Driver's Heart Rate Research Articles

Enhancing Driver Safety: Deep Learning Approach for Drowsiness Detection and Accident Prevention

Road safety concerns have spurred the development of innovative technologies aimed at reducing accidents, particularly those caused by driver fatigue. The main scope of the driver drowsiness detection system is to minimize road accidents caused by fatigue or sleepiness of drivers. This system leverages deep learning and computer vision, employing a Raspberry Pi camera to monitor facial expressions, including yawning, to assess the driver's alertness. Upon detecting signs of drowsiness, such as prolonged eye closure or altered facial expressions, the system triggers a buzzer alert by analyzing the Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) ratios. Integrated within an embedded system, it utilizes frontal face detection algorithms and Haar cascade classifiers to localize key facial features in real-time, facilitating efficient monitoring for signs of fatigue or distraction. Additionally, it issues vibration alerts if the seat belt is not fastened. The seat belt remainder will be helpful to ensure the safety of the driver, reduces accidents and monitors the driver's heart rate, triggering a PANIC message if irregularities are detected. At traffic signals, the system automatically reduces vehicle speed using a vibration motor. In the unfortunate event of an accident, the system initiates speed reduction and promptly notifies registered contacts through the Blynk application, thereby significantly reducing accidents caused by drowsy driving.

Not just more, but more diverse: Green landscapes along urban roads may significantly reduce drivers' psychophysiological fatigue

The impact of roadside greenness on driving fatigue in real urban settings has been insufficiently investigated, presenting a critical knowledge gap for researchers, policymakers, professionals, and the public. In this onsite driving experiment, 34 urban residents completed seven driving tasks on different urban road routes in a randomized order with one-day intervals. A total of 238 tasks were conducted, each lasting an hour, assessing psychophysiological, visual, and muscular fatigue. A cardiovascular activity monitor (BioHarness) continuously measured the driver's heart rate, with lower rates indicating reduced psychophysiological fatigue. Visual and muscular fatigue were self-reported using a Visual Analog Scale questionnaire administered before, at the midpoint, and after completing the driving task. Deep transfer learning semantic segmentation analyzed road landscape characteristics and traffic conditions recorded from the drivers' view. Statistical analysis demonstrated that higher mean and variation in greenness significantly predicted lower psychophysiological fatigue after adjusting for multiple covariates. These results indicate that enhancing both the quantity and diversity of green landscapes along urban roads is vital for reducing driver's psychophysiological fatigue. This study reveals that roadside landscapes in urban settings are not trivial decorations, and they should be considered an essential component of transportation infrastructure.

Analysis of Driving Fatigue Characteristics in Cold and Hypoxia Environment of High-Altitude Areas.

The cold and hypoxic environment at high altitudes can easily lead to driving fatigue. For improving highway safety in high-altitude areas, a driver fatigue test is conducted using the Kangtai PM-60A car heart rate and oxygen tester to collect drivers' heart rate oximetry in National Highway 214 in Qinghai Province. Standard deviation (SDNN), mean (M), coefficient of RR (two R heart rate waves), RR interval coefficient of variation (RRVC), and cumulative rate of driving fatigue based on the driver's heart rate RR interval are calculated using SPSS. This study aims to derive degree of driving fatigue (DFD) in high-altitude areas when driving from lower to higher altitude. The analysis shows that the DFD growth trend of different altitude ranges presents an S-shaped curve. The driving fatigue thresholds in the altitude range of 3000-3500, 3500-4000, 4000-4500, and 4500-5000 m are 2.86, 3.82, 4.54, and 10.2, which are significantly higher than that of ordinary roads in plain areas. The start times of severe fatigue in the four altitude ranges are 35, 34, 32, and 25 minutes. The start time of driving fatigue continued to advance with the increase of age, and the DFD continued to increase with the increase of age. Results provide an empirical basis for the design of the horizontal alignment index system and antifatigue strategies to improve highway safety in high-altitude areas.

Relationship between braking intensity and driver heart rate as a function of the size of the measurement window and its position

Heart Rate (HR) is often used to capture the driver's mental workload and stress. Usually, during a predefined time window (e.g., 10 s, 30 s, or longer). In addition, the center of that time window is commonly placed at the peak of events of interest (e.g., safety events such as hard braking). However, the choices of window size and window position relative to driving events are often not explained in the literature, and conceivably, other choices could have been made for estimating changes in HR. This research tested the effect of window size and position on HR estimates. Participants executed 12 braking events on a closed driving course and were monitored for HR and skin conductivity. We fitted a regression model on HR as a function of driver braking intensity, multiple times (N = 210), each time, we calculated the HR with different window sizes (1–70 s) and three window positions (onset of braking, peak deceleration, and end of braking). We found that the coefficients in the model on HR by braking intensity changed as a function of the window size and its position. Our sample's optimal time window size was different from conventional settings: it was 4-second long and centered at the end of the braking events. Our findings showcase the analysis procedure that we employed and can contribute to research on physiological markers (HR and others) and to the design of driver assistance systems.

Physical Characteristics of Lighting Environment and Physiological Change of Drivers in Tunnel

The physical characteristics (i.e., road surface luminance, sidewall luminance, overall uniformity of luminance, and longitudinal uniformity of luminance) of the tunnel and the physiological change (i.e., pupil diameter and heart rate) of the drivers were investigated by the real vehicle tests and the whole scale model. Experiments were carried out in the real tunnel and the whole scale model with the sidewalls covered by the energy-storage and self-luminous material coating (ESMC) and the fireproof material coating (FMC). The results also show that compared to FMC, the luminance and the luminance uniformity were higher, and the pupil diameter and heart rate of drivers were smaller for ESMC. Furthermore, the physical characteristics of the light environment are negatively correlated with the physiological change of drivers. Moreover, each decrease of 1 cd/mm2 in road surface luminance and sidewall luminance, the pupil diameter increase by 0.54 and 1.06 mm, the heart rate increase 7.29 and 14.15 times/min, respectively. Each decrease of 0.1 in luminance uniformity, the pupil diameter increase by 0.33 mm, the mean heart rate increase 4.41 times/min, respectively.

Correlating driving behavior with safety performance: from the heart to the pedals with a driving simulator

Driving behavior significantly affects road safety, pollutant emissions, and fuel consumption, and an in-depth analysis is crucial for any accurate evaluation of a transport system. To develop a comprehensive study, it is important to consider different variables and traffic conditions, the presence of vulnerable road users, road singularities, as well as driving behavior profiles (e.g., gear-shift strategy, acceleration style). In this research, fixed-based driving simulation tests were carried out to allow a global evaluation of driving behavior under two different traffic conditions (urban and highway). The main objective is to understand driver-road interaction and associated impacts in terms of the adopted gear-shift strategy, vehicle dynamics profile, driving volatility profile (a safety-related indicator), driver's heart rate monitoring (a driving comfort-related variable), and pollutant emissions modeling. Drivers’ performance was assessed for each case study and a database was created for different segment types and considering different drivers, using statistical data mining models. This work is relevant for a better understanding of driving behavior both from the viewpoint of policymaking as well as for driver awareness about the relative impact associated with his/her driving behavior.

Ivrr-PPG: An Illumination Variation Robust Remote-PPG Algorithm for Monitoring Heart Rate of Drivers

Heart rate can be accurately measured by Remote PhotoPlethysmoGraphy (rPPG) in stable illumination and noise-controlled environments such as indoors. However, heart rate measure in automotive scene is still faced with several challenges since the lighting illumination in the cab often changes dramatically. To address such issues, a novel method named Illumination Variation Robust Remote-PhotoPlethysmoGraphy (Ivrr-PPG) is proposed for monitoring the heart rate of drivers during on-road driving. The prototype is based on a commercial off-the-shelf imaging system, which includes a monochrome camera with 940 nm narrowband NIR filter and two 940 nm NIR active light sources to collect real-time video of drivers. Based on a dichromatic model, the core idea of our method is to model the non-linear relationship between illumination changing and the rPPG signal. This paper also proposed to use a background region to sense the illumination changing and consequently suppress its impact on the pulse signal. A singletone sinusoidal signal-based frequency estimation method is then used to compute the heart rate. Experimental results conducted on four different on-road driving scenarios show that the Ivrr-PPG significantly outperforms the state-of-the-art single-channel narrowband NIR algorithms.

A fundamental study on the relationship between cognitive function and psychological load while driving in older adult drivers.

Although many media outlets have sounded the alarm about dangerous driving by elderly drivers, serious accidents do not occur on a daily basis, and the dangerous events of daily driving have not been clarified. However, serious accidents do not occur on a daily basis, and the risk events of daily driving have not been clarified. Also, the relationship between cognitive function and drivers' cognitive function has not been clarified. In addition, cognitive function fluctuates from day to day, and it is difficult to obtain an accurate picture of cognitive function from a one-time test. However, in Japan, the system currently determines whether a driver's license is revoked or not depending on the results of the test, and it is difficult to say that we can properly diagnose the driving of elderly drivers. In this study, we observed the heart rate during driving and examined the changes in psychological load that occur during driving. I observed 80 subjects' driving data. As a result, it was found that the heart rate of drivers with cognitive decline increased when they were approached by a trivial voice or an unexpected obstacle, followed by a rapid decrease in the heart rate and a state of excessive relaxation.In addition, when they were in the state of excessive relaxation, they slowed down their obstacle avoidance behavior and forgot to activate the blinker. Therefore, it is necessary to find a way to equalize the tension during driving in order to support the safe driving of elderly drivers.

Research on catastrophe progression of driving load in mountainous road based on visual and environmental characteristics

AbstractWhen driving on mountain roads, the drivers bear the dynamic load from the road environment. The sudden change of driving load will produce great safety risks and even lead to traffic accidents. In order to explore the driving risk of two‐lane road in mountainous areas, it is necessary to analyze the change of driving load from a quantitative point of view. Using the road driving experiment, 10 male drivers were selected according to the experimental road conditions, collect the driver's visual and heart rate indicators, analyze the driver's physiological indicators, and study the driver's driving load evolution process on the mountain road. Combined with the classification of road environment, a driving load evaluation model based on catastrophe progression method is constructed. The driving load is divided into four states: low load state, affordable state, chaotic state and high load state. It is found that most sections of mountain roads are in a low load and bearable state. In semi closed and closed environment, the driving load is in a chaotic state, which affects the driving safety. The evaluation model can be used to evaluate the traffic safety of road environment.

Applications of internet of things for monitoring drivers-a comprehensive study

<span>Driving is a complex task that involves interacting adequately with the vehicle and the environmental changes simultaneously. Drivers' health is an essential factor in determining performance outcomes and enhancing road safety. It is a known reality that drivers with sudden health complications are most likely to be involved in road accidents and suffer several injuries. Besides that, drunk driving is another aspect of a significant public health issue, where drivers under the influence of alcohol show a clear vision loss and vehicle control. The internet of things (IoT) is a trendsetting advancement in which all sensor data can be collected in the cloud. In this paper, an active monitoring tool is developed to record the driver's heart rate if these readings reach vital values while on the move. Additionally, the tool monitors the driver's alcohol concentration, and if it rises beyond a certain threshold, an alarm is sent to the designated emergency contact. The tool has been tested and has been found to work satisfactorily.</span>

Relationship between pre-driving heart rate and driving performance in formula car racing: a case study.

Formula car racing is highly competitive and induces significant physical stress. Previous studies have shown that intense physical stresses, such as g-force, accelerate the driver's heart rate (HR). In contrast, it remains unclear whether psychological stress affects the physiological states of racers and racing performance. To investigate this phenomenon, we developed a wearable monitor that can track the driver's HR during a race. The HR and driving performance of two professional drivers were monitored in real racing situations. Changes in HR were then evaluated based on changes in the racing situation and car behavior. The results suggest that HR acceleration is strongly correlated with race situations such as free practice or qualifying sessions, and that such changes are related to subsequent driving performance.

Physiological Study of Blood Pressure and Heart Rate at Road Intersections


 
 
 Intersections are the critical road network where two or more roads meet and are connected. There were two types of intersections, which are signalized and unsignalized. Next, the physiological factor of a driver is one of the indicators for the condition of the driver. A condition such as drowsiness and fatigue that could affect the focus and performance of drivers during driving could be the major cause of traffic accidents. Besides that, stress and tension could lead to a temporary increased in blood pressure and heart rate of drivers. In this research, drivers aged 20 to 30 years old have been selected and the driver’s blood pressure and heart rate have been sampled by using OMRON digital wrist blood pressure monitor before, during, and after driving, including their driving speed. Thus, the focus of this study was to study Physiological behavior while driving throughout a signalized intersection. An analysis by using the Regression Method to obtain the r-squared value has been applied to determine the correlation level between both of the variables. According to the sampling software, 36 participants are required to be tested. But due to Movement Control Order (MCO), only six participants were successfully sampled. A signalized T-junction of Jalan Persiaran Kayangan near Universiti Selangor (UNISEL), Shah Alam has been selected, and by the data obtained, we could determine the driving behavior of drivers in a real-life situation.
 
 

Context-Aware Driver’s Behaviour Monitoring System in Vehicular Ad-Hoc Network

The number of deaths resulting from road accidents and mishaps has increased at an alarming rate over the years. Road transportation is the most popularly used means of transportation in developing countries like Nigeria and most of these road accidents are associated with reckless driving habits. Context-aware systems provide intelligent recommendations allowing digital devices to make correct and timely recommendations when required. Furthermore, in a Vehicular Ad-hoc Network (VANET), communication links between vehicles and roadside units are improved thus enabling vehicle and road safety. Hence, a non-intrusive driver behaviour detection system that incorporates context-aware monitoring features in VANET is proposed in this study. By making use of a one-dimensional highway (1D) road with one-way traffic movement and incorporating GSM technology, irregular actions (high speed, alcohol while driving, and pressure) exhibited by drivers are monitored and alerts are sent to other nearby vehicles and roadside units to avoid accidents. The proposed system adopted a real-time VANET prototype with three entities involved in the context-aware driver’s behaviour monitoring system namely, the driver, vehicle, and environment. The analytical tests with actual data set indicate that, when detected, the model measures the pace of the vehicle, the level of alcohol in the breath, and the driver's heart rate in-breath per minute (BPM). Therefore, it can be used as an appropriate model for the Context-aware driver’s monitoring system in VANET.

HARMONY: A Human-Centered Multimodal Driving Study in the Wild

Effective shared autonomy requires a clear understanding of driver's behavior, which is governed by multiple psychophysiological and environmental variables. Disentangling this intricate web of interactions requires understanding the driver's state and behaviors in different real-world scenarios, longitudinally. Naturalistic Driving Studies (NDS) have shown to be an effective approach to understanding the driver's state and behavior in real-world scenarios. However, due to the lack of technological and computing capabilities, former NDS only focused on vision-based approaches, ignoring important psychophysiological factors such as cognition and emotion. The main objective of this paper is to introduce HARMONY, a human-centered multimodal naturalistic driving study, where driver's behaviors and states are monitored through (1) in-cabin and outside video streams (2) physiological signals including driver's heart rate and hand acceleration (IMU data), (3) ambient noise, light, and the vehicle's GPS location, and (4) music logs, including song features such as tempo. HARMONY is the first study that collects long-term naturalistic facial, physiological, and environmental data simultaneously. This paper summarizes HARMONY's goals, framework design, data collection and analysis, and the on-going and future research efforts. Through a presented case study, we first demonstrate the importance of longitudinal driver state sensing through using Kernel Density Estimation Methods. Second, we leverage the application of Bayesian Change Point detection methods to demonstrate how we can identify driver behaviors and responses to the environmental conditions by fusing psychophysiological information with features extracted from video streams.

Use of Wearable Technology to Measure Influence of Driving Stress on Heart Rate of Professional Drivers

Background: Developments in wearable gadgets provides opportunity to measure human physiological parameters withaccuracy and low cost.Objective: To determine the effect of driving stress on heart rate (HR) among professional drivers with a wrist-worn smartband. Cross-sectiona study conducted in . Department of Medicine B.V. Hospital Bahawalpur from November 2018 toDecember 2018.Material and Methods: Thirty professional minivan drivers were recruited, Heart Rate was measured during real-time drivingconditions with Xiaomi Mi Band 3; we also recorded HR of passengers during the journey for comparison.Results: Heart Rate of drivers remained significantly higher as compared to passengers at different times during journey. HRof drivers at 30 min of journeying was 98.8+ 19.4, (P = 0.0001) at 60 min of journeying was 110.8 + 16.7, (P = <0.0001) at 90min of journeying was 133.4 + 22.5, (P = <0.0001) at the end of journeying was 153.9 + 19.1.Conclusion: Driving is a notable stress factor affecting cardiovascular health.

Monitoring driver health status in real time.

Nowadays, surveillance systems have evolved significantly; hence, in order to meet the specific needs of the health sector and to monitor the patients' health conditions, intelligent systems have been proposed. These innovations represent a primordial role in road safety, which reduce the risk of traffic accidents. This paper describes an intelligent system design for remote monitoring (tele-monitoring) of a driver's health condition in real time. The measurement using new hardware and software devices is made possible through the contact between the driver contact and an intelligent steering wheel, which is coupled either to an integrated monitor or to a bluetooth link with a local Android smartphone. The driver's heart rate is calculated through the continuous collection of the electrocardiographic signal as well as the blood oxygen saturation SpO2 by using the photoplethysmographic technique. Consequently, it is necessary to monitor the two vital functions of the driver, cardiac and respiratory activity. This information is transmitted to a remote tele-vigilance center in the case of abnormalities in these functions under the transmission control protocol/internet protocol involving a 4G/3G connection. The application is associated with the system that triggers high and low alarms locally and remotely in the events of tachycardia, bradycardia, or cardiac arrhythmia. Furthermore, another alarm is also triggered in the event of respiratory decompensation.

Effect of geometry on driver heart rate

Driving is a complex task incorporating most of the body organs and sensory system. Coordinated performance of eyes, ears, hands and legs are required in harmony with mental or cerebral actuations. Depending upon the stimulus the cardio vascular performance of a driver varies. The objective of this paper is to model the heart rate kinetics of drivers in response to variation in highway curve geometry. The study was done on 114 horizontal curves of two lane rural highways of Kerala. Heart rates of 30 drivers were collected during their drive along the study stretches. Cross sectional details of curve like radius of curve, length of curve, deflection angle, superelevation, width of road, width of shoulder and available sight distance were measured. Similarly the length of preceding tangent to the curve was also considered. Findings of the study revealed the correlation of sight distance and shoulder width with average heart rate. A nonlinear regression model was proposed based on analysis.

Pedestrians influence on the traffic flow parameters and road safety indicators at the pedestrian crossing

The interaction between drivers and pedestrians at a pedestrian crossing is studied using the method of ergonomic design. The galvanic skin response (GSR) and changes of the heart rate of drivers have been measured to monitor any emotional state change when encountering a pedestrian at a pedestrian crossing. If the driver moves away from the pedestrian standing at the roadside edge, the driver experiences a minimal emotional change. However, if there is no vehicle speed and path variation, the driver experiences significant shift in the GSR and heart rate, thus highlighting a possible increase of the stress level. It was based on the research where the distance and current speed have a significant impact on the drivers and pedestrians with the following consequences for their health. To assess the influence of a pedestrian presence on the road on drivers, it was proposed to use the angular velocity of the vehicle with respect to pedestrians. The probability of the pedestrians presence in a dangerous condition for drivers at different distances and speeds of pedestrian crossing made it possible to find an optimal pedestrian position on the road. Thus, the paper proposed a safe distance for pedestrians on the road at a pedestrian crossing in order to reduce the driver stress level in the traffic flow.

Neural Network Based Luminance Variation Resistant Remote-Photoplethysmography for Driver’s Heart Rate Monitoring

The driver's physiological status has enormous value to public traffic safety and cannot be ignored nowadays. In addition, heart rate (HR) is one of the most important indicators of human's health status. When detecting the driver's HR, using traditional contact-type devices might bring about the driver's distraction or discomfort. On the contrary, the remote photoplethysmography (rPPG) technique is a better way to monitor a driver's HR in vehicle applications simply by using a web-camera without interfering the driver. Most of the rPPG studies intended to reduce the interference caused by facial motion or luminance changes in the indoor or controlled scenario, but there are relatively fewer discussions on outdoor scenarios. Consequently, the purpose of this paper is to enhance the rPPG technique to make it suitable for the outdoor driving scenarios and for monitoring the driver's HR in different weather conditions, including daytime and nighttime. We first utilize artificial neural network (ANN) and train multiple personalized ANN models for each driver. For predicting the drivers' HR beat more precisely, we propose the approach, adaptive neural network model selection (ANNMS), which adaptively selects a personalized ANN model based on different noise conditions. Our algorithm eliminates the effect of noises caused by the variations of facial luminance in eight outdoor driving scenarios. The proposed driver's HR beat monitoring system has been evaluated against the state-of-the-art rPPG techniques that are Chrominance signal-based (CHRO) and k-nearest neighbors-based (kNN) algorithms. Compared with the CHRO and kNN algorithms, the ANNMS reduces the mean absolute error from 14.71 bpm (CHRO) and 9.91 (kNN) to 4.51 bpm (ANNMS) and enhances the success-rate-10, the probability in which the absolute error is below 10 bpm, from 44.1% (CHRO) and 56.3% (kNN) to 91.5% (ANNMS).

The effect of alcohol on the physiological performance of the driver

The objective of this research was to study the physiological performance of young novice male drivers after they were administered different doses of alcohol. The physiological performance of drivers under the influence of alcohol (DUI) was compared with that of drivers not under the influence of alcohol (non-DUI). After the administration of alcohol, the average heart rate of drivers increased, and the percentage of the differences between adjacent R–R intervals greater than 50 ms decreased. Regarding the total fixation duration of area of interest, a significant effect was found in the middle third region between the DUI group and non-DUI group for straight and curved roads. The chi-square tests suggested that the total fixation counts of DUI and that of non-DUI groups were significantly different on the road types. The results of this study can be used to improve vehicle safety and reduce accidents due to alcohol-influenced driving on roads.