Literature DB >> 31726896

Quantitative analysis of the Oculus Rift S in controlled movement.

Tyler A Jost1, Bradley Nelson1, Jonathan Rylander1.   

Abstract

PURPOSE: To assess the translational and rotational tracking performance of the Oculus Rift S using controlled robotic motion and a gold-standard motion tracking system.
MATERIALS AND METHODS: An Oculus Rift S headset and controller were each placed in an industrial robot arm and had Vicon passive markers placed on them. The robotic arm was then translated in three perpendicular directions and performed three orthogonal rotations about three orthogonal axes while the spatial position of the headset and controller were recorded by Vicon motion capture cameras as well as Unity. Positional data was analyzed to determine the difference in tracking between Unity and Vicon to establish the accuracy of the Rift S' tracking capabilities.
RESULTS: It was determined that the translational accuracy of the system was 1.66 ± 0.74 mm for the head-mounted display and 4.36 ± 2.91 mm for the controller, and the rotational accuracy of the system was 0.34 ± 0.38° for the HMD and 1.13 ± 1.23° for the controller.
CONCLUSIONS: The high level of accuracy and precision combined with the low cost of the Oculus Rift S and its use of inside-out tracking make it a viable candidate for clinicians looking to incorporate virtual reality.Implications for RehabilitationThe Oculus Rift S can report the position and orientation of the user's headmounted display and controllers during gameplay.The Oculus Rift S provides a more portable VR system which does not require external sensors to track motion, allowing it to be used in a variety of rehabilitation scenarios while still providing adequate motion tracking.

Keywords:  HTC Vive; Oculus Rift; Oculus Rift S; VR; Virtual reality; exergaming

Year:  2019        PMID: 31726896     DOI: 10.1080/17483107.2019.1688398

Source DB:  PubMed          Journal:  Disabil Rehabil Assist Technol        ISSN: 1748-3107


  3 in total

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Journal:  Proc SPIE Int Soc Opt Eng       Date:  2021-02-15

2.  Adversarial Autoencoder and Multi-Armed Bandit for Dynamic Difficulty Adjustment in Immersive Virtual Reality for Rehabilitation: Application to Hand Movement.

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Journal:  Sensors (Basel)       Date:  2022-06-14       Impact factor: 3.847

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Journal:  Sensors (Basel)       Date:  2022-07-23       Impact factor: 3.847

  3 in total

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