Driver Stress Research Articles

Assessment of Driver's Stress State Using Smart T-Shirt Textile Electrodes and Multimodal Cross-Attention Networks

Assessment of Driver's Stress State Using Smart T-Shirt Textile Electrodes and Multimodal Cross-Attention Networks

Advanced Necklace for Real-Time PPG Monitoring in Drivers.

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.

The relationship between perceived stress and emotional eating in bus drivers: the effect of shift work

This study discusses the relationship between perceived stress and emotional eating based on shiftwork in city bus drivers. It was conducted with 1403 male city bus drivers working in Istanbul. Questions about the drivers’ physical activity and dietary habits, the Healthy Diet Index (HDI), Emotional Appetite Questionnaire and Perceived Stress Scale-10 data were collected and evaluated in two groups: shift and non-shift employees. It was found that the shift workers (SWs) have lower perceived stress, physical activity, HDI and positive emotional appetite averages. In addition, for SWs, a positive correlation was found between negative emotional appetite scores and physical activity and HDI scores. In conclusion, the drivers working in shifts were found to be slightly overweight and had lower physical activity levels, had lower HDI scores, and their nutritional status was more negatively impacted than their counterparts.

IoT-Based Assessment of a Driver's Stress Level.

Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver's arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.

Stress and Sleep Deprivation Experienced by Bus Drivers of Government Bus Depots in an Urban Area of Chengalpattu District in South India.

Background Driving a bus is far more stressful than other professions. Bus drivers also take more sick leave than other workers because of the physical and mental demands of their employment. Bus drivers are especially vulnerable because of their intense work environment, poor sleep, and poor food pattern. The aim of the study was to assess stress and sleep deprivation of the government bus drivers in Chengalpattu District in Tamil Nadu and to identify the sociodemographic factors influencing them. Methodology An analytical cross-sectional study was conducted among 429 government bus drivers working under Tamil Nadu State Transport Corporation (TNSTC )in selected government bus depots in the urban area of Chengalpattu District in Tamil Nadu, India. The principal investigator collected the data by using a semi-structured questionnaire which comprises six parts, i.e., sociodemographic variables, job-related factors, comorbidity, habits, stress assessed by using the Perceived Stress Scale (PSS-10), and sleep deprivation assessed by using the Epworth Sleepiness Scale (ESS). Results In the present study, the mean age of participants was 47.683 years. Hindus comprise 95.3% of the sample, and 96.7% are married. Approximately 47.6% of the drivers have achieved a higher secondary education. The average time spent working as a heavy vehicle driver was 20.4 years. About 37.1% (159 individuals) have hypertension, making it the most prevalent comorbidity. In this study, 47.3% (203 participants) reported very poor sleep, 35.7% (153 participants) reported average sleep, and 17.0% (73 participants) reported good sleep. Inferential statistics revealed that those drivers who were degree holderstook less than three breaks in duty time, slept over six hours at night, had the habit of smoking and drinking alcohol, and took more than 60 minutes to fall asleep experienced very poor sleep according to the ESS. In this study, 57.1% (245 participants) reported moderate stress levels, 24.2% (104 participants) showed high levels of perceived stress, and 18.6% (80 participants) reported low stress levels. Inferential statistics revealed that those drivers who were Muslims, degree holders, those with primary education, smokers, alcohol consumers, drivers with very poor sleep, and those who took more than six days of casual leave in the past six months experienced high stress according to the PSS-10. Conclusion Implementing effective health management strategies and minimizing work-related stress will lead to a decrease in work-related disorders among drivers. TNSTC should ensure enough lodging facilities for drivers at depots, bus terminals, and outstations. They require a minimum of eight hours of sleep every day to maintain optimal physical well-being.

Improved time series models for the prediction of lane-change intention

ABSTRACT To improve the accuracy of lane-change intention prediction and analyze the influence of driving styles on prediction outcomes, the T-Encoder-Sequence model is proposed in this paper. It integrates the Transformer’s encoder module with various recurrent neural network (RNN) models and introduces a multimodal fusion input structure. Building on this, a risk indicator model, capable of reflecting driver stress, is established to calculate the model’s input parameters. Consequently, the model can simultaneously capture global information and consider the impact of vehicle classes on drivers. Furthermore, the k-means++ algorithm is employed to categorize vehicle trajectories into conservative, conventional, and aggressive types for further analysis. The results demonstrate that training the model with risk indicator parameters markedly enhances prediction performance. Under identical input parameters, the T-Encoder-Sequence model exhibits notably superior prediction efficacy compared to the original model. The T-Encoder-Sequence model, trained with risk indicator parameters, demonstrates substantial advantages compared to other studies.

PENGARUH STRES KERJA, PROGRAM INSENTIF DAN MOTIVASI TERHADAP KINERJA DRIVER OJEK ONLINE

This study aims to examine the effect of work stress, incentive programs and work motivation of online motorcycle taxi drivers for study on Shopee Food drivers who are domiciled in DKI Jakarta. The independent variables in this study are work stress, incentive programs and work motivation. The dependent variable in this study is the performance of Shopee Food drivers. This study uses the PLS or Partial Least Square method with SmartPLS 4.0 software. The data collection technique uses the Googleform application questionnaire to collect respondent information. The sample in this study are all members of the Shopee Food driver population who live in DKI Jakarta with a total sample of 102 respondents. The results of the analysis show that work stress has no negative effect on the performance of Shopee Food drivers. The Incentive Program has a positive and significant effect on the performance of Shopee Food drivers. Work motivation has a positive and significant effect on the performance of Shopee Food drivers.

Attitudes Towards the Stress Created by Ever Increasing Traffic in the Maltese Islands

The relatively large and rapid increase in population in the Maltese Islands over the past decade has placed significant strain on the infrastructure of the country. These factors, together with the fact that issues with the public transport system have never been completely sorted, has led to an over-reliance on private cars as the main means of transport in most cases. Increased transport has been shown to increase stress and aggressive behaviour in drivers, amongst other possible negative consequences, leading to a negative effect on personal wellbeing. In order to assess the perceptions of the Maltese about the stress created by traffic, a cross-sectional study was conducted by means of telephone interviews with a stratified sample of 400 Maltese participants. A purposely designed questionnaire with twelve items scored on a seven-point Likert scale was used for this purpose. While more than four fifths of participants stated that they owned at least one car, and most agreed that traffic increases stress, both in the short and long term, less than one third of participants strongly agreed to preferring to use public transport rather than their private vehicle, even if public transport were more reliable. Females showed more anxiety than males with reference to the traffic situation and 18–29 year-olds showed the lowest level of agreement overall with the statements in the survey – a surprising result given this age group’s awareness of the negative effects of climate change on wellbeing.

Event-related driver stress detection with smartphones in an urban environment: a naturalistic driving study

Driving in urban areas can be challenging and encounter acute stress. To detect driver stress, collecting data on real roads without interfering the driver is preferred. A smartphone-based data collection protocol was developed to support a naturalistic driving study. Sixty-one participants drove on predetermined real road routes, and driving information as well as physiological, psychological, and facial data were collected. The algorithm identified potentially stressful events based on the collected data. Participants classified these events as low, medium, or highly stressful events by watching recorded videos after the experiment. These events were then used to train prediction models. The best model achieved an accuracy of 92.5% in classifying low/medium/highly stressful events. The contribution of physiological, psychological, and facial expression indices and individual profile information was evaluated. The method can be applied to visualise the geographical distribution of stressors, monitor driver behaviour, and help drivers regulate their driving habits.

Development and validation of the Driver Attention Regulation Scale: A measure of the perceived ability to regulation attention on the road

The ability to regulate attention while driving is a vital skill for remaining safe on the road. However, there is no current self-report method that measures such a construct, potentially limiting research in the area of attention and distraction. This study aimed to develop and validate a new measure, the Driver Attention Regulation Scale (DARS), that can be used to identify how drivers perceive their ability to regulate attention while driving. To achieve this aim, two online surveys were shared with unique samples of Australian drivers. Following the development of an initial item pool, principal components analysis and follow up confirmatory factor analysis were used to test the structure of the items and revealed that three factors emerged: attentional presence (e.g., focus; vigilance), attentional flexibility (e.g., awareness; shifting), and attentional capacity (e.g., processing speed; cognitive resources). Each of the subscales (α = 0.75–0.88) and the total scale (α = 0.87–0.91) exhibited a good internal consistency across the samples. Evidence of external validity was also demonstrated, as the DARS was positively associated with generalised attention regulation measures, and driver confidence, but negatively associated with emotion regulation difficulties, driver stress, and driving errors/lapses. Mediation analyses also indicated that the attentional constructs measured by the DARS may indirectly influence driving behaviours, via their effect on driver stress and confidence. Overall, the DARS has demonstrated promise as a new measure of driving-related attentional skills that can be used in future research to better understand how situational factors may impact drivers’ confidence, and also how confidence may influence driving behaviours (e.g., speeding).

Impulsivity and Driver Stress in Mexico and Spain

Impulsivity and Driver Stress in Mexico and Spain

Stress influence on real-world driving identified by monitoring heart rate variability and morphologic variability of electrocardiogram signals: the case of intercity roads

Objectives. This study examines which of the heart rate variability (HRV) and morphologic variability (MV) metrics may have the highest accuracy in different stress detection during real-world driving. Methods. The cross-sectional study was carried out among 93 intercity mini-bus male drivers aged 22–67 years. The Trillium 5000 Holter Recorder and GARMIN Virb Elite camera were used to determine heart rate and vehicle speed measurements along the path, respectively. We considered the HRV and MV metrics of electrocardiogram (ECG) signals including the mean RR interval (mRR), mean heart rate (mHR), normalized low-frequency spectrum (nLF), normalized high-frequency spectrum (nHF), normalized very low-frequency spectrum (nVLF), difference of normalized low-frequency spectrum and normalized high-frequency spectrum (dLFHF), and sympathovagal balance index (SVI). Results. The analysis showed that the HRV metrics mHR, mRR, nVLF, nLF, nHF, dLFHF and SVI are effective in mental stress detection while driving as compared to rest time. We obtained a high accuracy of stress detection for MV metrics as compared to the traditional HRV analysis, of approximately 92%. Conclusions. Our findings indicate that driver stress could be detected with an accuracy of 92% using MV metrics as an accurate physiological index of the driver’s state.

Tree-Based Personalized Clustered Federated Learning: A Driver Stress Monitoring Through Physiological Data Case Study

Tree-Based Personalized Clustered Federated Learning: A Driver Stress Monitoring Through Physiological Data Case Study

An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs

Unexpected traffic incidents cause safety concerns and intense traffic congestion on crowded urban road networks. Vehicular ad-hoc network (VANET)-aided Intelligent Transport Systems (ITS) aim to mitigate these risks through timely dissemination of alert messages. However, conventional Radio frequency (RF) mobile ad-hoc routing protocols are ill-suited for dynamic VANET environments due to high mutual interference, packet collisions, high end-to-end delay from frequent route discoveries, and periodic beaconing requirements. Fortunately, the quickly emerging Visible Light Communications (VLC) provide complementary short-range connectivity with high bandwidth and low interference. This paper proposes an efficient adaptive routing protocol for emergency messages in dense VANET scenarios leveraging a hybrid RF/VLC system. When an incident or congestion happens, the source vehicle disseminates the information to the incoming vehicles as quickly as possible using a combination of VLC and RF communication networks. Multi-hop relays extend the connectivity if the direct links are blocked. The coverage area is partitioned into zones based on road segments, intersections, and traffic flows. The Road Side Units (RSU)s are intelligently assigned to zones and they analyze the historical traffic data to characterize each zone and decide a response strategy. We also propose a congestion detection scheme that utilizes traffic simulations to forecast the clearance times under different response strategies. The highest-scoring strategy is selected based on the predicted impacts on travel time, emissions, and driver stress levels. The proposed algorithm adaptively uses the selected strategy to proactively alleviate the predicted congestion through optimized routing and control. Overall, the protocol maximizes safety and efficiency during emergencies by leveraging the hybrid RF/VLC, incorporating real-time congestion forecasting and dynamic rerouting into the response strategies.

Subjective and Objective Evaluation of Driving Behaviour in Depression

Abstract Introduction Road traffic collisions and related casualties are a major issue for public health and society. Depression is characterized by mental, emotional and executive dysfunction, which may have an impact on driving behaviour. Among its symptoms sleep disturbance is very common. Objectives The current study examined behaviour and attitude towards driving in a group of patients with depression and a group of controls, considering sleep disturbances. Methods Participants (patients N = 39 and controls N = 30) were asked to complete the Driver Stress Inventory (DSI) and Driver Behavior Questionnaire (DBQ). Afterwards, they were asked to drive on a driving simulator in a motorway scenario, and in urban and rural scenarios under low and high traffic load, all in good weather conditions. Driving simulator data included speed, lateral position (LP) and safety distance from the preceding vehicle. Results Gender, age and, to a lesser extent, body mass index influenced almost all variables. Patients with depression did not differ from the controls regarding driving behaviour as assessed through the DSI and DBQ questionnaires. Subjective fatigue was found to be positively correlated with aggression (β = 0.027, p = 0.005), dislike of driving (β = 0.036, p = 0.006), hazard monitoring (β = 0.046, p = 0.001) and traffic violations (β = 0.010, p = 0.020). On the driving simulator, patients maintained a longer distance from the preceding vehicle. Epworth Sleepiness Scale and Athens Insomnia Scale scores were positively associated with safety distance and with the Standard Deviation of LP, denoting lower ability to keep a stable vehicle track (β = 2.108, p = 0.003 and β = 0.047, p = 0.004 respectively). Conclusions It seems that although certain symptoms of depression (insomnia, fatigue and somnolence) may affect driving performance, this effect is lessened by patients driving more carefully. There are parameters (age and sex) which influence driving behaviour and attitude similarly for both groups. Key messages • Patients with depression did not differ from controls regarding driving behaviour and attitude. • Patients seem to realize the effects of symptoms of depression on their driving ability and, consequently, drive more carefully.

A driver stress detection model via data augmentation based on deep convolutional recurrent neural network

Excessive stress generally leads to degraded driving performance, which increases the risk of road accidents. Therefore, real-time driver stress detection is of great significance to improve road safety. To this end, this paper proposes a deep convolutional recurrent neural network with Wasserstein generative adversarial network-based data augmentation (WGAN-DCRNN) for real-time driver stress detection, where only the pupillary response data are used. The proposed model can learn the sequence features from the input data more effectively and address the class-imbalance problem of the dataset. First, two types of DCRNN models (i.e., CNN-LSTM and CNN-GRU) are established and compared with baseline models (i.e., CNN, LSTM and GRU). The best baseline model (i.e., CNN) achieves 93.02% accuracy on the overall samples of the test set, while the CNN-LSTM and CNN-GRU improve the accuracy to 94.68% and 94.71%, respectively. This suggests that the performance of the CNN can be improved by integrating the RNN to consider the temporal dependencies of the pupillary response data. Second, deep generative model-based data augmentation is used to address the class-imbalance problem of the dataset. Specifically, the WGAN is trained to generate new samples, which are used to expand and balance the training set. To validate the effect of the proposed data augmentation method, comparative experiments are further conducted. The results show that the accuracies of CNN-LSTM and CNN-GRU are further improved to 95.39% and 95.30%, respectively. More importantly, the recall rates of minority categories achieve significant improvement. This indicates that the proposed model overcomes the class-imbalance problem and that the generalization ability of the recognition model has been further improved by using data augmentation. In addition, the WGAN-DCRNN outperforms models in existing driver stress detection studies. The proposed model can be used for different classification tasks to reduce road accident risks caused by drivers.

Evaluation of driver stress intervention with guided breathing and positive comments

While many methods have been proposed to detect driver stress with high accuracy, few studies have explored how to mitigate stress during driving effectively. This study proposed and evaluated two driver stress intervention methods, i.e., auditory Positive Comments w/o haptic Breathing guidance (BPC and PC). Sixty drivers were randomly assigned to four groups (i.e., no stress, stressful but no intervention-NI, PC, and BPC) and completed a simulated driving task with their physiological, psychological, and behavioral data collected. Driver stress was effectively induced by challenging simulated driving events. Haptic guidance provided by smartwatches efficiently regulated the breathing rate to the target. Engaging in the intervention was associated with increased RMSSD and did not worsen driving performance. Participants perceived moderate to large comfort effects. The complexity of driving scenarios should be considered for choosing interventions. Breathing intervention was less effective when complex maneuvers were required than normal driving. The findings provided implications regarding the design of in-vehicle stress intervention systems for intelligent transportation.

Aggressive Driving, Rule-Breaking, and Driver Stress in Spanish-speaking countries

Driver Aggression is a phenomenon many have studied in all five continents. It has been the focus of curiosity for all sorts of disciplines, and science has been unable to curb it, much less park it. Driver Stress, Road Aggression and Road Rule-Breaking are crucial elements in traffic psychology. Those who do not respect the rules are a major factor in increased risk and the occurrence of major traffic accidents. Little work has been done on how these phenomena behave in Spanish-speaking countries, much less the development of useful instruments to evaluate them. This study aims to generate two unique scales that can be used in Spanish speaking countries regardless of culture or geography, as well as determining the influence Driver Stress has on Driver Aggression and Rule-Breaking. This study comprises a sample of 1827 drivers from Mexico, Guatemala, Costa Rica, and Spain. Through this paper, a small yet significant relationship was found between Driver Stress, Driver Aggression, and Driver Rule-Breaking. This suggests that personality factors are at play in these relationships in Spanish-speaking countries, and that these variables are an important factor on how drivers behave. This study also delivers two useful tools for evaluating Driver Aggression (LatinCAT) and Driver Rule-Breaking (LatinDANT) that have excellent construct validity for further research in Spanish-speaking countries.

The Effect of Work-Life Balance on Job Stress and Fatigue in Taxi Drivers.

This is a descriptive study that was conducted to examine the effect of work-life balance on job stress and fatigue in taxi drivers. The study included 150 taxi drivers residing in Ankara. As a data collection tool, we used Work-Life Balance, Chalder Fatigue, and Work Stress Scales. We used the Mann-Whitney U test and multiple linear regression analysis. The model established after multiple linear regression analysis was statistically significant ( F [5.142] = 5.442, P < 0.001). A 1-unit increase in the work-life balance variable caused a 2.885-unit decrease in the work stress score and a 0.143-unit decrease in the fatigue score. We can suggest that taxi drivers who have a work-life balance will be able to participate in social activities in their daily lives, and leisure time activities with family and friends can help motivate individuals more and increase energy.

Assessment of Driver's Stress using Multimodal Biosignals and Regularized Deep Kernel Learning.

In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and respiration rate (RESP) from N = 10 healthy drivers in experiments of 25min duration with different stress states (5min resting, 10min driving, 10min driving + answering cognitive questions). We manually remove unusable segments and approximately 4h of data remain. Multimodal time and frequency features are extracted and employed to regularized deep kernel machine learning based on a fusion framework. Task-specific representations of different physiological signals are combined using intermediate fusion. Subsequently, the fused multimodal features are fed a support vector machine (SVM) and a random forest (RF) for stress classification. The experimental results show that the proposed approach can discriminate between stress states. The combination of PPG and ECG using RF as classifier yields the highest F1-score of 0.97 in the test set. PPG only and RF yield a maximum F1-score of 0.90. Furthermore, subject-specific cross-validation improves performance. ECG and PPG signals are reliable in classifying the stress state of a car driver. In summary, the proposed framework could be extended to real-time stress state assessment in driving conditions.