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Hand-free natural user interface for VR HMD with IR based facial gesture tracking sensor

Published: 08 November 2017 Publication History

Abstract

We proposed a hand-free Natural User Interface (NUI) for VR Head-Mounted-Display (HMD) with infra-red (IR) based sensor tracking facial gestures. We have realized NUI based on the real-time recognition of user intuitions for VR HMD without any additional control devices except for a built-in Gyroscope and IR couplers with readout circuitry integrated in the foam interface of an HMD. We implemented seven control commands affordable for 3D interactions with virtual objects. The experimental data show that the proposed system provides a convenient and efficient 2D/3D user interface for both manipulating objects and controlling commands while wearing a VR HMD.

References

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Sangho Lee, Gyutae Ha, Jaekwang Cha, Jinhyeok Kim, Hojun Lee, and Shiho Kim. 2015. CyberTouch-Touch and Cursor Interface for VR HMD. In International Conference on Human-Computer Interaction. Springer, 503--507.
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  1. Hand-free natural user interface for VR HMD with IR based facial gesture tracking sensor

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      cover image ACM Conferences
      VRST '17: Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology
      November 2017
      437 pages
      ISBN:9781450355483
      DOI:10.1145/3139131
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      New York, NY, United States

      Publication History

      Published: 08 November 2017

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      Author Tags

      1. facial gesture recognition
      2. head-mounted-display
      3. natural user interface
      4. virtual reality
      5. wearable sensors

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      • Ministry of Science and ICT

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      VRST '17

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      Overall Acceptance Rate 66 of 254 submissions, 26%

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