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. Single samples were parsed through Long Short-Term Memory (LSTM) neural network model using Python. To determine minimal simulation times, multiple data splits for training were trialled (10/20/30/40%). Scanning electron microscopy (SEM) was performed for material surface evaluation. RESULTS: 3D printed tooth material (G5) had the lowest wear resistance (59 ± 35.71 µm) whereas conventional PMMA with microfillers (G3) shown the highest wear rate (303 ± 0.06 µm) after 48 months of simulation. The LSTM model successfully predicted up to 48 months wear using 30% of the collected data. Compared with the actual data, the model had a root-mean-square error range between 6.23 and 88.56 µm, mean-absolute-percentage-error 12.43-23.02% and mean-absolute-error 7.47-70.71 µm. SEM images revealed additional plastic deformations and chipping of materials, that may have introduced data artifacts. CONCLUSIONS: 3D printed denture teeth materials showed the lowest wear out of all studied for 48 months simulation. LSTM model was successfully developed to predict wear of various denture teeth. The developed LSTM model has the potential to reduce simulation duration and specimen number for wear testing of various dental materials, while potentially improving the accuracy and reliability of wear testing predictions. This work paves the way for generalized multi-sample models enhanced with empirical information.

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. 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.

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. 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.

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. METHODS: Healthy human male subjects (n = 27) underwent constant whole-body yaw rotation with eyes open and closed in the clockwise (CW) and anticlockwise (ACW) directions, at 90°/s for two minutes. Subjects also viewed the same spinning environments on video in a VR headset. RESULTS: CW spinning significantly decreased parasympathetic vagal activity in all conditions (CW open p = 0.0048, CW closed p = 0.0151, CW VR p = 0.0019,), but not ACW spinning (ACW open p = 0.2068, ACW closed p = 0.7755, ACW VR p = 0.1775,) as indicated by an HRV metric, the root mean square of successive RR interval differences (RMSSD). There were no direction-dependent effects of constant spinning on sympathetic activity inferred through the HRV metrics, stress index (SI), sympathetic nervous system index (SNS index) and pupil diameter. Neuroplasticity in the CW eyes closed and CW VR conditions post stimulation was observed. CONCLUSIONS: Only one direction of yaw spinning, and visual flow caused vagal nerve neuromodulation and neuroplasticity, resulting in an inhibition of parasympathetic activity on the heart, to the same extent in either vestibular or visual stimulation. These results indicate that visual flow in VR can be used as a non-electrical method for vagus nerve inhibition without the need for body motion in the treatment of disorders with vagal overactivity. The findings are also important for VR and spinning chair based autonomic nervous system modulation protocols, and the effects of motion integrated VR.