Prediction and detection of virtual reality induced cybersickness: a spiking neural network approach using spatiotemporal EEG brain data and heart rate variability.

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.

Development of predictive algorithms for the wear resistance of denture teeth materials.

Objectives: To investigate the wear resistance of conventional, CAD-milled and 3D-printed denture teeth in vitro with simulated aging. To use the collected data to train single time series sample model LSTM and provide proof of concept.

Methods: Six denture teeth materials (Three Conventional; Double-cross linked PMMA (G1), Nanohybrid composite (G2), PMMA with microfillers (G3), CAD-milled (G4), two 3D-printed teeth (G5, G6) (Total n = 60) underwent simulation for 24 and 48 months of linear reciprocating wear using a universal testing machine (UFW200, NeoPlus) under 49 N load, 1 Hz and linear stroke of 2 mm in an artificial saliva medium.

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

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.

Directional effects of whole-body spinning and visual flow in virtual reality on vagal neuromodulation.

Background: Neural circuits allow whole-body yaw rotation to modulate vagal parasympathetic activity, which alters beat-to-beat variation in heart rate. The overall output of spinning direction, as well as vestibular-visual interactions on vagal activity still needs to be investigated.

Objective: This study investigated direction-dependent effects of visual and natural vestibular stimulation on two autonomic responses: heart rate variability (HRV) and pupil diameter.