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Support data for "Human Factors Related to Cybersickness Tolerance in Virtual Environment"

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posted on 2022-07-21, 00:57 authored by Adrian Ka Tsun Ng, Cheryl H. Y. Leung, Leith Kin Yip ChanLeith Kin Yip Chan, Henry Ying Kei Lau

  

The uploaded data contains research data supporting “Human factors related to cybersickness tolerance in virtual environment”.

We used the Richards-Campbell Sleep Questionnaire (RCSQ) to measure the perceived sleep quality of participants. In the current study, participants were asked to rate their perceived sleep quality for their previous night's sleep. Cybersickness tolerance is defined by the duration (in seconds) participant stayed in the VE until they reported feeling any sickness symptoms, however mild. The Simulator Sickness Questionnaire (SSQ) was used to measure participants' severity of cybersickness symptoms. The total severity score and subscores were computed in the recommended manner. We used correlation to test between these factors.

Then we analysed participants' pain sensitivity, we used a digital algometer to measure pressure pain threshold. Pressure were applied with the digital algometer gently at a rate of 1kg/cm^2/s on participants’ trapezius muscle on the shoulder (mid-point between the spinous process C7 and the acromion). Participants were instructed to notify the experimenter if, at any time during the measurement, they felt a noticeably unpleasant pain sensation. We assessed pressure pain threshold two times successively with 10 s break while participants seated on a chair, and take the average value for analysis. Participants were classified post-hoc as having low pain threshold (lower than 1 SD) and high pain threshold, with the cut-off level of 2.15. We used t-test to test between the two groups with other cybersickness measures. 

The Motion Sickness Susceptibility Questionnaire Short version (MSSQ-Short) was used to measure participants' cybersickness susceptibility. The child and adult subscores represented susceptibility based on the experiences at the corresponding period of life.

We also examine participants’ spatial ability. Mental rotation tasks based on the Vandenberg and Kuse Mental Rotation Test were formulated using 3D figures in the Library of Shepard and Metzler-type Mental Rotation Stimuli. The 12-question test was carried out by paper and pen with a time limit of 5 min. In each question, a reference stimulus was positioned on top of four potential matching blocks, which were positioned in various orientations and labelled as option “A”, “B”, “C”, and “D” respectively. Participants were asked to orient mental representations of the stimuli for dynamic comparisons, then choose two figures that shared the same configuration with the reference stimuli. All stimuli were in rotation around the horizontal axis and presented in an identical white frame against a white background. A practice trial with feedback was provided before the timed test in order to ensure participants' full understanding. The practice trial comprised 3 questions with no time limit. Participants’ responses were scored by the experimenter. One score was awarded when both choices are correct, which can accumulate to a maximum of 12 marks.

Lastly, we used a balance board to measure participants' instantaneous postural stability. Participants stood on the balance board with their eyes closed for 1 min. They were instructed to stand still with their hands at their sides and look forward, and refrain from moving or speaking. The data from the balance board was computed into a time series centre of pressure (CoP) position data in anterior-posterior and medio-lateral axes. To ensure that participants reached a steady posture, postural data sampled in the first 10 s was removed. The positional variability of the CoP position was computed to evaluate the spatial magnitude of postural activity. In particular, the standard deviation (SD) of CoP positions of the two axes was used. The data were processed in MATLAB (Version R2021b) using custom code. We used correlation to test between MSSQ-Short, spatial ability, postural stability, and other cybersickness measures. 

Raw and pre-processed data are available upon request from adriang@connect.hku.hk.

Funding

Characterization of human performance in virtual environment using a multi-modal deep learning framework

University Grants Committee

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