Integrating eye-tracking technologies in Extended Reality (XR) headsets has enabled intuitive, hands-free system interaction, such as gaze-based menu navigation. However, there is a lack of comprehensive comparisons and consensus in the literature on the optimal use of gaze-based menu navigation. This paper presents a comparative analysis of gaze-based menu navigation in virtual environments, focusing on two common menu layouts: pie and list menus, with three interaction methods: gaze-based dwell, controller-based, and a multimodal approach combining gaze and controller inputs. We conducted a 19-participant within-subject study, measuring task completion time, error rate, usability, and user preference for each condition. The results indicate that while the pie layout was statistically faster and less erroneous than the list layout, novice users tend to favour list layouts. Furthermore, we found that users preferred the multimodal interaction method, despite its lower task completion times and higher error rates compared to controller-based navigation. Based on our findings, we offer design guidelines and recommendations for implementing gaze-based menu systems.

The MASTER project introduces an open Extended Reality (XR) platform designed to enhance human-robot collaboration and train workers in robotics within manufacturing settings. It includes modules for creating safe workspaces, intuitive robot programming, and user-friendly human-robot interactions (HRI), including eye-tracking technologies. The development of the platform is supported by two open calls targeting technical SMEs and educational institutes to enhance and test its functionalities. By employing the learning-by-doing methodology and integrating effective teaching principles, the MASTER platform aims to provide a comprehensive learning environment, preparing students and professionals for the complexities of flexible and collaborative manufacturing settings.

Mobile eye tracking studies involve analyzing areas of interest (AOIs) and visual attention to these AOIs to understand how people process visual information. However, accurately annotating the data collected for user studies can be a challenging and time-consuming task. Current approaches for automatically or semi-automatically analyzing head-mounted eye tracking data in mobile eye tracking studies have limitations, including a lack of annotation flexibility or the inability to adapt to specific target domains. To address this problem, we present IMETA, an architecture for semi-automatic fixation-to-AOI mapping. When an annotator assigns an AOI label to a sequence of frames based on the respective fixation points, an interactive video object segmentation method is used to estimate the mask proposal of the AOI. Then, we use the 3D reconstruction of the visual scene created from the eye tracking video to map these AOI masks to 3D. The resulting 3D segmentation of the AOI can be used to suggest labels for the rest of the video, with the suggestions becoming increasingly accurate as more samples are provided by an annotator using interactive machine learning (IML). IMETA has the potential to reduce the annotation workload and speed up the evaluation of mobile eye tracking studies.

Identity tracking and instance segmentation are crucial in several areas of biological research. Behavior analysis of individuals in groups of similar animals is a task that emerges frequently in agriculture or pharmaceutical studies, among others. Automated annotation of many hours of surveillance videos can facilitate a large number of biological studies/experiments, which otherwise would not be feasible. Solutions based on machine learning generally perform well in tracking and instance segmentation; however, in the case of identical, unmarked instances (e.g., white rats or mice), even state-of-the-art approaches can frequently fail. We propose a pipeline of deep generative models for identity tracking and instance segmentation of highly similar instances, which, in contrast to most region-based approaches, exploits edge information and consequently helps to resolve ambiguity in heavily occluded cases. Our method is trained by synthetic data generation techniques, not requiring prior human annotation. We show that our approach greatly outperforms other state-of-the-art unsupervised methods in identity tracking and instance segmentation of unmarked rats in real-world laboratory video recordings.

Password entry is common authentication approach in Extended Reality (XR) applications for its simplicity and familiarity, but it faces challenges in public and dynamic environments due to its cumbersome nature and susceptibility to observation attacks. Manual password input can be disruptive and prone to theft through shoulder surfing or surveillance. While alternative knowledge-based approaches exist, they often require complex physical gestures and are impractical for frequent public use. We present GazeLock, an eye-tracking and lock pattern-based authentication method. This method aims to provide an easy-to-learn and efficient alternative by leveraging familiar lock patterns operated through gaze. It ensures resilience to external observation, as physical interaction is unnecessary and eyes are obscured by the headset. Its hands-free, discreet nature makes it suitable for secure public use. We demonstrate this method by simulating the unlocking of a smart lock via an XR headset, showcasing its potential applications and benefits in real-world scenarios.

With the integration of eye tracking technologies in Augmented Reality (AR) and Virtual Reality (VR) headsets, gaze-based interactions have opened up new possibilities for user interface design, including menu navigation. Prior research in gaze-based menu navigation in VR has predominantly focused on pie menus, yet recent studies indicate a user preference for list layouts. However, the comparison of gaze-based interactions on list menus is lacking in the literature. This work aims to fill this gap by exploring the viability of list menus for multi-level gaze-based menu navigation in VR and evaluating the efficiency of various gaze-based interactions, such as dwelling and border-crossing, against traditional controller navigation and multi-modal interaction using gaze and button press.