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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Related with: Long-short term memory prediction of user's locomotion in Virtual Reality publication (Dataset)

Authors: Jesus Mayor; Pablo Calleja; Felix Fuentes; Nobuyuki Umetani;

Related with: Long-short term memory prediction of user's locomotion in Virtual Reality publication (Dataset)

Abstract

Dataset: Captured motion data from 44 users. Scenes: SL -> Scene Lab. SR -> Escape Room. MF -> Shooter forest. Since it is recorded inside a game engine and all records take place inside their processing, the timestamp is written down for each register (Time_sice_startup field). Additionally, the anonymized identification of the user is recorded (User field). The dataset includes the following characteristics for Oculus Quest 2 HMD and each controller. DevicePosition (x, y, z): Position recorded. DeviceRotation (w, x, y, z): Rotation expressed with a quaternion. Forward (x, y, z): The unit vector that points to the specific device in the forward direction used in our new model. It can also be obtained by rotating $(0,0,1)$ with the quaternion. DeviceVelocity (x, y, z): Linear velocity of that device in that frame. It represents the rate of change in position. DeviceAcceleration (x, y, z): Linear acceleration of that device in that frame. DeviceAngularVelocity (x, y, z): The angular velocity vector in that frame of the device is measured in radians per second. DeviceAngularAcceleration (x, y, z): The angular acceleration at that frame. Also for each goal in the scene: GoalName (x, y, z): Position of that goal. If the element is static, the same position will always be recorded. GoalName_Quat (w, x, y, z): As in the previously defined fields, a rotation is expressed as a quaternion. GoalName_LocalScale (x, y, z): Scale of that element locally related to its parent in the hierarchy. They have no relatives in their hierarchy, so it is the real scale.

Keywords

virtual reality, user prediction, deep learning

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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