VR Vertigo Level Classification Using a Multi-Dimensional Taylor Network Approach
Users often experience motion sickness symptoms after using Virtual Reality (VR) systems, which can jeopardize their health. If we can rapidly diagnose the vertigo levels of VR users and dynamically take anti-motion sickness measures based on the levels, such as adjusting the movement of objects in...
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IEEE
2023-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10265256/ |
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| author | Ziyan Wang Ying Yan Jun Cai Chengcheng Hua Na Liu Qi Chen Ming Li Danxu Zhang |
| author_facet | Ziyan Wang Ying Yan Jun Cai Chengcheng Hua Na Liu Qi Chen Ming Li Danxu Zhang |
| author_sort | Ziyan Wang |
| collection | DOAJ |
| description | Users often experience motion sickness symptoms after using Virtual Reality (VR) systems, which can jeopardize their health. If we can rapidly diagnose the vertigo levels of VR users and dynamically take anti-motion sickness measures based on the levels, such as adjusting the movement of objects in VR glasses, adding visual reference points, and changing image brightness, contrast, and refresh rate, we can quickly alleviate motion sickness symptoms. Therefore, rapid assessment of the VR vertigo levels becomes crucial. Deep learning methods can accurately diagnose vertigo levels, but due to the complex structure and high computational requirements of these deep models, they may not fully meet the need for speed. Additionally, these complex models may be challenging to implement in devices like VR glasses. Unlike deep neural networks, BP-MTN represents complex nonlinear functions as polynomial functions using a simple network structure based solely on addition and multiplication operations. This design significantly reduces model complexity. However, traditional MTN models are primarily used for prediction tasks and are not suitable for classification. To address this issue, this paper proposes a Backpropagation Multivariate Taylor Network (BP-MTN) classifier for diagnosing VR vertigo levels. Compared to the traditional MTN, the BP-MTN includes the following modifications: 1) adding fully connected layers to handle inconsistent input and output dimensions of the BP-MTN; 2) introducing a softmax layer after the traditional MTN’s output layer to enable classification; 3) incorporating activation functions after each output node to enhance the model’s ability for fitting nonlinearity. Experimental results demonstrate that the BP-MTN classifier achieves higher classification accuracy and faster speed compared to some deep learning models. |
| format | Article |
| id | doaj-art-9b405d378f144a889e4d236f2f89b4a6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-9b405d378f144a889e4d236f2f89b4a62025-08-20T03:33:34ZengIEEEIEEE Access2169-35362023-01-011110894410895510.1109/ACCESS.2023.332004310265256VR Vertigo Level Classification Using a Multi-Dimensional Taylor Network ApproachZiyan Wang0Ying Yan1https://orcid.org/0000-0002-3609-0496Jun Cai2Chengcheng Hua3Na Liu4Qi Chen5Ming Li6Danxu Zhang7https://orcid.org/0000-0002-9317-846XC-MEIC, ICAEET, School of Automation, Nanjing University of Information Science and Technology, Nanjing, ChinaC-MEIC, ICAEET, School of Automation, Nanjing University of Information Science and Technology, Nanjing, ChinaC-MEIC, ICAEET, School of Automation, Nanjing University of Information Science and Technology, Nanjing, ChinaC-MEIC, ICAEET, School of Automation, Nanjing University of Information Science and Technology, Nanjing, ChinaDepartment of Respiratory Medicine, Nanjing Chest Hospital, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaShanghai Institute of Spaceflight Control Technology, Shanghai, ChinaSchool of Mechanical and Electric Engineering, Suzhou University, Suzhou, ChinaMCE Team, GE Digital, Bothell, WA, USAUsers often experience motion sickness symptoms after using Virtual Reality (VR) systems, which can jeopardize their health. If we can rapidly diagnose the vertigo levels of VR users and dynamically take anti-motion sickness measures based on the levels, such as adjusting the movement of objects in VR glasses, adding visual reference points, and changing image brightness, contrast, and refresh rate, we can quickly alleviate motion sickness symptoms. Therefore, rapid assessment of the VR vertigo levels becomes crucial. Deep learning methods can accurately diagnose vertigo levels, but due to the complex structure and high computational requirements of these deep models, they may not fully meet the need for speed. Additionally, these complex models may be challenging to implement in devices like VR glasses. Unlike deep neural networks, BP-MTN represents complex nonlinear functions as polynomial functions using a simple network structure based solely on addition and multiplication operations. This design significantly reduces model complexity. However, traditional MTN models are primarily used for prediction tasks and are not suitable for classification. To address this issue, this paper proposes a Backpropagation Multivariate Taylor Network (BP-MTN) classifier for diagnosing VR vertigo levels. Compared to the traditional MTN, the BP-MTN includes the following modifications: 1) adding fully connected layers to handle inconsistent input and output dimensions of the BP-MTN; 2) introducing a softmax layer after the traditional MTN’s output layer to enable classification; 3) incorporating activation functions after each output node to enhance the model’s ability for fitting nonlinearity. Experimental results demonstrate that the BP-MTN classifier achieves higher classification accuracy and faster speed compared to some deep learning models.https://ieeexplore.ieee.org/document/10265256/Multidimensional Taylor networkvirtual realityvertigoclassificationEEG |
| spellingShingle | Ziyan Wang Ying Yan Jun Cai Chengcheng Hua Na Liu Qi Chen Ming Li Danxu Zhang VR Vertigo Level Classification Using a Multi-Dimensional Taylor Network Approach IEEE Access Multidimensional Taylor network virtual reality vertigo classification EEG |
| title | VR Vertigo Level Classification Using a Multi-Dimensional Taylor Network Approach |
| title_full | VR Vertigo Level Classification Using a Multi-Dimensional Taylor Network Approach |
| title_fullStr | VR Vertigo Level Classification Using a Multi-Dimensional Taylor Network Approach |
| title_full_unstemmed | VR Vertigo Level Classification Using a Multi-Dimensional Taylor Network Approach |
| title_short | VR Vertigo Level Classification Using a Multi-Dimensional Taylor Network Approach |
| title_sort | vr vertigo level classification using a multi dimensional taylor network approach |
| topic | Multidimensional Taylor network virtual reality vertigo classification EEG |
| url | https://ieeexplore.ieee.org/document/10265256/ |
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