incomplete. Exercise break is proven to be an effective remedy, especially if accompanied by the ANS actions. The normalized parasympathetic index HF(NU), the normalized sympathetic index LF(NU), and the sympatho-vagal balance index LF/HF are three most promising parameters that could be further developed to monitor driver fatigue. <s> BIB006 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> Effects of driver's states adaptive driving support systems is highly expected for the prevention of traffic accidents. In order to create this constituent technology, detecting driver's psychosomatic states which occurs just before a traffic accident is essential. Therefore driver's distraction is thought as one of important factors. This study focused on detecting driver's cognitive distraction, a state which can easily lead to a traffic accident. We reproduced the cognitive distraction by imposing conversation or arithmetic loads to the subjects on a driving simulator. A stereo camera system were used as the means to track a subject's eyes, and head movements, which were set as classification features for pattern recognition on the Support Vector Machine (hereafter, SVM) basis used in the previous study of the AIDE project, a part of EU 6th Framework Programme. Diameter of pupil as well as the interval between heart R-waves (hereafter, heart rate RRI) from an ECG (electrocardiogram) were added for classification features to further improve the accuracy of driver's cognitive distraction detection. Based on this study, we established the methodology for more precise and faster driver's cognitive detection by using the AdaBoost. <s> BIB007 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> Driver drowsiness is one of the major causes of serious traffic accidents, which makes this an area of great socioeconomic concern. Continuous monitoring of drivers’ drowsiness thus is of great importance to reduce drowsiness-caused accidents. This proposed research developed a real-time, nonintrusive driver drowsiness detection system by building biosensors on the automobile steering wheel and driver’s seat to measure driver’s heart beat signals. Heart rate variability (HRV), a physiological signal that has established links to waking/sleepiness stages, is analyzed from the heat beat pulse signals for the detection of driver drowsiness. The novel design of measuring heat beat signal from biosensors on the steering wheel means this drowsiness detection system has almost no annoyance to the drivers, and the use of a physiological signal can ensure the drowsiness detection accuracy. <s> BIB008 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological 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> BIB009 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> Abstract Two complexity parameters of EEG, i.e. approximate entropy ( ApEn ) and Kolmogorov complexity ( Kc ) are utilized to characterize the complexity and irregularity of EEG data under the different mental fatigue states. Then the kernel principal component analysis (KPCA) and Hidden Markov Model (HMM) are combined to differentiate two mental fatigue states. The KPCA algorithm is employed to extract nonlinear features from the complexity parameters of EEG and improve the generalization performance of HMM. The investigation suggests that ApEn and Kc can effectively describe the dynamic complexity of EEG, which is strongly correlated with mental fatigue. Both complexity parameters are significantly decreased ( P <s> BIB010 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-97% on an average across all subjects. <s> BIB011 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> Research highlights? In this study we show heart rate variability which can be used as a passive means to quantify drowsiness. Frequency domain components of HRV is used for early detection of fatigue. ? We also designed a neural network based artificial intelligent algorithm which detects whether the given HRV signal is in alert of fatigue state. ? The frequency domain components of HRV were also used to distinguish between parasympathetic (HF) and sympathetic (LF) activities using LF/HF ratio. Statistical analysis was also performed to identify the difference between LF/HF ratio during alert and fatigue states. Long duration driving is a significant cause of fatigue related accidents on motorways. Fatigue caused by driving for extended hours can acutely impair driver's alertness and performance. This papers presents an artificial intelligence based system which could detect early onset of fatigue in drivers using heart rate variability (HRV) as the human physiological measure. The detection performance of neural network was tested using a set of electrocardiogram (ECG) data recorded under laboratory conditions. The neural network gave an accuracy of 90%. This HRV based fatigue detection technique can be used as a fatigue countermeasure. <s> BIB012 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> Provision of ubiquitous healthcare solutions which provide healthcare services at anytime anywhere has become more favorable nowadays due to the emphasis on healthcare awareness and also the growth of mobile wireless technologies. Following this approach, an Android™ smart phone device is proposed as a mobile monitoring terminal to observe and analyze ECG (electrocardiography) waveforms from wearable ECG devices in real time under the coverage of a wireless sensor network (WSN). The exploitation of WSN in healthcare is able to substitute the complicated wired technology, moving healthcare away from a fixed location setting. As an extension to the monitoring scheme, medicine care is taken into consideration by utilizing the mobile phone as a barcode decoder, to verify and assist out-patients in the medication administration process, providing a better and more comprehensive healthcare service. <s> BIB013 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks ( p-value <;0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications. <s> BIB014 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> In long-term prevention and in rehabilitation of health of elderly people the recording of vital signs plays an important role. Especially the progress of rehabilitation can be deduced from the recording of an electrocardigram (ECG), blood pressure and body temperature. In this paper we present a wireless coupled recording device for long-term monitoring of these vital sign signals. We record the ECG, the blood pressure and the skin temperature and include a 3D-acceleration sensor for the determination of the movements during recording. To deal with motion artifacts in all recorded properties we use data fusion to reject or correct distorted vital sign signals. <s> BIB015 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> This work presents a novel easy-to-use system intended for the fast and noninvasive monitoring of the Lead I electrocardiogram (ECG) signal by using a wireless steering wheel. The system uses a dual ground electrode configuration connected to a low-power analog front-end to reduce 50/60 Hz interference and it is able to show a stable ECG signal with good enough quality for monitoring purposes in less than 5 s. A novel heart rate detection algorithm based on the continuous wavelet transform has been implemented, which is specially designed to be robust against the most common sources of noise and interference present when acquiring the ECG in the hands, i.e., electromyographic (EMG) noise and baseline wandering. The algorithm shows acceptable performance even under non-ordinary high levels of EMG noise and yields a positive predictivity value of 100.00% and a sensitivity of 99.75% when tested in normal use with subjects of different age, gender, and physical condition. <s> BIB016 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological Measures <s> We developed nonintrusive methods for simultaneous electrocardiogram, photoplethysmogram, and ballistocardiogram measurements that do not require direct contact between instruments and bare skin. These methods were applied to the design of a diagnostic chair for unconstrained heart rate and blood pressure monitoring purposes. Our methods were operationalized through capacitively coupled electrodes installed in the chair back that include high-input impedance amplifiers, and conductive textiles installed in the seat for capacitive driven-right-leg circuit configuration that is capable of recording electrocardiogram information through clothing. Photoplethysmograms were measured through clothing using seat mounted sensors with specially designed amplifier circuits that vary in light intensity according to clothing type. Ballistocardiograms were recorded using a film type transducer material, polyvinylidenefluoride (PVDF), which was installed beneath the seat cover. By simultaneously measuring signals, beat-to-beat heart rates could be monitored even when electrocardiograms were not recorded due to movement artifacts. Beat-to-beat blood pressure was also monitored using unconstrained measurements of pulse arrival time and other physiological parameters, and our experimental results indicated that the estimated blood pressure tended to coincide with actual blood pressure measurements. This study demonstrates the feasibility of our method and device for biological signal monitoring through clothing for unconstrained long-term daily health monitoring that does not require user awareness and is not limited by physical activity. <s> BIB017 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Physiological 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> BIB018
As drivers become drowsy, their head begins to sway and the vehicle may wander away from the center of the lane. The previously described vehicle-based and vision based measures become apparent only after the driver starts to sleep, which is often too late to prevent an accident. However, physiological signals start to change in earlier stages of drowsiness. Hence, physiological signals are more suitable to detect drowsiness with few false positives; making it possible to alert a drowsy driver in a timely manner and thereby prevent many road accidents. Many researchers have considered the following physiological signals to detect drowsiness: electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and electro-oculogram (EoG) ( Table 3) . Some researchers have used the EoG signal to identify driver drowsiness through eye movements BIB011 BIB004 BIB005 . The electric potential difference between the cornea and the retina generates an electrical field that reflects the orientation of the eyes; this electrical field is the measured EoG signal. Researchers have investigated horizontal eye movement by placing a disposable Ag-Cl electrode on the outer corner of each eye and a third electrode at the center of the forehead for reference BIB004 . The electrodes were placed as specified so that the parameters -Rapid eye movements (REM) and Slow Eye Movements (SEM) which occur when a subject is awake and drowsy respectively, can be detected easily BIB001 . The heart rate (HR) also varies significantly between the different stages of drowsiness, such as alertness and fatigue BIB006 BIB007 . Therefore, heart rate, which can be easily determined by the ECG signal, can also be used to detect drowsiness. Others have measured drowsiness using Heart Rate Variability (HRV), in which the low (LF) and high (HF) frequencies fall in the range of 0.04-0.15 Hz and 0.14-0.4 Hz, respectively BIB011 BIB012 . HRV is a measure of the beat-to-beat (R-R Intervals) changes in the heart rate. The ratio of LF to HF in the ECG decreases progressively as the driver progresses from an awake to a drowsy state BIB009 BIB012 . The Electroencephalogram (EEG) is the physiological signal most commonly used to measure drowsiness. The EEG signal has various frequency bands, including the delta band (0.5-4 Hz), which corresponds to sleep activity, the theta band (4-8 Hz), which is related to drowsiness, the alpha band (8-13 Hz), which represents relaxation and creativity, and the beta band (13-25 Hz), which corresponds to alertness BIB002 BIB014  BIB010 . A decrease in the power changes in the alpha frequency band and an increase in the theta frequency band indicates drowsiness. Akin et al. observed that the success rate of using a combination of EEG and EMG signals to detect drowsiness is higher than using either signal alone BIB002 . The measurement of raw physiological signals is always prone to noise and artifacts due to the movement that is involved with driving. Hence, in order to eliminate noise, various preprocessing techniques, such as low pass filter, digital differentiators, have been used (Table 2 ). In general, an effective digital filtering technique would remove the unwanted artifacts in an optimal manner BIB003 . A number of statistical features are then extracted from the processed signal using various feature extraction techniques, including Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) BIB002 BIB014  . The extracted features are then classified using Artificial Neural Networks (ANN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), or other similar methods BIB011 BIB004 BIB005 . The reliability and accuracy of driver drowsiness detection by using physiological signals is very high compared to other methods. However, the intrusive nature of measuring physiological signals remains an issue to be addressed. To overcome this, researchers have used wireless devices to measure physiological signals in a less intrusive manner by placing the electrodes on the body and obtaining signals using wireless technologies like Zigbee  , Bluetooth BIB015 . Some researchers have gone further ahead by measuring physiological signals in a non intrusive way; by placing electrodes on the steering wheel BIB008 BIB016 or on the driver's seat BIB008 BIB017 . The signals obtained were then processed in android based smart phone devices BIB018 BIB013 and the driver was alerted on time. The accuracy of a non-intrusive system is relatively less due to movement artifacts and errors that occur due to improper electrode contact. However, researchers are considering to use this because of its user friendliness. In recent years, experiments are conducted to validate non-intrusive systems BIB016 BIB017 . The advantages and disadvantages of the different type of measures are summarized in Table 4 . 