Respiration and Heart Rate Modulation Due to Competing Cognitive Tasks While Driving

Road to serenity: Individual variations in the efficacy of unobtrusive respiratory guidance for driving stress regulation

Stress impacts driving-related cognitive functions like attention and decision-making, and may arise in automated vehicles due to non-driving tasks. Unobtrusive relaxation techniques are needed to regulate stress without distracting from driving. Tactile wearables have shown efficacy in stress regulation through respiratory guidance, but individual variations may affect their efficacy. This study assessed slow-breathing tactile guidance under different stress levels on 85 participants. Physiological, behavioral and subjective data were collected. The influence of individual variations (e.g., driving habits and behavior, personality) using logistic regression analysis was explored. Participants could follow the guidance and adjust breathing while driving, but subjective efficacy depended on individual variations linked to different efficiency in using the technique, in relation with its attentional cost. An influence of factors linked to the evaluation of context criticality was also found. The results suggest that considering individual and contextual variations is crucial in designing and using such techniques in demanding driving contexts. In this line some design recommendations and insights for further studies are provided.

A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study

Driving behaviour analysis has drawn much attention in recent years due to the dramatic increase in the number of traffic accidents and casualties, and based on many studies, there is a relationship between the driving environment or behaviour and the driver's state. To the best of our knowledge, these studies mostly investigate relationships between one vital sign and the driving circumstances either inside or outside the cabin. Hence, our paper provides an analysis of the correlation between the driver state (vital signs, eye state, and head pose) and both the vehicle maneuver actions (caused by the driver) and external events (carried out by other vehicles or pedestrians), including the proximity to other vehicles. Our methodology employs several models developed in our previous work to estimate respiratory rate, heart rate, blood pressure, oxygen saturation, head pose, eye state from in-cabin videos, and the distance to the nearest vehicle from out-cabin videos. Additionally, new models have been developed using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to classify the external events from out-cabin videos, as well as a Decision Tree classifier to detect the driver's maneuver using accelerometer and gyroscope sensor data. The dataset used includes synchronized in-cabin/out-cabin videos and sensor data, allowing for the estimation of the driver state, proximity to other vehicles and detection of external events, and driver maneuvers. Therefore, the correlation matrix was calculated between all variables to be analysed. The results indicate that there is a weak correlation connecting both the maneuver action and the overtaking external event on one side and the heart rate and the blood pressure (systolic and diastolic) on the other side. In addition, the findings suggest a correlation between the yaw angle of the head and the overtaking event and a negative correlation between the systolic blood pressure and the distance to the nearest vehicle. Our findings align with our initial hypotheses, particularly concerning the impact of performing a maneuver or experiencing a cautious event, such as overtaking, on heart rate and blood pressure due to the agitation and tension resulting from such events. These results can be the key to implementing a sophisticated safety system aimed at maintaining the driver's stable state when aggressive external events or maneuvers occur.

Drivers' self-regulatory behaviors in active and responsive scenarios

OBJECTIVE

Drivers usually appear to self-regulate their driving behaviors in situations considered to be challenging, such as mobile phone-distracted driving. It is important to clarify how drivers self-regulate their actual behaviors. In addition, few studies investigated driver distraction in active and responsive scenarios. Therefore, the present study aimed to gain a better understanding of drivers' actual self-regulation of driving behaviors and phone use behaviors while mobile phone-distracted driving in active and responsive scenarios. The contribution of compensatory beliefs to self-regulation was also explored.

METHODS

This study was conducted using a 2 (mobile phone use behaviors: phone calling vs. WeChat messaging) × 2 (scenarios: active vs. responsive) within-group design. A total of 34 participants completed a driving simulation experiment. The dependent variables of drivers' driving behaviors, phone use behaviors, and physiological data were collected. Participants' compensatory belief was also measured.

RESULTS

The results showed that the speed reduction in the stages with WeChat messaging was significantly greater than that in the stages with phone calls, and the speed reduction in the responsive scenario was significantly greater than that in the active scenario. Participants would adopt relatively equal phone-use-related self-regulatory behaviors in active and responsive scenarios. Participants with higher compensatory beliefs had relatively greater speed reduction in most scenarios, but fewer phone-use-related self-regulatory behaviors. In addition, the respiratory rate could contribute to evaluating the changes in drivers' physiological status during phone calling-distracted driving.

CONCLUSIONS

Participants would self-regulate driving behaviors and phone use behaviors according to different distracted driving tasks and scenarios. The driving-related self-regulation in WeChat messaging scenarios and responsive scenarios was greater. There was a trend in the effect of compensatory beliefs on actual self-regulatory behaviors, which needs to be further verified in the future. This study contributes to the verification of the different actual driving-related and phone-use-related self-regulatory behavior of drivers in active and responsive mobile phone distracted driving scenarios.

A Systematic Review of In-Vehicle Physiological Indices and Sensor Technology for Driver Mental Workload Monitoring

The concept of vehicle automation ceases to seem futuristic with the current advancement of the automotive industry. With the introduction of conditional automated vehicles, drivers are no longer expected to focus only on driving activities but are still required to stay alert to resume control. However, fluctuations in driving demands are known to alter the driver's mental workload (MWL), which might affect the driver's vehicle take-over capabilities. Driver mental workload can be specified as the driver's capacity for information processing for task performance. This paper summarizes the literature that relates to analysing driver mental workload through various in-vehicle physiological sensors focusing on cardiovascular and respiratory measures. The review highlights the type of study, hardware, method of analysis, test variable, and results of studies that have used physiological indices for MWL analysis in the automotive context.

Spontaneous Breathing Rate Variations Linked to Social Exclusion and Emotion Self-assessment

The emotional reactions to social exclusion can be associated with physiological responses that could allow researchers to estimate the valence and intensity of the ongoing affective state. In this work, respiratory activity was analysed to verify whether breathing rate variations can be considered as predictive factors of subsequent positive and negative affect after inclusion and exclusion in young women. A standard Cyberball task was implemented and manipulated information was provided to the participants to create both conditions. The participants were socially excluded by limiting their participation to 6% of the total number of passes among three teammates and providing negative feedback about them. The results suggest that breathing rate can be a good option to infer subjective feelings during social interactions and a promising feature to incorporate into modern emotion monitoring systems as an alternative to other physiological measures. Furthermore, the interaction between metaemotion and physiology was studied by recording breathing rate while completing the Positive and Negative Affect Schedule, evidencing a breathing rate increase during the emotion self-assessment only after exclusion.

Differences in stress system (re-)activity between single and dual- or multitasking in healthy adults: A systematic review and meta-analysis

AbstractIn the age of digitization, multitasking requirements are ubiquitous, especially in the workplace. Multitasking (MT) describes the activity of performing multiple (at least two) tasks at the same time. Dual tasking (DT) refers to the sequential switching between two tasks. The aim of our systematic review and meta-analysis was first to investigate whether physiological stress systems become activated in response to or during MT/DT and, secondly, whether this (re-)activity is higher compared to single tasking. We focused on the Sympathetic Nervous System (SNS), the Parasympathetic Nervous System (PNS), the hypothalamic-pituitary adrenal (HPA) axis, and the immune system. The systematic review has been pre-registered with PROSPERO (CRD42020181415). A total of twenty-five articles were identified as eligible, in which n = 26 studies were reported, with N = 1,142 participants. Our main findings are that SNS activity is significantly higher and PNS activity is significantly lower during MT/DT than during single tasking. Only two studies were found, in which HPA axis (re-)activity was surveyed. No eligible study was identified in which immune system (re-)activity was investigated. This is the first systematic synthesis of the literature base showing that stress system activity is increased during MT/DT in comparison to single-tasking.

The Current State of Subjective Training Load Monitoring: Follow-Up and Future Directions

This article addresses several key issues that have been raised related to subjective training load (TL) monitoring. These key issues include how TL is calculated if subjective TL can be used to model sports performance and where subjective TL monitoring fits into an overall decision-making framework for practitioners. Regarding how TL is calculated, there is conjecture over the most appropriate (1) acute and chronic period lengths, (2) smoothing methods for TL data and (3) change in TL measures (e.g., training stress balance (TSB), differential load, acute-to-chronic workload ratio). Variable selection procedures with measures of model-fit, like the Akaike Information Criterion, are suggested as a potential answer to these calculation issues with examples provided using datasets from two different groups of elite athletes prior to and during competition at the 2016 Olympic Games. Regarding using subjective TL to model sports performance, further examples using linear mixed models and the previously mentioned datasets are provided to illustrate possible practical interpretations of model results for coaches (e.g., ensuring TSB increases during a taper for improved performance). An overall decision-making framework for determining training interventions is also provided with context given to where subjective TL measures may fit within this framework and the determination if subjective measures are needed with TL monitoring for different sporting situations. Lastly, relevant practical recommendations (e.g., using validated scales and training coaches and athletes in their use) are provided to ensure subjective TL monitoring is used as effectively as possible along with recommendations for future research.

Let Complexity Bring Clarity: A Multidimensional Assessment of Cognitive Load Using Physiological Measures

The effects of cognitive load on driver behavior and traffic safety are unclear and in need of further investigation. Reliable measures of cognitive load for use in research and, subsequently, in the development and implementation of driver monitoring systems are therefore sought. Physiological measures are of interest since they can provide continuous recordings of driver state. Currently, however, a few issues related to their use in this context are not usually taken into consideration, despite being well-known. First, cognitive load is a multidimensional construct consisting of many mental responses (cognitive load components) to added task demand. Yet, researchers treat it as unidimensional. Second, cognitive load does not occur in isolation; rather, it is part of a complex response to task demands in a specific operational setting. Third, physiological measures typically correlate with more than one mental state, limiting the inferences that can be made from them individually. We suggest that acknowledging these issues and studying multiple mental responses using multiple physiological measures and independent variables will lead to greatly improved measurability of cognitive load. To demonstrate the potential of this approach, we used data from a driving simulator study in which a number of physiological measures (heart rate, heart rate variability, breathing rate, skin conductance, pupil diameter, eye blink rate, eye blink duration, EEG alpha power, and EEG theta power) were analyzed. Participants performed a cognitively loading n-back task at two levels of difficulty while driving through three different traffic scenarios, each repeated four times. Cognitive load components and other coinciding mental responses were assessed by considering response patterns of multiple physiological measures in relation to multiple independent variables. With this approach, the construct validity of cognitive load is improved, which is important for interpreting results accurately. Also, the use of multiple measures and independent variables makes the measurements (when analyzed jointly) more diagnostic-that is, better able to distinguish between different cognitive load components. This in turn improves the overall external validity. With more detailed, diagnostic, and valid measures of cognitive load, the effects of cognitive load on traffic safety can be better understood, and hence possibly mitigated.

Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving.

Cognitive and body manifestations of driving anxiety according to different onsets

BACKGROUND

Driving anxiety can have deleterious effects not only on driving behavior, but also on life quality. The interaction between motor vehicle collision (MVC) experiences and driving anxiety has been studied from different standpoints. However, the comparison with other events triggering it has been scarcely considered. Objectives: To analyze the body manifestations and the driving cognitions related to the accident, social and panic concerns in people suffering from different levels of driving anxiety. Method: A total of 260 participants suffering from driving anxiety were included in a survey, including Driving Cognition Questionnaire and Body Sensation Questionnaire. Results: Panic attacks and criticisms are the most relevant onsets of driving anxiety, more than MVC. Only 11.4% of MVC victims considered it as the onset. People with MVC history showed lower scores in social concerns than people without MVC experience and neither the responsibility of the MVC nor the role (driver/passenger) seemed to have an impact on the anxiety level. Conclusions: Although the most relevant body sensations, heart palpitations and sweating, were the same in people with panic attack experiences and MVC victims, a discrimination of the emotions behind the concept of "driving anxiety" is desirable to clarify the psychological effects of different onsets.

Breathing Biofeedback for Police Officers in a Stressful Virtual Environment: Challenges and Opportunities

As part of the Dutch national science program “Professional Games for Professional Skills” we developed a stress-exposure biofeedback training in virtual reality (VR) for the Dutch police. We aim to reduce the acute negative impact of stress on performance, as well as long-term consequences for mental health by facilitating physiological stress regulation during a demanding decision task. Conventional biofeedback applications mainly train physiological regulation at rest. This might limit the transfer of the regulation skills to stressful situations. In contrast, we provide the user with the opportunity to practice breathing regulation while they carry out a complex task in VR. This setting poses challenges from a technical – (real-time processing of noisy biosignals) as well as from a user-experience perspective (multi-tasking). We illustrate how we approach these challenges in our training and hope to contribute a useful reference for researchers and developers in academia or industry who are interested in using biosignals to control elements in a dynamic virtual environment.

Ultra-Short Window Length and Feature Importance Analysis for Cognitive Load Detection from Wearable Sensors

Human cognitive capabilities are under constant pressure in the modern information society. Cognitive load detection would be beneficial in several applications of human–computer interaction, including attention management and user interface adaptation. However, current research into accurate and real-time biosignal-based cognitive load detection lacks understanding of the optimal and minimal window length in data segmentation which would allow for more timely, continuous state detection. This study presents a comparative analysis of ultra-short (30 s or less) window lengths in cognitive load detection with a wearable device. Heart rate, heart rate variability, galvanic skin response, and skin temperature features are extracted at six different window lengths and used to train an Extreme Gradient Boosting classifier to detect between cognitive load and rest. A 25 s window showed the highest accury (67.6%), which is similar to earlier studies using the same dataset. Overall, model accuracy tended to decrease as the window length decreased, and lowest performance (60.0%) was observed with a 5 s window. The contribution of different physiological features to the classification performance and the most useful features that react in short windows are also discussed. The analysis provides a promising basis for future real-time applications with wearable sensors.

Relationships Between Different Internal and External Training Load Variables and Elite International Women's Basketball Performance

PURPOSE

To investigate the relationships between internal and external training load (TL) metrics with elite international women's basketball performance.

METHODS

Sessional ratings of perceived exertion, PlayerLoad™/minute, and training duration were collected from 13 elite international-level female basketball athletes (age 29.0 [3.7] y, stature 186.0 [9.8] cm, body mass 77.9 [11.6] kg) during the 18 weeks prior to the International Basketball Federation Olympic qualifying event for the 2016 Rio Olympic Games. Training stress balance, differential load, and the training efficiency index were calculated with 3 different smoothing methods. These TL metrics and their change in the last 21 days prior to competition were examined for their relationship to competition performance as coach ratings of performance.

RESULTS

For a number of TL variables, there were consistent significant small to moderate correlations with performance and significant small to large differences between successful and unsuccessful performances. However, these differences were only evident for external TL when using exponentially weighted moving averages to calculate TL. The variable that seemed most sensitive to performance was the change in training efficiency index in the last 21 days prior to competition (performance r = .47-.56, P < .001 and difference between successful and unsuccessful performance P < .001, f2 = 0.305-0.431).

CONCLUSIONS

Internal and external TL variables were correlated with performance and distinguished between successful and unsuccessful performances among the same players during international women's basketball games. Manipulating TL in the last 3 weeks prior to competition may be worthwhile for basketball players' performance, especially in internal TL.

Towards Mindless Stress Regulation in Advanced Driver Assistance Systems: A Systematic Review

Background: Stress can frequently occur in the driving context. Its cognitive effects can be deleterious and lead to uncomfortable or risky situations. While stress detection in this context is well developed, regulation using dedicated advanced driver-assistance systems (ADAS) is still emergent. Objectives: This systematic review focuses on stress regulation strategies that can be qualified as "subtle" or "mindless": the technology employed to perform regulation does not interfere with an ongoing task. The review goal is 2-fold: establishing the state of the art on such technological implementation in the driving context and identifying complementary technologies relying on subtle regulation that could be applied in driving. Methods: A systematic review was conducted using search operators previously identified through a concept analysis. The patents and scientific studies selected provide an overview of actual and potential mindless technology implementations. These are then analyzed from a scientific perspective. A classification of results was performed according to the different stages of emotion regulation proposed by the Gross model. Results: A total of 47 publications were retrieved, including 21 patents and 26 studies. Six of the studies investigated mindless stress regulation in the driving context. Patents implemented strategies mostly linked to attentional deployment, while studies tended to investigate response modulation strategies. Conclusions: This review allowed us to identify several ADAS relying on mindless computing technologies to reduce stress and better understand the underlying mechanisms allowing stress reduction. Further studies are necessary to better grasp the effect of mindless technologies on driving safety. However, we have established the feasibility of their implementation as ADAS and proposed directions for future research in this field.

Psychophysiological responses to takeover requests in conditionally automated driving

In SAE Level 3 automated driving, taking over control from automation raises significant safety concerns because drivers out of the vehicle control loop have difficulty negotiating takeover transitions. Existing studies on takeover transitions have focused on drivers' behavioral responses to takeover requests (TORs). As a complement, this exploratory study aimed to examine drivers' psychophysiological responses to TORs as a result of varying non-driving-related tasks (NDRTs), traffic density and TOR lead time. A total number of 102 drivers were recruited and each of them experienced 8 takeover events in a high fidelity fixed-base driving simulator. Drivers' gaze behaviors, heart rate (HR) activities, galvanic skin responses (GSRs), and facial expressions were recorded and analyzed during two stages. First, during the automated driving stage, we found that drivers had lower heart rate variability, narrower horizontal gaze dispersion, and shorter eyes-on-road time when they had a high level of cognitive load relative to a low level of cognitive load. Second, during the takeover transition stage, 4 s lead time led to inhibited blink numbers and larger maximum and mean GSR phasic activation compared to 7 s lead time, whilst heavy traffic density resulted in increased HR acceleration patterns than light traffic density. Our results showed that psychophysiological measures can indicate specific internal states of drivers, including their workload, emotions, attention, and situation awareness in a continuous, non-invasive and real-time manner. The findings provide additional support for the value of using psychophysiological measures in automated driving and for future applications in driver monitoring systems and adaptive alert systems.

Subjective Evaluation of Performance in a Collaborative Task Is Better Predicted From Autonomic Response Than From True Achievements

Whereas the fundamental role of the body in social cognition seems to be generally accepted, elucidating the bodily mechanisms associated with non-verbal communication and cooperation between two or more persons is still a challenging endeavor. In this article we propose a fresh approach for investigating the function of the autonomic nervous system that is reflected in parameters of heart rate variability, respiration, and electrodermal activity in a social setting. We analyzed autonomic parameters of dyads solving a target-tracking task together with the partner or individually. A machine classifier was trained to predict the subjects' rating of performance and collaboration either from tracking error data or from the set of autonomic parameters. When subjects collaborated, this classifier could predict the subjective performance ratings better from the autonomic response than from the objective performance of the subjects. However, when they solved the task individually, predictability from autonomic parameters dropped to the level of objective performance, indicating that subjects were more rational in rating their performance in this condition. Moreover, the model captured general knowledge about the population that allows it to predict the performance ratings of an unseen subject significantly better than chance. Our results suggest that, in particular in situations that require collaboration with others, evaluation of performance is shaped by the bodily processes that are quantified by autonomic parameters. Therefore, subjective performance assessments appear to be modulated not only by the output of a rational or discriminative system that tracks the objective performance but to a significant extent also by interoceptive processes.

Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification

One debatable issue in traffic safety research is that the cognitive load by secondary tasks reduces primary task performance, i.e., driving. In this paper, the study adopted a version of the n-back task as a cognitively loading secondary task on the primary task, i.e., driving; where drivers drove in three different simulated driving scenarios. This paper has taken a multimodal approach to perform 'intelligent multivariate data analytics' based on machine learning (ML). Here, the k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF) are used for driver cognitive load classification. Moreover, physiological measures have proven to be sophisticated in cognitive load identification, yet it suffers from confounding factors and noise. Therefore, this work uses multi-component signals, i.e., physiological measures and vehicular features to overcome that problem. Both multiclass and binary classifications have been performed to distinguish normal driving from cognitive load tasks. To identify the optimal feature set, two feature selection algorithms, i.e., sequential forward floating selection (SFFS) and random forest have been applied where out of 323 features, a subset of 42 features has been selected as the best feature subset. For the classification, RF has shown better performance with F1-score of 0.75 and 0.80 than two other algorithms. Moreover, the result shows that using multicomponent features classifiers could classify better than using features from a single source.

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.