Fidelity metrics for virtual environment simulations based on spatial memory awareness states

This paper describes a methodology based on human judgments of memory awareness states for assessing the simulation fidelity of a virtual environment (VE) in relation to its real scene counterpart. To demonstrate the distinction between task performance-based approaches and additional human evaluation of cognitive awareness states, a photorealistic VE was created. Resulting scenes displayed on a headmounted display (HMD) with or without head tracking and desktop monitor were then compared to the real-world task situation they represented, investigating spatial memory after exposure. Participants described how they completed their spatial recollections by selecting one of four choices of awareness states after retrieval in an initial test and a retention test a week after exposure to the environment. These reflected the level of visual mental imagery involved during retrieval, the familiarity of the recollection and also included guesses, even if informed. Experimental results revealed variations in the distribution of participants’ awareness states across conditions while, in certain cases, task performance failed to reveal any. Experimental conditions that incorporated head tracking were not associated with visually induced recollections. Generally, simulation of task performance does not necessarily lead to simulation of the awareness states involved when completing a memory task. The general premise of this research focuses on how tasks are achieved, rather than only on what is achieved. The extent to which judgments of human memory recall, memory awareness states, and presence in the physical and VE are similar provides a fidelity metric of the simulation in question

Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a three-dimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.Comment: Tp appear in the CCNC 2019 Conferenc

Spherical clustering of users navigating 360{\deg} content

In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on their common navigation patterns is a first direction to understand users behaviour. However, classical clustering techniques fail in identifying these common paths, since they are usually focused on minimizing a simple distance metric. In this paper, we argue that minimizing the distance metric does not necessarily guarantee to identify users that experience similar navigation path in the VR domain. Therefore, we propose a graph-based method to identify clusters of users who are attending the same portion of the spherical content over time. The proposed solution takes into account the spherical geometry of the content and aims at clustering users based on the actual overlap of displayed content among users. Our method is tested on real VR user navigation patterns. Results show that our solution leads to clusters in which at least 85% of the content displayed by one user is shared among the other users belonging to the same cluster.Comment: 5 pages, conference (Published in: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Usability Evaluation in Virtual Environments: Classification and Comparison of Methods

Virtual environments (VEs) are a relatively new type of human-computer interface in which users perceive and act in a three-dimensional world. The designers of such systems cannot rely solely on design guidelines for traditional two-dimensional interfaces, so usability evaluation is crucial for VEs. We present an overview of VE usability evaluation. First, we discuss some of the issues that differentiate VE usability evaluation from evaluation of traditional user interfaces such as GUIs. We also present a review of VE evaluation methods currently in use, and discuss a simple classification space for VE usability evaluation methods. This classification space provides a structured means for comparing evaluation methods according to three key characteristics: involvement of representative users, context of evaluation, and types of results produced. To illustrate these concepts, we compare two existing evaluation approaches: testbed evaluation [Bowman, Johnson, & Hodges, 1999], and sequential evaluation [Gabbard, Hix, & Swan, 1999]. We conclude by presenting novel ways to effectively link these two approaches to VE usability evaluation