To share a virtual reality (VR) experience remotely together, users usually record videos from an individual's point of view and then co-watch these videos. However, co-watching recorded videos limits users to reliving their memories from the perspective from which the video was captured. In this paper, we describe ReliveInVR, a new time-machine-like VR experience sharing method. ReliveInVR allows multiple users to immerse themselves in the relived experience together and independently view the experience from any perspective. We conducted a 1x3 within-subject study with 26 dyads to compare ReliveInVR with (1) co-watching 360-degree videos on desktop, and (2) co-watching 360-degree videos in VR. Our results suggest that participants reported higher levels of immersion and social presence in ReliveInVR. Participants in ReliveInVR also understood the shared experience better, discovered unnoticed things together and found the sharing experience more fulfilling. We discuss the design implications for sharing VR experiences over time and space.
As smart devices are becoming commonplace in homes, we need to explore the needs of not just the residents of the home, but also of secondary stakeholders who may be granted access to these devices from outside of the home. We conducted a mixed methods study, which included a survey of 163 smart home device owners and a follow-up interview with 13 individuals who currently share their smart home devices with others outside of their home. Nearly half (47.8%) of our survey participants shared at least one smart home device with someone that did not live with them. Individuals sought greater safety and security by providing remote access to trusted family members or friends. By understanding users' perspectives about privacy and trust in relation to sharing smart home devices beyond the home, we build a case for community-based access control of smart home devices in the Internet of Things.
Public speaking anxiety is one of the most common social phobias. We explore the feasibility of using a conversational agent to reduce this anxiety. We developed a public-speaking tutor on the Amazon Alexa platform that enables users to engage in cognitive reconstruction exercises. We also investigated how the sociability of the agent might affect its performance as a tutor. A user study of 53 college students with fear of public speaking showed that the interaction with the agent served to assuage pre-speech state anxiety. Agent sociability improved the sense of interpersonal closeness, which was associated with lower pre-speech anxiety. Moreover, sociability of the agent increased participants' satisfaction and their willingness to continue engagement. Our findings, thus, have implications not only for addressing public speaking anxiety in a scalable way but also for the design of future conversational agents using smart speaker platforms.
Autonomous vehicles have been rapidly progressing towards full autonomy using fixed driving styles, which may differ from individual passenger preferences. Violating these preferences may lead to passenger discomfort or anxiety. We studied passenger responses to different driving style parameters in a physical autonomous vehicle. We collected galvanic skin response, heart rate, and eye-movement patterns from 20 participants, along with self-reported comfort and anxiety scores. Our results show that the presence and proximity of a lead vehicle not only raised the level of all measured physiological responses, but also exaggerated the existing effect of the longitudinal acceleration and jerk parameters. Skin response was also found to be a significant predictor of passenger comfort and anxiety. By using multiple independent events to isolate different driving style parameters, we demonstrate a method to control and analyze such parameters in future studies.
Data analysis is central to sports training. Today, cutting-edge digital technologies are deployed to measure and improve athletes' performance. But too often researchers focus on the technology collecting performance data at the expense of understanding athletes' experiences with data. This is particularly the case in the understudied context of collegiate athletics, where competition is fierce, tools for data analysis abound, and the institution actively manages athletes' lives. By investigating how student-athletes analyze their performance data and are analyzed in turn, we can better understand the individual and institutional factors that make data literacy practices in athletics meaningful and productive—or not. Our pilot interview study of student-athletes at one Division I university reveals a set of opportunities for student-athletes to engage with and learn from data analytics practices. These opportunities come with a set of contextual tensions that should inform the design of new technologies for collegiate sports settings.