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Objective Quantification of Circular Vection in Immersive Environments

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Virtual, Augmented and Mixed Reality: Design and Development (HCII 2022)

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Abstract

Human interaction in the computer environment requires conduciveness with minimal cybersickness. One such sickness is vection, where the subjects undergo illusory perception of self-motion in response to visual stimulus. The present research quantifies the perceptual parameter. An optokinetic drum (OKD) is used to induce circular vection on a virtual reality (VR), and the inertial measurement unit (IMU) in a head-mounted display (HMD) is used to track the head rotation about x, y, z axes. The study quantifies the vection in terms of the vection index (VI). The VI depends on the ratio of the angular velocity of HMD to the angular velocity of OKD. There is a significant difference from resting state to higher angular speeds in clockwise (CW) as well as anticlockwise (ACW) direction \((p<0.05)\). Also, the circular vection along the y-axis imparts the motion along the x and z axes. The magnitude of vection increases with speed in CW and ACW directions till the optimum speed of OKD. The vection is absent during very low and high speeds of OKD. Most participants experience the self-motion in an angular displacement range of 30–97\(^\circ /\)s in both CW and ACW directions. The vection in ACW compensates for the vection in CW direction about x, y and z axes.

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  1. Touch Lab, Department Applied Mechanics, Indian Institute of Technology Madras, Chennai, 600 113, India

    Debadutta Subudhi, P. Balaji & Manivannan Muniyandi

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  1. Debadutta Subudhi
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  2. P. Balaji
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  1. U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA

    Jessie Y. C. Chen

  2. U.S. Army Combat Capabilities Development Command Soldier Center, Orlando, FL, USA

    Gino Fragomeni

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Subudhi, D., Balaji, P., Muniyandi, M. (2022). Objective Quantification of Circular Vection in Immersive Environments. In: Chen, J.Y.C., Fragomeni, G. (eds) Virtual, Augmented and Mixed Reality: Design and Development. HCII 2022. Lecture Notes in Computer Science, vol 13317. Springer, Cham. https://doi.org/10.1007/978-3-031-05939-1_17

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