Evaluation of visual-induced motion sickness from head-mounted display using heartbeat evoked potential: a cognitive load-focused approach

Author:

Park Sangin,Kim Laehyun,Kwon Jangho,Choi Soo Ji,Whang MincheolORCID

Abstract

AbstractBased on sensory conflict theory, motion sickness is strongly related to the information processing capacity or resources of the brain to cope with the multi-sensory stimuli experienced by watching virtual reality (VR) content. The purpose of this research was to develop a method of measuring motion sickness using the heart-evoked potential (HEP) phenomenon and propose new indicators for evaluating motion sickness. Twenty-eight undergraduate volunteers of both genders (14 females) participated in this study by watching VR content on both 2D and head-mounted devices (HMD) for 15 min. The responses of HEP measures such as alpha power, latency, and amplitude of first and second HEP components were compared using paired t-tests and ANCOVA. This study confirmed that motion sickness leads to a decline in cognitive processing, as demonstrated by increasing in alpha power of HEP. Also, the proposed indicators such as latency and amplitude of the HEP waveform showed significant differences during the experience of motion sickness and exhibited high correlations with alpha power measures. Latencies of the first HEP component, in particular, are recommended as better quantitative evaluators of motion sickness than other measures, following the multitrait-multimethod matrix. The proposed model for motion sickness was implemented in a support vector machine with a radial basis function kernel, and validated on twenty new participants. The accuracy, F1 score, precision, recall, and area under the curve (AUC) of the motion-sickness classification results were 0.875, 0.865, 0.941, 0.8, and 0.962, respectively.

Funder

Institute of Information & communications Technology Planning & Evaluation

Electronics and Telecommunications Research Institute

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Human-Computer Interaction,Software

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Compressive sensing applied to SSVEP-based brain–computer interface in the cloud for online control of a virtual wheelchair;Biomedical Signal Processing and Control;2024-03

2. Artificial Intelligence-based Motion Sickness Detection: A Survey;2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS);2023-12-04

3. Assessment of Virtual Reality Motion Sickness Severity Based on EEG via LSTM/BiLSTM;IEEE Sensors Journal;2023-10-15

4. When XR and AI Meet - A Scoping Review on Extended Reality and Artificial Intelligence;Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems;2023-04-19

5. Brain activity during cybersickness: a scoping review;Virtual Reality;2023-04-12

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