Measuring Latency in Virtual Reality Systems

Raaen, Kjetil; Kjellmo, Ivar

doi:10.1007/978-3-319-24589-8_40

Cited by 61 publications

(41 citation statements)

References 7 publications

(9 reference statements)

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“…EEG data were acquired with 500 samples per second, and drift correction with a threshold of 2 ms was applied. The specification of latency of the used VR system is not officially available, however testing using older generation of the HMD demonstrated 45 ms (Raaen and Kjellmo, 2015) or 46 ms latency time (Oculus, 2013), while unofficial information 5 on motion-to-photon latency of the used HMD claims it to be <5 ms. Consequently, the majority of delay between the participant and system is created during the epoching. Epochs were created using a sliding window each 1/16th of a second, meaning that feedback was generated based on 62.5-ms-old data.…”

Section: Methodsmentioning

confidence: 99%

Progressive Training for Motor Imagery Brain-Computer Interfaces Using Gamification and Virtual Reality Embodiment

Škola

Tinková

Liarokapis

2019

Front. Hum. Neurosci.

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This paper presents a gamified motor imagery brain-computer interface (MI-BCI) training in immersive virtual reality. The aim of the proposed training method is to increase engagement, attention, and motivation in co-adaptive event-driven MI-BCI training. This was achieved using gamification, progressive increase of the training pace, and virtual reality design reinforcing body ownership transfer (embodiment) into the avatar. From the 20 healthy participants performing 6 runs of 2-class MI-BCI training (left/right hand), 19 were trained for a basic level of MI-BCI operation, with average peak accuracy in the session = 75.84%. This confirms the proposed training method succeeded in improvement of the MI-BCI skills; moreover, participants were leaving the session in high positive affect. Although the performance was not directly correlated to the degree of embodiment, subjective magnitude of the body ownership transfer illusion correlated with the ability to modulate the sensorimotor rhythm.

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Section: Methodsmentioning

confidence: 99%

Progressive Training for Motor Imagery Brain-Computer Interfaces Using Gamification and Virtual Reality Embodiment

Škola

Tinková

Liarokapis

2019

Front. Hum. Neurosci.

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“…While using a Titan V video card, it is possible to detect charts in almost real-time, so for most high-end specs cards, it is possible to use this process on these time-intensive applications [62]. Even if it is not acceptable for frame-by-frame real-time use in time-intensive applications [63], some shortcuts can be used, such as frame skipping, resolution scaling, and object tracking to minimize the perceived latency for the users. For an augmented reality mobile application, a high-end video card could be part of a cloud service that does the heavy computation, allowing for the mobile device to position the results correctly.…”

Section: Discussionmentioning

confidence: 99%

A Real-World Approach on the Problem of Chart Recognition Using Classification, Detection and Perspective Correction

Araújo

Chagas

Alves

et al. 2020

Sensors

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Data charts are widely used in our daily lives, being present in regular media, such as newspapers, magazines, web pages, books, and many others. In general, a well-constructed data chart leads to an intuitive understanding of its underlying data. In the same way, when data charts have wrong design choices, a redesign of these representations might be needed. However, in most cases, these charts are shown as a static image, which means that the original data are not usually available. Therefore, automatic methods could be applied to extract the underlying data from the chart images to allow these changes. The task of recognizing charts and extracting data from them is complex, largely due to the variety of chart types and their visual characteristics. Other features in real-world images that can make this task difficult are photo distortions, noise, alignment, etc. Two computer vision techniques that can assist this task and have been little explored in this context are perspective detection and correction. These methods transform a distorted and noisy chart in a clear chart, with its type ready for data extraction or other uses. This paper proposes a classification, detection, and perspective correction process that is suitable for real-world usage, when considering the data used for training a state-of-the-art model for the extraction of a chart in real-world photography. The results showed that, with slight changes, chart recognition methods are now ready for real-world charts, when taking time and accuracy into consideration.

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“…A photosensorbased approach has been also proposed by Seo et al [86]. Raaen and Kjellmo presented [80] a system consisting of a laser pointer, light sensor and an oscilloscope, which could measure the delay also in sudden movements, unlike previous measurement setups which relied on constant motion. High-speed cameras have also been used both for manual and automated delay measurements [34,13].…”

Section: Measurement Methods For Delay-sensitive Mobile Applicationsmentioning

confidence: 99%

Towards pervasive and mobile gaming with distributed cloud infrastructure

Kämäräinen¹,

Siekkinen²,

Xiao³

et al. 2014

2014 13th Annual Workshop on Network and Systems Support for Games

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Measuring Latency in Virtual Reality Systems

Cited by 61 publications

References 7 publications

Progressive Training for Motor Imagery Brain-Computer Interfaces Using Gamification and Virtual Reality Embodiment

Progressive Training for Motor Imagery Brain-Computer Interfaces Using Gamification and Virtual Reality Embodiment

A Real-World Approach on the Problem of Chart Recognition Using Classification, Detection and Perspective Correction

Towards pervasive and mobile gaming with distributed cloud infrastructure

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