Pourang Irani

Diego Monteiro, Hai-Ning Liang, Xiaohang Tang et al.

Identifying cybersickness in virtual reality (VR) applications such as games in a fast, precise, non-intrusive, and non-disruptive way remains challenging. Several factors can cause cybersickness, and their identification will help find its origins and prevent or minimize it. One such factor is virtual movement. Movement, whether physical or virtual, can be represented in different forms. One way to represent and store it is with a temporally annotated point sequence. Because a sequence is memory-consuming, it is often preferable to save it in a compressed form. Compression allows redundant data to be eliminated while still preserving changes in speed and direction. Since changes in direction and velocity in VR can be associated with cybersickness, changes in compression rate can likely indicate changes in cybersickness levels. In this research, we explore whether quantifying changes in virtual movement can be used to estimate variation in cybersickness levels of VR users. We investigate the correlation between changes in the compression rate of movement data in two VR games with changes in players' cybersickness levels captured during gameplay. Our results show (1) a clear correlation between changes in compression rate and cybersickness, and(2) that a machine learning approach can be used to identify these changes. Finally, results from a second experiment show that our approach is feasible for cybersickness inference in games and other VR applications that involve movement.

Stela H. Seo, James E. Young, Pourang Irani

While teleoperated robots continue to proliferate in domains including search and rescue, field exploration, or the military, human error remains a primary cause for accidents or mistakes. One challenge is that teleoperating a remote robot is cognitively taxing as the operator needs to understand the robot's state and monitor all its sensor data. In a multi-robot team, an operator needs to additionally monitor other robots' progress, states, notifications, errors, and so on to maintain team cohesion. One strategy for supporting the operator to comprehend this information is to improve teleoperation interface designs to carefully present data. We present a set of prototypes that simplify complex team robot states and actions, with an aim to help the operator to understand information from the robots easily and quickly. We conduct a series of pilot studies to explore a range of design parameters used in our prototypes (text, icon, facial expression, use of color, animation, and number of team robots), and develop a set of guidelines for graphically representing team robot states in the remote team teleoperation.