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

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.