Toward Accurate Cybersickness Prediction in Virtual Reality: A Multimodal Physiological Modeling Approach.

Yang Long, Tieyan Wang, Xiaoliang Liu, Yujiang Li, Da Tao

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yang LongInstitute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.

Tieyan WangInstitute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.

Xiaoliang LiuInstitute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.

Yujiang LiInstitute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.

Da TaoInstitute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0003-2319-359X

Funding

Foundation of Shenzhen Science and Technology Innovation Committee JCYJ20230808105219038National Natural Science Foundation of China 32271130Natural Science Foundation of Guangdong Province of China 2023A1515012843Natural Science Foundation of Guangdong Province of China 2024B1515020007

6 · The paper itself

Abstract

Cybersickness poses a significant challenge to the widespread adoption of virtual reality (VR), as it impairs user experience and operational performance. This study proposes a physiological modeling approach to objectively assess cybersickness severity during VR experience. An interactive VR experiment was conducted, inducing varying levels of cybersickness through VR navigation tasks under different field-of-view and graphic quality settings. Physiological signals (i.e., electrodermal activity (EDA) and electrocardiogram (ECG)) were continuously recorded and extracted to build multiple machine learning regression models for cybersickness prediction. The results showed that EDA-based models consistently outperformed ECG-based models across all algorithms, with the Ensemble Learning model achieving the highest predictive accuracy (R

Indexed as

Virtual RealityAdultAlgorithmsElectrocardiographyFemaleGalvanic Skin ResponseHumansMachine LearningMaleYoung Adultcybersicknessmachine learningmultimodal modelingphysiological measuresvirtual reality

Identifiers

PMID41013063

DOI10.3390/s25185828

PMCPMC12473799

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.