Search

Virtual Reality (VR) allows users to interact with 3D immersive environments and has the potential to be a key technology across many domain applications, including access to a future metaverse. Yet, consumer adoption of VR technology is limited by cybersickness (CS)—a debilitating sensation accompanied by a cluster of symptoms, including nausea, oculomotor issues and dizziness. A leading problem is the lack of automated objective tools to predict or detect CS in individuals, which can then be used for resistance training, timely warning systems or clinical intervention. This paper explores the spatiotemporal brain dynamics and heart rate variability involved in cybersickness and uses this information to both predict and detect CS episodes. The present study applies deep learning of EEG in a spiking neural network (SNN) architecture to predict CS prior to using VR (85.9%, F7) and detect it (76.6%, FP1, Cz). ECG-derived sympathetic heart rate variability (HRV) parameters can be used for both prediction (74.2%) and detection (72.6%) but at a lower accuracy than EEG. Multimodal data fusion of EEG and sympathetic HRV does not change this accuracy compared to ECG alone. The study found that Cz (premotor and supplementary motor cortex) and O2 (primary visual cortex) are key hubs in functionally connected networks associated with both CS events and susceptibility to CS. F7 is also suggested as a key area involved in integrating information and implementing responses to incongruent environments that induce cybersickness. Consequently, Cz, O2 and F7 are presented here as promising targets for intervention. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article presents the results of a study that examined students' ability to retain what they have learned in an anatomy course after thirty days via using various learning tools for twenty minutes. Fifty-two second-year medical students were randomly assigned to three learning tools: text-only, three-dimension visualisation in a two-dimensional screen (3DM), or mixed reality (MR). An anatomy test lasting for twenty minutes measuring spatial and nominal knowledge was taken immediately after the learning intervention and another thirty days later. Psychometric tests were also used to measure participants' memory, reasoning and concentration abilities. Additionally, electroencephalogram data was captured to measure the participants' awakeness during the learning session. Results of this study showed that the MR group performed poorly in the nominal questions compared to the other groups; however, the MR group demonstrated higher retention in both the nominal and spatial type information for at least a month compared to the other groups. Furthermore, participants in the 3DM and MR groups reported increased engagement. The results of this study suggest that three-dimensional visualiser tools are likely to enhance learning in anatomy education. However, the study itself has several limitations; some include limited sample size and various threats to internal validity. [ABSTRACT FROM AUTHOR]

Auricular vasomotor responses are considered to be signs of clinical conditions including migraine. The mechanisms of auricular vasomotor control are still debatable. This study aimed at investigating perivascular co-transmitters of vasomotor control in the auricle. Another aim was to provide three-dimensional arterial maps of the auricle, as a proxy of periarterial autonomic innervation. Twelve paired human auricles were used to visualize the arteries following Spalteholz clearing and μ-CT-based reconstruction. Perivascular innervation staining was conducted using anti-tyrosine hydroxylase (TH), anti-neuropeptide Y (NPY), anti-vasoactive intestinal peptide (VIP) and anti-choline acetyl transferase (ChAT). The combined Spalteholz technique and μ-CT revealed a highly consistent arrangement of the auricular vasculature. The superficial temporal (STA) and posterior auricular artery (PAA) supply the helical rim arcade and arcade, with the STA mainly forming the superior and the PAA forming the middle and inferior auricular artery. Co-existence of sympathetic NPY+ and TH+ terminals mediating vasoconstriction, and VIP+ and ACh+ indicating cholinergic vasodilatation, was found in the perivascular zone. The presence of both sympathetic vasoconstriction and cholinergic co-innervation for active vasodilatation was shown in the perivascular auricular zone. Assuming that the highly-consistent vasculature gives way to these terminals, this periarterial innervation may be found spread out across the helix. [ABSTRACT FROM AUTHOR]