Social Virtual Reality (VR) platforms have surged in popularity in recent years, including among people with disabilities (PWD). Previous research has documented accessibility challenges, harassment, and negative experiences for PWD using disability signifiers in VR, primarily focusing on those with visible disabilities who encounter negative experiences. Yet, little is known about the experiences of people with invisible disabilities in social VR environments, and whether positive experiences are also common. To address these gaps, we designed inclusive avatars (avatars with disability signifiers) and investigated the lived experiences of 26 individuals with both visible and invisible disabilities immersing themselves in social interactions in VRChat for a week. We utilized a mixed methods experience sampling design and multilevel regression to explore the relationships between social interactions of PWD in VR and various psychological outcomes. Our results indicate that PWD, both visible and invisible, experienced positive and negative social interactions in VR. These interactions, in turn, significantly influenced users’ overall experience with inclusive avatars, affecting aspects such as emotional responses, engagement levels, satisfaction with the avatar’s design, and perceptions of inclusion in VR. Qualitative interviews of 18 participants allowed for a more nuanced exploration of the experiences of PWD by giving voice to users who are rarely studied in depth. Findings provided unique insights into both the positive and negative experiences of PWD, as well as identified key design factors influencing user experience in social VR.

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.

In a world where diversity is increasingly recognised and celebrated, it is important for HCI to embrace the evolving methods and theories for technologies to reflect the diversity of its users and be ability-centric. Interdependence Theory, an example of this evolution, highlights the interpersonal relationships between humans and technologies and how technologies should be designed to meet shared goals and outcomes for people, regardless of their abilities. This necessitates a contemporary understanding of "ability-diverse collaboration," which motivated this review. In this review, we offer an analysis of 117 papers sourced from the ACM Digital Library spanning the last two decades. We contribute (1) a unified taxonomy and the Ability-Diverse Collaboration Framework, (2) a reflective discussion and mapping of the current design space, and (3) future research opportunities and challenges. Finally, we have released our data and analysis tool to encourage the HCI research community to contribute to this ongoing effort.

This work investigates and compares the performance of node-link diagrams, adjacency matrices, and bipartite layouts for visualizing networks. In a crowd-sourced user study ( n=150), we measure the task accuracy and completion time of the three representations for different network classes and properties. In contrast to the literature, which covers mostly topology-based tasks (e.g., path finding) in small datasets, we mainly focus on overview tasks for large and directed networks. We consider three overview tasks on networks with 500 nodes: (T1) network class identification, (T2) cluster detection, and (T3) network density estimation, and two detailed tasks: (T4) node in-degree vs. out-degree and (T5) representation mapping, on networks with 50 and 20 nodes, respectively. Our results show that bipartite layouts are beneficial for revealing the overall network structure, while adjacency matrices are most reliable across the different tasks.

We present an exploratory study on the accessibility of images in publications when viewed with color vision deficiencies (CVDs). The study is based on 1,710 images sampled from a visualization dataset (VIS30K) over five years. We simulated four CVDs on each image. First, four researchers (one with a CVD) identified existing issues and helpful aspects in a subset of the images. Based on the resulting labels, 200 crowdworkers provided 30,000 ratings on present CVD issues in the simulated images. We analyzed this data for correlations, clusters, trends, and free text comments to gain a first overview of paper figure accessibility. Overall, about 60 % of the images were rated accessible. Furthermore, our study indicates that accessibility issues are subjective and hard to detect. On a meta-level, we reflect on our study experience to point out challenges and opportunities of large-scale accessibility studies for future research directions.

Authoring situated visualizations in-situ is challenging due to the need of writing code in a mobile and highly dynamic fashion. To provide better support for that, we define requirements for text input methods that target situated visualization authoring. We identify wearable chorded keyboards as a potentially suitable method that fulfills some of these requirements. To further investigate this approach, we tailored a chorded keyboard device to visualization authoring, developed a learning application, and conducted a pilot user study. Our results confirm that learning a high number of chords is the main barrier for adoption, as in other application areas. Based on that, we discuss ideas on how chorded keyboards with a strongly reduced alphabet, hand gestures, and voice recognition might be used as a viable, multi-modal support for authoring situated visualizations in-situ.

Ubiquitous, situated, and physical visualizations create entirely new possibilities for tasks contextualized in the real world, such as doctors inserting needles. During the development of situated visualizations, evaluating visualizations is a core requirement. However, performing such evaluations is intrinsically hard as the real scenarios are safety-critical or expensive to test. To overcome these issues, researchers and practitioners adapt classical approaches from ubiquitous computing and use surrogate empirical methods such as Augmented Reality (AR), Virtual Reality (VR) prototypes, or merely online demonstrations. This approach's primary assumption is that meaningful insights can also be gained from different, usually cheaper and less cumbersome empirical methods. Nevertheless, recent efforts in the Human-Computer Interaction (HCI) community have found evidence against this assumption, which would impede the use of surrogate empirical methods. Currently, these insights rely on a single investigation of four interactive objects. The goal of this work is to investigate if these prior findings also hold for situated visualizations. Therefore, we first created a scenario where situated visualizations support users in do-it-yourself (DIY) tasks such as crafting and assembly. We then set up five empirical study methods to evaluate the four tasks using an online survey, as well as VR, AR, laboratory, and in-situ studies. Using this study design, we conducted a new study with 60 participants. Our results show that the situated visualizations we investigated in this study are not prone to the same dependency on the empirical method, as found in previous work. Our study provides the first evidence that analyzing situated visualizations through different empirical (surrogate) methods might lead to comparable results.

Heatmaps are a popular visualization technique that encode 2D density distributions using color or brightness. Experimental studies have shown though that both of these visual variables are inaccurate when reading and comparing numeric data values. A potential remedy might be to use 3D heatmaps by introducing height as a third dimension to encode the data. Encoding abstract data in 3D, however, poses many problems, too. To better understand this tradeoff, we conducted an empirical study (N=48) to evaluate the user performance of 2D and 3D heatmaps for comparative analysis tasks. We test our conditions on a conventional 2D screen, but also in a virtual reality environment to allow for real stereoscopic vision. Our main results show that 3D heatmaps are superior in terms of error rate when reading and comparing single data items. However, for overview tasks, the well-established 2D heatmap performs better.

Subtitles play a crucial role in cross-lingual distribution of multimedia content and help communicate information where auditory content is not feasible (loud environments, hearing impairments, unknown languages). Established methods utilize text at the bottom of the screen, which may distract from the video. Alternative techniques place captions closer to related content (e.g., faces) but are not applicable to arbitrary videos such as documentations. Hence, we propose to leverage live gaze as indirect input method to adapt captions to individual viewing behavior. We implemented two gaze-adaptive methods and compared them in a user study (n=54) to traditional captions and audio-only videos. The results show that viewers with less experience with captions prefer our gaze-adaptive methods as they assist them in reading. Furthermore, gaze distributions resulting from our methods are closer to natural viewing behavior compared to the traditional approach. Based on these results, we provide design implications for gaze-adaptive captions.

We investigate how Mixed Reality (MR) can be used to guide human body motions, such as in physiotherapy, dancing, or workout applications. While first MR prototypes have shown promising results, many dimensions of the design space behind such applications remain largely unexplored. To better understand this design space, we approach the topic from different angles by contributing three user studies. In particular, we take a closer look at the influence of the perspective, the characteristics of motions, and visual guidance on different user performance measures. Our results indicate that a first-person perspective performs best for all visible motions, whereas the type of visual instruction plays a minor role. From our results we compile a set of considerations that can guide future work on the design of instructions, evaluations, and the technical setup of MR motion guidance systems.

Finding relevant documents is essential for researchers of all disciplines. We investigated an approach for supporting searchers in their relevance decision in a digital library by automatically highlighting the most important keywords in abstracts. We conducted an eye-tracking study with 25 subjects and observed very different search and reading behavior which lead to diverse results. Some of the participants liked that highlighted abstracts accelerate their relevance decision, while others found that they disturb the reading flow. What many agree on is that the quality of highlighting is crucial for trust and system credibility.