UX Analysis

Overview of optical radiation safety requirements for eye tracking systems

Eye-tracking systems have gained significant attention in various applications, including human-computer interaction, and direct eye projection applications such as near-to-eye displays and augmented/virtual/mixed reality. These systems use optical sensors to monitor eye movements and gaze direction in order to adapt and optimize the displayed image. The working principles of these sensors are based on the emission and detection of optical radiation of light emitting diodes or lasers. Therefore, the optical radiation safety needs to be considered during development of such eye-tracking systems to enable safe use. The aim of the paper is to provide an overview of existing standards and technical specifications related to eye tracking and to highlight potential gaps. As a result, it contributes to the responsible and safe deployment of eye-tracking technology across various applications.

2 versions available

Personalized Course Recommendations Leveraging Machine and Transfer Learning Toward Improved Student Outcomes

University advising at matriculation must operate under strict information constraints, typically without any post-enrolment interaction history.We present a unified, leakage-free pipeline for predicting early dropout risk and generating cold-start programme recommendations from pre-enrolment signals alone, with an optional early-warning variant incorporating first-term academic aggregates. The approach instantiates lightweight multimodal architectures: tabular RNNs, DistilBERT encoders for compact profile sentences, and a cross-attention fusion module evaluated end-to-end on a public benchmark (UCI id 697; n = 3630 students across 17 programmes). For dropout, fusing text with numerics yields the strongest thresholded performance (Hybrid RNN–DistilBERT: f1-score ≈ 0.9161, MCC ≈ 0.7750, and simple ensembling modestly improves threshold-free discrimination (Area Under Receiver Operating Characteristic Curve (AUROC) up to ≈0.9488). A text-only branch markedly underperforms, indicating that numeric demographics and early curricular aggregates carry the dominant signal at this horizon. For programme recommendation, pre-enrolment demographics alone support actionable rankings (Demographic Multi-Layer Perceptron (MLP): Normalized Discounted Cumulative Gain @ 10 (NDCG@10) ≈ 0.5793, Top-10 ≈ 0.9380, exceeding a popularity prior by 25–27 percentage points in NDCG@10); adding text offers marginal gains in hit rate but not in NDCG on this cohort. Methodologically, we enforce leakage guards, deterministic preprocessing, stratified splits, and comprehensive metrics, enabling reproducibility on non-proprietary data. Practically, the pipeline supports orientation-time triage (high-recall early-warning) and shortlist generation for programme selection. The results position matriculation-time advising as a joint prediction–recommendation problem solvable with carefully engineered pre-enrolment views and lightweight multimodal models, without reliance on historical interactions.

1 version available:

Physdrive: A multimodal remote physiological measurement dataset for in-vehicle driver monitoring

Robust and unobtrusive in-vehicle physiological monitoring is crucial for ensuring driving safety and user experience. While remote physiological measurement (RPM) offers a promising non-invasive solution, its translation to real-world driving scenarios is critically constrained by the scarcity of comprehensive datasets. Existing resources are often limited in scale, modality diversity, the breadth of biometric annotations, and the range of captured conditions, thereby omitting inherent real-world challenges in driving. Here, we present PhysDrive, the first large-scale multimodal dataset for contactless in-vehicle physiological sensing with dedicated consideration on various modality settings and driving factors. PhysDrive collects data from 48 drivers, including synchronized RGB, near-infrared camera, and raw mmWave radar data, accompanied with six synchronized ground truths (ECG, BVP, Respiration, HR, RR, and SpO2). It covers a wide spectrum of naturalistic driving conditions, including driver motions, dynamic natural light, vehicle types, and road conditions. We extensively evaluate both signal-processing and deep-learning methods on PhysDrive, establishing a comprehensive benchmark across all modalities, and release full open-source code with compatibility for mainstream public toolboxes. We envision PhysDrive will serve as a foundational resource and accelerate research on multimodal driver monitoring and smart-cockpit systems.

1 version available:

Real-time detection method of angry driving behavior based on bracelet data

Year: 2025 | Published by: 1.Key Laboratory of Automotive Transportation Safety Assurance Technology for Transportation Industry，Chang'an University，Xi'an 710064，China 2.School of Automobile，Chang'an University，Xi'an 710064，China

Authors: Shi-feng NIU(1,2),Shi-jie YU(2),Yan-jun LIU(2),Chong MA(2)

A method for detecting drivers' angry driving behavior has been designed using widely used popular smart bracelet， which provides a new way and method for effective monitoring of angry driving behavior. 50 drivers were recruited to conduct a simulated driving experiment， and a simulated driving scene that caused anger was designed. Then， heart rate index HR and eight heart rate variability （HRV） indexes such as RR.mean， SDNN， RMSSD， PNN50， SDSD， HF， LF and LF/HF obtained from bracelet collection data were used to study the correlation between the acquisition indexes and the angry driving behavior， and screen the significant influence indexes Finally， using three methods， namely support vector machine （SVM）， K-nearest neighbor （KNN） and linear discriminant analysis （LDA）， established and verified the detection model of angry driving behavior. The results show that the model based on KNN algorithm has the best performance on anger recognition. The accuracy of anger intensity recognition can reach 75%， and the accuracy of anger state recognition is 86 %. The results show that the wearable device （smart bracelet） can reasonably detect the driver 's anger state and anger intensity. Key words: vehicle application engineering, anger driving behavior, machine learning, smart bracelet, heart rate variability

1 version available: Journal of Jilin University(Engineering and Technology Edition)

Research Methods in Cognitive Translation and Interpreting Studies, Chapter 10. Dynamic eyetracking

As digital advancements reshape communication, researchers need interdisciplinary methods to understand the cognitive processes involved. This essential reference for advanced students and researchers provides a comprehensive introduction to innovative research methods in cognitive translation and interpreting studies (CTIS). International experts from diverse disciplines share best practices for investigating cognitive processes in multilectal mediated communication. They emphasize the application of these methods across research domains situated at the interface of cognition and communication. The book offers an in-depth analysis of key research methods, explaining their rationales, uses, affordances, and limitations. Each chapter focuses on one or two closely related research methods and their tools, including surveys, interviews, introspective techniques, keylogging, eyetracking, and neuroimaging. The book guides readers in planning research projects and in making informed methodological choices. It also helps readers understand the basics of popular tools, fostering more rigorous research practices in data collection. Additionally, it provides practical suggestions on study design, participant profiling, and data analysis to deepen our understanding of texts, tasks, and their users.

2 versions available

Road Signs Perception: Eye Tracking Case Study in Real Road Traffic

This study investigates driver visual perception of road traffic signs under real road conditions. Using mobile eye tracking technology, we analyzed glance behavior toward various traffic signs and advertisements along urban and highway routes during daytime and nighttime conditions. Results showed significant differences in glance duration and frequency based on sign type, environmental conditions, and the presence of advertisements. Drivers primarily focused on speed limit and directional signs, while advertisements attracted longer glance durations despite their lower frequency of detection. Nighttime conditions generally led to increased glance durations and higher frequencies for most traffic sign types. These findings highlight the importance of optimizing road signage design and placement to improve driver attention and road safety, especially in environments with high visual clutter. Limitations include the exclusion of peripheral vision effects and potential biases introduced by experimental settings.

1 version available:

Role of Eye-Tracking Technology and Software Algorithms in Enhancing ADHD Detection and Diagnosis: A Systematic Literature Review

This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye-tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD. The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified through eye-tracking technology and analyzed using software algorithms. These technologies have shown promise in improving diagnostic accuracy, with machine learning models further enhancing their potential. However, the effectiveness of these interventions varies across age groups and study designs, highlighting the need for further research to refine these tools for clinical application. Eye-tracking technology and assessment software provide a valuable supplement to traditional diagnostic methods but require further validation and standardization before widespread clinical use in children and adults.

1 version available:

Speech planning depends on next-speaker selection: evidence from pupillometry in question–answer sequences in naturalistic triadic conversation

Next-speaker selection, which controls who should speak next, is fundamental to turn taking. While it is central in Conversation Analysis, little is known about its cognitive repercussions. We draw on turn-taking and pupillometric data in triadic interaction, investigating open-floor questions, which license more than one participant to respond, and closed-floor questions, which license only a single participant to answer. Comparing pupil size changes in answerers versus not- answerers at turn transitions, we find that answerers’ pupils dilate irrespective of question type, while not-answerers’ pupils dilate only in open-floor questions, indicating that in closed-floor questions not-selected participants do not prepare a response, whereas in open-floor questions both recipients engage in response planning, even if only one responds. Mutual gaze, by contrast, is not found to have an effect on pupil size at turn transitions. We propose an extension to the current model of language processing in turn taking, including next-speaker selection as a relevant variable impacting interlocutors’ behavior and their mental processes.

1 version available:

Technologies and Advanced Tools for Human-Augmentation in Smart Industry

The technologies disseminated since the Industry 4.0 phenomenon have changed industrial systems and the role of the user within production processes with the introduction of tools that increase the transmission and dissemination of information. This phenomenon has reached its maximum expansion with the introduction of Industry 5.0, which with its proposed paradigms emphasises the need to introduce advanced systems designed according to a Human-Centred Design approach. By surveying the interaction and control systems introduced in recent years, with a particular focus on the manufacturing sector, it was possible to identify the latest instances in the industrial sector relating to human-machine interaction, between real and virtual space, physical and sensory perception and physical-directed and cognitive-neural control. Through methodologies and analysis tools, research has enabled the acquisition of new forms of knowledge oriented towards scientific knowledge with the aim of aligning design knowledge and industrial and production progress.

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The aesthetic nature of Chinese-inscribed poetry and painting texts—evidence from eye movements

Previous studies on traditional Chinese inscribed poems and paintings have primarily been subjective qualitative analyses conducted by researchers. In this study, eye-tracking techniques and equipment were utilized to objectively analyze hotspot maps, Area of Interest (AOI) first gaze duration, AOI gaze duration, and sight-switching frequency of viewers when observing inscribed poems and paintings. This research presents these analyses impartially in the form of quantitative data. It reports the eye-tracking data and aesthetic tendencies of three groups with varying art education backgrounds when viewing inscribed poetic paintings. The experiments demonstrated statistically significant differences in aesthetic appreciation, revealing that the inscribed poems and paintings significantly influence the aesthetics of individuals with different art education backgrounds. In the group with the original inscribed poems and paintings, significant differences were observed in the eye-tracking data across the three groups with varying art education backgrounds. Participants with professional art backgrounds focused more on the interaction between the poetry text and the painting. In contrast, in the text + audio narration group, the eye-tracking data of all three groups overlapped significantly, suggesting that understanding the meaning of the poetic text enhanced aesthetic appreciation, particularly for those without professional art backgrounds. Our findings provide insights into the aesthetic appreciation of traditional Chinese paintings, audience perceptions and understandings of artworks, and art museum display practices.

2 versions available

Publication Hub Archive

Overview of optical radiation safety requirements for eye tracking systems

Personalized Course Recommendations Leveraging Machine and Transfer Learning Toward Improved Student Outcomes

Physdrive: A multimodal remote physiological measurement dataset for in-vehicle driver monitoring

Real-time detection method of angry driving behavior based on bracelet data

Research Methods in Cognitive Translation and Interpreting Studies, Chapter 10. Dynamic eyetracking

Road Signs Perception: Eye Tracking Case Study in Real Road Traffic

Role of Eye-Tracking Technology and Software Algorithms in Enhancing ADHD Detection and Diagnosis: A Systematic Literature Review

Speech planning depends on next-speaker selection: evidence from pupillometry in question–answer sequences in naturalistic triadic conversation

Technologies and Advanced Tools for Human-Augmentation in Smart Industry

The aesthetic nature of Chinese-inscribed poetry and painting texts—evidence from eye movements