Sensitivity of Physiological Measures of Acute Driver Stress: A Meta-Analytic Review

A new era for stress research: supporting user performance and experience in the digital age

Stress is both a driver of objective performance impairments and a source of negative user experience of technology. This review addresses future directions for research on stress and ergonomics in the digital age. The review is structured around three levels of analysis. At the individual user level, stress is elicited by novel technologies and tasks including interaction with AI and robots, working in Virtual Reality, and operating autonomous vehicles. At the organisational level, novel, potentially stressful challenges include maintaining cybersecurity, surveillance and monitoring of employees supported by technology, and addressing bias and discrimination in the workplace. At the sociocultural level, technology, values and norms are evolving symbiotically, raising novel demands illustrated with respect to interactions with social media and new ethical challenges. We also briefly review the promise of neuroergonomics and emotional design to support stress mitigation. We conclude with seven high-level principles that may guide future work.

Open-DPSM: An open-source toolkit for modeling pupil size changes to dynamic visual inputs

Pupil size change is a widely adopted, sensitive indicator for sensory and cognitive processes. However, the interpretation of these changes is complicated by the influence of multiple low-level effects, such as brightness or contrast changes, posing challenges to applying pupillometry outside of extremely controlled settings. Building on and extending previous models, we here introduce Open Dynamic Pupil Size Modeling (Open-DPSM), an open-source toolkit to model pupil size changes to dynamically changing visual inputs using a convolution approach. Open-DPSM incorporates three key steps: (1) Modeling pupillary responses to both luminance and contrast changes; (2) Weighing of the distinct contributions of visual events across the visual field on pupil size change; and (3) Incorporating gaze-contingent visual event extraction and modeling. These steps improve the prediction of pupil size changes beyond the here-evaluated benchmarks. Open-DPSM provides Python functions, as well as a graphical user interface (GUI), enabling the extension of its applications to versatile scenarios and adaptations to individualized needs. By obtaining a predicted pupil trace using video and eye-tracking data, users can mitigate the effects of low-level features by subtracting the predicted trace or assess the efficacy of the low-level feature manipulations a priori by comparing estimated traces across conditions.

Leveraging Wearable Sensors in Virtual Reality Driving Simulators: A Review of Techniques and Applications

Virtual reality (VR) driving simulators are very promising tools for driver assessment since they provide a controlled and adaptable setting for behavior analysis. At the same time, wearable sensor technology provides a well-suited and valuable approach to evaluating the behavior of drivers and their physiological or psychological state. This review paper investigates the potential of wearable sensors in VR driving simulators. Methods: A literature search was performed on four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) using appropriate search terms to retrieve scientific articles from a period of eleven years, from 2013 to 2023. Results: After removing duplicates and irrelevant papers, 44 studies were selected for analysis. Some important aspects were extracted and presented: the number of publications per year, countries of publication, the source of publications, study aims, characteristics of the participants, and types of wearable sensors. Moreover, an analysis and discussion of different aspects are provided. To improve car simulators that use virtual reality technologies and boost the effectiveness of particular driver training programs, data from the studies included in this systematic review and those scheduled for the upcoming years may be of interest.

Exploring hazard anticipation and stress while driving in light of defensive behavior theory

In driving, poor hazard anticipation would provide drivers less time to prepare an appropriate response, increasing the urgency of the situation and generating more stress. Assuming this, the current study seeks to determine whether a predictable road hazard triggers hazard anticipation in drivers that can mitigate the ensuing stress response, and whether the stress response is influenced by driving experience. In a simulated road environment, a cue was used to trigger hazard anticipation, and a road hazard to induce a stress response. Heart rate, pupil diameter, driving speed, subjective stress, arousal, and negative emotions, were retrieved from 36 drivers who all faced the cue followed by the hazard (i.e. a predictable hazard), the cue only, and the hazard only. In the light of work on defensive behaviors, the findings indicate that a predictable hazard triggers hazard anticipation detectable via (1) freezing behavior-characterized by cardiac deceleration-(2) anticipatory pupil dilation and (3) anticipatory speed deceleration. The results also point to a beneficial role for hazard anticipation in reducing driver stress, as evidenced by reductions in peak heart rate levels, as well as in reported levels of stress and negative emotions. Finally, the findings showed an influence of driving experience on reported levels of stress. Overall, this study shows how previous work on defensive behaviors can be used to gain insight into the processes and driving behaviors involved in hazard anticipation and stress.