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Can Urban Forests Alleviate Eye Strain? Evidence from Eye-Tracking Metrics

With the surge in digital screen use, eye strain has become a growing public health concern. While urban forests are known to support psychological and physiological restoration, their capacity to alleviate eye strain remains underexplored. This study investigates whether short-term exposure to different types of urban forests can facilitate ocular recovery from screen-induced eye strain, using objective eye-tracking metrics. A total of 30 participants were fitted with wearable eye-tracking glasses and watched a 20-minute high-intensity video to induce eye strain before spending 10 min in a designated environment (open green space, semi-open green space, enclosed green space, green space with large water area, green space with small water area, and an indoor control). Eye metrics including blink rate, pupil size, fixation count/duration, and saccade count/duration were recorded at baseline, post-stress, and post-recovery. Significant recovery was observed in participants exposed to open green spaces and water-area settings, with reductions in pupil size and blink rate (p < 0.01). In contrast, the indoor environment failed to improve and even exacerbated eye strain. Among all indicators, pupil size change emerged as the strongest correlate of composite recovery. Urban blue-green spaces, especially those that are open and feature water elements, can effectively promote ocular recovery from acute eye strain. These findings underscore the value of incorporating open and water-rich natural elements into urban forestry design and management strategies that promote public well-being.

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Wearable eye-tracking of visuomotor strategies in table tennis players of diverse expertise and cognitive function in a naturalistic environment

Understanding how gaze behaviour and visuomotor control vary across populations is crucial for optimizing performance and training in fast-paced sports. However, studies involving athletes with cognitive disabilities remain limited, particularly in naturalistic environments. This study employed wearable eye-tracking technology to examine gaze behaviour and oculomotor control in table tennis players of differing skill levels and cognitive profiles. Forty-six participants were grouped as Professional athletes, Amateur players, individuals with Down syndrome (DS), or intellectual disabilities (ID). All completed table tennis-specific tasks in naturalistic environment training conditions while wearing a head-mounted eye-tracker. Oculomotor metrics, including fixation frequency and duration, saccade frequency and velocity, and pupil diameter, were analysed. Fixation duration did not differ across groups (≈272–301 ms; p = 0.984, η2 = −0.032), whereas fixation frequency varied: ID participants (80.67 ± 6.81 %) and Amateurs (78.98 ± 5.22 %) showed higher and more consistent rates, DS participants were lower and more variable (74.56 ± 17.37 %), and Professionals maintained moderately lower but strategically balanced frequency (77.78 ± 12.64 %). Although saccade metrics were not statistically significant, trends suggested more controlled patterns in Professionals (right eye (RE) length: 1414.63 ± 720.47 mm; longitudinal velocity: 13,888.52 ± 4242.25 mm/s) and higher variability in DS participants (RE length: 2254.03 ± 3215.55 mm; longitudinal velocity: 16,274.78 ± 6,837.21 mm/s). Pupil diameter was significantly larger in Professionals (RE: 5.26 ± 0.79 mm; left eye (LE): 5.40 ± 0.81 mm; p < 0.001), indicating higher visual engagement and cognitive arousal. Binocular vergence metrics remained stable across groups, and gaze heat maps revealed more focused visual strategies in Professionals, while participants with DS and ID exhibited dispersed, less task-relevant fixations. These findings indicate that the accuracy of eye movements, rather than their duration, serves as a sensitive indicator of visuomotor expertise. In conclusion, wearable eye-tracking in naturalistic sport environment offers valuable insights into visual strategies across diverse populations and supports the development of tailored visual training programs, particularly for athletes with cognitive disabilities.

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A study of the effect of monitoring request and takeover request time interval on takeover characteristics

With the advancement of Level-3 conditional automated driving technology, drivers are increasingly engaging in non-driving related tasks (NDRTs) while the vehicle operates autonomously. However, this behavior can reduce takeover performance and compromise driving safety. The monitoring request (MR) mechanism alerts drivers in advance to resume monitoring traffic conditions, thereby improving takeover performance and ensuring safety. This study investigated the effect of four time intervals—3 s, 5 s, 7 s, and 9 s—on driver takeover characteristics, including takeover reaction time, maximum longitudinal deceleration, minimum collision time, fixation count, and the percentage of area of interest (AOI) fixation time. Results showed that longer time intervals significantly enhanced driver takeover performance and situational awareness. In particular, the 7 s and 9 s intervals resulted in significantly better performance and situational awareness compared to the 3 s and 5 s intervals, yet the difference between 7 s and 9 s was not statistically significant. To determine the optimal interval, the CRITIC-TOPSIS evaluation model was applied, which ranked the 7 s interval as the most effective, followed by 9 s, 5 s, and 3 s. These findings indicate that the 7 s interval achieves the best balance between driver preparedness and takeover performance, while the 3 s interval performed the worst. This study provides valuable insights into the design of the time interval between monitoring requests and takeover requests in automated driving systems, contributing to improved driving safety and user satisfaction.

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A systematic and bibliometric review on physiological monitoring systems and wearable sensing devices for mental status monitoring in construction: Trends …

Advancements in physiological monitoring systems (PMSs) and wearable sensing devices (WSDs) have enabled real-time, objective assessments of workers’ mental status in construction. However, existing studies lack a comprehensive synthesis of mental status monitoring and classification approaches used in construction, including data collection, preprocessing, as well as postprocessing techniques. This paper systematically and bibliometrically reviews 223 studies following PRISMA guidelines, providing a structured framework for PMS and WSD applications in construction. The findings identified ten sensor types used to assess four mental status factors: risk perception, mental workload and fatigue, mental stress, and emotional states. For each sensor, the review details data collection procedures, including sensor brands and models, placements, and sampling rates. Additionally, it examines preprocessing techniques (i.e., noise filtering and artifact removal) and postprocessing methods, including feature extraction and metric computation, data interpretation, as well as mental status classification using rule-based and AI-based methods. Identified limitations and future research directions are also discussed. This study serves as a comprehensive guide for researchers and practitioners, promoting the broader adoption of PMSs and WSDs for mental status monitoring in construction.

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A Systematic Review of Research on Urban Streets and Parks Based on Eye-Tracking Technology.

In recent years, the application of eye-tracking technology in urban studies has garnered increasing attention from researchers across various disciplines. This study aims to provide a comprehensive review of the current applications of eye-tracking technology in these urban environments through a systematic literature analysis. Our findings indicate that eye-tracking technology has played a significant role in exploring visual preferences and the restorative effects of urban streets, as well as the visual preferences and restorative potential of urban landscapes. Certain visual elements in streets and parks, such as artificial and natural elements, can elicit different psychological and visual responses from people. This is of great reference value for understanding how urban street and park design can better meet people's visual preferences and exert the therapeutic effects of urban streets and parks. Moreover, characterised by its portability and reliability, eye-tracking technology has significant advantages in capturing real-time visual behaviour and cognitive responses in natural urban settings and can become a powerful tool for future research. Furthermore, eye-tracking technology holds great potential for extending its applications to other urban public spaces, such as plazas, waterfront areas, and urban greenways. This expansion can provide deeper insights into how people interact with and perceive various urban environments, ultimately contributing to more effective urban planning and design strategies.

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Auton kuljettajan tarkkaamattomuuden ennustaminen silmänliikkeiden avulla

This study discusses the prediction of car driver inattention using eye movements. The measurements for the study were performed at the University of Jyväskylä’s Drive-In Laboratory. The goal of the study was to find out what kind of glance metrics might be connected to inattention, as measured in the Drive-In laboratory. The research question was how well and with what kind of glance metrics, inattention can be predicted. Data was collected from 48 test subjects who performed tasks related to car functions that interfere with driving in the simulation. Inattention was measured using the HMET system (Head Mounted Eye Tracker). Various glance metrics were collected from the recorded eye movements. Glances were measured between two different Areas of Interest (AOI). The areas were the road and the car’s on-board computer control panel, which is the area where the tasks were performed. Additionally, during the study, it was decided to investigate other explanatory variables related to the research alongside the glance metrics, such as the order of the execution and the number of the task executions within three minutes. This led to creation of two different multi-level models, one of which only eye movement metrics were considered, and in other, variables from outside of the eye movements were also considered. The research resulted in the following result: Decrease in inattention was predicted by a higher number of glances toward the task, a greater total glance time on the road, a longer average duration toward the task and a greater number of execution repetitions within the task time limit. Task performed later predicted increase in inattention. All these connections were significant, but they all had small effect size. Furthermore, the study found that large part of the variance could be explained by differences between the test subjects and the tasks. Possible learning effects and fatigue could be observed in the test subjects as the experimental situation progressed. The topic requires much further research and better data to achieve clearer results. The slightly imprecise measurement method for glance metrics was the biggest weakness of the study’s validity. Keywords: inattention, distraction, eye movement, glance metrics, driving simulation study

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Cabin Environment Matters: Psycho-Physiological Pathways from In-Vehicle Climate to Driver Behaviors in Conditional Automated Driving

Year: 2025

Authors: Z Wang, A Wang, H Sheng, F Gu, L Zhao, The Hong Kong University of Science and Technology(Guangzhou), Department of Civil and Environmental Engineering, The Hong Kong University of Science andTechnology, Hong Kong SAR, China, Thrust of Robotics and Autonomous Systems, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China, HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Guangdong, China

Physical environments shape how humans think, feel, and act, yet their influence on cognition and behavior in automated driving remains underexplored. This study introduces and validates a psycho-physiological framework explaining how in-cabin temperature and CO₂ affect driver behavior during conditional automated driving, based on a high-fidelity driving simulator study with 60 participants. Structural equation modeling was used to test pathways linking environmental factors to driver physiology, subjective states, and takeover performance. Results show that at the psycho-physiological level, slightly cool temperatures reduced drowsiness through direct environmental input but also increased drowsiness via parasympathetic activation and cold discomfort. Slightly warm conditions elevated drowsiness through warm discomfort, underscoring the mediating role of comfort. CO₂ exposure degraded perceived air quality but produced limited downstream effects. At the behavioral level, cool conditions shortened takeover time by reducing drowsiness but impaired post-takeover control through suppressed muscle activation, whereas warm conditions exerted competing influences on takeover time through drowsiness and physiological arousal. These findings advance understanding of how cabin environments shape driver states and behaviors and provide insights for adaptive climate control strategies in human-centered automated vehicles.

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CogMamba: Multi-Task Driver Cognitive Load and Physiological Non-Contact Estimation with Multimodal Facial Features

The cognitive load of drivers directly affects the safety and practicality of advanced driving assistant systems, especially in autonomous driving scenarios where drivers need to quickly take control of the vehicle after performing non-driving-related tasks (NDRTs). However, existing driver cognitive load detection methods have shortcomings such as the inability to deploy invasive detection equipment inside vehicles and limitations to eye movement detection, which restrict their practical application. To achieve more efficient and practical cognitive load detection, this study proposes a multi-task non-contact cognitive load and physiological state estimation model based on RGB video, named CogMamba. The model utilizes multimodal features extracted from facial video and introduces the Mamba architecture to efficiently capture local and global temporal dependencies, thereby further jointly estimating cognitive load, heart rate (HR), and respiratory rate (RR). Experimental results demonstrate that CogMamba exhibits superior performance on two public datasets and shows excellent robustness under the cross-dataset generalization test. This study provides insights for non-contact driver state monitoring in real-world driving scenarios.

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Collision Risk Perception Models Using Physiological and Eye-tracking Signals

Accurate risk perception is essential for safe driving, particularly in dynamic and high-risk traffic environments. This study develops machine learning (ML)-based user risk perception models using physiological recording systems to assess driving risks across various scenarios. We evaluate model performance at different granularity levels, ranging from binary classification (e.g., risk versus no-risk), to detailed classifications, resulting in more classes (e.g., incorporating collision subject types, pedestrian types, numbers, vehicle speeds and deceleration rates). Results demonstrate that despite increased model complexity, the proposed risk perception models consistently achieve high performance, with accuracy exceeding 0.92 in most cases except for high-granularity models in vehicle collision scenarios. Models trained for pedestrian-related risks outperformed those for vehicle-related risks, indicating a stronger physiological response to pedestrian hazards. Feature importance analysis reveals that electroencephalogram (EEG) signals (midline channels from frontal to parietal lobes: Cz, Fz and Pz) play a dominant role, with pupil center shift degree (PCD) and pupil center shift magnitude (PCM) as secondary key contributors. In contrast, features derived from skin temperature and electrodermal activity (EDA) exhibit limited importance due to slower response times. These findings suggest that EEG and pupil features are optimal for real-time risk perception models, with heart activity features serving as complementary factors in enhancing model accuracy and reliability. We also discuss practical applications of these models in driver-vehicle interaction and intelligent transportation systems. By integrating physiological data with environmental perception sensors, these models offer a promising approach to enhancing safety in semi-autonomous driving systems

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Could music reduce driver fatigue? An investigation on music effects in various weather conditions

Fatigue impairs drivers’ performance and increases the occurrence likelihood of traffic incidents. This study aims to explore the capability of music as an intervention to reduce driver fatigue under different weather conditions by conducting a driving simulation experiment. The Eysenck Personality Questionnaire-Neuroticism (EPQ-N) scale is used to assess drivers’ personalities. A mixed factorial analysis of variance (ANOVA) is applied to analyze the influence of music on the driving behavior of different drivers under various weather conditions, including sunny, rainy, and foggy scenarios. Results indicated that drivers’ fatigue levels are reduced by the music under less complicated weather conditions, such as sunny and foggy conditions. The results also revealed that drivers with low EPQ-N scores could demonstrate better physical reactions and driving performance in sunny and rainy conditions, whereas those with high EPQ-N scores perform better in foggy conditions. These findings could help prevent traffic accidents caused by driving fatigue in sunny and foggy conditions through the strategic use of music.

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Can Urban Forests Alleviate Eye Strain? Evidence from Eye-Tracking Metrics

Wearable eye-tracking of visuomotor strategies in table tennis players of diverse expertise and cognitive function in a naturalistic environment

A study of the effect of monitoring request and takeover request time interval on takeover characteristics

A systematic and bibliometric review on physiological monitoring systems and wearable sensing devices for mental status monitoring in construction: Trends …

A Systematic Review of Research on Urban Streets and Parks Based on Eye-Tracking Technology.

Auton kuljettajan tarkkaamattomuuden ennustaminen silmänliikkeiden avulla

Cabin Environment Matters: Psycho-Physiological Pathways from In-Vehicle Climate to Driver Behaviors in Conditional Automated Driving

CogMamba: Multi-Task Driver Cognitive Load and Physiological Non-Contact Estimation with Multimodal Facial Features

Collision Risk Perception Models Using Physiological and Eye-tracking Signals

Could music reduce driver fatigue? An investigation on music effects in various weather conditions

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