4 research outputs found

    Distance Perception Through Head-Mounted Displays

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    It has been shown in numerous research studies that people tend to underestimate distances while wearing head-mounted displays (HMDs). We investigated various possible factors affecting the perception of distance is HMDs through multiple studies. Many contributing factors has been identified by researchers in the past decades, however, further investigation is required to provide a better understanding of this problem. In order to find a baseline for distance underestimation, we performed a study to compare the distance perception in real world versus a fake headset versus a see-through HMD. Users underestimated distances while wearing the fake headset or the see-through HMD. The fake headset and see-through HMD had similar result, while they had significant difference with the real-world results. Since the fake headset and the HMD had similar underestimation results, we decided to focus on the FOV of the headset which was a common factor between these two conditions. To understand the effects of FOV on the perception of distance in a virtual environment we performed a study through a blind-throwing task. FOVs at three different diagonal angles, 60°, 110° and 200° were compared with each other. The results showed people underestimate the distances more in restricted FOVs. As this study was performed using static 360° images of a single environment, we decided to see if the results can be extended to various 3D environments. A mixed-design study to compare the effect of horizontal FOV and vertical FOV on egocentric distance perception in four different realistic VEs was performed. The results indicated more accurate distance judgement with larger horizontal FOV with no significant effect of vertical FOV. More accurate distance judgement in indoor VEs compared to outdoor VEs was observed. Also, participants judged distances more accurately in cluttered environments versus uncluttered environments. These results highlights the importance of the environment in distance-critical VR applications and also shows that wider horizontal FOV should be considered for an improved distance judgment

    Effects of Clutter on Egocentric Distance Perception in Virtual Reality

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    To assess the impact of clutter on egocentric distance perception, we performed a mixed-design study with 60 participants in four different virtual environments (VEs) with three levels of clutter. Additionally, we compared the indoor/outdoor VE characteristics and the HMD's FOV. The participants wore a backpack computer and a wide FOV head-mounted display (HMD) as they blind-walked towards three distinct targets at distances of 3m, 4.5m, and 6m. The HMD's field of view (FOV) was programmatically limited to 165{\deg}×\times110{\deg}, 110{\deg}×\times110{\deg}, or 45{\deg}×\times35{\deg}. The results showed that increased clutter in the environment led to more precise distance judgment and less underestimation, independent of the FOV. In comparison to outdoor VEs, indoor VEs showed more accurate distance judgment. Additionally, participants made more accurate judgements while looking at the VEs through wider FOVs.Comment: This paper was not published yet in any venue or conference/journal, ACM conference format was used for the paper, authors were listed in order from first to last (advisor), 10 pages, 10 figure

    Investigating The Value Of Privacy Within The Internet Of Things

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    Many companies within the Internet of Things (IoT) sector rely on the personal data of users to deliver and monetize their services, creating a high demand for personal information. A user can be seen as making a series of transactions, each involving the exchange of personal data for a service. In this paper, we argue that privacy can be described quantitatively, using the game- theoretic concept of value of information (VoI), enabling us to assess whether each exchange is an advantageous one for the user. We introduce PrivacyGate, an extension to the Android operating system built for the purpose of studying privacy of IoT transactions. An example study, and its initial results, are provided to illustrate its capabilities
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