Eye Tracking during reading provides information about how readers process English texts when English is their second language (L2). We use unsupervised learning to derive reader profiles from eye tracking and comprehension data in MECO L2 dataset. For each participant, we compute eight features: skipping, regression in, refixation, reading rate, progressive duration, rereading duration, lookback duration, and comprehension accuracy. After standardization of these calculated features, a k-means clustering is deployed with and without reading rate to separate the influence of speed from other aspects of reading behavior. A gradient-boosted tree model serves as a surrogate classifier to interpret the resulting three profiles, which differ in speed, regression rate, and accuracy. We discuss how this profiling pipeline assists in studying individual differences in L2 reading and relates cluster-level patterns to existing reading style typologies.

This study integrates Bundesen’s Theory of Visual Attention (TVA) with task-evoked pupillary responses (TEPR) to examine how typog- raphy affects letter recognition. Twenty-one participants completed a whole-report task with Danish letters in three fonts (Cambria, Garamond, Roboto)× two styles (Regular, Italic) across eight expo- sure durations (10–200 ms). Bayesian posterior analysis revealed that italic processing-speed penalties scale with the degree of structural redesign: Garamond’s script-derived italic imposed the largest cost (Δ= 27.0 elem/s, (Δ > 0)= 1.00), Cambria’s glyph substitutions a moderate cost (Δ= 16.8, = 1.00), and Roboto’s oblique the smallest (Δ= 4.5, = 0.82, CI spanning zero). Pupillary metrics showed no significant font effects; only exposure duration drove dilation, though dPPD revealed a significant Font× Style interaction. This dissociation font-sensitive TVA parameters but font-insensitive pupillary am- plitude suggests typography modulates encoding efficiency rather than cognitive effort, although pupillometric

While Generative AI models like Large Language Models (LLMs) are capable of generating extensive text, their efficacy in producing readable content for human participants in experimental settings remains to be evaluated. Further, eye-tracking technology is increas- ingly utilized to study cognition and behavior, yet its application to readers’ cognitive processes when exposed to AI-generated versus human-authored texts remains unexplored. This study investigates how text generated by LLMs influences reading by analyzing gaze patterns. The study collects gaze data from 13 participants as they read AI- generated and human-authored passages. A comparative analysis is conducted within subjects to assess gaze patterns between authors and between text types based on the robust two-means clustering (I2MC) algorithm to identify fixations. In addition, pupil dilation and reading speed were examined. Our findings reveal significant differences in fixation character- istics not only between authors but also between AI-generated and human-authored texts.

While adaptive reading interfaces are capable of providing flexible typographical adjustments in real-time, readers are challenged to keep track of the context. This paper aims to contribute by introducing context preservation, which enables readers to resume reading faster after applying typographical adjustments, using eye-tracking. Typography adjustments are applied through so-called interventions, and the reading application currently has four intervention designs: Popup, Undo, Notification, and Gradual. To explore how much text is required to resume reading quickly, context-preservation functionality was applied and evaluated on 22 participants through within-subjects experiment design. Our findings reveal significant differences in reading-resume time (RRT) between interventions. Furthermore, context-preservation in a gradual intervention mode is the fastest and most liked intervention design by the participants.

Head movement is widely used as a uniform type of input for human-computer interaction. However, there are fundamental differences between head movements coupled with gaze in support of our visual system, and head movements performed as gestural expression. Both Head-Gaze and Head Gestures are of utility for interaction but differ in their affordances. To facilitate the treatment of Head-Gaze and Head Gestures as separate types of input, we developed HeadBoost as a novel classifier, achieving high accuracy in classifying gaze-driven versus gestural head movement (F1-Score: 0.89). We demonstrate the utility of the classifier with three applications: gestural input while avoiding unintentional input by Head-Gaze; target selection with Head-Gaze while avoiding Midas Touch by head gestures; and switching of cursor control between Head-Gaze for fast positioning and Head Gesture for refinement. The classification of Head-Gaze and Head Gesture allows for seamless head-based interaction while avoiding false activation.

Within the last 20 years, gaze interaction has become a successful communication solution for numerous people with motor challenges. In this paper, we present two new cases of gaze interactive assistive technology: i) gaze control of an exoskeleton for stroke rehabilitation, and ii) gaze interactive reading support for people with low vision. By applying a Universal Design approach [Mace 1998] both cases are assessed through an ability analysis to identify issues with gaze interaction specific to our applications that need to be further addressed. Finally, we suggest how solutions in our applications may be mainstreamed for a broader user group.

Type 2 diabetes (T2D) is a large disease burden worldwide and represents an increasing and complex challenge for all societies. For the individual, T2D is a complex, multi-dimensional, and long-term challenge to manage, and it is challenging to establish and maintain good communication between the patient and healthcare professionals. This article presents DiaFocus, which is a mobile health sensing application for long-term ambulatory management of T2D. DiaFocus supports an adaptive collection of physiological, behavioral, and contextual data in combination with ecological assessments of psycho-social factors. This data is used for improving patient-clinician communication during consultations. DiaFocus is built using a generic data collection framework for mobile and wearable sensing and is highly extensible and customizable. We deployed DiaFocus in a 6-week feasibility study involving 12 patients with T2D. The patients found the DiaFocus approach and system useful and usable for diabetes management. Most patients would use such a system, if available as part of their treatment. Analysis of the collected data shows that mobile sensing is feasible for longitudinal ambulatory assessment of T2D, and helped identify the most appropriate target users being early diagnosed and technically literate T2D patients.

Knowledge of passenger flow underpins any optimal public transport application, such as new facilities design and operations. The interactions between passengers and sensors may provide the foundation to measure this flow and dismiss users and staff from the measures’ validation loop. Removing humans and their errors may impact significantly and improve measures’ cost and quality. The literature considered smartphones the leading enabler due to market penetration and embodied sensors. Smartphones allow users’ localization, identification, authentication, and billing. Via Bluetooth, smartphones detect short-range implicit interactions, device-to-device. We model passenger states on buses, either be-in or be-out (BIBO). The BIBO use case identifies a fundamental building block of continuously-valued passenger flow, which this paper describes through a Human-Computer interaction experimental setting involving two autonomous buses and a proprietary smartphone-Bluetooth sensing platform. The resulting dataset of 14,000 observations/sensors contains two ground-truth levels: the first is the passengers’ validation; the second is validation by video cameras surveilling buses and tracks. This study verifies separately classification based on Bluetooth and GPS signals, as well as an inertial navigation system, evaluating signals and related machine learning (ML) classifiers against measurement and ground-truth noise. The paper contributes a Monte Carlo simulation of labels-flip to emulate human errors in the labeling process, as in smartphone surveys, and a novel unsupervised variational auto-encoder classifier. Experimental results indicate error-free human validation is unlikely. The impact of mistakes on model performance bias can be significant. This use case supports the potential substitution of human validation with independent Bluetooth validation.

A dry-electrode head-mounted sensor for visually-evoked electroencephalogram (EEG) signals has been introduced to the gamer market, and provides wireless, low-cost tracking of a user’s gaze fixation on target areas in real-time. Unlike traditional EEG sensors, this new device is easy to set up for non-professionals. We conducted a Fitts’ law study (N = 6) and found the mean throughput (TP) to be 0.82 bits/s. The sensor yielded robust performance with error rates below 1%. The overall median activation time (AT) was 2.35 s with a minuscule difference between one or nine concurrent targets. We discuss whether the method might supplement camera-based gaze interaction, for example, in gaze typing or wheelchair control, and note some limitations, such as a slow AT, the difficulty of calibration with thick hair, and the limit of 10 concurrent targets.

The growing commercial success of smart speaker devices following recent advancements in speech recognition technology has surfaced new opportunities for collecting self-reported health and well-being data. Speech-enabled conversational agents (CAs) in particular, deployed in home environments using just such systems, may offer increasingly intuitive and engaging means of self-report. To date, however, few real-world studies have examined users’ experiences of engaging in the self-report of mental health using such devices or the challenges of deploying these systems in the home context. With these aims in mind, this article recounts findings from a 4-week “in-the-wild” study during which 20 individuals with depression or bipolar disorder used a speech-enabled CA named “Sofia” to maintain a daily diary log, responding also to the World Health Organization–Five Well-Being Index WHO-5 scale every 2 weeks. Thematic analysis of post-study interviews highlights actions taken by participants to overcome CAs’ limitations, diverse personifications of a speech-enabled agent, and unique forms of valuing of this system among users’ personal and social circles. These findings serve as initial evidence for the potential of CAs to support the self-report of mental health and well-being, while highlighting the need to address outstanding technical limitations in addition to design challenges of conversational pattern matching, filling unmet interpersonal gaps, and the use of self-report CAs in the at-home social context. Based on these insights, we discuss implications for the future design of CAs to support the self-report of mental health and well-being.

A novel eye-tracked measure of pupil diameter oscillation is derived as an indicator of cognitive load. The new metric, termed the Low/High Index of Pupillary Activity (LHIPA), is able to discriminate cognitive load (vis-a-vis task difficulty) in several experiments where the Index of Pupillary Activity fails to do so. Rationale for the LHIPA is tied to the functioning of the human autonomic nervous system yielding a hybrid measure based on the ratio of Low/High frequencies of pupil oscillation. The paper's contribution is twofold. First, full documentation is provided for the calculation of the LHIPA. As with the IPA, it is possible for researchers to apply this metric to their own experiments where a measure of cognitive load is of interest. Second, robustness of the LHIPA is shown in analysis of three experiments, a restrictive fixed-gaze number counting task, a less restrictive fixed-gaze n-back task, and an applied eye-typing task.

In this paper, we have measured cognitive load during an interactive eye-tracking task. Eye-typing was chosen as the task, because of its familiarity, ubiquitousness and ease. Experiments with 18 participants, where they memorized and eye-typed easy and difficult sentences over four days, were used to compare the difficulty levels of the tasks using subjective scores and eye-metrics like blink duration, frequency and interval and pupil dilation were explored, in addition to performance measures like typing speed, error rate and attended but not selected rate. Typing performance lowered with increased task difficulty, while blink frequency, duration and interval were higher for the difficult tasks. Pupil dilation indicated the memorization process, but did not demonstrate a difference between easy and difficult tasks.

Recent eye tracking research in the field of software engineering has proposed novel visualizations linking developer's gazes with the source code artifacts to better understand how developers comprehend source code artifacts potentially consisting of several different files. In addition, it is well established that cognitive processes can be monitored by recording the change in pupil dilation. Recent pupillometry studies in the software engineering field have shown that pupil dilation can be used either as an indicator of cognitive load or task difficulty. We envision to create a tool for visualizing pupil dilation linked to source code artifacts that can help to better understand the cognitive processes of a developer during code comprehension tasks in terms of cognitive load. In this paper, we describe a feasibility study we conducted to enable a more fine-grained analysis of pupil dilation and we demonstrate some preliminary results.

Head-mounted displays offer full control over lighting conditions. When equipped with eye tracking technology, they are well suited for experiments investigating pupil dilation in response to cognitive tasks, emotional stimuli, and motor task complexity, particularly for studies that would otherwise have required the use of a chinrest, since the eye cameras are fixed with respect to the head. This paper analyses pupil dilations for 13 out of 27 participants completing a Fitts' law task using a virtual reality headset with built-in eye tracking. The largest pupil dilation occurred for the condition subjectively rated as requiring the most physical and mental effort. Fitts' index of difficulty had no significant effect on pupil dilation, suggesting differences in motor task complexity may not affect pupil dilation.

This paper presents a Fitts' law experiment and a clinical case study performed with a head-mounted display (HMD). The experiment compared gaze, foot, and head pointing. With the equipment setup we used, gaze was slower than the other pointing methods, especially in the lower visual field. Throughputs for gaze and foot pointing were lower than mouse and head pointing and their effective target widths were also higher. A follow-up case study included seven participants with movement disorders. Only two of the participants were able to calibrate for gaze tracking but all seven could use head pointing, although with throughput less than one-third of the non-clinical participants.