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Modeling Online Adaptive Navigation in Virtual Environments Based on PID Control

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1964))

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Abstract

It is well known that locomotion-dominated navigation tasks may highly provoke cybersickness effects. Past research has proposed numerous approaches to tackle this issue based on offline considerations. In this work, a novel approach to mitigate cybersickness is presented based on online adaptive navigation. Considering the Proportional-Integral-Derivative (PID) control method, we proposed a mathematical model for online adaptive navigation parametrized with several parameters, taking as input the users’ electro-dermal activity (EDA), an efficient indicator to measure the cybersickness level, and providing as output adapted navigation accelerations. Therefore, minimizing the cybersickness level is regarded as an argument optimization problem: find the PID model parameters which can reduce the severity of cybersickness. User studies were organized to collect non-adapted navigation accelerations and the corresponding EDA signals. A deep neural network was then formulated to learn the correlation between EDA and navigation accelerations. The hyperparameters of the network were obtained through the Optuna open-source framework. To validate the performance of the optimized online adaptive navigation developed through the PID control, we performed an analysis in a simulated user study based on the pre-trained deep neural network. Results indicate a significant reduction of cybersickness in terms of EDA signal analysis and motion sickness dose value. This is a pioneering work which presented a systematic strategy for adaptive navigation settings from a theoretical point.

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Notes

  1. 1.

    https://www.empatica.com/research/e4/.

  2. 2.

    One pair of data includes longitudinal and rotational accelerations and the corresponding EDA signal from one user session; one pair can be regarded as one data sample.

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Authors and Affiliations

  1. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

    Yuyang Wang

  2. Arts et Metiers Institute of Technology, LISPEN, HESAM Université, Paris, France

    Jean-Rémy Chardonnet & Frédéric Merienne

Authors
  1. Yuyang Wang
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  2. Jean-Rémy Chardonnet
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  3. Frédéric Merienne
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Corresponding author

Correspondence to Yuyang Wang .

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Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Wang, Y., Chardonnet, JR., Merienne, F. (2024). Modeling Online Adaptive Navigation in Virtual Environments Based on PID Control. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1964. Springer, Singapore. https://doi.org/10.1007/978-981-99-8141-0_25

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  • DOI: https://doi.org/10.1007/978-981-99-8141-0_25

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