Numerous studies have investigated the effects of system fidelity as a whole on one’s total sense of presence in virtual reality (VR). The Fidelity-based Presence Scale (FPS), a recently introduced presence questionnaire, provides a method for investigating the effects of different system fidelities (interaction, scenario, and display) on different aspects of one’s sense of presence. In this paper, we present one of the first studies to investigate those effects for a locomotion task by conducting a 2 × 2 × 2 within-subjects experiment that reveals insight on how the components of system fidelity affect sense of presence. Like recent research, our results indicate that interaction fidelity and display fidelity significantly affect one’s interaction presence and display presence, respectively. However, unlike prior work, we did not find that changes in scenario fidelity significantly affected one’s scenario presence. We discuss other results and the possible implications of this research.

Within the virtual reality (VR) research community, there have been several efforts to develop questionnaires with the aim of better understanding the sense of presence. Despite having numerous surveys, the community does not have a questionnaire that informs which components of a VR application contributed to the sense of presence. Furthermore, previous literature notes the absence of consensus on which questionnaire or questions should be used. Therefore, we conducted a Delphi study, engaging presence experts to establish a consensus on the most important presence questions and their respective verbiage. We then conducted a validation study with an exploratory factor analysis (EFA). The efforts between our two studies led to the creation of the Fidelity-based Presence Scale (FPS). With our consensus-driven approach and fidelity-based factoring, we hope the FPS will enable better communication within the research community and yield important future results regarding the relationship between VR system fidelity and presence.

We conducted a co-design study with 26 users in dyadic groups who were assigned to the opposing roles of "Streamers'' and "Viewers'' to design interactive, wearable telepresence prototypes for interpersonal use. The goal was to elicit values, identify value tensions, and resolve these tensions in the design of a system that allows Streamers to share their live experiences with a remote Viewer. Leveraging the lens of value-sensitive design (VSD), we found that these different stakeholders prioritized different values in design, but possibly due to our prompt to design for someone they cared about/knew, often accounted for one another's needs in their solutions to arrive at designs that were affordable, unobtrusive, and socially acceptable for the Streamer, while giving the Viewer a sense of autonomy. Our work highlights the strengths of co-design when eliciting important human values in the design of sociotechnical systems for wearable telepresence, reconciling value tensions, and conceptualizing novel hardware- and software-based solutions for enhancing the interpersonal telepresence experience of both viewers and streamers. A key insight from our study is that no single system will meet all users' needs; therefore, we should move towards building customizable toolkits to account for differing values and needs.

Standard selection techniques such as ray casting fail when virtual objects are partially or fully occluded. In this paper, we present two novel approaches that combine cone-casting, world-in-miniature, and grasping metaphors to disocclude objects in the representation local to the user. Through a within-subject study where we compared 4 selection techniques across 3 levels of object occlusion, we found that our techniques outperformed an alternative one that also focuses on maintaining the spatial relationships between objects. We discuss application scenarios and future research directions for these types of selection techniques.

Training simulations in virtual reality (VR) have become a focal point of both research and development due to allowing users to familiarize themselves with procedures and tasks without needing physical objects to interact with or needing to be physically present. However, the increasing popularity of VR training paradigms raises the question: Are VR-based training assessments accurate? Many VR training programs, particularly those focused on inspection tasks, employ simple pass or fail assessments. However, these types of assessments do not necessarily reflect the user’s knowledge.

In recent years, pass-through cameras have resurfaced as inclusions for virtual reality (VR) hardware. With modern cameras that now have increased resolution and frame rate, Video See-Through (VST) Head-Mounted Displays (HMD) can be used to provide an Augmented Reality (AR) experience. However, because users see their surroundings through video capture and HMD lenses, there is question surrounding how people perceive their environment with these devices. We conducted a user study with 26 participants to help understand if distance perception is altered when viewing surroundings with a VST HMD. Although previous work shows that distance estimation in VR with an HTC Vive is comparable to that in the real world, our results show that the inclusion of a ZED Mini pass-through camera causes a significant difference between normal, unrestricted viewing and that through a VST HMD.

We are witnessing a revolution in body area sensing, with applications ranging from biometric-based security to personalized healthcare to sports performance training, among numerous others. The key application driver has been the emergence of wireless and/or contact-free technology for sensing human physiology, motion, and posture. We posit an analogous revolution enabled by advancements in sensing of human emotion. The applications are similarly diverse, spanning social skills education, business intelligence, monitoring of doctor-patient dynamics, and numerous others. This paper explores the sensing foundations necessary to achieve reliable detection and classification of human emotions. The approach combines sensing of speech characteristics, natural language processing, facial landmark monitoring, and machine learning.