A computational model of motion sickness dynamics during passive self-motion in the dark.

Predicting the time course of motion sickness symptoms enables the evaluation of provocative stimuli and the development of countermeasures for reducing symptom severity. In pursuit of this goal, we present an Observer-driven model of motion sickness for passive motions in the dark. Constructed in two stages, this model predicts motion sickness symptoms by bridging sensory conflict (i.e., differences between actual and expected sensory signals) arising from the Observer model of spatial orientation perception (stage 1) to Oman's model of motion sickness symptom dynamics (stage 2; presented in 1982 and 1990) through a proposed "Normalized Innovation Squared" statistic. The model outputs the expected temporal development of human motion sickness symptom magnitudes (mapped to the Misery Scale) at a population level, due to arbitrary, 6-degree-of-freedom, self-motion stimuli. We trained model parameters using individual subject responses collected during fore-aft translations and off-vertical axis of rotation motions. Improving on prior efforts, we only used datasets with experimental conditions congruent with the perceptual stage (i.e., adequately provided passive motions without visual cues) to inform the model. We assessed model performance by predicting an unseen validation dataset, producing a Q2 value of 0.91. Demonstrating this model's broad applicability, we formulate predictions for a host of stimuli, including translations, earth-vertical rotations, and altered gravity, and we provide our implementation for other users. Finally, to guide future research efforts, we suggest how to rigorously advance this model (e.g., incorporating visual cues, active motion, responses to motion of different frequency, etc.).

Effects of passenger body movements in a visual task during and after vehicle rotation on post-rotatory illusion and motion sickness.

We explored how body movements influence illusory body motion intensity and their association with motion sickness. Twelve individuals who were seated in the back of a passenger car, performed a visual task and were subjected to continuous rotations followed by driving in a straight line. The body movements during and immediately after rotation were categorized as follows: (A) upright posture; (B) leaning the body in the yaw direction towards the rotation center, returning the yaw angle to zero upon transitioning to straight line travel, and tilting in the roll condition and gradually returning to upright; and (C) tilting in roll conditions towards the centripetal direction during rotation and becoming upright upon transitioning to straight line travel. In experiment-1, after spanning half a lap, participants reported the intensity of the illusory motion experienced during straight line travel immediately after rotation. In experiment-2, after travelling up to eight laps, the participants reported the symptom level of motion sickness experienced during two straight sections per lap using the MIsery SCale (MISC). Experiment-1 revealed that condition (C) had significantly larger illusions than Conditions (A) and (B). Experiment-2 revealed that motion sickness progressed significantly more in Condition (C) than in Condition (A). A significant positive correlation was found between the observed MISC and the illusion strength. Our findings suggest that body movements during and immediately after continuous rotation have a significant impact on the illusion strength. Additionally, illusory motion could serve as an indicator of impending motion sickness.

Visual vertigo and motion sickness is different between persistent postural-perceptual dizziness and vestibular migraine.

INTRODUCTION: Persistent postural-perceptual dizziness (PPPD) and vestibular migraine (VM) share symptoms of visual vertigo and motion sickness that can be confusing for clinicians to distinguish. We compare the severity of these symptoms and dynamic subjective visual vertical (dSVV) in these two common vestibular conditions. METHOD: Twenty-nine patients with PPPD, 37 with VM, and 29 controls were surveyed for subjective symptoms using the visual vertigo analogue scale (VVAS) and motion sickness susceptibility questionnaire during childhood (MSA) and the past 10 years (MSB). dSVV is a measure of visual dependence measures perception of verticality against a rotating background (5 deg./s). RESULTS: VVAS revealed contextual differences for dizziness between those with PPPD and VM. Ratings of visual vertigo were most severe in PPPD, less in VM, and mild in controls (VVAS PPPD 27.1, VM 11.2, control 4.6, p < 0.001). MSA was more severe in VM than in PPPD or control (12.8 vs 7.6 vs 8.5, p = 0.01). MSB was more severe in VM than controls (MSB score 12.9 VS 8.1 p = 0.009) but was not different than PPPD (MSB score 10.0, p = 0.10). dSVV alignment was similar among the three groups (p = 0.83). Both VM and PPPD groups had greater simulator sickness than controls after completing the dSVV. CONCLUSIONS: Patients with PPPD report more visual vertigo than those with VM, but a history of motion sickness as a child is more common in VM. Additionally, the environmental context that induces visual vertigo is different between PPPD and VM.

Classification of Visually Induced Motion Sickness Based on Phase-Locked Value Functional Connectivity Matrix and CNN-LSTM.

To effectively detect motion sickness induced by virtual reality environments, we developed a classification model specifically designed for visually induced motion sickness, employing a phase-locked value (PLV) functional connectivity matrix and a CNN-LSTM architecture. This model addresses the shortcomings of traditional machine learning algorithms, particularly their limited capability in handling nonlinear data. We constructed PLV-based functional connectivity matrices and network topology maps across six different frequency bands using EEG data from 25 participants. Our analysis indicated that visually induced motion sickness significantly alters the synchronization patterns in the EEG, especially affecting the frontal and temporal lobes. The functional connectivity matrix served as the input for our CNN-LSTM model, which was used to classify states of visually induced motion sickness. The model demonstrated superior performance over other methods, achieving the highest classification accuracy in the gamma frequency band. Specifically, it reached a maximum average accuracy of 99.56% in binary classification and 86.94% in ternary classification. These results underscore the model's enhanced classification effectiveness and stability, making it a valuable tool for aiding in the diagnosis of motion sickness.

Feature extraction method of EEG based on wavelet packet reconstruction and deep learning model of VR motion sickness feature classification and prediction.

The surging popularity of virtual reality (VR) technology raises concerns about VR-induced motion sickness, linked to discomfort and nausea in simulated environments. Our method involves in-depth analysis of EEG data and user feedback to train a sophisticated deep learning model, utilizing an enhanced GRU network for identifying motion sickness patterns. Following comprehensive data pre-processing and feature engineering to ensure input accuracy, a deep learning model is trained using supervised and unsupervised techniques for classifying and predicting motion sickness severity. Rigorous training and validation procedures confirm the model's robustness across diverse scenarios. Research results affirm our deep learning model's 84.9% accuracy in classifying and predicting VR-induced motion sickness, surpassing existing models. This information is vital for improving the VR experience and advancing VR technology.

EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation.

BACKGROUND: Virtual reality motion sickness (VRMS) is a key issue hindering the development of virtual reality technology, and accurate detection of its occurrence is the first prerequisite for solving the issue. OBJECTIVE: In this paper, a convolutional neural network (CNN) EEG detection model based on multi-scale feature correlation is proposed for detecting VRMS. METHODS: The model uses multi-scale 1D convolutional layers to extract multi-scale temporal features from the multi-lead EEG data, and then calculates the feature correlations of the extracted multi-scale features among all the leads to form the feature adjacent matrixes, which converts the time-domain features to correlation-based brain network features, thus strengthen the feature representation. Finally, the correlation features of each layer are fused. The fused features are then fed into the channel attention module to filter the channels and classify them using a fully connected network. Finally, we recruit subjects to experience 6 different modes of virtual roller coaster scenes, and collect resting EEG data before and after the task to verify the model. RESULTS: The results show that the accuracy, precision, recall and F1-score of this model for the recognition of VRMS are 98.66 %, 98.65 %, 98.68 %, and 98.66 %, respectively. The proposed model outperforms the current classic and advanced EEG recognition models. SIGNIFICANCE: It shows that this model can be used for the recognition of VRMS based on the resting state EEG.

Data augmentation for generating synthetic electrogastrogram time series.

To address an emerging need for large number of diverse datasets for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram time series. We used electrogastrography (EGG) data from an open database to set model parameters and statistical tests to evaluate synthesized data. Additionally, we illustrated method customization for generating artificial EGG time series alterations caused by the simulator sickness. Proposed data augmentation method generates synthetic EGG data with specified duration, sampling frequency, recording state (postprandial or fasting state), overall noise and breathing artifact injection, and pauses in the gastric rhythm (arrhythmia occurrence) with statistically significant difference between postprandial and fasting states in > 70% cases while not accounting for individual differences. Features obtained from the synthetic EGG signal resembling simulator sickness occurrence displayed expected trends. The code for generation of synthetic EGG time series is not only freely available and can be further customized to assess signal processing algorithms but also may be used to increase data diversity for training artificial intelligence (AI) algorithms. The proposed approach is customized for EGG data synthesis but can be easily utilized for other biosignals with similar nature such as electroencephalogram.

Detection of virtual reality motion sickness based on EEG using asymmetry of entropy and cross-frequency coupling.

The existence of Virtual Reality Motion Sickness (VRMS) is a key factor restricting the further development of the VR industry, and the premise to solve this problem is to be able to accurately and effectively detect its occurrence. In view of the current lack of high-accuracy and effective detection methods, this paper proposes a VRMS detection method based on entropy asymmetry and cross-frequency coupling value asymmetry of EEG. First of all, the EEG of the four selected pairs of electrodes on the bilateral brain are subjected to Multivariate Variational Mode Decomposition (MVMD) respectively, and three types of entropy values on the low-frequency and high-frequency components are calculated, namely approximate entropy, fuzzy entropy and permutation entropy, as well as three types of phase-amplitude coupling features between the low-frequency and high-frequency components, namely the mean value, standard deviation and correlation coefficient; Secondly, the difference of the entropies and the cross-frequency coupling features between the left electrodes and the right electrodes are calculated; Finally, the final feature set are selected via t-test and fed into the SVM for classification, thus realizing the automatic detection of VRMS. The results show that the three classification indexes under this method, i.e., accuracy, sensitivity and specificity, reach 99.5 %, 99.3 % and 99.7 %, respectively, and the value of the area under the ROC curve reached 1, which proves that this method can be an effective indicator for detecting the occurrence of VRMS.

Concussion can increase the risk of visually induced motion sickness.

Concussion can lead to various symptoms such as balance problems, memory impairments, dizziness, and/or headaches. It has been previously suggested that during self-motion relevant tasks, individuals with concussion may rely heavily on visual information to compensate for potentially less reliable vestibular inputs and/or problems with multisensory integration. As such, concussed individuals may also be more sensitive to other visually-driven sensations such as visually induced motion sickness (VIMS). To investigate whether concussed individuals are at elevated risk of experiencing VIMS, we exposed participants with concussion (n = 16) and healthy controls (n = 15) to a virtual scene depicting visual self-motion down a grocery store aisle at different speeds. Participants with concussion were further separated into symptomatic and asymptomatic groups. VIMS was measured with the SSQ before and after stimulus exposure, and visual dependence, self-reported dizziness, and somatization were recorded at baseline. Results showed that concussed participants who were symptomatic demonstrated significantly higher SSQ scores after stimulus presentation compared to healthy controls and those who were asymptomatic. Visual dependence was positively correlated with the level of VIMS in healthy controls and participants with concussion. Our results suggest that the presence of concussion symptoms at time of testing significantly increased the risk and severity of VIMS. This finding is of relevance with regards to the use of visual display devices such as Virtual Reality applications in the assessment and rehabilitation of individuals with concussion.

Neuro-ophthalmic and Neuro-otologic Evaluation in Individuals with Motion Sickness Susceptibility.

Since the physiological background of motion sickness is not entirely clear, it was aimed to examine the physiological differences in groups consisting of individuals susceptible and non-susceptible to motion sickness. Sixty subjects [motion sickness (MS) group: 33 female, 3 male; 28.8 ± 8.1 years; control group: 19 female, 5 male; 24.5 ± 4.3 years] were included in the study. Near visual acuity test on the treadmill in the presence of visual stimulation, pattern visual-evoked potentials, oculomotor tests, and computerized dynamic posturography were applied. Receiver operating characteristic analysis was performed to determine the parameter that provides the excellent discrimination between the groups. The most effective parameter in differentiating the study groups was determined as dynamic visual acuity with 77.8% sensitivity and 95.8% specificity. Significant differences were found in the vestibular (mean ± standard deviation: 0.63 ± 0.17), visual (0.77 ± 0.18), and composite scores (73.11 ± 11.89) of the patients (P=.000) in posturographic evaluation. In the visual-evoked potential examination, a significant decrease was found in the amplitude values between the P100-N145 waves in the binocular (5.0 ± 2.8, P=.002), right eye (7.6 ± 3.2, P=.009) and left eye (7.9 ± 2.9, P=.016) in the symptomatic patients. In binocular oculomotor evaluation, directional asymmetric findings were obtained. It has been shown that the most effective test parameter that distinguishes the MS susceptible and non-susceptible individuals is the dynamic visual acuity value. Based on the results of neuro-physiological tests, it was suggested that a possible visual-vestibular integration disorder in individuals susceptible to motion sickness may affect visual and vestibular performance.

Alpha tACS on Parieto-Occipital Cortex Mitigates Motion Sickness Based on Multiple Physiological Observation.

Approximately one third of the population is prone to motion sickness (MS), which is associated with the dysfunction in the integration of sensory inputs. Transcranial alternating current stimulation (tACS) has been widely used to modulate neurological functions by affecting neural oscillation. However, it has not been applied in the treatment of motion sickness. This study aims to investigate changes in brain oscillations during exposure to MS stimuli and to further explore the potential impact of tACS with the corresponding frequency and site on MS symptoms. A total of 19 subjects were recruited to be exposed to Coriolis stimuli to complete an inducing session. After that, they were randomly assigned to tACS stimulation group or sham stimulation group to complete a stimulation session. Electroencephalography (EEG), electrocardiogram, and galvanic skin response were recorded during the experiment. All the subjects suffering from obvious MS symptoms after inducing session were observed that alpha power of four channels of parieto-occipital lobe significantly decreased (P7: t =3.589, p <0.001; P8: t =2.667, p <0.05; O1: t =3.556, p <0.001; O2: t =2.667, p <0.05). Based on this, tACS group received the tACS stimulation at 10Hz from Oz to CPz. Compared to sham group, tACS stimulation significantly improved behavioral performance and entrained the alpha oscillation in individuals whose alpha power decrease during the inducing session. The findings show that parieto-occipital alpha oscillation plays a critical role in the integration of sensory inputs, and alpha tACS on parieto-occipital can become a potential method to mitigate MS symptoms.

Galvanic vestibular stimulation: a novel modulatory countermeasure for vestibular-associated movement disorders / Estimulacao galvanica vestibular para corrigir transtornos neurologicos associados a disfuncao vestibular

Motion sickness or kinetosis is the result of the abnormal neural output originated by visual, proprioceptive and vestibular mismatch, which reverses once the dysfunctional sensory information becomes coherent. The space adaptation syndrome or space sickness relates to motion sickness; it is considered to be due to yaw, pith, and roll coordinates mismatch. Several behavioural and pharmacological measures have been proposed to control these vestibular-associated movement disorders with no success. Galvanic vestibular stimulation has the potential of up-regulating disturbed sensory-motor mismatch originated by kinetosis and space sickness by modulating the GABA-related ion channels neural transmission in the inner ear. It improves the signal-to-noise ratio of the afferent proprioceptive volleys, which would ultimately modulate the motor output restoring the disordered gait, balance and human locomotion due to kinetosis, as well as the spatial disorientation generated by gravity transition.

A cinetose ou doença do movimento resulta de uma resposta neural anormal originada do desequilíbrio entre estímulos visuais, proprioceptivos e vestibulares, que melhora quando esse desequilíbrio é corrigido. A síndrome de adaptação espacial ou doença do espaço está relacionada à doença do movimento e é desencadeada por mudanças bruscas de direção, inclinação e rotação da cabeça. Têm sido propostas várias medidas comportamentais e farmacológicas para controlar esses transtornos do movimento associados com o sistema vestibular, mas sem sucesso. A estimulação galvânica vestibular pode regular o desequilíbrio sensitivo-motor causado pela cinetose e pela doença do espaço modulando os canais iônicos GABA, relacionados à transmissão de impulsos nervosos no ouvido interno. Essa estimulação melhora a relação sinal-ruído dos impulsos proprioceptivos que acabam modulando a resposta motora, restabelecendo o equilíbrio e a marcha, recuperando a desorientação espacial causada pelos diversos gradientes de gravidade.

Incidentes fisiológicos durante entrenamiento en cámara hipobárica entre los años 1983-1992 / Physiological training in altitude chamber in the chilean airforce: years 1983-1992

En el presente trabajo, se ha analizado el Programa de Entrenamiento Fisiológico en el Departamento de Medicina Aeroespacial de la Dirección de Sanidad de la Fuerza Aérea de Chile, en lo que respecta a incidentes producidos durante el desarrollo de dicho programa y al que fueron sometidos tanto pilotos alumnos como instructores de vuelo de cámara hipobárica, durante los años 1983-1992. En este período se entrenó a 2.118 alumnos en vuelos de hipoxia y vuelos de descompresión rápida no registrándose ningún accidente, teniendo presente además, que los incidentes que se produjeron fueron sin consecuencias graves, tanto para alumnos como instructores. En el período analizado se produjeron un total de 274 incidentes en los alumnos correspondiendo a barotitis, barotitis tardía y barosinusitis, los porcentajes más altos. Asimismo, en los instructores el número total de incidentes fue de 56, siendo barotitis, barotitis tardía y barosinusitis los más frecuentes. Otros hechos importantes son la realización de 23 vuelos médicos y de 24 vuelos de prueba, todos sin consecuencia y con resultados altamente positivos. Cabe destacar que durante este período los incidentes han sido superados efectiva y oportunamente gracias a la preparación, capacidad y a las medidas de seguridad que en cada circunstancia específica se aplicaron