The use of psychophysiology to assess driver status

Gaze entropy metrics for mental workload estimation are heterogenous during hands-off level 2 automation

As the level of vehicle automation increases, drivers are more likely to engage in non-driving related tasks which take their hands, eyes, and/or mind away from the driving task. Consequently, there has been increased interest in creating Driver Monitoring Systems (DMS) that are valid and reliable for detecting elements of driver state. Workload is one element of driver state that has remained elusive within the literature. Whilst there has been promising work in estimating mental workload using gaze-based metrics, the literature has placed too much emphasis on point estimate differences. Whilst these are useful for establishing whether effects exist, they ignore the inherent variability within individuals and between different drivers. The current work builds on this by using a Bayesian distributional modelling approach to quantify the within and between participants variability in Information Theoretical gaze metrics. Drivers (N = 38) undertook two experimental drives in hands-off Level 2 automation with their hands and feet away from operational controls. During both drives, their priority was to monitor the road before a critical takeover. During one drive participants had to complete a secondary cognitive task (2-back) during the hands-off Level 2 automation. Changes in Stationary Gaze Entropy and Gaze Transition Entropy were assessed for conditions with and without the 2-back to investigate whether consistent differences between workload conditions could be found across the sample. Stationary Gaze Entropy proved a reliable indicator of mental workload; 92 % of the population were predicted to show a decrease when completing 2-back during hands-off Level 2 automated driving. Conversely, Gaze Transition Entropy showed substantial heterogeneity; only 66 % of the population were predicted to have similar decreases. Furthermore, age was a strong predictor of the heterogeneity of the average causal effect that high mental workload had on eye movements. These results indicate that, whilst certain elements of Information Theoretic metrics can be used to estimate mental workload by DMS, future research needs to focus on the heterogeneity of these processes. Understanding this heterogeneity has important implications toward the design of future DMS and thus the safety of drivers using automated vehicle functions. It must be ensured that metrics used to detect mental workload are valid (accurately detecting a particular driver state) as well as reliable (consistently detecting this driver state across a population).

Psychophysiological models of hypovigilance detection: A scoping review

Hypovigilance represents a major contributor to accidents. In operational contexts, the burden of monitoring/managing vigilance often rests on operators. Recent advances in sensing technologies allow for the development of psychophysiology-based (hypo)vigilance prediction models. Still, these models remain scarcely applied to operational situations and need better understanding. The current scoping review provides a state of knowledge regarding psychophysiological models of hypovigilance detection. Records evaluating vigilance measuring tools with gold standard comparisons and hypovigilance prediction performances were extracted from MEDLINE, PsychInfo, and Inspec. Exclusion criteria comprised aspects related to language, non-empirical papers, and sleep studies. The Quality Assessment tool for Diagnostic Accuracy Studies (QUADAS) and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were used for bias evaluation. Twenty-one records were reviewed. They were mainly characterized by participant selection and analysis biases. Papers predominantly focused on driving and employed several common psychophysiological techniques. Yet, prediction methods and gold standards varied widely. Overall, we outline the main strategies used to assess hypovigilance, their principal limitations, and we discuss applications of these models.

Changes in the levels of IL-1β, cortisol and chromogranin A in saliva of subjects with occupational fatigue

Changes in the levels of interleukin-1β (IL-1β), cortisol and chromogranin A (CgA) in saliva of subjects with occupational fatigue were investigated. Doctors in the Emergency Department were selected as research subjects. Saliva was collected before work (after full rest) and after work (≥24 h). Electroencephalogram (EEG) was performed. Enzyme-linked immunosorbent assay (ELISA) was used to detect the levels of IL-1β, cortisol and CgA in saliva. In order to obtain permission for human specimens, the study was approved by the Ethics Committee of the Affiliated Hospital of Hebei University of Engineering and registered for clinical trials (registration no. ChiC-TR-DCD-14005746). As there were only 4 subjects in this study without fatigue waves in EEG, and the number of these subjects was not sufficient to constitute a control group, the comparison of the contents of IL-1β, cortisol and CgA of all subjects before and after working for 18 h was just a confirmation of the statistical results of 43 cases with fatigue waves in the EEG. According to the results, there was no change in the contents of IL-1β and cortisol in the saliva of subjects with occupational fatigue before and after fatigue, whereas, there was a significant change in the content of CgA before and after fatigue. However, there was no correlation between the content of CgA and fatigue. The results of the present study revealed that IL-1β, cortisol and CgA indicators are not suitable diagnostic markers for occupational fatigue.

Using Electroencephalography (EEG) Power Responses to Investigate the Effects of Ambient Oxygen Content, Safety Shoe Type, and Lifting Frequency on the Worker's Activities

Objective

The study assesses the changes in electroencephalography (EEG) power spectral density of individuals in hypoxia when wearing a different type of safety shoes under different lifting frequencies. It also assesses the EEG response behavior induced via the process of lifting loads related to these variables.

Methods

The study was conducted in two consecutive phases: training and acclimatization phase and experimental lifting phase. Ten male college students participated in this study. A four-way repeated measures design was used in this research with independent variables: ambient oxygen content (“15%, 18%, and 20%”), safety shoes type (“light-duty, medium-duty, and heavy-duty”), lifting frequency (“1 and 4 lifts/min”), and replication (“first and second”). And the dependent variables were alpha, theta, beta, gamma, θ/α, θ/β, α/β, β/α, (θ + α)/β, and (θ + α)/(α + β). The participant was allowed to determine his maximum acceptable weight of lift (MAWL) in fifteen minutes of lifting using psychophysically technique. Then, he continued lifting the MAWL for another five minutes, where all the data were collected.

Results

Results showed that the EEG responses at lower levels of the independent variables were significantly high than at higher levels; except for oxygen content, the EEG responses at lower levels were considerably lower than at a higher level. It also showed that an upsurge in the physical demand increased lifting frequency and replication and caused decreasing in alpha power, theta/beta, alpha/beta, (theta + alpha)/beta, (theta + alpha)/(alpha + beta) and increasing in the theta power and the gamma power. Furthermore, several interactions among independent variables had significant effects on the EEG responses.

Conclusion

The EEG implementation for the investigation of neural responses to physical demands allows for the possibility of newer nontraditional and faster methods of human performance monitoring. These methods provide effective and reliable results as compared to other traditional methods. This study will safeguard the physical capabilities and possible health risks of industrial workers. And the applications of these tasks can occur in almost all working environments (factories, warehouses, airports, building sites, farms, hospitals, offices, etc.) that are at high altitudes. It can include lifting boxes at a packaging line, handling construction materials, handling patients in hospitals, and cleaning.

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.

EEG-Based Neurocognitive Metrics May Predict Simulated and On-Road Driving Performance in Older Drivers

The number of older drivers is steadily increasing, and advancing age is associated with a high rate of automobile crashes and fatalities. This can be attributed to a combination of factors including decline in sensory, motor, and cognitive functions due to natural aging or neurodegenerative diseases such as HIV-Associated Neurocognitive Disorder (HAND). Current clinical assessment methods only modestly predict impaired driving. Thus, there is a need for inexpensive and scalable tools to predict on-road driving performance. In this study EEG was acquired from 39 HIV+ patients and 63 healthy participants (HP) during: 3-Choice-Vigilance Task (3CVT), a 30-min driving simulator session, and a 12-mile on-road driving evaluation. Based on driving performance, a designation of Good/Poor (simulator) and Safe/Unsafe (on-road drive) was assigned to each participant. Event-related potentials (ERPs) obtained during 3CVT showed increased amplitude of the P200 component was associated with bad driving performance both during the on-road and simulated drive. This P200 effect was consistent across the HP and HIV+ groups, particularly over the left frontal-central region. Decreased amplitude of the late positive potential (LPP) during 3CVT, particularly over the left frontal regions, was associated with bad driving performance in the simulator. These EEG ERP metrics were shown to be associated with driving performance across participants independent of HIV status. During the on-road evaluation, Unsafe drivers exhibited higher EEG alpha power compared to Safe drivers. The results of this study are 2-fold. First, they demonstrate that high-quality EEG can be inexpensively and easily acquired during simulated and on-road driving assessments. Secondly, EEG metrics acquired during a sustained attention task (3CVT) are associated with driving performance, and these metrics could potentially be used to assess whether an individual has the cognitive skills necessary for safe driving.

Assessment of Driver's Drowsiness Based on Fractal Dimensional Analysis of Sitting and Back Pressure Measurements

The most effective way of preventing motor vehicle accidents caused by drowsy driving is through a better understanding of drowsiness itself. Prior research on the detection of symptoms of drowsy driving has offered insights on providing drivers with advance warning of an elevated risk of crash. The present study measured back and sitting pressures during a simulated driving task under both high and low arousal conditions. Fluctuation of time series of center of pressure (COP) movement of back and sitting pressure was observed to possess a fractal property. The fractal dimensions were calculated to compare the high and low arousal conditions. The results showed that under low arousal (the drowsiness state) the fractal dimension was significantly lower than what was calculated with high arousal. Accumulated drowsiness thus contributed to the loss of self-similarity and unpredictability of time series of back and sitting pressure measurement. Drowsiness further reduces the complexity of the posture control system as viewed from back and sitting pressure. Thus, fractal dimension is a necessary and sufficient condition of a decreased arousal level. It further is a necessary condition for detecting the interval or point in time with high risk of crash.

EEG classification of driver mental states by deep learning

Driver fatigue is attracting more and more attention, as it is the main cause of traffic accidents, which bring great harm to society and families. This paper proposes to use deep convolutional neural networks, and deep residual learning, to predict the mental states of drivers from electroencephalography (EEG) signals. Accordingly we have developed two mental state classification models called EEG-Conv and EEG-Conv-R. Tested on intra- and inter-subject, our results show that both models outperform the traditional LSTM- and SVM-based classifiers. Our major findings include (1) Both EEG-Conv and EEG-Conv-R yield very good classification performance for mental state prediction; (2) EEG-Conv-R is more suitable for inter-subject mental state prediction; (3) EEG-Conv-R converges more quickly than EEG-Conv. In summary, our proposed classifiers have better predictive power and are promising for application in practical brain-computer interaction .

How Physical Activities Affect Mental Fatigue Based on EEG Energy, Connectivity, and Complexity

Many studies have verified that there is an interaction between physical activities and mental fatigue. However, few studies are focused on the effect of physical activities on mental fatigue. This study was to analyze the states of mental fatigue based on electroencephalography (EEG) and investigate how physical activities affect mental fatigue. Fourteen healthy participants participated in an experiment including a 2-back mental task (the control) and the same mental task with cycling simultaneously (physical-mental task). Each experiment consisted of three 20 min fatigue-inducing sessions repeatedly (mental fatigue for mental tasks or mental fatigue plus physical activities for physical-mental tasks). During the evaluation sessions (before and after the fatigue-inducing sessions), the states of the participants were assessed by EEG parameters. Wavelet Packet Energy (WPE), Spectral Coherence Value (SCV), and Lempel-Ziv Complexity (LZC) were used to indicate mental fatigue from the perspectives of activation, functional connectivity, and complexity of the brain. The indices are the beta band energy E_β, the energy ratio E_α/β, inter-hemispheric SCV of beta band SCV_β and LZC. The statistical analysis shows that mental fatigue was detected by E_β, E_α/β, SCV_β, and LZC in physical-mental task. The slopes of the linear fit on these indices verified that the mental fatigue increased more fast during physical-mental task. It is concluded form the result that physical activities can enhance the mental fatigue and speed up the fatigue process based on brain activation, functional connection, and complexity. This result differs from the traditional opinion that physical activities have no influence on mental fatigue, and finds that physical activities can increase mental fatigue. This finding helps fatigue management through exercise instruction.

Electrophysiological signatures of the resting-state fMRI global signal: A simultaneous EEG-fMRI study

BACKGROUND

The global signal of resting-state functional magnetic resonance imaging (fMRI) constitutes an intrinsic fluctuation and presents an opportunity to characterize and understand the activity of the whole brain. Recently, evidence that the global signal contains neurophysiologic information has been growing, but the global signal of electroencephalography (EEG) has never been determined.

NEW METHODS

We developed a new method to obtain the EEG global signal. The EEG global signal was reconstructed by the reference electrode standardization technique and represented the outer cortical electrophysiological activity. To investigate its relationship with the global signal of resting-state fMRI, a simultaneous EEG-fMRI signal was recorded, and this was analyzed in 24 subjects.

RESULTS

We found that the global signal of resting-state fMRI showed a positive correlation with power fluctuations of the EEG global signal in the γ band (30-45 Hz) and a negative correlation in the low-frequency band (4-20 Hz).

COMPARISON WITH EXISTING METHOD(S)

Compared with the global signal of fMRI, the global signal of EEG provides more temporal information about outer cortical neural activity.

CONCLUSIONS

These results provide new evidence for the electrophysiology information of the global signal of resting-state fMRI. More importantly, due to its high correlation with the fMRI global signal, the EEG global signal may serve as a new biomarker for neurological disorders.

The effects on mental fatigue and the cognitive function of mechanical massage and binaural beats (brain massage) provided by massage chairs

OBJECTIVE

To verify whether the mechanical massage using massage chairs and binaural beats (brain massage) affect the mental fatigue recovery and cognitive enhancements.

METHODS

25 healthy adults used massage chairs that could provide mechanical massage and binaural beats (brain massage) for 20 min. Mental fatigue and cognitive function were assessed before and after receiving brain massage using electroencephalogram (EEG) and 5 prolonged cognitive tests.

RESULTS

When a person received a brain massage on the massage chair, the decrease in mental fatigue was statistically significant compared to taking a rest or receiving a mechanical massage only on the massage chair. In addition, sustained attention, verbal short-term and long-term memory and non-verbal long-term memory were statistically significantly increased after using brain massage.

CONCLUSION

Brain massage (mechanical massage and binaural beats) are effective in reducing mental fatigue and improving the cognitive function.

A comparison of ground reaction force components according to the foothold heights in 16-t truck during downward step

The aim of this study is to compare and analyze the components of ground reaction force (GRF) relative to the foothold heights during downward step of 16-t truck. Adult males (n= 10) jumped downward from each 1st, 2nd, 3rd foothold step and driver’s seat orderly using hand rail. Sampling rate of force components of 3 axis (medial-lateral [ML] GRF, anterior-posterior [AP] GRF, peak vertical force [PVF]), variables (COPx, COPy, COP area) of center of pressure (COP), loading rate, and stability index (ML, AP, vertical, and dynamic postural stability index [DPSI]) processed from GRF system was cut off at 1,000 Hz. and variables was processed with repeated one-way analysis of variance. AP GRF, PVF and loading rate showed higher value in case of not used hand rail than that used hand rail in all 1st, 2nd, and 3rd of foothold step. DPSI showed more lowered stability in order of 2nd, 3rd step than 1st foothold step used with hand rail, of which showed lowest stability from driver’s seat. COPx, COPy, and COP area showed higher value in case of 2nd and 3rd than that of 1st of foothold step, and showed lowest stability from driver’s seat. It is more desirable for cargo truck driver to utilize an available hand rail in order of 3rd, 2nd, and 1st of foothold step than downward stepping directly, thus by which may results in decrease of falling injuries and minimization of impulsive force transferring to muscular-skeletal system.

Dual Frequency Head Maps: A New Method for Indexing Mental Workload Continuously during Execution of Cognitive Tasks

One goal of advanced information and communication technology is to simplify work. However, there is growing consensus regarding the negative consequences of inappropriate workload on employee's health and the safety of persons. In order to develop a method for continuous mental workload monitoring, we implemented a task battery consisting of cognitive tasks with diverse levels of complexity and difficulty. We conducted experiments and registered the electroencephalogram (EEG), performance data, and the NASA-TLX questionnaire from 54 people. Analysis of the EEG spectra demonstrates an increase of the frontal theta band power and a decrease of the parietal alpha band power, both under increasing task difficulty level. Based on these findings we implemented a new method for monitoring mental workload, the so-called Dual Frequency Head Maps (DFHM) that are classified by support vectors machines (SVMs) in three different workload levels. The results are in accordance with the expected difficulty levels arising from the requirements of the tasks on the executive functions. Furthermore, this article includes an empirical validation of the new method on a secondary subset with new subjects and one additional new task without any adjustment of the classifiers. Hence, the main advantage of the proposed method compared with the existing solutions is that it provides an automatic, continuous classification of the mental workload state without any need for retraining the classifier—neither for new subjects nor for new tasks. The continuous workload monitoring can help ensure good working conditions, maintain a good level of performance, and simultaneously preserve a good state of health.

Surface EEG-Transcranial Direct Current Stimulation (tDCS) Closed-Loop System

Conventional transcranial direct current stimulation (tDCS) protocols rely on applying electrical current at a fixed intensity and duration without using surrogate markers to direct the interventions. This has led to some mixed results; especially because tDCS induced effects may vary depending on the ongoing level of brain activity. Therefore, the objective of this preliminary study was to assess the feasibility of an EEG-triggered tDCS system based on EEG online analysis of its frequency bands. Six healthy volunteers were randomized to participate in a double-blind sham-controlled crossover design to receive a single session of 10[Formula: see text]min 2[Formula: see text]mA cathodal and sham tDCS. tDCS trigger controller was based upon an algorithm designed to detect an increase in the relative beta power of more than 200%, accompanied by a decrease of 50% or more in the relative alpha power, based on baseline EEG recordings. EEG-tDCS closed-loop-system was able to detect the predefined EEG magnitude deviation and successfully triggered the stimulation in all participants. This preliminary study represents a proof-of-concept for the development of an EEG-tDCS closed-loop system in humans. We discuss and review here different methods of closed loop system that can be considered and potential clinical applications of such system.

A method for predicting the risk of virtual crashes in a simulated driving task using behavioural and subjective drowsiness measures

This study proposed a procedure for predicting the point in time with high risk of virtual crash using a control chart methodology for behavioural measures during a simulated driving task. Tracking error, human back pressure, sitting pressure and horizontal and vertical neck bending angles were measured during the simulated driving task. The time with a high risk of a virtual crash occurred in 9 out of 10 participants. The time interval between the successfully detected point in time with high risk of virtual crash and the point in time of virtual crash ranged from 80 to 324 s. The proposed procedure for predicting the point in time with a high risk of a crash is promising for warning drivers of the state of high risk of crash. Practitioner Summary: Many fatal crashes occur due to drowsy driving. We proposed a method to predict the point in time with high risk of virtual crash before such a virtual crash occurs. This is done using behavioural measures during a simulated driving task. The effectiveness of the method is also demonstrated.

Human Factors and Neurophysiological Metrics in Air Traffic Control: A Critical Review

This paper provides a focused and organized review of the research progress on neurophysiological indicators, also called "neurometrics," to show how they can effectively address some of the most important human factors (HFs) needs in the air traffic management (ATM) field. In order to better understand and highlight available opportunities of such neuroscientific applications, state of the art on the most involved HFs and related cognitive processes (e.g., mental workload and cognitive training) are presented together with examples of possible applications in current and future ATM scenarios. Furthermore, this paper will discuss the potential enhancements that further research and development activities could bring to the efficiency and safety of the ATM service.

The antecedents, experience, and coping strategies of driver boredom in young adult males

INTRODUCTION

Road crash statistics are evidence of the severe consequences resulting from human error, especially among young adult males. Drivers perform best and safest when they are adequately engaged in the driving task. Boredom and a lack of engagement in the driving task may cause risk taking and phone use. However, the antecedents to driver boredom, the subjective experience itself, as well as the coping strategies to combat boredom are not well understood. The aim of this study was to investigate these aspects.

METHOD

We carried out a qualitative study in a simulated, safe, yet highly immersive driving environment. The 24 participants included male drivers aged 18 to 25 susceptible to risky driving and phone use. A phenomenological framework was used to analyze their accounts of the experience of boredom while driving.

RESULTS

Results indicate that situations giving rise to driver boredom include low traffic, slow or constant speed, and routine drives. Feelings comprising the experience were frustration, vigilance, relaxing, autopilot, mind wandering, and discomfort. Coping mechanisms manifest themselves in approach strategies related to the driving task such as speeding, which are often dangerous, and avoidance strategies, which include phone use.

CONCLUSIONS

We conclude that driver boredom bears similarities to the experience of boredom at work (unlike boredom at home) due to the situational constraints, where people feel stuck, trapped, or obliged to remain vigilant.

PRACTICAL APPLICATIONS

The findings present an opportunity for the road safety and automotive technology community to address the issue of under-stimulation through safety interventions aimed at increased task engagement. Our work can also aid in investigating driver experiences in partially automated driving, which is likely to induce boredom as well.

Development of Active and Passive Fatigue Manipulations Using a Driving Simulator

The present study investigates driving simulator methodologies for inducing qualitatively different patterns of subjective response. The study tested Desmond and Hancock's (2001) theory that there may be two types of fatigue: active and passive. 108 undergraduates participated. There were 3 conditions (active, passive, control) and 3 durations (10, 30, 50 minutes). The active condition used simulated wind gusts to increase the required number of steering and acceleration changes. The passive condition was fully automated. In the control condition, drivers were in full control of steering and acceleration. Task engagement (e.g., energy) was lowest in the passive fatigue condition, followed by the control and active conditions. Distress (e.g., negative mood) was found to be highest in the active fatigue condition. The time course of fatigue responses was also determined. The results suggest methods for developing manipulations to determine the impact of fatigue on performance.

Driving Safety

Driving is a common and hazardous activity that is a prominent cause of death worldwide. Driver behavior represents a predominant cause, contributing to over 90% of crashes. In this review, I will focus on how driver behavior influences driving safety by describing the types of crashes and their general causes, the driving process, the perceptual and cognitive characteristics of drivers, and driver types and impairments. Evidence from each of these perspectives suggests that breakdowns of a multilevel control process are the fundamental factors that undermine driving safety. Drivers adapt and drive safely in a broad range of situations but fail when expectations are violated or when feedback is inadequate. The review concludes by considering driving safety from a societal risk management perspective.

Implementation and Validation of Engagement Monitoring in an Engagement Enhancing Rehabilitation System

Enhancing engagement of patients during stroke rehabilitation exercises are in the focus of current research. Various methods and computer supported tools have been developed for this purpose, which try to avoid mundane exercising that is prone to become a routine or even boring for the patients and leads to ineffective training. This paper introduces an engagement enhancing cyber-physical stroke rehabilitation system (CP-SRS) aiming at enhancing the patient's engagement during rehabilitation training exercises. This paper focuses on introducing the implementation and validation of the engagement monitoring subsystem (EMS) in the CP-SRS. The EMS is expected to evaluate the patient's actual engagement levels in motor, perceptive, cognitive and emotional aspects. Experiments in these four aspects were conducted separately, in order to characterize the range and accuracy of the engagement indicators by influencing the subjects into different engaged states. During the experiments, different setups were created to mimic the situations in which the subject was engaged or not engaged. The subjects involved in the experiments were healthy subjects. Results showed that the measurement in motor, perceptive, cognitive, and emotional aspects can represent the corresponding engagement level. More experiments will be conducted in the future to validate the efficiency of the CP-SRS in enhancing the engagement with stroke patients.

Development of a method to predict crash risk using trend analysis of driver behavior changes over time

OBJECTIVE

This study aimed at identifying and predicting in advance the point in time with a high risk of a virtual accident before a virtual accident actually occurs using the change of behavioral measures and subjective rating on drowsiness over time and the trend analysis of each behavioral measure.

METHODS

Behavioral measures such as neck bending angle and tracking error in steering maneuvering during the simulated driving task were recorded under the low arousal condition of all participants who stayed up all night without sleeping. The trend analysis of each evaluation measure was conducted using a single regression model where time and each measure of drowsiness corresponded to an independent variable and a dependent variable, respectively. Applying the trend analysis technique to the experimental data, we proposed a method to predict in advance the point in time with a high risk of a virtual accident (in a real-world driving environment, this corresponds to a crash) before the point in time when the participant would have encountered a crucial accident if he or she continued driving a vehicle (we call this the point in time of a virtual accident).

RESULTS

On the basis of applying the proposed trend analysis method to behavioral measures, we found that the proposed approach could predict in advance the point in time with a high risk of a virtual accident before the point in time of a virtual accident.

CONCLUSION

The proposed method is a promising technique for predicting in advance the time zone with potentially high risk (probability) of being involved in an accident due to drowsy driving and for warning drivers of such a drowsy and risky state.

Should drivers be operating within an automation-free bandwidth? Evaluating haptic steering support systems with different levels of authority

OBJECTIVE

The aim of this study was to compare continuous versus bandwidth haptic steering guidance in terms of lane-keeping behavior, aftereffects, and satisfaction.

BACKGROUND

An important human factors question is whether operators should be supported continuously or only when tolerance limits are exceeded. We aimed to clarify this issue for haptic steering guidance by investigating costs and benefits of both approaches in a driving simulator.

METHODS

Thirty-two participants drove five trials, each with a different level of haptic support: no guidance (Manual); guidance outside a 0.5-m bandwidth (Band1); a hysteresis version of Band1, which guided back to the lane center once triggered (Band2); continuous guidance (Cont); and Cont with double feedback gain (ContS). Participants performed a reaction time task while driving. Toward the end of each trial, the guidance was unexpectedly disabled to investigate aftereffects.

RESULTS

All four guidance systems prevented large lateral errors (>0.7 m). Cont and especially ContS yielded smaller lateral errors and higher time to line crossing than Manual, Band1, and Band2. Cont and ContS yielded short-lasting aftereffects, whereas Band1 and Band2 did not. Cont yielded higher self-reported satisfaction and faster reaction times than Band1.

CONCLUSIONS

Continuous and bandwidth guidance both prevent large driver errors. Continuous guidance yields improved performance and satisfaction over bandwidth guidance at the cost of aftereffects and variability in driver torque (indicating human-automation conflicts).

APPLICATION

The presented results are useful for designers of haptic guidance systems and support critical thinking about the costs and benefits of automation support systems.

State of science: mental workload in ergonomics

Mental workload (MWL) is one of the most widely used concepts in ergonomics and human factors and represents a topic of increasing importance. Since modern technology in many working environments imposes ever more cognitive demands upon operators while physical demands diminish, understanding how MWL impinges on performance is increasingly critical. Yet, MWL is also one of the most nebulous concepts, with numerous definitions and dimensions associated with it. Moreover, MWL research has had a tendency to focus on complex, often safety-critical systems (e.g. transport, process control). Here we provide a general overview of the current state of affairs regarding the understanding, measurement and application of MWL in the design of complex systems over the last three decades. We conclude by discussing contemporary challenges for applied research, such as the interaction between cognitive workload and physical workload, and the quantification of workload 'redlines' which specify when operators are approaching or exceeding their performance tolerances.

Google Glass

Objective:

We assess the driving distraction potential of texting with Google Glass (Glass), a mobile wearable platform capable of receiving and sending short-message-service and other messaging formats.

Background:

A known roadway danger, texting while driving has been targeted by legislation and widely banned. Supporters of Glass claim the head-mounted wearable computer is designed to deliver information without concurrent distraction. Existing literature supports the supposition that design decisions incorporated in Glass might facilitate messaging for drivers.

Method:

We asked drivers in a simulator to drive and use either Glass or a smartphone-based messaging interface, then interrupted them with an emergency brake event. Both the response event and subsequent recovery were analyzed.

Results:

Glass-delivered messages served to moderate but did not eliminate distracting cognitive demands. A potential passive cost to drivers merely wearing Glass was also observed. Messaging using either device impaired driving as compared to driving without multitasking.

Conclusion:

Glass in not a panacea as some supporters claim, but it does point the way to design interventions that effect reduced load in multitasking.

Application:

Discussions of these identified benefits are framed within the potential of new in-vehicle systems that bring both novel forms of distraction and tools for mitigation into the driver’s seat.

Effects of chronotype and time of day on the vigilance decrement during simulated driving

The current study tested for the first time the effect of individual differences in circadian rhythmicity (chronotype) on both driving performance and its evolution along time on task. Morning-type and evening-type female participants were tested in morning (8 am) and evening (8 pm) sessions, in which we controlled for prior sleep duration and prior wake. Measures of body temperature, subjective activation and affect, reaction times (RT) in the Psychomotor Vigilance Task (PVT), behavioral performance (error position) and EEG alpha power during simulated driving were collected. The main result showed strong linear increments of mean and standard deviation of error position along time on task (vigilance decrement) when evening-type participants drove at their non-optimal time of day, that is, during the morning session. In contrast, driving performance in the morning-type group remained stable over time on task and was not affected by time of day. This finding can be due to differences in personality traits (e.g., conscientiousness, sensation seeking) and task appraisal associated to extreme chronotypes. The consideration of chronotype in vigilance and driving tasks can enhance safety and human performance by promoting work schedules and countermeasures to prevent failures in the accomplishment of tasks under non-optimal circadian conditions.

A strategically timed verbal task improves performance and neurophysiological alertness during fatiguing drives

OBJECTIVE

The objective of this study was to investigate if a verbal task can improve alertness and if performance changes are associated with changes in alertness as measured by EEG.

BACKGROUND

Previous research has shown that a secondary task can improve performance on a short, monotonous drive. The current work extends this by examining longer, fatiguing drives. The study also uses EEG to confirm that improved driving performance is concurrent with improved driver alertness.

METHOD

A 90-min, monotonous simulator drive was used to place drivers in a fatigued state. Four secondary tasks were used: no verbal task, continuous verbal task, late verbal task, and a passive radio task.

RESULTS

When engaged in a secondary verbal task at the end of the drive, drivers showed improved lane-keeping performance and had improvements in neurophysiological measures of alertness.

CONCLUSION

A strategically timed concurrent task can improve performance even for fatiguing drives.

APPLICATION

Secondary-task countermeasures may prove useful for enhancing driving performance across a range of driving conditions.

Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness

This paper reviews published papers related to neurophysiological measurements (electroencephalography: EEG, electrooculography EOG; heart rate: HR) in pilots/drivers during their driving tasks. The aim is to summarise the main neurophysiological findings related to the measurements of pilot/driver's brain activity during drive performance and how particular aspects of this brain activity could be connected with the important concepts of "mental workload", "mental fatigue" or "situational awareness". Review of the literature suggests that exists a coherent sequence of changes for EEG, EOG and HR variables during the transition from normal drive, high mental workload and eventually mental fatigue and drowsiness. In particular, increased EEG power in theta band and a decrease in alpha band occurred in high mental workload. Successively, increased EEG power in theta as well as delta and alpha bands characterise the transition between mental workload and mental fatigue. Drowsiness is also characterised by increased blink rate and decreased HR values. The detection of such mental states is actually performed "offline" with accuracy around 90% but not online. A discussion on the possible future applications of findings provided by these neurophysiological measurements in order to improve the safety of the vehicles will be also presented.

The prevalence and nature of stopped on-the-road driving tests and the relationship with objective performance impairment

INTRODUCTION AND OBJECTIVES

The on-the-road driving test in normal traffic is applied to examine the impact of drugs on driving performance. Although participants are accompanied by a licensed driving instructor, under Dutch law, the driver is primarily responsible for safe driving and is not permitted to continue driving when it is judged that the drug compromises safety. This review examined the prevalence and nature of stopped driving tests, and the relationship with Standard Deviation of Lateral Position (SDLP), i.e. the "weaving of the car".

MATERIALS AND METHODS

A literature search was conducted to gather all publications on clinical trials that applied the on-the-road driving test, examining the effects of Central Nervous System (CNS)-drugs such as anxiolytics, antidepressants, antihistamines, analgesics, and hypnotics.

RESULTS

47 papers reported on 50 Dutch clinical trials in which 1059 subjects participated (903 healthy volunteers and 156 patients). A total of 7232 driving tests were performed; 5050 after drug treatment and 2042 after placebo. 3.1% of all driving tests were terminated before completion: 4.1% after drug treatment, and 0.7% after placebo. The decision to stop a driving test was 3-4 times more often made by the driving instructor than the subject. The most common reasons for stopping were the driver feeling tired or sleepy, or the driving instructor noticing signs of drowsiness and performance impairment. Although SDLP values of stopped driving tests are sometimes high, there is no clear relationship between SDLP (changes from placebo) and the decision to stop a driving test. Based on 8 studies that reported exact data, 39.6% of stopped drivers had a lower and 60.4% had a higher SDLP than 35 cm, i.e. the cut-off point of safe driving. This confirms that perception of the driver as well as judgment by the instructor of driving to be 'unsafe' differs between individuals.

CONCLUSION

Driving tests are sometimes stopped after drug treatment or placebo. The decision to stop driving is not a good correlate of objective performance.

The impact of cognitive workload on physiological arousal in young adult drivers: a field study and simulation validation

Physiological measures provide a continuous and relatively non-invasive method of characterising workload. The extent to which such measures provide sensitivity beyond that provided by driving performance metrics is more open to question. Heart rate and skin conductance were monitored during actual highway driving in response to systematically increased levels of cognitive demand using an auditory delayed digit recall task. The protocol was consistent with an earlier simulator study, providing an opportunity to assess the validity of physiological measures recorded during driving simulation. The pattern of change in heart rate with increased cognitive demand was highly consistent between field and simulator. The findings meet statistical criteria for both relative and absolute validity, although there was a trend for absolute levels to be higher under actual driving conditions. For skin conductance level, the pattern in both environments was also quite similar and a reasonable case for overall relative validity can be made. STATEMENT OF RELEVANCE: Growing complexity and multiple demands on modern drivers' attention highlight the significance of determining whether physiological measures provide increased sensitivity in workload detection. Better understanding, including whether simulator assessments provide valid measures of real-world response patterns, has implications in evaluating and refining interface designs and for developing advanced workload managers.

EEG-derived estimators of present and future cognitive performance

Previous electroencephalography (EEG)-based fatigue-related research primarily focused on the association between concurrent cognitive performance and time-locked physiology. The goal of this study was to investigate the capability of EEG to assess the impact of fatigue on both present and future cognitive performance during a 20-min sustained attention task, the 3-choice active vigilance task (3CVT), that requires subjects to discriminate one primary target from two secondary non-target geometric shapes. The current study demonstrated the ability of EEG to estimate not only present, but also future cognitive performance, utilizing a single, combined reaction time (RT), and accuracy performance metric. The correlations between observed and estimated performance, for both present and future performance, were strong (up to 0.89 and 0.79, respectively). The models were able to consistently estimate "unacceptable" performance throughout the entire 3CVT, i.e., excessively missed responses and/or slow RTs, while acceptable performance was recognized less accurately later in the task. The developed models were trained on a relatively large dataset (n = 50 subjects) to increase stability. Cross-validation results suggested the models were not over-fitted. This study indicates that EEG can be used to predict gross-performance degradations 5-15 min in advance.

Influence of task combination on EEG spectrum modulation for driver workload estimation

OBJECTIVE

This study investigates the feasibility of using a method based on electroencephalography (EEG) for deriving a driver's mental workload index.

BACKGROUND

The psychophysiological signals provide sensitive information for human functional states assessment in both laboratory and real-world settings and for building a new communication channel between driver and vehicle that allows for driver workload monitoring.

METHODS

An experiment combining a lane-change task and n-back task was conducted. The task load levels were manipulated in two dimensions, driving task load and working memory load, with each containing three task load conditions.

RESULTS

The frontal theta activity showed significant increases in the working memory load dimension, but differences were not found with the driving task load dimension. However, significant decreases in parietal alpha activity were found when the task load was increased in both dimensions. Task-related differences were also found. The driving task load contributed more to the changes in alpha power, whereas the working memory load contributed more to the changes in theta power. Additionally, these two task load dimensions caused significant interactive effects on both theta and alpha power.

CONCLUSION

These results indicate that EEG technology can provide sensitive information for driver workload detection even if the sensitivities of different EEG parameters tend to be task dependent.

APPLICATION

One potential future application of this study is to establish a general driver workload estimator that uses EEG signals.

The effect of concurrent bandwidth feedback on learning the lane-keeping task in a driving simulator

OBJECTIVE

The aim of this study was to investigate whether concurrent bandwidth feedback improves learning of the lane-keeping task in a driving simulator.

BACKGROUND

Previous research suggests that bandwidth feedback improves learning and that off-target feedback is superior to on-target feedback. This study aimed to extend these findings for the lane-keeping task.

METHOD

Participants without a driver's license drove five 8-min lane-keeping sessions in a driver training simulator: three practice sessions, an immediate retention session, and a delayed retention session I day later. There were four experimental groups (n=15 per group): (a) on-target, receiving seat vibrations when the center of the car was within 0.5 m of the lane center; (b) off-target, receiving seat vibrations when the center of the car was more than 0.5 m away from the lane center; (c) control, receiving no vibrations; and (d) realistic, receiving seat vibrations depending on engine speed. During retention, all groups were provided with the realistic vibrations.

RESULTS

During practice, on-target and off-target groups had better lane-keeping performance than the nonaugmented groups, but this difference diminished in the retention phase. Furthermore, during late practice and retention, the off-target group outperformed the on-target group.The off-target group had a higher rate of steering reversal and higher steering entropy than the nonaugmented groups, whereas no clear group differences were found regarding mean speed, mental workload, or self-reported measures.

CONCLUSION

Off-target feedback is superior to on-target feedback for learning the lane-keeping task.

APPLICATION

This research provides knowledge to researchers and designers of training systems about the value of feedback in simulator-based training of vehicular control.

Potential Benefits and Costs of Concurrent Task Engagement to Maintain Vigilance

Objective: The objective of this study was to investigate the nature of concurrent task interference during a vigilance task and to determine whether a concurrent task improves performance with decreased vigilance.

Background: Research has repeatedly shown that engaging in a cell phone conversation while driving increases the risk of getting into crashes. At the same time, it has also been found that task monotony could lead to an increase in crash risk. There is evidence that suggests that engaging in a concurrent task reduces the effects of monotony, leading to an improvement in vigilance task performance.

Method: A monotonous drive in a driving simulator was used to investigate the effects of a concurrent verbal task. Three task conditions were used: no verbal task, continuous verbal task, and late verbal task.

Results: When engaged in a secondary verbal task, drivers showed improved lane-keeping performance and steering control when vigilance was lowest.

Conclusion: A strategically placed concurrent task can improve performance when vigilance is at its lowest.

Application: There is potential for the design of a countermeasure system that can be strategically activated by an automated system monitoring driver performance.

The Berlin Brain-Computer Interface: Non-Medical Uses of BCI Technology

Brain-computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.

Sleepiness and prediction of driver impairment in simulator studies using a Cox proportional hazard approach

Cox proportional hazard models were used to study relationships between the event that a driver is leaving the lane caused by sleepiness and different indicators of sleepiness. In order to elucidate different indicators' performance, five different models developed by Cox proportional hazard on a data set from a simulator study were used. The models consisted of physiological indicators and indicators from driving data both as stand alone and in combination. The different models were compared on two different data sets by means of sensitivity and specificity and the models' ability to predict lane departure was studied. In conclusion, a combination of blink indicators based on the ratio between blink amplitude and peak closing velocity of eyelid (A/PCV) (or blink amplitude and peak opening velocity of eyelid (A/POV)), standard deviation of lateral position and standard deviation of lateral acceleration relative road (ddy) was the most sensitive approach with sensitivity 0.80. This is also supported by the fact that driving data only shows the impairment of driving performance while blink data have a closer relation to sleepiness. Thus, an effective sleepiness warning system may be based on a combination of lane variability measures and variables related to eye movements (particularly slow eye closure) in order to have both high sensitivity (many correct warnings) and acceptable specificity (few false alarms).

Driving with a Congestion Assistant; mental workload and acceptance

New driver support systems are developed and introduced to the market at increasing speed. In conditions of traffic congestion drivers may be supported by a "Congestion Assistant", a system that combines the features of a Congestion Warning System (acoustic warning and gas pedal counterforce) and a Stop & Go system (automatic gas and brake pedal during congestion). To gain understanding of the effects of driving with a Congestion Assistant on drivers, mental workload of drivers was registered under different conditions as well as acceptance of the system. Mental workload was measured by means of physiological registrations, i.e. heart rate, a secondary task and with the aid of subjective scaling techniques. Acceptance was measured with an acceptance scale. The study was carried out in an advanced driving simulator. Driving with the Congestion Assistant while in congestion potentially leads to decreased driver mental workload, whereas just before congestion starts, i.e. developing just noticeable, the system may add to the workload of the driver. Acceptance is generally high after experiencing the system, though not in all respects.

Evaluation of experience-based fatigue countermeasures

BACKGROUND AND OBJECTIVE

Strategies adopted by drivers in order to cope with fatigue and falling asleep at the wheel include a variety of activities that may invigorate the body and/or the mind. The objectives of the current study were to examine the effectiveness of an energy drink and a non-traditional manual-dexterity/mastication activity as fatigue countermeasures.

METHOD

Twenty subjects participated in this driving simulator study. Each driving session lasted 2h and each driver drove under three conditions: after consumption of an energy drink, while engaged in a self-paced manual-dexterity/mastication secondary task (shelling and eating sunflower seeds), and in a control condition with neither. Fatigue effects were assessed on three dimensions: subjective evaluations (using the Swedish Occupational Fatigue Inventory), a physiological indicator (heart rate variability), and driving performance measures (speed, steering, and lane deviations).

RESULTS

The subjective and physiological measures showed a significant effect of both treatments in counteracting the effects of fatigue when compared to the control condition. The results of the driving performance measures indicated that the energy drink was effective in counteracting fatigue, while the secondary task was as effective as the energy drink in counteracting fatigue on measures that did not rely on hand movements.

CONCLUSIONS

Drinking an energy drink prior to the driving task has a significant, positive effect in counteracting fatigue, though it may have long-term negative rebound effects. The manual-dexterity/mastication secondary task can temporarily counteract the subjective and physiological effects of fatigue while driving, but can interfere with vehicle handling.