Evaluation of driver drowsiness based on respiratory metrics.

BACKGROUND: The transition from alertness to drowsiness can cause considerable changes in the respiratory system, providing an opportunity to detect driver drowsiness. OBJECTIVE: The aim of this study was to determine which respiratory features indicate driver drowsiness and then use these features to classify the level of drowsiness and alertness. METHODS: Twenty male students (mean age 25.6±2.41 years) participated in the study using a driving simulator, and eight features, including expiration duration (ED), inspiration duration (ID), peak-to-peak amplitude (PA), inspiration-to-expiration time ratio (I/E ratio), driving, timing, respiration rate (RR), and yawning, were extracted from the respiratory signal generated by abdominal motions using a belt equipped with a force sensor. RESULTS: All eight features were statistically significant at the significance level of 0.05. Drowsiness can be detected using respiratory features with 88% accuracy, 82% precision, 86% recall, and an 90% F1 score. CONCLUSION: The findings of this study may be useful in the development of driver drowsiness monitoring systems based on less intrusive respiratory signal analysis, particularly for specific process automation applications when vehicle control is not in the hands of the driver.

A hybrid approach for driver drowsiness detection utilizing practical data to improve performance system and applicability.

BACKGROUND: Numerous systems for detecting driver drowsiness have been developed; however, these systems have not yet been widely used in real-time. OBJECTIVE: The purpose of this study was to investigate at the feasibility of detecting alert and drowsy states in drivers using an integration of features from respiratory signals, vehicle lateral position, and reaction time and out-of-vehicle ways of data collection in order to improve the system's performance and applicability in the real world. METHODS: Data was collected from 25 healthy volunteers in a driving simulator-based study. Their respiratory activity was recorded using a wearable belt and their reaction time and vehicle lateral position were measured using tests developed on the driving simulator. To induce drowsiness, a monotonous driving environment was used. Different time domain features have been extracted from respiratory signals and combined with the reaction time and lateral position of the vehicle for modeling. The observer of rating drowsiness (ORD) scale was used to label the driver's actual states. The t-tests and Man-Whitney test was used to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features then combined to investigate the improvement in performance using the Multilayer Perceptron (MLP), the Support Vector Machines (SVMs), the Decision Trees (DTs), and the Long Short Term Memory (LSTM) classifiers. The models were implemented in Python library 3.6. RESULTS: The experimental results illustrate that the support vector machine classifier achieved accuracy of 88%, precision of 85%, recall of 83%, and F1 score of 84% using selected features. CONCLUSION: These results indicate the possibility of very accurate detection of driver drowsiness and a viable solution for a practical driver drowsiness system based on combined measurement using less-intrusive and out-of-vehicle recording methods.

Persistent effects of mobile phone conversation while driving after disconnect: Physiological evidence and driving performance.

Cognitive workload has been known as a key factor in traffic accidents, which can be highly increased by talking on the phone while driving. A wide range of studies around the world investigated the effects of mobile phone conversations on driving performance and traffic accidents. But less noticed is the durability of cognitive effects of mobile phone conversations. This study aimed to determine the effects of different types of mobile phone conversations on physiological response and driving performance during and after the conversation. Heart rate, heart rate variability (physiological response), Standard deviation of lane position (SDLP), and the relative distance between two cars (driving performance) of 34 samples (male and female) in the driving simulator were recorded. In this study, three types of conversations (neutral, cognitive, and arousal) were used. Neutral conversation did not pursue specific purpose questions. Cognitive conversations were simple mathematical problem-solving questions and arousal conversations aimed at arousing participant emotions. Each conversation was used as a secondary task in a condition. The study had three conditions; in each condition the participant drove for 15 min. Each condition consisted of 5 min of driving (Background), 5 min of driving and conversation (dual tasks) and 5 min of driving after conversation to trace the effects of the conversation. Vehicle speed was 110 km/h in each of the three conditions using car-following scenario. The results showed that neutral conversations had no significant effects on physiological response. Though, arousal conversations had significant effects on physiological responsiveness and driving performance during conversations, where it was even more significant after disconnection. Therefore, the content of the conversation determines the amount of cognitive load imposed on the driver. Considering the persistence of cognitive effects caused by conversation, the risk of traffic accidents is still high even after disconnection.

Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks.

The high number of fatal crashes caused by driver drowsiness highlights the need for developing reliable drowsiness detection methods. An ideal driver drowsiness detection system should estimate multiple levels of drowsiness accurately without intervening in the driving task. This paper proposes a multi-level drowsiness detection system by a deep neural network-based classification system using a combination of electrocardiogram and respiration signals. The proposed method is based on a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for classifying drowsiness by concurrently using heart rate variability (HRV), power spectral density of HRV, and respiration rate signal as inputs. Two models, a CNN-based model and a hybrid CNN-LSTM-based model were used for multi-level classifications. The performance of the proposed method was evaluated on experimental data collected from 30 subjects in a simulated driving environment. The performance and the results of both models are presented and compared. The best performance for both three-level and five-level drowsiness classifications was achieved by the CNN-LSTM model. The results indicate that the three-level and five-level classifications of drowsiness can be achieved with 91 and 67% accuracy, respectively.

Driver drowsiness detection using facial thermal imaging in a driving simulator.

Driver drowsiness causes fatal driving accidents. Thermal imaging is a suitable drowsiness detection method as it is non-invasive and robust against changes in the ambient light. In this paper, driver drowsiness is detected by measuring the forehead temperature at the region covering the supratrochlear artery and also the cheek temperature. About 30 subjects drove on a highway in a driving simulator in two sessions. A thermal camera was used to monitor the facial temperature pattern. The subjects' drowsiness levels were estimated by three human observers. The forehead and the cheek regions were located and tracked in each frame. The forehead and the cheek skin temperatures were obtained at three levels of drowsiness. The Support Vector Machine, the K-Nearest Neighbor, and the regression tree classifiers were used. From wakefulness to extreme drowsiness, the forehead skin temperature and the absolute cheek-forehead skin temperature gradient decreased by 0.46°C and 0.81°C, respectively. But the cheek skin temperature increased by 0.35°C in two sessions. The gradient difference is on average 50% higher than the forehead or the cheek temperature change alone. The results indicate that drowsiness can be detected with an accuracy of 82%, sensitivity of 85%, specificity of 90%, and precision of 84%. Driver drowsiness can be detected by monitoring changes in the forehead and the cheek temperature signal. Also, the temperature gradient can be used as a more robust and sensitive indicator of drowsiness.

Tracing the physiological response and behavioral performance of drivers at different levels of mental workload using driving simulators.

INTRODUCTION: The use of mobile phones while driving is known to be a distraction factor and a cause of accidents. The way in which different kinds of conversations affect the behavioral performance of the driver as well as the persistence of the effects are not yet fully understood. METHOD: In this study, in addition to comparing brain function and behavioral function in dual task conditions in three conversations types, the persistent effects of these types of conversations have also been traced. RESULTS: The results show that the content of the mobile phone conversation while driving is the cause of the persistent changes in behavioral and brain functions. Increased time headway and lane departure was observed during and up to 5 min after the emotional conversation was finished. EEG bands also varied in different types of conversations. Cognitive conversations caused an increase in the activity of the alpha and beta bands while emotional conversations enhanced the rate of gamma and beta bands. A meaningful correlation was found between changes in the theta and alpha bands and changes in behavioral performance both during the dual task condition and after the conversation was finished, was also observed. CONCLUSIONS: The content of the conversation is one of the most important factors that increase the risk of road accidents. This can also deteriorate the behavioral performance of the driver and can have persistent effects on behavioral performance and the brain. Practical applications: The findings of this study provide a basis to measure and tracing drivers' cognitive distractions induced by different levels of mental workload through physiological and behavioral performances.

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space.

Although the potential value of RNA as a target for new small molecule therapeutics is becoming increasingly credible, the physicochemical properties required for small molecules to selectively bind to RNA remain relatively unexplored. To investigate the druggability of RNAs with small molecules, we have employed affinity mass spectrometry, using the Automated Ligand Identification System (ALIS), to screen 42 RNAs from a variety of RNA classes, each against an array of chemically diverse drug-like small molecules (~50,000 compounds) and functionally annotated tool compounds (~5100 compounds). The set of RNA-small molecule interactions that was generated was compared with that for protein-small molecule interactions, and naïve Bayesian models were constructed to determine the types of specific chemical properties that bias small molecules toward binding to RNA. This set of RNA-selective chemical features was then used to build an RNA-focused set of ~3800 small molecules that demonstrated increased propensity toward binding the RNA target set. In addition, the data provide an overview of the specific physicochemical properties that help to enable binding to potential RNA targets. This work has increased the understanding of the chemical properties that are involved in small molecule binding to RNA, and the methodology used here is generally applicable to RNA-focused drug discovery efforts.

Polyurethane-carbon microfiber composite coating for electrical heating of concrete pavement surfaces.

Electrically-heated pavements have attracted attention as alternatives to the traditional ice/snow removal practices. Electrically conductive polymer-carbon composite coatings provide promising properties for this application. Based on the concept of joule heating, the conductive composite can be utilized as a resistor that generates heat by electric current and increases the surface temperature to melt the ice and snow on the pavement surface. This research investigates the feasibility of applying an electrically conductive composite coating made with a Polyurethane (PU) binder and micrometer-scale carbon fiber (CMF) filler as the electrical heating materials on the surface of Portland cement concrete (PCC) pavements. PU-CMF composite coatings were prepared using different volume fractions of CMF, applied on the PCC surfaces, and evaluated in terms of volume conductivity, resistive heating ability, durability, and surface friction properties at the proof-of-concept level. A conceptual cost analysis was performed to compare this method with other heated pavement systems with respect to economic viability. Percolative behavior of CMF in PU matrix was captured and most desirable CMF dosage rates in terms of each performance parameter were investigated. Two percolation transition zones were identified for CMF in PU matrix at dosage rate ranges of 0.25-1% and 4-10%. The composites exhibited their most desirable performance and properties at CMF dosage rates greater than 10% and smaller than 15%.

Driver drowsiness detection based on classification of surface electromyography features in a driving simulator.

Driver drowsiness is a significant cause of fatal crashes every year in the world. In this research, driver's drowsiness is detected by classifying surface electromyography signal features. The tests are conducted on 13 healthy subjects in a driving simulator with a monotonous route. The surface electromyography signal from the upper arm and shoulder muscles are measured including mid deltoid, clavicular portion of the pectoralis major, and triceps and biceps long heads. Signals are separated into 30-s epochs. Five features including range, variance, relative spectral power, kurtosis, and shape factor are extracted. The Observer Rating of Drowsiness evaluates the level of drowsiness. A binormal function is fitted for each feature. For classification, six classifiers are applied. The results show that the k-nearest neighbor classifier predicts drowsiness by 90% accuracy, 82% precision, 77% sensitivity, and 92% specificity.

Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection.

This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.

Discovery of Selective RNA-Binding Small Molecules by Affinity-Selection Mass Spectrometry.

Recent advances in understanding the relevance of noncoding RNA (ncRNA) to disease have increased interest in drugging ncRNA with small molecules. The recent discovery of ribocil, a structurally distinct synthetic mimic of the natural ligand of the flavin mononucleotide (FMN) riboswitch, has revealed the potential chemical diversity of small molecules that target ncRNA. Affinity-selection mass spectrometry (AS-MS) is theoretically applicable to high-throughput screening (HTS) of small molecules binding to ncRNA. Here, we report the first application of the Automated Ligand Detection System (ALIS), an indirect AS-MS technique, for the selective detection of small molecule-ncRNA interactions, high-throughput screening against large unbiased small-molecule libraries, and identification and characterization of novel compounds (structurally distinct from both FMN and ribocil) that target the FMN riboswitch. Crystal structures reveal that different compounds induce various conformations of the FMN riboswitch, leading to different activity profiles. Our findings validate the ALIS platform for HTS screening for RNA-binding small molecules and further demonstrate that ncRNA can be broadly targeted by chemically diverse yet selective small molecules as therapeutics.

Virtual reality and anxiety in primiparous women during episiotomy repair.

BACKGROUND: In recent studies, using virtual reality (VR) has been proposed as a nonpharmacological method for anxiety reduction, but until this time, its effects have not been assessed on anxiety during episiotomy repair. This study aimed to determine the effect of audiovisual distraction (VR) on anxiety in primiparous women during episiotomy repair. MATERIALS AND METHODS: This clinical trial was conducted on 30 primigravida from May to July 2012 in the maternity unit of the Omolbanin Hospital, Mashhad city, Iran. The samples were divided randomly into two groups with the toss of a coin. Anxiety were evaluated by the numeric 0-10 anxiety self-report, in the first and during labor. However, after delivery, anxiety was measured with the Spilberger scale. Mann-Whitney, Chi-square, Fisher tests, and repeated-measures analysis of variance were used to analyze data. RESULTS: Anxiety scores were not significantly different between the two groups (wearing video-glass and receiving routine care), but anxiety scores were lower in the intervention group during and after repair (P = 0.000). CONCLUSIONS: VR are safe, appropriate, and nonpharmacologic to decrease and manage the anxiety-associated episiotomy.

Correlation between Driver Subjective Fatigue and Bus Lateral Position in a Driving Simulator.

BACKGROUND: Driver fatigue as a leading cause of death in the transportation industry can impair the driving performance in long-distance driving task. Studies on the links of driver subjective fatigue and the bus lateral position are still an exploratory issue that requires further investigation. This study aimed to determine the correlation between the driver subjective fatigue and the bus lateral position in a driving simulator. METHODS: This descriptive-analytical research was conducted on 30 professional male bus drivers participated in a two-hour driving session. The driver subjective fatigue was assessed by the Fatigue Visual Analogue Scale (F-VAS) at 10-min intervals. Simultaneously, the performance measures of lane drifting as the mean and standard deviation of the bus lateral position (SDLP) were calculated during the simulated driving task. Descriptive statistics and the Spearman correlation coefficient were used to describe and analyze the data. RESULTS: Fatigue levels had an increasing trend as the time-on-task of driving increased. Time-on-task of driving had the greatest effect on the fatigue self-evaluation (r = 0.605, p < 0.0001). The results showed a significant correlation between fatigue self-evaluation and bus lateral position (r = 0.567, p < 0.0001). CONCLUSION: As the time of driving increased, driving performance was affected adversely, as shown by the increase in the SDLP. Even so, the effect of individual differences on driving performance should not be overlooked. This work concludes that predicting the state of a driver fatigue based on the group mean data has some complications for any application.

Selective small-molecule inhibition of an RNA structural element.

Riboswitches are non-coding RNA structures located in messenger RNAs that bind endogenous ligands, such as a specific metabolite or ion, to regulate gene expression. As such, riboswitches serve as a novel, yet largely unexploited, class of emerging drug targets. Demonstrating this potential, however, has proven difficult and is restricted to structurally similar antimetabolites and semi-synthetic analogues of their cognate ligand, thus greatly restricting the chemical space and selectivity sought for such inhibitors. Here we report the discovery and characterization of ribocil, a highly selective chemical modulator of bacterial riboflavin riboswitches, which was identified in a phenotypic screen and acts as a structurally distinct synthetic mimic of the natural ligand, flavin mononucleotide, to repress riboswitch-mediated ribB gene expression and inhibit bacterial cell growth. Our findings indicate that non-coding RNA structural elements may be more broadly targeted by synthetic small molecules than previously expected.

Presenting a model for dynamic facial expression changes in detecting drivers' drowsiness.

Drowsiness while driving is a major cause of accidents. A driver fatigue detection system that is designed to sound an alarm, when appropriate, can prevent many accidents that sometime leads to the loss of life and property. In this paper, we classify drowsiness detection sensors and their strong and weak points. A compound model is proposed that uses image processing techniques to study the dynamic changes of the face to recognize drowsiness during driving.

The Effect of Virtual Reality on Pain in Primiparity Women during Episiotomy Repair: A Randomize Clinical Trial.

BACKGROUND: Pain is one of the side effects of episiotomy. The virtual reality (VR) is a non-pharmacological method for pain relief. The purpose of this study was to determine the effect of using video glasses on pain reduction in primiparity women during episiotomy repair. METHODS: This clinical trial was conducted on 30 primiparous parturient women having labor at Omolbanin Hospital (Mashhad, Iran) during May-July 2012. Samples during episiotomy repair were randomly divided into two equal groups. The intervention group received the usual treatment with VR (video glasses and local infiltration 5 ml solution of lidocaine 2%) and the control group only received local infiltration (5 ml solution of lidocaine 2%). Pain was measured using the Numeric Pain Rating Scale (0-100 scale) before, during and after the episiotomy repair. Data were analyzed using Fisher's exact test, Chi-square, Mann-Whitney and repeated measures ANOVA tests by SPSS 11.5 software. RESULTS: There were statistically significant differences between the pain score during episiotomy repair in both groups (P=0.038). CONCLUSION: Virtual reality is an effective complementary non-pharmacological method to reduce pain during episiotomy repair. TRIAL REGISTRATION NUMBER: IRCT138811063185N1.

Detecting Driver Mental Fatigue Based on EEG Alpha Power Changes during Simulated Driving.

BACKGROUND: Driver fatigue is one of the major implications in transportation safety and accounted for up to 40% of road accidents. This study aimed to analyze the EEG alpha power changes in partially sleep-deprived drivers while performing a simulated driving task. METHODS: Twelve healthy male car drivers participated in an overnight study. Continuous EEG and EOG records were taken during driving on a virtual reality simulator on a monotonous road. Simultaneously, video recordings from the driver face and behavior were performed in lateral and front views and rated by two trained observers. Moreover, the subjective self-assessment of fatigue was implemented in every 10-min interval during the driving using Fatigue Visual Analog Scale (F-VAS). Power spectrum density and fast Fourier transform (FFT) were used to determine the absolute and relative alpha powers in the initial and final 10 minutes of driving. RESULTS: The findings showed a significant increase in the absolute alpha power (P = 0.006) as well as F-VAS scores during the final section of driving (P = 0.001). Meanwhile, video ratings were consistent with subjective self-assessment of fatigue. CONCLUSION: The increase in alpha power in the final section of driving indicates the decrease in the level of alertness and attention and the onset of fatigue, which was consistent with F-VAS and video ratings. The study suggested that variations in alpha power could be a good indicator for driver mental fatigue, but for using as a countermeasure device needed further investigations.

Data fusion to develop a driver drowsiness detection system with robustness to signal loss.

This study proposes a drowsiness detection approach based on the combination of several different detection methods, with robustness to the input signal loss. Hence, if one of the methods fails for any reason, the whole system continues to work properly. To choose correct combination of the available methods and to utilize the benefits of methods of different categories, an image processing-based technique as well as a method based on driver-vehicle interaction is used. In order to avoid driving distraction, any use of an intrusive method is prevented. A driving simulator is used to gather real data and then artificial neural networks are used in the structure of the designed system. Several tests were conducted on twelve volunteers while their sleeping situations during one day prior to the tests, were fully under control. Although the impact of the proposed system on the improvement of the detection accuracy is not remarkable, the results indicate the main advantages of the system are the reliability of the detections and robustness to the loss of the input signals. The high reliability of the drowsiness detection systems plays an important role to reduce drowsiness related road accidents and their associated costs.

Structural analysis of RNA backbone using in-line probing.

To determine the secondary structure characteristics of nucleotides in an RNA polymer.

Impact of headway distance and car speed on drivers' decisions to answer an incoming call.

OBJECTIVE: Previous studies on factors affecting drivers' decisions to use their mobile phones while driving are mostly focused on psychological and sociocultural contexts. Few investigations have been carried out on the role of traffic circumstances on drivers' decisions to use or not to use a mobile phone. METHODS: In this research, the effects of headway distance, speed, and the type of road as well as individual variables including age and driving experience are investigated. Forty-two subjects participated in observing 24 scenarios in a driving simulator. RESULTS: Logistic regression models showed that car speed, headway distance, and age can predict whether drivers' answer an incoming call while driving. The results indicated that traffic circumstances at the onset of phone calls are stronger predictors of drivers' decisions to answer a cell phone compared to driver-related variables. CONCLUSIONS: Headway distance and car speed can be used by in-vehicle information systems (IVIS) to warn drivers against using a mobile phone in certain traffic situations. Traffic safety campaigns for young drivers should focus on keeping safe headway distance and speed while driving.