Detecting Driver Drowsiness Based on Sensors: A Review
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paper_title: Estimating vigilance level by using EEG and EMG signals

paper_content:

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%.
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paper_title: The relationship between subjective and objective sleepiness and performance during a simulated night-shift with a nap countermeasure

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Abstract The aim of the present study was to investigate the relationship between perceived and actual sleepiness and performance during a simulated night-shift that included a 30-min night-nap as an on-duty sleepiness countermeasure. Twenty-four healthy young adults (nine males, fifteen females) participated in a repeated measures design comprising two experimental conditions: no night-nap and 30-min night-nap. Both groups were given a 2-h prophylactic afternoon sleep opportunity (1500–1700 h). Measures of subjective sleepiness (Stanford Sleepiness Scale, Karolinska Sleepiness Scale and Visual Analogue Scale), objective sleepiness (sleep latency tests), objective performance (Symbol–Digit Substitution Task) and reaction time (Psychomotor Vigilance Task) were taken before the night-nap (0230 h) and at several intervals post-nap. Time–series correlation analyses indicated that subjective sleepiness was less correlated with objective sleepiness and objective performance when participants were given a 30-min night-nap. However subjective sleepiness and reaction time performance was strongly correlated in both conditions, and there was no significant difference between the nap and no-nap conditions. Consistent with previous research, results of the present study indicate that subjective and objective indicators of sleepiness and performance may not always correspond, and this relationship may be reduced by the inclusion of a night-nap.
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paper_title: A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue

paper_content:

A new scheme for driver fatigue detection is presented, which is based on the nonlinear unscented Kalman filter and eye tracking. Assuming a probability distribution than to approximate an arbitrary nonlinear function or transformation, eye nonlinear tracking can be achieved using an unscented transformation (UT), which adopts a set of deterministic sigma points to match the posterior probability density function of the eye movement. Driver fatigue can be detected using the percentage of eye closure (PERCLOS) framework in a realistic driving condition after the eye nonlinear tracking. This system was tested adequately in realistic driving environments with subjects of different genders, with/without glasses, in day/night driving, being commercial/noncommercial drivers, in continuous driving time, and under different road conditions. The last experimental results show that the proposed method not only improves the robustness for nonlinear eye tracking, but also can provide more accurate estimation than the traditional Kalman filter.
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paper_title: A driver fatigue recognition model based on information fusion and dynamic Bayesian network

paper_content:

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.
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paper_title: Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm

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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.
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paper_title: Yawning Detection Based on Gabor Wavelets and LDA

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To improve driving safety,the authors propose an approach to locate a driver's mouth by a web camera and extract texture features from mouth comers for monitoring drivers' yawning.Firstly,it detects drivers' left and right mouth corners by gray projection based on the result of driver face detection,and then it extracts texture features of drivers' mouth corners by Gabor wavelets.Finally,LDA is used to classify Gabor features for yawning detection.The proposed approach is tested on 3 000 images from thirty subjects with variations in illuminations,poses,and facial accessories(glasses).Yawning is also detected by the ratio of mouth height to width as a baseline.Experiment results show that the proposed approach is suitable for real time yawning detection,Gabor features are more powerful than geometric features for yawning representation,and an average recognition rate of 91.97%is achieved which is much better than the baseline.
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paper_title: Fatigue, sleep restriction and driving performance.

paper_content:

We ran a randomized cross-over design study under sleep-deprived and non-sleep-deprived driving conditions to test the effects of sleep restriction on real driving performance. The study was performed in a sleep laboratory and on an open French highway. Twenty-two healthy male subjects (age = 21.5 +/- 2 years; distance driven per year = 12,225 +/- 4739 km (7641 +/- 2962 miles) [mean +/- S.D.]) drove 1000 km (625 miles) over 10 h during five 105 min sessions on an open highway. Self-rated fatigue and sleepiness before each session, number of inappropriate line crossings from video recordings and simple reaction time (RT) were measured. Total crossings increased after sleep restriction (535 crossings in the sleep-restricted condition versus 66 after non-restricted sleep (incidence rate ratio (IRR): 8.1; 95% confidence interval (95% CI): 3.2-20.5; p < 0.001)), from the first driving session. The interaction between the two factors (conditionxtime of day) was also significant (F(5, 105) = 3.229; p < 0.05). Increasing sleepiness score was associated with increasing crossings during the next driving session in the sleep-restricted (IRR: 1.9; 95% CI: 1.4-2.4) but not in the non-restricted condition (IRR: 1.0; 95% CI: 0.8-1.3). Increasing self-perceived fatigue was not associated with increasing crossings in either condition (IRR: 0.95; 95% CI: 0.93-0.98 and IRR: 1.0; 95% CI: 0.98-1.02). Rested subjects drove 1000 km with four shorts breaks with only a minor performance decrease. Sleep restriction induced important performance degradation even though time awake (8h) and session driving times (105 min) were relatively short. Major inter-individual differences were observed under sleep restriction. Performance degradation was associated with sleepiness and not fatigue. Sleepiness combined with fatigue significantly affected RT. Road safety campaigns should encourage drivers to avoid driving after sleep restriction, even on relatively short trips especially if they feel sleepy.
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paper_title: A study of heart rate and brain system complexity and their interaction in sleep-deprived subjects

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In this work we investigate the synchronization of the dynamic behaviour of heart rate (ECG) and brain (EEG) signals using sample entropy as a measure of complexity. EEG and ECG recordings were collected during experiment with sleep-deprived subjects exposed to real field driving conditions. The degree to which brain and heart complexity loose complexity in a synchronous manner, indicating a possible interaction between the two systems is investigated. Preliminary results obtained from the examination of four subjects show the existence of a weak-to-intermediate cross-correlation between these pairs of biological oscillators. Furthermore, the frequency content in both heart rate and brain signals was calculated via power spectrum analysis and the association of synchronisation patterns with prevalent frequencies in the two systems was investigated.
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paper_title: Efficient driver drowsiness detection at moderate levels of drowsiness.

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Previous research on driver drowsiness detection has focused primarily on lane deviation metrics and high levels of fatigue. The present research sought to develop a method for detecting driver drowsiness at more moderate levels of fatigue, well before accident risk is imminent. Eighty-seven different driver drowsiness detection metrics proposed in the literature were evaluated in two simulated shift work studies with high-fidelity simulator driving in a controlled laboratory environment. Twenty-nine participants were subjected to a night shift condition, which resulted in moderate levels of fatigue; 12 participants were in a day shift condition, which served as control. Ten simulated work days in the study design each included four 30-min driving sessions, during which participants drove a standardized scenario of rural highways. Ten straight and uneventful road segments in each driving session were designated to extract the 87 different driving metrics being evaluated. The dimensionality of the overall data set across all participants, all driving sessions and all road segments was reduced with principal component analysis, which revealed that there were two dominant dimensions: measures of steering wheel variability and measures of lateral lane position variability. The latter correlated most with an independent measure of fatigue, namely performance on a psychomotor vigilance test administered prior to each drive. We replicated our findings across eight curved road segments used for validation in each driving session. Furthermore, we showed that lateral lane position variability could be derived from measured changes in steering wheel angle through a transfer function, reflecting how steering wheel movements change vehicle heading in accordance with the forces acting on the vehicle and the road. This is important given that traditional video-based lane tracking technology is prone to data loss when lane markers are missing, when weather conditions are bad, or in darkness. Our research findings indicated that steering wheel variability provides a basis for developing a cost-effective and easy-to-install alternative technology for in-vehicle driver drowsiness detection at moderate levels of fatigue.
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paper_title: Predicting driver drowsiness using vehicle measures: recent insights and future challenges.

paper_content:

INTRODUCTION ::: Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs. ::: ::: ::: METHOD ::: The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time. ::: ::: ::: RESULTS ::: Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time. ::: ::: ::: CONCLUSIONS ::: Further research is necessary to examine more complex functions, as well as individual differences between drivers. ::: ::: ::: IMPACT ON INDUSTRY ::: A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.
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paper_title: MULTISCALE DYNAMIC FEATURES BASED DRIVER FATIGUE DETECTION

paper_content:

Driver fatigue is a significant factor in many traffic accidents. We propose a novel approach for driver fatigue detection from facial image sequences, which is based on multiscale dynamic features. First, Gabor filters are used to get a multiscale representation for image sequences. Then Local Binary Patterns are extracted from each multiscale image. To account for the temporal aspect of human fatigue, the LBP image sequence is divided into dynamic units, and a histogram of each dynamic unit is computed and concatenated as dynamic features. Finally a statistical learning algorithm is applied to extract the most discriminative features from the multiscale dynamic features and construct a strong classifier for fatigue detection. The proposed approach is validated under real-life fatigue conditions. The test data includes 600 image sequences with illumination and pose variations from 30 people's videos. Experimental results show the validity of the proposed approach, and a correct rate of 98.33% is achieved which is much better than the baselines.
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paper_title: Changes in Physiological Parameters Induced by Indoor Simulated Driving: Effect of Lower Body Exercise at Mid-Term Break

paper_content:

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.
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paper_title: Predicting driver drowsiness using vehicle measures: recent insights and future challenges.

paper_content:

INTRODUCTION ::: Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs. ::: ::: ::: METHOD ::: The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time. ::: ::: ::: RESULTS ::: Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time. ::: ::: ::: CONCLUSIONS ::: Further research is necessary to examine more complex functions, as well as individual differences between drivers. ::: ::: ::: IMPACT ON INDUSTRY ::: A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.
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paper_title: Impact of Narrower Lane Width Comparison Between Fixed-Base Simulator and Real Data

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This driving simulator study investigated differences in the influence of lane width reduction on speed profiles and lateral positions in real-world and simulator conditions. The use of driving simulators has been expanding in transportation research. This use raises the question of the simulators' validity. The study was developed in two steps. In the first step, an experiment was conducted with the INRETS (French National Institute for Transport and Safety Research) driving simulator. Forty-three drivers drove in the simulator. Two configurations of lane widths (3.5 m and 3 m) were tested, both chosen in reference to a previous French field study carried out on a rural road to assess the impact of lane width reduction and the provision of a hard shoulder on driver behaviors (speed, lateral positioning). In the second step, results were compared with the results of the French field study. The comparison showed that, as in the field study, reducing the lane width had no impact on speeds but did induce the participants to drive closer to the center of the road. It also showed that oncoming vehicles induced subjects to move toward the right side of their lanes. Finally, the results showed a relative behavioral validity.
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paper_title: A driver fatigue recognition model based on information fusion and dynamic Bayesian network

paper_content:

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.
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paper_title: The impact of perceptual treatments on driver's behavior: from driving simulator studies to field tests--first results.

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Our study focused on the lateral position of drivers in relation to risk on rural crest vertical curves, using a field site proposed by a local operator of the French road network (Conseil Général de Maine-et-Loire, 49). The final goal was to test one road treatment on this field site. The study consisted of three stages. The first, using driving simulators, selected two perceptual treatments (i.e., rumble strips on both sides of the centerline and sealed shoulders) from five that were tested in order to help drivers maintain lateral control when driving on crest vertical curves. The rumble strips were installed first on the field site. The second stage was to develop a diagnostic device specifically in order to evaluate, on the field site, the impact of a perceptual treatment on the driver's performance (i.e., lateral position). This diagnostic device was installed in the field upstream and downstream of the target crest vertical curve. The third stage was to collect the data during two periods, before and after the centerline rumble strips were installed. We then compared the results obtained in the field study with those from the driving simulator studies. The comparison showed that, as in the simulator studies, the centerline rumble strips on the crest vertical curve affected lateral positions, causing the participants to drive closer to the center of the lane. Finally, the results showed the usefulness of driving simulators in the road design process.
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paper_title: Physiological responses to simulated and on-road driving

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Driving simulators have become an increasingly popular tool to study and assess drivers. Physiological measurements not only provide an important index of an individual's presence in the virtual environment, but they also permit us to compare simulated and on-road experiences. However, at this point, few studies examining the ecological validity of simulated driving have included physiological variables. In a first study, we embedded three surprising events into a typical simulated road circuit. The first event consisted of a car pulling out suddenly from the shoulder of the road, while the remaining two events consisted of a green traffic light changing to amber as the driver approached the intersection. We noted statistically significant elevations in the mean heart rate (MHR) response to virtual events of about 4beats per minute (bpm) during the 15s immediately following the events. In a second study, we directly compared heart rate, oxygen consumption (VO(2)), and mean ventilation (MV(E)) responses to similar simulated and on-road drives. The change in physiological variables from baseline to driving was similar between simulated and on-road conditions, and a very strong correlation between simulated and on-road driving values for MV(E) (r=0.90) was observed. MHR and maximum heart rate (HR(max)) were nonetheless significantly higher during on-road drives. These studies suggest that the level of immersion of a fixed base simulator is great enough to elicit presence, and achieve both relative and absolute validity for certain physiological parameters. Nonetheless, the absolute responses between virtual and real world experiences remain different. For both research and evaluation purposes, it is critical that we better understand the impact of the driver's perceived level of risk or difficulty during simulation on their driving behaviour and physiological responses. Language: en
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paper_title: Fatigue, sleep restriction and driving performance.

paper_content:

We ran a randomized cross-over design study under sleep-deprived and non-sleep-deprived driving conditions to test the effects of sleep restriction on real driving performance. The study was performed in a sleep laboratory and on an open French highway. Twenty-two healthy male subjects (age = 21.5 +/- 2 years; distance driven per year = 12,225 +/- 4739 km (7641 +/- 2962 miles) [mean +/- S.D.]) drove 1000 km (625 miles) over 10 h during five 105 min sessions on an open highway. Self-rated fatigue and sleepiness before each session, number of inappropriate line crossings from video recordings and simple reaction time (RT) were measured. Total crossings increased after sleep restriction (535 crossings in the sleep-restricted condition versus 66 after non-restricted sleep (incidence rate ratio (IRR): 8.1; 95% confidence interval (95% CI): 3.2-20.5; p < 0.001)), from the first driving session. The interaction between the two factors (conditionxtime of day) was also significant (F(5, 105) = 3.229; p < 0.05). Increasing sleepiness score was associated with increasing crossings during the next driving session in the sleep-restricted (IRR: 1.9; 95% CI: 1.4-2.4) but not in the non-restricted condition (IRR: 1.0; 95% CI: 0.8-1.3). Increasing self-perceived fatigue was not associated with increasing crossings in either condition (IRR: 0.95; 95% CI: 0.93-0.98 and IRR: 1.0; 95% CI: 0.98-1.02). Rested subjects drove 1000 km with four shorts breaks with only a minor performance decrease. Sleep restriction induced important performance degradation even though time awake (8h) and session driving times (105 min) were relatively short. Major inter-individual differences were observed under sleep restriction. Performance degradation was associated with sleepiness and not fatigue. Sleepiness combined with fatigue significantly affected RT. Road safety campaigns should encourage drivers to avoid driving after sleep restriction, even on relatively short trips especially if they feel sleepy.
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paper_title: Driver perception of roadside configurations on two-lane rural roads: Effects on speed and lateral placement.

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This paper reports the results of a driving simulator study which sought to analyze the effect that: (a) three roadside configurations on a two-lane rural road lined with trees have on speed and lateral position of the driver, depending on different cross-sections as well as geometric elements; (b) the beginning of the guardrail barrier has upon the driver's behavior whenever this occurs on the left curve, right curve or tangent. A two-lane rural road lined with trees was designed and implemented in an advanced-interactive driving simulator. Two different cross-sections (with and without a shoulder), which were combined with three roadside configurations (only trees, trees and barriers, trees and barriers having undergone a treatment), were tested. Six road scenarios were then analyzed. Thirty-six drivers (33 were deemed to be valid and used for the analysis) drove in the simulator using these scenarios and the speed and lateral placement values were collected. Statistical analysis showed that the driver behavior was only affected by the cross-sections and geometric elements but not by roadside configurations. Although the presence of trees along the road represents a factor that increases the severity of run-off-road accidents, drivers do not change their behavior when barriers are not present. Concerning the effects of the beginning of the barrier, MANOVA revealed a main effect for roadside configuration on lateral position but not on speed. There was also a clear tendency of drivers to "cut" both the right curves as well as the left curves in order to minimize the speed reduction in the tangent-curve-tangent transition. These main results allow useful suggestions to be made as regards safety measures for improving road safety on two-lane rural roads lined with trees.
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paper_title: Vehicle accidents related to sleep: a review

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UNLABELLED ::: Falling asleep while driving accounts for a considerable proportion of vehicle accidents under monotonous driving conditions. Many of these accidents are related to work--for example, drivers of lorries, goods vehicles, and company cars. Time of day (circadian) effects are profound, with sleepiness being particularly evident during night shift work, and driving home afterwards. Circadian factors are as important in determining driver sleepiness as is the duration of the drive, but only duration of the drive is built into legislation protecting professional drivers. Older drivers are also vulnerable to sleepiness in the mid-afternoon. Possible pathological causes of driver sleepiness are discussed, but there is little evidence that this factor contributes greatly to the accident statistics. Sleep does not occur spontaneously without warning. Drivers falling asleep are unlikely to recollect having done so, but will be aware of the precursory state of increasing sleepiness; probably reaching a state of fighting off sleep before an accident. Self awareness of sleepiness is a better method for alerting the driver than automatic sleepiness detectors in the vehicle. None of these have been proved to be reliable and most have shortcomings. Putative counter measures to sleepiness, adopted during continued driving (cold air, use of car radio) are only effective for a short time. The only safe counter measure to driver sleepiness, particularly when the driver reaches the stage of fighting sleep, is to stop driving, and--for example, take a 30 minute break encompassing a short (< 15 minute) nap or coffee (about 150 mg caffeine), which are very effective particularly if taken together. Exercise is of little use. ::: ::: ::: CONCLUSIONS ::: More education of employers and employees is needed about planning journeys, the dangers of driving while sleepy, and driving at vulnerable times of the day.
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paper_title: On-road and simulated driving: concurrent and discriminant validation.

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INTRODUCTION ::: A converging pair of studies investigated the validity of a simulator for measuring driving performance/skill. STUDY 1: A concurrent validity study compared novice driver performance during an on-road driving test with their performance on a comparable simulated driving test. ::: ::: ::: RESULTS ::: Results showed a reasonable degree of concordance in terms of the distribution of driving errors on-road and errors on the simulator. Moreover, there was a significant relationship between the two when driver performance was rank ordered according to errors, further establishing the relative validity of the simulator. However, specific driving errors on the two tasks were not closely related suggesting that absolute validity could not be established and that overall performance is needed to establish the level of skill. STUDY 2: A discriminant validity study compared driving performance on the simulator across three groups of drivers who differ in their level of experience--a group of true beginners who had no driving experience, a group of novice drivers who had completed driver education and had a learner's permit, and a group of fully licensed, experienced drivers. ::: ::: ::: RESULTS ::: The findings showed significant differences among the groups in the expected direction--the various measures of driving errors showed that beginners performed worse than novice drivers and that experienced drivers had the fewest errors. Collectively, the results of the concurrent and discriminant validity studies support the use of the simulator as a valid measure of driving performance for research purposes. ::: ::: ::: IMPACT ON INDUSTRY ::: These findings support the use of a driving simulator as a valid measure of driving performance for research purposes. Future research should continue to examine validity between on-road driving performance and performance on a driving simulator and the use of simulated driving tests in the evaluation of driver education/training programs.
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paper_title: Predicting driver drowsiness using vehicle measures: recent insights and future challenges.

paper_content:

INTRODUCTION ::: Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs. ::: ::: ::: METHOD ::: The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time. ::: ::: ::: RESULTS ::: Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time. ::: ::: ::: CONCLUSIONS ::: Further research is necessary to examine more complex functions, as well as individual differences between drivers. ::: ::: ::: IMPACT ON INDUSTRY ::: A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.
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paper_title: Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers

paper_content:

The effect of partial sleep deprivation and driving duration on subsequent alertness and performance in car drivers was investigated. Twenty healthy male subjects, between 25 and 55 years of age, free from any sleep disorder, took part in two simulated driving sessions carried out between 2 p.m. and 4 p.m. Before one session, subjects were sleep deprived as they were allowed to sleep only between 3 a.m. and 7 a.m. during the preceding night. Throughout the driving task, the subjects' driving performance, electroencephalogram and Karolinska Sleepiness Scale (KSS) score were recorded. The results revealed that sleep deprivation had an effect on KSS score but not on the (alpha+theta) spectral power, while driving duration had an effect on these two parameters. This effect was also influenced by sleep restriction. Time on driving task alone had a significant effect on driving performance; the sleep restriction having only an effect on one of the performances indices studied: the number of right edge-line crossings. These results are interpreted in terms of the relationship between level of alertness and performance impairment.
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paper_title: Driver's visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers' eye movements in day, night and rain driving.

paper_content:

Road crashes are the main cause of death of young people in the developed world. The reasons that cause traffic crashes are numerous; however, most researchers agree that a lack of driving experience is one of the major contributing factors. In addition it has been demonstrated that environmental factors such as driving during night and rain increases the risk of a crash. Both of these factors may be related to drivers' visual search strategies that become more efficient with increased experience. In the present study we recorded the eye movements of driving instructors and learner drivers while they drove three virtual routes that included day, night and rain routes in a driving simulator. The results showed that driving instructors had an increased sampling rate, shorter processing time and broader scanning of the road than learner drivers. This broader scanning of the road could be possibly explained by the mirror inspection pattern which revealed that driving instructors fixated more on the side mirrors than learner drivers. Also it was found that poor visibility conditions, especially rain, decrease the effectiveness of drivers' visual search. The lack of interaction between driving experience and visibility suggests that some aspects of visual search are affected by general rather than situation specific driving experience. The present findings support the effect of driving experience in modifying eye movement strategies. The high accident risk of night and rain driving could be partly explained by the decrement in visual search strategies during these conditions. Finally it is argued that the use of driving simulators can provide valuable insights regarding driving safety.
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paper_title: Driving Simulator Validity: Some Considerations

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The validity of driving simulators for behavioral research is discussed. The concept of validity is introduced and explained, and a survey of validation studies follows, in the TNO driving simulator and others, comparing field and simulator study results. Results for mid-level driving simulators show that generally absolute validity of route choice behavior is obtained and relative validity of speed and lateral control behavior is obtained. There is evidence suggesting that for a number of applications the presence of a moving base and possibly a higher image resolution might increase the validity of a driving simulator.
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paper_title: Monotony of road environment and driver fatigue: a simulator study

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Studies have shown that drowsiness and hypovigilance frequently occur during highway driving and that they may have serious implications in terms of accident causation. This paper focuses on the task induced factors that are involved in the development of these phenomena. A driving simulator study was conducted in order to evaluate the impact of the monotony of roadside visual stimulation using a steering wheel movement (SWM) analysis procedure. Fifty-six male subjects each drove during two different 40-min periods. In one case, roadside visual stimuli were essentially repetitive and monotonous, while in the other one, the environment contained disparate visual elements aiming to disrupt monotony without changing road geometry. Subject's driving performance was compared across these conditions in order to determine whether disruptions of monotony can have a positive effect and help alleviate driver fatigue. Results reveal an early time-on-task effect on driving performance for both driving periods and more frequent large SWM when driving in the more monotonous road environment, which implies greater fatigue and vigilance decrements. Implications in terms of environmental countermeasures for driver fatigue are discussed.
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paper_title: Biosignal Based Discrimination between Slight and Strong Driver Hypovigilance by Support-Vector Machines

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In the area of transportation research, there is a growing need for robust and reliable measures of hypovigilance, particularly due to the current volume of research in the development and validation of Fatigue Monitoring Technologies (FMT). Most of the currently emerging FMT is vision based. The parameter Percentage of Eyelid Closure (PERCLOS) is used for the fatigue detection. The development and validation of PERCLOS based FMT require an independent reference standard of drivers’ hypovigilance. Most approaches utilized electrooculography (EOG) and electroencephalography (EEG) combined with descriptive statistics of a few time or spectral domain features. Typically, the power spectral densities (PSD) averaged in four to six spectral bands is used for fatigue characterization. This constricted approach led to sometimes contradicting results and questioned the validity of the EEG and EOG as gold standard for driver fatigue, wrongly as we will show. Here we present a more general approach using generalized EEG and EOG PSD features in combination with data fusion and advanced computational intelligence methods, such as Support-Vector Machines (SVM). Biosignal based discrimination of driver hypovigilance was performed by independent class labels which were derived from Karolinska Sleepiness Scale (KSS) and from variation of lane deviation (VLD). The first is a measure of subjectively self-experienced hypovigilance, whereas the second is an objective measure of performance decrements. For simplicity, two label classes were discriminated: slight and strong hypovigilance. The discrimination results of PERCLOS were compared with results from single and combined EEG and EOG channels. We conclude that EEG and EOG biosignals are substantially more suited to assess driver’s hypovigilance than the PERCLOS biosignals. In addition, computational intelligence performed better when objective class labels were used instead of subjective class labels.
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paper_title: Driver drowsiness detection with eyelid related parameters by Support Vector Machine

paper_content:

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.
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paper_title: Subjective sleepiness, simulated driving performance and blink duration: examining individual differences.

paper_content:

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
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paper_title: Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers

paper_content:

The effect of partial sleep deprivation and driving duration on subsequent alertness and performance in car drivers was investigated. Twenty healthy male subjects, between 25 and 55 years of age, free from any sleep disorder, took part in two simulated driving sessions carried out between 2 p.m. and 4 p.m. Before one session, subjects were sleep deprived as they were allowed to sleep only between 3 a.m. and 7 a.m. during the preceding night. Throughout the driving task, the subjects' driving performance, electroencephalogram and Karolinska Sleepiness Scale (KSS) score were recorded. The results revealed that sleep deprivation had an effect on KSS score but not on the (alpha+theta) spectral power, while driving duration had an effect on these two parameters. This effect was also influenced by sleep restriction. Time on driving task alone had a significant effect on driving performance; the sleep restriction having only an effect on one of the performances indices studied: the number of right edge-line crossings. These results are interpreted in terms of the relationship between level of alertness and performance impairment.
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paper_title: Video based detection of driver fatigue

paper_content:

This thesis addresses the problem of drowsy driver detection using computer vision techniques applied to the human face. Specifically we explore the possibility of discriminating drowsy from alert video segments using facial expressions automatically extracted from video. Several approaches were previously proposed for the detection and prediction of drowsiness. There has recently been increasing interest in computer vision approaches as it is a potentially promising approach due to its non-invasive nature for detecting drowsiness. Previous studies with vision based approaches detect driver drowsiness primarily by making pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to explore, understand and exploit actual human behavior during drowsiness episodes. We have collected two datasets including facial and head movement measures. Head motion is collected through an accelerometer for the first dataset (UYAN-1) and an automatic video based head pose detector for the second dataset (UYAN-2). We use outputs of the automatic classifiers of the facial action coding system (FACS) for detecting drowsiness. These facial actions include blinking and yawn motions, as well as a number of other facial movements. These measures are passed to a learning-based classifier based on multinomial logistic regression. In UYAN-1 the system is able to predict sleep and crash episodes during a driving computer game with 0.98 performance area under the receiver operator characteristic curve for across subjects tests. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis reveals new information about human facial behavior during drowsy driving. In UYAN-2 fine discrimination of drowsy states are also explored on a separate dataset. The degree to which individual facial action units can predict the difference between moderately drowsy to acutely drowsy is studied. Signal processing techniques and machine learning methods are employed to build a person independent acute drowsiness detection system. Temporal dynamics are captured using a bank of temporal filters. Individual action unit predictive power is explored with an MLR based classifier. Best performing five action units have been determined for a person independent system. The system is able to obtain 0.96 performance of area under the receiver operator characteristic curve for a more challenging dataset with the combined features of the best performing 5 action units. Moreover the analysis reveals new markers for different levels of drowsiness.
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paper_title: Impairment of Driving Performance Caused by Sleep Deprivation or Alcohol: A Comparative Study

paper_content:

A study was conducted to assess the relative impact of partial sleep deprivation (restriction to 4 h sleep before testing) and full sleep deprivation (no sleep on the night before testing) on 2 h of simulated driving, compared with an alcohol treatment (mean blood alcohol content = 0.07%). Data were collected from the 64 male participants on the primary driving task, psychophysiology (0.1 Hz heart rate variability), and subjective self-assessment. The results revealed that the full sleep deprivation and alcohol group exhibited a safety-critical decline in lane-keeping performance. The partial sleep deprivation group exhibited only noncritical alterations in primary task performance. Both sleep-deprived groups were characterized by subjective discomfort and an awareness of reduced performance capability. These subjective symptoms were not perceived by the alcohol group. The findings are discussed with reference to the development of systems for the online diagnosis of driver fatigue. Potential applications of this research include the formulation of performance criteria to be encompassed within a driver impairment monitoring system.
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paper_title: An on-board system for detecting driver drowsiness based on multi-sensor data fusion using Dempster-Shafer theory

paper_content:

This paper presents a data fusion method for the on-board detection of driver drowsiness in real time. Multiple sensors including camera to capture the driver's eye status, angle sensor to measure the driver's steering behavior, and clock to indicate the time on task were implemented. A data fusion framework based on Dempster-Shafer theory is built for modeling and combining the pieces of evidence, and to generate an overall inference of the driver's drowsiness level. The method has been validated in an experiment on a driving simulator. The results suggest that the data fusion process could reduce the uncertainty in the drowsiness inference and obtain a better system performance compared with any single sensor.
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paper_title: Subjective sleepiness, simulated driving performance and blink duration: examining individual differences.

paper_content:

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
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paper_title: Predicting driver drowsiness using vehicle measures: recent insights and future challenges.

paper_content:

INTRODUCTION ::: Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs. ::: ::: ::: METHOD ::: The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time. ::: ::: ::: RESULTS ::: Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time. ::: ::: ::: CONCLUSIONS ::: Further research is necessary to examine more complex functions, as well as individual differences between drivers. ::: ::: ::: IMPACT ON INDUSTRY ::: A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.
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paper_title: Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers

paper_content:

The effect of partial sleep deprivation and driving duration on subsequent alertness and performance in car drivers was investigated. Twenty healthy male subjects, between 25 and 55 years of age, free from any sleep disorder, took part in two simulated driving sessions carried out between 2 p.m. and 4 p.m. Before one session, subjects were sleep deprived as they were allowed to sleep only between 3 a.m. and 7 a.m. during the preceding night. Throughout the driving task, the subjects' driving performance, electroencephalogram and Karolinska Sleepiness Scale (KSS) score were recorded. The results revealed that sleep deprivation had an effect on KSS score but not on the (alpha+theta) spectral power, while driving duration had an effect on these two parameters. This effect was also influenced by sleep restriction. Time on driving task alone had a significant effect on driving performance; the sleep restriction having only an effect on one of the performances indices studied: the number of right edge-line crossings. These results are interpreted in terms of the relationship between level of alertness and performance impairment.
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paper_title: Effects of dexamphetamine with and without alcohol on simulated driving

paper_content:

RationaleIn party circuits dexamphetamine is frequently used in combination with alcohol. It is hypothesized that co-administration of dexamphetamine to alcohol might reduce the sedative effects of alcohol, but may potentiate risk-taking behaviour.ObjectivesThe study was aimed at assessing the effects of alcohol, dexamphetamine and the combination of both on simulated driving and cognitive performance.MethodEighteen subjects participated in a randomized, crossover, placebo-controlled study employing four conditions: 10 mg dexamphetamine, 0.8 g/kg alcohol, 10 mg dexamphetamine + 0.8 g/kg alcohol, and placebo. Fundamental driving skills and risk-taking behaviour were assessed in a driving simulator. Subjects also completed vigilance and divided attention tasks, and subjective ratings.ResultsMean BAC levels during simulated driving were between 0.91‰ and 0.64‰. Subjects using alcohol showed a significantly larger mean standard deviation of lateral position and shorter accepted gap time and distance. Use of alcohol or dexamphetamine + alcohol was associated with a higher frequency of red light running and collisions than the dexamphetamine or placebo conditions. Performance of vigilance and divided attention tasks was significantly impaired in the alcohol condition and, to a lesser degree, in the dexamphetamine + alcohol condition.ConclusionSingle doses of 0.8 g/kg alcohol increased risk-taking behaviours and impaired tracking, attention and reaction time during a 3-h period after drinking when BACs declined from 0.9 to 0.2 mg/ml. The stimulatory effects of co-administration of dexamphetamine 10 mg were not sufficient to overcome the impairing effects of alcohol on skills related to driving.
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paper_title: Differentiating Alcohol-Induced Driving Behavior Using Steering Wheel Signals

paper_content:

Detection of alcohol-induced driving impairment through vehicle-based sensor signals is of paramount importance for road safety. To differentiate the driving conditions with and without alcohol-induced impairment, data were collected from 108 drivers under both conditions in a high-fidelity driving simulator. With this data set, various quantitative measures of steering wheel movement, including not only simple statistics such as the mean and the standard deviation but nonlinear dynamic invariant measures such as sample entropy and Lyapunov exponent as well, are compared in terms of their differentiating capabilities. Nonlinear invariant measures are more robust and consistent than the simple measures in differentiating the impairment. Furthermore, people respond to alcohol-induced impairment quite differently, and for a certain group of people, the alcohol-induced impairment can be well detected using these nonlinear invariant measures. Many interesting insights into characterizing the effect of alcohol on driving behavior are obtained in this paper. This paper lays a foundation for the future development of a real-time detection method for alcohol-induced impairment.
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paper_title: Monotony of road environment and driver fatigue: a simulator study

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Studies have shown that drowsiness and hypovigilance frequently occur during highway driving and that they may have serious implications in terms of accident causation. This paper focuses on the task induced factors that are involved in the development of these phenomena. A driving simulator study was conducted in order to evaluate the impact of the monotony of roadside visual stimulation using a steering wheel movement (SWM) analysis procedure. Fifty-six male subjects each drove during two different 40-min periods. In one case, roadside visual stimuli were essentially repetitive and monotonous, while in the other one, the environment contained disparate visual elements aiming to disrupt monotony without changing road geometry. Subject's driving performance was compared across these conditions in order to determine whether disruptions of monotony can have a positive effect and help alleviate driver fatigue. Results reveal an early time-on-task effect on driving performance for both driving periods and more frequent large SWM when driving in the more monotonous road environment, which implies greater fatigue and vigilance decrements. Implications in terms of environmental countermeasures for driver fatigue are discussed.
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paper_title: Head pose estimation using isophote features for driver assistance systems

paper_content:

In this paper, we consider the problem of estimating the pose of a driver from video data. We propose to use isophote features to improve classification performance when illumination varies. In particular, we both use the direction and curvature features. The features are encoded into direction histogram and curvature histogram in the blocks of the image. Experimental results show that the proposed features could well describe the structure of driver face image, and have a good performance in real scene.
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paper_title: Determining driver visual attention with one camera

paper_content:

This paper presents a system for analyzing human driver visual attention. The system relies on estimation of global motion and color statistics to robustly track a person's head and facial features. The system is fully automatic, it can initialize automatically, and reinitialize when necessary. The system classifies rotation in all viewing directions, detects eye/mouth occlusion, detects eye blinking and eye closure, and recovers the three dimensional gaze of the eyes. In addition, the system is able to track both through occlusion due to eye blinking, and eye closure, large mouth movement, and also through occlusion due to rotation. Even when the face is fully occluded due to rotation, the system does not break down. Further the system is able to track through yawning, which is a large local mouth motion. Finally, results are presented, and future work on how this system can be used for more advanced driver visual attention monitoring is discussed.
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paper_title: A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue

paper_content:

A new scheme for driver fatigue detection is presented, which is based on the nonlinear unscented Kalman filter and eye tracking. Assuming a probability distribution than to approximate an arbitrary nonlinear function or transformation, eye nonlinear tracking can be achieved using an unscented transformation (UT), which adopts a set of deterministic sigma points to match the posterior probability density function of the eye movement. Driver fatigue can be detected using the percentage of eye closure (PERCLOS) framework in a realistic driving condition after the eye nonlinear tracking. This system was tested adequately in realistic driving environments with subjects of different genders, with/without glasses, in day/night driving, being commercial/noncommercial drivers, in continuous driving time, and under different road conditions. The last experimental results show that the proposed method not only improves the robustness for nonlinear eye tracking, but also can provide more accurate estimation than the traditional Kalman filter.
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paper_title: Head Pose Estimation and Augmented Reality Tracking: An Integrated System and Evaluation for Monitoring Driver Awareness

paper_content:

Driver distraction and inattention are prominent causes of automotive collisions. To enable driver-assistance systems to address these problems, we require new sensing approaches to infer a driver's focus of attention. In this paper, we present a new procedure for static head-pose estimation and a new algorithm for visual 3-D tracking. They are integrated into the novel real-time (30 fps) system for measuring the position and orientation of a driver's head. This system consists of three interconnected modules that detect the driver's head, provide initial estimates of the head's pose, and continuously track its position and orientation in six degrees of freedom. The head-detection module consists of an array of Haar-wavelet Adaboost cascades. The initial pose estimation module employs localized gradient orientation (LGO) histograms as input to support vector regressors (SVRs). The tracking module provides a fine estimate of the 3-D motion of the head using a new appearance-based particle filter for 3-D model tracking in an augmented reality environment. We describe our implementation that utilizes OpenGL-optimized graphics hardware to efficiently compute particle samples in real time. To demonstrate the suitability of this system for real driving situations, we provide a comprehensive evaluation with drivers of varying ages, race, and sex spanning daytime and nighttime conditions. To quantitatively measure the accuracy of system, we compare its estimation results to a marker-based cinematic motion-capture system installed in the automotive testbed.
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paper_title: Detecting deteriorated vigilance using percentage of eyelid closure time during behavioral maintenance of wakefulness tests.

paper_content:

Abstract Several researchers have investigated the relation between vigilance and ocular variables such as saccade, slow eye movement, pupil, blink, or eyelid closure. This study was undertaken to find the most effective indicator among these ocular variables for evaluating short-term (1 min) fluctuation of vigilance, and to investigate the ability of the most effective ocular variable for predicting deteriorated vigilance during behavioral maintenance of the wakefulness test (Oxford sleep resistance test: OSLER test). Nine healthy volunteers (two women, 19–30 years old, 23.4 ± 3.9 years old) participated in this study. Ocular variables were recorded during the OSLER test at 10 A.M. and 2 P.M. before and after partial sleep deprivation (4 h sleep). The periods during the OSLER test were divided into 1 min epochs. Each epoch was classified according to the number of consecutive missed responses. Decreased blink frequency and pupil diameter as well as increased percentage of eyelid closure time (PERCLOS) and slow eye movement were observed as the consecutive missed responses increased. Among these variables, PERCLOS showed the highest ability to detect occurrence of any missed response and three or more consecutive missed responses. Moreover, a missed response seldom occurred (0.2 ± 0.2/20 trial/min) when PERCLOS was less than 11.5% per minute. Results suggest that, among the ocular variables, PERCLOS can prevent error or accident caused by low vigilance most effectively.
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paper_title: Yawning Detection Based on Gabor Wavelets and LDA

paper_content:

To improve driving safety,the authors propose an approach to locate a driver's mouth by a web camera and extract texture features from mouth comers for monitoring drivers' yawning.Firstly,it detects drivers' left and right mouth corners by gray projection based on the result of driver face detection,and then it extracts texture features of drivers' mouth corners by Gabor wavelets.Finally,LDA is used to classify Gabor features for yawning detection.The proposed approach is tested on 3 000 images from thirty subjects with variations in illuminations,poses,and facial accessories(glasses).Yawning is also detected by the ratio of mouth height to width as a baseline.Experiment results show that the proposed approach is suitable for real time yawning detection,Gabor features are more powerful than geometric features for yawning representation,and an average recognition rate of 91.97%is achieved which is much better than the baseline.
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paper_title: Driver drowsiness warning system using visual information for both diurnal and nocturnal illumination conditions

paper_content:

Every year, traffic accidents due to human errors cause increasing amounts of deaths and injuries globally. To help reduce the amount of fatalities, in the paper presented here, a new module for Advanced Driver Assistance System (ADAS) which deals with automatic driver drowsiness detection based on visual information and Artificial Intelligence is presented. The aim of this system is to locate, track, and analyze both the drivers face and eyes to compute a drowsiness index, where this real-time system works under varying light conditions (diurnal and nocturnal driving). Examples of different images of drivers taken in a real vehicle are shown to validate the algorithms used.
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paper_title: Fatigue, sleep restriction and driving performance.

paper_content:

We ran a randomized cross-over design study under sleep-deprived and non-sleep-deprived driving conditions to test the effects of sleep restriction on real driving performance. The study was performed in a sleep laboratory and on an open French highway. Twenty-two healthy male subjects (age = 21.5 +/- 2 years; distance driven per year = 12,225 +/- 4739 km (7641 +/- 2962 miles) [mean +/- S.D.]) drove 1000 km (625 miles) over 10 h during five 105 min sessions on an open highway. Self-rated fatigue and sleepiness before each session, number of inappropriate line crossings from video recordings and simple reaction time (RT) were measured. Total crossings increased after sleep restriction (535 crossings in the sleep-restricted condition versus 66 after non-restricted sleep (incidence rate ratio (IRR): 8.1; 95% confidence interval (95% CI): 3.2-20.5; p < 0.001)), from the first driving session. The interaction between the two factors (conditionxtime of day) was also significant (F(5, 105) = 3.229; p < 0.05). Increasing sleepiness score was associated with increasing crossings during the next driving session in the sleep-restricted (IRR: 1.9; 95% CI: 1.4-2.4) but not in the non-restricted condition (IRR: 1.0; 95% CI: 0.8-1.3). Increasing self-perceived fatigue was not associated with increasing crossings in either condition (IRR: 0.95; 95% CI: 0.93-0.98 and IRR: 1.0; 95% CI: 0.98-1.02). Rested subjects drove 1000 km with four shorts breaks with only a minor performance decrease. Sleep restriction induced important performance degradation even though time awake (8h) and session driving times (105 min) were relatively short. Major inter-individual differences were observed under sleep restriction. Performance degradation was associated with sleepiness and not fatigue. Sleepiness combined with fatigue significantly affected RT. Road safety campaigns should encourage drivers to avoid driving after sleep restriction, even on relatively short trips especially if they feel sleepy.
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paper_title: Drowsy Driver Detection Through Facial Movement Analysis

paper_content:

The advance of computing technology has provided the means for building intelligent vehicle systems. Drowsy driver detection system is one of the potential applications of intelligent vehicle systems. Previous approaches to drowsiness detection primarily make preassumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy driving.
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paper_title: Evaluation of eye metrics as a detector of fatigue.

paper_content:

OBJECTIVES ::: This study evaluated oculometrics as a detector of fatigue in Air Force-relevant tasks after sleep deprivation. Using the metrics of total eye closure duration (PERCLOS) and approximate entropy (ApEn), the relation between these eye metrics and fatigue-induced performance decrements was investigated. ::: ::: ::: BACKGROUND ::: One damaging effect to the successful outcome of operational military missions is that attributed to sleep deprivation-induced fatigue. Consequently, there is interest in the development of reliable monitoring devices that can assess when an operator is overly fatigued. ::: ::: ::: METHOD ::: Ten civilian participants volunteered to serve in this study. Each was trained on three performance tasks: target identification, unmanned aerial vehicle landing, and the psychomotor vigilance task (PVT). Experimental testing began after 14 hr awake and continued every 2 hr until 28 hr of sleep deprivation was reached. ::: ::: ::: RESULTS ::: Performance on the PVT and target identification tasks declined significantly as the level of sleep deprivation increased.These performance declines were paralleled more closely by changes in the ApEn compared to the PERCLOS measure. ::: ::: ::: CONCLUSION ::: The results provide evidence that the ApEn eye metric can be used to detect fatigue in relevant military aviation tasks. ::: ::: ::: APPLICATION ::: Military and commercial operators could benefit from an alertness monitoring device.
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paper_title: Real-time system for monitoring driver vigilance

paper_content:

We present a non-intrusive prototype computer vision system for real-time monitoring driver's vigilance. It is based on a hardware system, for real time acquisition of driver's images using an active IR illuminator, and their software implementation for monitoring some visual behaviours that characterize a driver's level of vigilance. These are the eyelid movements and the pose face. The system has been tested with different sequences recorded on night and day driving conditions in a motorway and with different users. We show some experimental results and some conclusions about the performance of the system.
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paper_title: MULTISCALE DYNAMIC FEATURES BASED DRIVER FATIGUE DETECTION

paper_content:

Driver fatigue is a significant factor in many traffic accidents. We propose a novel approach for driver fatigue detection from facial image sequences, which is based on multiscale dynamic features. First, Gabor filters are used to get a multiscale representation for image sequences. Then Local Binary Patterns are extracted from each multiscale image. To account for the temporal aspect of human fatigue, the LBP image sequence is divided into dynamic units, and a histogram of each dynamic unit is computed and concatenated as dynamic features. Finally a statistical learning algorithm is applied to extract the most discriminative features from the multiscale dynamic features and construct a strong classifier for fatigue detection. The proposed approach is validated under real-life fatigue conditions. The test data includes 600 image sequences with illumination and pose variations from 30 people's videos. Experimental results show the validity of the proposed approach, and a correct rate of 98.33% is achieved which is much better than the baselines.
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paper_title: Drowsiness Detection Based on Eyelid Movement

paper_content:

Fatigue driving easily causes traffic accidents. drowsiness is an important hint of Fatigue. A novel drowsiness detection algorithms based on eyelid movement is proposed in the paper. The cascaded classifiers algorithm is used to detect driver's face and the diamond searching algorithm used to trace the face. A simple feature is then extracted from temporal difference image and used to analyze rules of eyelid movement in drowsiness. Furthermore, three criterions are also presented and used to judge whether a driver is drowsy or not. Experimental results show that this new algorithm achieves a satisfied performance for drowsiness detection.
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paper_title: Evaluation of fatigue monitoring technologies

paper_content:

The purpose of this study was to evaluate commercially available devices for driver fatigue monitoring with particular focus on the needs of the mining industry. We present an overview of fatigue monitoring technologies (FMTs) and propose means to evaluate the devices. Three video-based devices were selected and used in an overnight driving simulation study to test their accuracy. In total 14 healthy volunteers performed the driving simulation tasks in eight test runs separated by breaks of approximately 10 min. EEG and EOG were recorded during the driving periods. The output variable of the FMT devices (percentage of eye closures, PERCLOS), subjectively rated fatigue on the Karolinska sleepiness scale (KSS), and driving performance in terms of standard deviation of lateral position in lane (SDL) were also recorded throughout testing sessions. Regression analysis revealed that PERCLOS is significantly related to higher KSS scores and to SDL. Calculations at a finer temporal resolution as well as on an intra-subject level showed decreased correlation coefficients. Discriminant analysis of PERCLOS and EEG/EOG suggested that PERCLOS does not differentiate well between mild and strong fatigue. The results suggest that under laboratory conditions current FMT devices are reliable when temporal resolution is not too fine (>30 min) and data averaged across several subjects is utilized, but fail to give a valid prediction of subjective fatigue as well as of driving performance on an individual level.
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paper_title: Estimating vigilance level by using EEG and EMG signals

paper_content:

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%.
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paper_title: Real-time Nonintrusive Detection of Driver Drowsiness

paper_content:

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.
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paper_title: Driver drowsiness detection with eyelid related parameters by Support Vector Machine

paper_content:

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.
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paper_title: A driver fatigue recognition model based on information fusion and dynamic Bayesian network

paper_content:

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.
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paper_title: MULTI-CLASSIFIER FOR HIGHLY RELIABLE DRIVER DROWSINESS DETECTION IN ANDROID PLATFORM

paper_content:

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 ...
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paper_title: Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm

paper_content:

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.
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paper_title: EEG-based estimation of mental fatigue by using KPCA-HMM and complexity parameters

paper_content:

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
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paper_title: A critical review of the psychophysiology of driver fatigue

paper_content:

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.
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paper_title: Applying neural network analysis on heart rate variability data to assess driver fatigue

paper_content:

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.
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paper_title: A Fast and Easy-to-Use ECG Acquisition and Heart Rate Monitoring System Using a Wireless Steering Wheel

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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.
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paper_title: EEG and HRV markers of sleepiness and loss of control during car driving

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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.
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paper_title: A Smart Health Monitoring Chair for Nonintrusive Measurement of Biological Signals

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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.
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paper_title: Driver's cognitive distraction detection using physiological features by the adaboost

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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.
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paper_title: Mobile wearable device for long term monitoring of vital signs

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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.
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paper_title: The ANN-based computing of drowsy level

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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.
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paper_title: A Comprehensive Ubiquitous Healthcare Solution on an Android™ Mobile Device

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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.
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paper_title: Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System

paper_content:

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.
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paper_title: Changes in Physiological Parameters Induced by Indoor Simulated Driving: Effect of Lower Body Exercise at Mid-Term Break

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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.
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paper_title: Effects of visual and cognitive load in real and simulated motorway driving

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Abstract As part of the HASTE European Project, effects of visual and cognitive demand on driving performance and driver state were systematically investigated by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS). The present paper reports results from simulated and real motorway driving. Data were collected in a fixed base simulator, a moving base simulator and an instrumented vehicle driven in real traffic. The data collected included speed, lane keeping performance, steering wheel movements, eye movements, physiological signals and self-reported driving performance. The results show that the effects of visual and cognitive load affect driving performance in qualitatively different ways. Visual demand led to reduced speed and increased lane keeping variation. By contrast, cognitive load did not affect speed and resulted in reduced lane keeping variation. Moreover, the cognitive load resulted in increased gaze concentration towards the road centre. Both S-IVIS had an effect on physiological signals and the drivers’ assessment of their own driving performance. The study also investigated differences between the three experimental settings (static simulator, moving base simulator and field). The results are discussed with respect to the development of a generic safety test regime for In-vehicle Information Systems.
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paper_title: Fatigue, sleep restriction and driving performance.

paper_content:

We ran a randomized cross-over design study under sleep-deprived and non-sleep-deprived driving conditions to test the effects of sleep restriction on real driving performance. The study was performed in a sleep laboratory and on an open French highway. Twenty-two healthy male subjects (age = 21.5 +/- 2 years; distance driven per year = 12,225 +/- 4739 km (7641 +/- 2962 miles) [mean +/- S.D.]) drove 1000 km (625 miles) over 10 h during five 105 min sessions on an open highway. Self-rated fatigue and sleepiness before each session, number of inappropriate line crossings from video recordings and simple reaction time (RT) were measured. Total crossings increased after sleep restriction (535 crossings in the sleep-restricted condition versus 66 after non-restricted sleep (incidence rate ratio (IRR): 8.1; 95% confidence interval (95% CI): 3.2-20.5; p < 0.001)), from the first driving session. The interaction between the two factors (conditionxtime of day) was also significant (F(5, 105) = 3.229; p < 0.05). Increasing sleepiness score was associated with increasing crossings during the next driving session in the sleep-restricted (IRR: 1.9; 95% CI: 1.4-2.4) but not in the non-restricted condition (IRR: 1.0; 95% CI: 0.8-1.3). Increasing self-perceived fatigue was not associated with increasing crossings in either condition (IRR: 0.95; 95% CI: 0.93-0.98 and IRR: 1.0; 95% CI: 0.98-1.02). Rested subjects drove 1000 km with four shorts breaks with only a minor performance decrease. Sleep restriction induced important performance degradation even though time awake (8h) and session driving times (105 min) were relatively short. Major inter-individual differences were observed under sleep restriction. Performance degradation was associated with sleepiness and not fatigue. Sleepiness combined with fatigue significantly affected RT. Road safety campaigns should encourage drivers to avoid driving after sleep restriction, even on relatively short trips especially if they feel sleepy.
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paper_title: Differences between vehicle lateral displacement on the road and in a fixed-base simulator.

paper_content:

This work investigates differences in lateral displacement when driving on curved and straight road sections in real-road and simulator conditions. We observed 100 licensed drivers on a rural road and 100 in a fixed-base simulator. Speed and lateral position on the real road were measured using videocameras. The analysis indicates that the mean vehicle lateral displacement is in general higher on the real road than in the simulator. However, these differences decrease for higher speeds at curved sections and for lower speeds at straight sections. It was also found that the standard deviation of the vehicle lateral displacement is significantly lower on the real road than the corresponding values in the simulator, at either curved or straight sections. Actual or potential applications of this research are related to a more realistic assessment of driving behavior scenarios derived on the basis of simulation experiment results.
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paper_title: Driver drowsiness detection based on multisource information

paper_content:

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.
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paper_title: A driver fatigue recognition model based on information fusion and dynamic Bayesian network

paper_content:

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.
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paper_title: Development of neckband mounted active bio-electrodes for non-restraint lead method of ECG R wave

paper_content:

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.
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paper_title: Real-time physiological and vision monitoring of vehicle driver for non-intrusive drowsiness detection

paper_content:

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.
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paper_title: MULTI-CLASSIFIER FOR HIGHLY RELIABLE DRIVER DROWSINESS DETECTION IN ANDROID PLATFORM

paper_content:

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 ...
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paper_title: Subjective sleepiness, simulated driving performance and blink duration: examining individual differences.

paper_content:

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
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paper_title: Controlled Inducement and Measurement of Drowsiness in a Driving Simulator

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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.
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Title: Detecting Driver Drowsiness Based on Sensors: A Review

Section 1: Introduction
Description 1: Provide an overview of the importance of detecting driver drowsiness and the scope of the paper.

Section 2: Defining Drowsiness
Description 2: Define what drowsiness means in the context of driving, including the stages of sleep and characteristics of drowsiness-related crashes.

Section 3: Simulated Environment for Drowsiness Manipulation
Description 3: Discuss the use of simulated environments for studying driver drowsiness and the various types of driving simulators used.

Section 4: Methods for Measuring Drowsiness
Description 4: Review the different methods used to measure driver drowsiness, including subjective measures, vehicle-based measures, behavioral measures, and physiological measures.

Section 5: Subjective Measures
Description 5: Explain methods that evaluate drowsiness based on the driver's self-assessment and provide examples of commonly used scales.

Section 6: Vehicle-Based Measures
Description 6: Describe methods that use data from vehicle components to detect drowsiness and their effectiveness.

Section 7: Behavioral Measures
Description 7: Analyze methods that track the driver's facial expressions and head movements to monitor drowsiness.

Section 8: Physiological Measures
Description 8: Discuss the use of physiological signals, such as ECG, EEG, EoG, and EMG, to measure driver drowsiness.

Section 9: Discussion
Description 9: Evaluate the various drowsiness detection measures, comparing simulated and real driving conditions, and the potential of hybrid systems.

Section 10: Conclusions
Description 10: Summarize the findings of the review, highlighting the most effective methods for detecting drowsiness and suggesting future directions for research.