Optics and Precision Engineering, Volume. 32, Issue 4, 595(2024)

Spatiotemporal multi-feature evaluation of visually induced motion sickness in virtual reality

Qifeng DONG1... Mei YU1,*, Zhidi JIANG2, Ziang LU1 and Gangyi JIANG1 |Show fewer author(s)
Author Affiliations
  • 1Faculty of Information Science and Engineering, Ningbo University, Ningbo352, China
  • 2College of Information Engineering, College of Science and Technology Ningbo University, Ningbo3151, China
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    Figures & Tables(11)
    Assessment model of visually induced motion sickness
    Foreground-background weighted motion feature extraction
    Relationship between pre-background weighted motion features and DMOS of partial videos in SPVCD
    Comparison of SI of similar videos in SPVCD
    Frame level motion feature value through sliding averaging processing
    Comparison of motion mutation feature in videos with similar Mk
    Sample videos in SPVCD database
    • Table 1. Performance indicators of different feature sets when tested on SPVCD

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      Table 1. Performance indicators of different feature sets when tested on SPVCD

      特征PLCCSROCCRMSE
      所有特征0.8210.7900.486
      不含运动特征0.6720.5990.666
      不含视差特征0.8030.7170.537
      不含空间特征0.7110.6170.640
      不含突变特征0.7960.7200.551
      非加权运动特征0.7780.6970.571
    • Table 2. Performance comparison under different statistical methods

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      Table 2. Performance comparison under different statistical methods

      方法统计方式PLCCSROCCRMSE
      Lu19Median RMSE0.8160.8750.740
      Median SROCC0.7960.8460.809
      Average0.8100.8370.763
      ProposedMedian RMSE0.8210.7900.486
      Median SROCC0.8250.7620.499
      Average0.8400.7510.489
    • Table 3. Overall and comparative performance indicators of the proposed model on Stanford database

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      Table 3. Overall and comparative performance indicators of the proposed model on Stanford database

      方法PLCCSROCCRMSE
      Lu190.6060.6140.713
      Proposed0.6780.6330.672
    • Table 4. Summary of results the proposed method and other model

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      Table 4. Summary of results the proposed method and other model

      方法所用数据库

      是否包含

      生理信号

      视频

      个数

      是否

      立体

      PLCCSROCCRMSE
      H. Oh3CYRE database52非立体0.6020.606/
      J. Kim7VR sickness database36非立体0.7240.710/
      J. Kim8CYRE database52非立体0.8620.861/
      W. Kim9VR-SP database100非立体0.7890.721/
      S. Lee10VRSA-DB-FR20非立体0.8300.8197.341
      S. Lee11

      VRSA-DB-shaking

      VRSA-DB-FR

      20

      20

      非立体

      非立体

      0.751

      0.801

      0.679

      0.671

      25.373

      22.937

      H. G. Kim12VR video database9非立体0.8850.88210.251
      S. Kim13VRSA-DB-FR20非立体0.8040.69827.406
      H. G. Kim14VRPS-DB-FR80非立体0.8310.8127.112
      S. Lee15

      VRSA-DB-shaking

      VRSA-DB-FR

      20

      20

      非立体

      非立体

      0.767

      0.837

      0.706

      0.719

      25.946

      20.350

      Lu19SPVCD116立体0.8160.8750.740
      ProposedSPVCD116立体0.8210.7900.486
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    Qifeng DONG, Mei YU, Zhidi JIANG, Ziang LU, Gangyi JIANG. Spatiotemporal multi-feature evaluation of visually induced motion sickness in virtual reality[J]. Optics and Precision Engineering, 2024, 32(4): 595

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    Paper Information

    Category:

    Received: Sep. 2, 2023

    Accepted: --

    Published Online: Apr. 2, 2024

    The Author Email: YU Mei (yumei@nbu.edu.cn)

    DOI:10.37188/OPE.20243204.0595

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