Avatars play a central role in virtual and augmented reality (VR/AR), yet little is known about how they are represented in research. To address this gap, we collected and analyzed 14 440 avatar images from 4 659 publications. Every image was hand-labeled for gender, ethnicity, body representation, and visual style, and linked to publication metadata such as keywords, affiliation, and year. Our large-scale analysis yields 14 findings: for example, the dominance of realistic white male avatars in VR studies, the use of lower-fidelity and more gender-diverse bodies in AR, the post-2021 rise of stylized designs and diversity labels, and the mismatch between the amount of female keywords and their avatar occurrences. Based on our findings, we offer practical implications for avatar designers and researchers, such as adopting balanced starter libraries, employing bias dashboards, and using simple representation checklists. These steps can help future VR and AR platforms reduce bias and improve representations in avatar systems.

Extensive sitting is unhealthy; thus, countermeasures are needed to react to the ongoing trend toward more prolonged sitting. A variety of studies and guidelines have long addressed the question of how we can improve our sitting habits. Nevertheless, sitting time is still increasing. Here, smart devices can provide a general overview of sitting habits for more nuanced feedback on the user’s sitting posture. Based on a literature review (N=223), including publications from engineering, computer science, medical sciences, electronics, and more, our work guides developers of posture systems. There is a large variety of approaches, with pressure-sensing hardware and visual feedback being the most prominent. We found factors like environment, cost, privacy concerns, portability, and accuracy important for deciding hardware and feedback types. Further, one should consider the user’s capabilities, preferences, and tasks. Regarding user studies for sitting posture feedback, there is a need for better comparability and for investigating long-term effects.

Previous research has shown that integrating haptic feedback can improve immersion and realism in automotive VR applications. However, current haptic feedback approaches primarily focus on a single feedback type. This means users must switch between devices to experience haptic stimuli for different feedback types, such as grabbing, collision, or weight simulation. This restriction limits the ability to simulate haptics realistically for complex tasks such as maintenance. To address this issue, we evaluated existing feedback devices based on our requirements analysis to determine which devices are most suitable for simulating these three feedback types. Since no suitable haptic feedback system can simulate all three feedback types simultaneously, we evaluated which devices can be combined. Based on that, we devised a new multi-type haptic feedback system combining three haptic feedback devices. We evaluated the system with different feedback-type combinations through a qualitative expert study involving twelve automotive VR experts. The results showed that combining weight and collision feedback yielded the best and most realistic experience. The study also highlighted technical limitations in current grabbing devices. Our findings provide insights into the effectiveness of haptic device combinations and practical boundaries for automotive virtual reality tasks.

We present STROE, a new ungrounded string-based weight simulation device. STROE is worn as an add-on to a shoe that in turn is connected to the user’s hand via a controllable string. A motor is pulling the string with a force according to the weight to be simulated. The design of STROE allows the users to move more freely than other state-of-the-art devices for weight simulation. It is also quieter than other devices, and is comparatively cheap. We conducted a user study that empirically shows that STROE is able to simulate the weight of various objects and, in doing so, increases users’ perceived realism and immersion of VR scenes.

The large potential of force feedback devices for interacting in Virtual Reality (VR) has been illustrated in a plethora of research prototypes. Yet, these devices are still rarely used in practice and it remains an open challenge how to move this research into practice. To that end, we contribute a participatory design study on the use of haptic feedback devices in the automotive industry. Based on a 10-month observing process with 13 engineers, we developed STRIVE, a string-based haptic feedback device. In addition to the design of STRIVE, this process led to a set of requirements for introducing haptic devices into industrial settings, which center around a need for flexibility regarding forces, comfort, and mobility. We evaluated STRIVE with 16 engineers in five different day-to-day automotive VR use cases. The main results show an increased level of trust and perceived safety as well as further challenges towards moving haptics research into practice.

Immersive analytics is a fast growing field that is often applied in virtual reality (VR). VR environments often lack immersion due to missing sensory feedback when interacting with data. Existing haptic devices are often expensive, stationary, or occupy the user’s hand, preventing them from grasping objects or using a controller. We propose PropellerHand, an ungrounded hand-mounted haptic device with two rotatable propellers, that allows exerting forces on the hand without obstructing hand use. PropellerHand is able to simulate feedback such as weight and torque by generating thrust up to 11 N in 2-DOF and a torque of 1.87 Nm in 2-DOF. Its design builds on our experience from quantitative and qualitative experiments with different form factors and parts. We evaluated our final version through a qualitative user study in various VR scenarios that required participants to manipulate virtual objects in different ways, while changing between torques and directional forces. Results show that PropellerHand improves users’ immersion in virtual reality.

We report on an interdisciplinary visual analytics project wherein automotive engineers analyze test drive videos. These videos are annotated with navigation-specific augmented reality (AR) content, and the engineers need to identify issues and evaluate the behavior of the underlying AR navigation system. With the increasing amount of video data, traditional analysis approaches can no longer be conducted in an acceptable timeframe. To address this issue, we collaboratively developed Caarvida, a visual analytics tool that helps engineers to accomplish their tasks faster and handle an increased number of videos. Caarvida combines automatic video analysis with interactive and visual user interfaces. We conducted two case studies which show that Caarvida successfully supports domain experts and speeds up their task completion time.

Recent advances in technology and miniaturization allow the building of self-levitating tangible interfaces. This includes flying tangibles, which extend the mid-air interaction space from 2D to 3D. While a number of theoretical concepts about interaction with levitating tangibles were previously investigated by various researchers, a user-centered evaluation of the presented interaction modalities has attracted only minor attention from prior research. We present Flyables, a system adjusting flying tangibles in 3D space to enable interaction between users and levitating tangibles. Interaction concepts were evaluated in a user study(N=17), using quadcopters as operable levitating tangibles. Three different interaction modalities are evaluated to collect quantitative data and qualitative feedback. Our findings show preferred user interaction modalities using Flyables. We conclude our work with a discussion and future research within the domain of human-drone interaction.