In this work, we explore the potential and limitations of simulating gaze-contingent tunnel vision conditions using Virtual Reality (VR) with built-in eye tracking technology. This approach promises an easy and accessible way of expanding study populations and test groups for visual training, visual aids, or accessibility evaluations. However, it is crucial to assess the validity and reliability of simulating these types of visual impairments and evaluate the extend to which participants with simulated tunnel vision can represent real patients. Two age-matched participant groups were acquired: The first group (n = 8, aged 20–60, average 49.1 ± 13.2) consisted of patients diagnosed with Retinitis pigmentosa (RP). The second group (n = 8, aged 27–59, average 46.5 ± 10.8) consisted of visually healthy participants with simulated tunnel vision. Both groups carried out different visual tasks in a virtual environment for 30 min per day over the course of four weeks. Task performances as well as gaze characteristics were evaluated in both groups over the course of the study. Using the ’two one-sided tests for equivalence’ method, the two groups were found to perform similar in all three visual tasks. Significant differences between groups were found in different aspects of their gaze behavior, though most of these aspects seem to converge over time. Our study evaluates the potential and limitations of using Virtual Reality technology to simulate the effects of tunnel vision within controlled virtual environments. We find that the simulation accurately represents performance of RP patients in the context of group averages, but fails to fully replicate effects on gaze behavior.

Introduction: Extended reality (XR) technologies, particularly gaze‐based interaction methods, have evolved significantly in recent years to improve accessibility and reach broader user communities. While previous research has improved the simplicity and inclusivity of gaze-based choice, the adaptability of such systems ‐ particularly in terms of user comfort and fault tolerance ‐ has not yet been fully explored.Methods: In this study, four gaze‐based interaction techniques were examined in a visual search game in virtual reality (VR). A total of 52 participants were involved. The techniques tested included selection by dwell time, confirmation by head orientation, nodding and smooth pursuit eye movements. Both subjective and objective performance measures were assessed, using the NASA‐TLX for perceived task load and time to complete the task and score for objective evaluation.Results: Significant differences were found between the interaction techniques in terms of NASA‐TLX dimensions, target search time and overall performance. The results indicate different levels of efficiency and intuitiveness of each method. Gender differences in interaction preferences and cognitive load were also found.Discussion: These findings highlight the importance of personalizing gaze‐based VR interfaces to the individual user to improve accessibility, reduce cognitive load and enhance the user experience. Personalizing gaze interaction strategies can support more inclusive and effective VR systems that benefit both general and accessibility‐focused populations.

In the rapidly advancing field of vision science, traditional research approaches struggle to accurately simulate and evaluate vision correction methods, leading to time-consuming evaluations with limited scope and flexibility. To overcome these challenges, we introduce ‘VisionaryVR’, a virtual reality (VR) simulation framework designed to enhance optical simulation fidelity and broaden experimental capabilities. VisionaryVR leverages a versatile VR environment to support dynamic vision tasks and integrates comprehensive eye-tracking functionality. Its experiment manager’s scene-loading feature fosters a scalable and flexible research platform. Preliminary validation through an empirical study has demonstrated VisionaryVR’s effectiveness in replicating a wide range of visual impairments and providing a robust platform for evaluating vision correction solutions. Key findings indicate a significant improvement in evaluating vision correction methods and user experience, underscoring VisionaryVR’s potential to transform vision science research by bridging the gap between theoretical concepts and their practical applications. This validation underscores VisionaryVR’s contribution to overcoming traditional methodological limitations and establishing a foundational framework for research innovation in vision science.

Bi-directional gaze-based communication offers an intuitive and natural way for users to interact with systems. This approach utilizes the user’s gaze not only to communicate intent but also to obtain feedback, which promotes mutual understanding and trust between the user and the system. In this review, we explore the state of the art in gaze-based communication, focusing on both directions: From user to system and from system to user. First, we examine how eye-tracking data is processed and utilized for communication from the user to the system. This includes a range of techniques for gaze-based interaction and the critical role of intent prediction, which enhances the system’s ability to anticipate the user’s needs. Next, we analyze the reverse pathway—how systems provide feedback to users via various channels, highlighting their advantages and limitations. Finally, we discuss the potential integration of these two communication streams, paving the way for more intuitive and efficient gaze-based interaction models, especially in the context of Artificial Intelligence. Our overview emphasizes the future prospects for combining these approaches to create seamless, trust-building communication between users and systems. Ensuring that these systems are designed with a focus on usability and accessibility will be critical to making them effective communication tools for a wide range of users.

In this manuscript, we describe the soft- and hardware architecture as well as the implementation of a modern Internet of Medical Things (IoMT) system for sensor-assisted telepsychotherapy. It enables telepsychotherapy sessions in which the patient exercises therapy-relevant behaviors in their home environment under the remote supervision of the therapist. Wearable sensor information (electrocardiogram (ECG), movement sensors, and eye tracking) is streamed in real time to the therapist to deliver objective information about specific behavior-triggering situations and the stress level of the patients. We describe the IT infrastructure of the system which uses open standards such as WebRTC and OpenID Connect (OIDC). We also describe the system’s security concept, its container-based deployment, and demonstrate performance analyses. The system is used in the ongoing study SSTeP-KiZ (smart sensor technology in telepsychotherapy for children and adolescents with obsessive-compulsive disorder) and shows sufficient technical performance.

With the increasing prevalence of AI–driven systems that assist humans in daily tasks, ensuring safety and fostering trust is pivotal. Moreover, addressing system errors is important, as conventional methods may be inaccessible and time consuming for people with motor impairments. This study explores implicit communication using eye-tracking features. We conducted a Virtual Reality (VR) experiment where participants used a robotic arm, encountering errors to be prevented through explicit communication. Findings reveal significant differences in eye-tracking features based on error recognition and demonstrate automated error detection feasibility. Using machine learning, we were able to classify when a user intends to stop the system before a potential collision using the eye features (86 – 90% accuracy). This research contributes to understanding eye movement behavior for error detection and offers insights for implementing implicit communication in AI –- driven assistive systems.