Machine learning methods for the study of cybersickness: a systematic review

Abstract

This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness.

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

“…Cybersickness is accompanied by a mix of unfavorable symptoms such as headache, nausea, dizziness, fatigue, oculomotor, and postural disturbances ( Kourtesis et al, 2023 ; Venkatakrishnan et al, 2023 ). An interesting overview of research on this problem using the machine learning approach was presented by Yang A. H. X. et al (2022) . ML can be used to detect them and is a step toward overcoming these adverse limitations of new technologies.…”

“…These methods have several key advantages over traditional statistical techniques, including learning from data and making predictions based on patterns and relationships present/hidden in the data ( Cristancho Cuervo et al, 2022 ). Consequently, ML methods in neuroscience and contemporary medical practice provide very effective support for accurate diagnosis ( Rosenfelder et al, 2023 ), assessment of beneficial or adverse effects of therapy and rehabilitation ( Yang A. H. X. et al, 2022 ), as well as natural aging using innovative IT/ICT technologies, which can effectively help maintain joy/satisfaction and good quality of life for the elderly ( Yousefi Babadi and Daneshmandi, 2021 ).…”

“…Various virtual protocols are being proposed and tested, showing comparable or even greater benefits of VR training compared to traditional physical training. An important thread related to the development of novel technologies is the computational approach for evaluating the usability and effectiveness of various systems offered via VR ( Cavedoni et al, 2020 ; Yang A. H. X. et al, 2022 ; Nieto et al, 2024 ; Veneziani et al, 2024 ; Wen et al, 2024 ). In clinical applications, machine learning algorithms are proving to be very useful ( Bao et al, 2019 ; Eichler et al, 2022 ).…”

Metaverse: Trend, emerging themes, and future directions

“…Among the 1000 plus documents about ("metaverse" OR "metaverses") published on or before 2022, 68 of them used a systematic review (or systematic literature review) approach to explore different aspects of metaverse. For example, researchers studied the use of metaverse in education and training, [34][35][36] commerce in virtual worlds, 37 ethical marketing in metaverse, 38 cybersickness induced by VR and metaverse, 39 and the use of metaverse in healthcare. 40,41 Yet there was scant literature on the bibliometric study of metaverse except the publications by Abbate et al, 42 Feng et al, 43 Liu et al, 44 and Trunfio and Rossi.…”

Prediction and Detection of Virtual Reality induced Cybersickness: A Spiking Neural Network Approach Using Spatiotemporal EEG Brain Data and Heart Rate Variability

“…Not only do these methods not allow for future prediction, but they are time ine cient and require manual input. With current technology at our disposal, objective biomarkers correlated with cybersickness can be collected from wearable devices and fed into machine learning algorithms for streamlined, automatic prediction and/or detection of cybersickness events [4]. The present study developed a machine learning algorithm that can detect CS events (75.0%) and predict it prior to VR usage at resting baseline (77.5%) using integrated electrocardiogram (ECG) and electroencephalogram (EEG) data.…”