Emotional and Cognitive Modulation of Cybersickness: The Role of Pain Catastrophizing and Body Awareness

An examination of scene complexity's role in cybersickness

This study explored the effects of scene complexity factor on cybersickness. In this between-subjects experiment, 44 participants played the Pendulum Chair VR game, half with a simple scene and half with a complex scene. The complex scene featured higher optic flow (lower-level perceptual factor) and higher familiarity (higher level factor). Dependent variables were cybersickness and task performance. Results were unexpected in that cybersickness did not differ significantly between the simple and complex scenes. These results suggest that the impact of optic flow and familiarity on cybersickness may be affected by each other or other factors, making them unreliable predictors of cybersickness if considered alone. Both lower level and higher-level factors would benefit from further research to deduce the conditions under which they affect cybersickness. VR designers could consider that optic flow and familiarity alone are not reliable factors when predicting the cybersickness-inducing effects of a new environment.

Multimodal sentiment system and method based on CRNN-SVM

Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people’s sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people’s needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs.

Inward VR: Toward a Qualitative Method for Investigating Interoceptive Awareness in VR

Immersive virtual reality (VR) technologies can produce powerful illusions of being in another place or inhabiting another body, and theories of presence and embodiment provide valuable guidance to designers of VR applications that use these illusions to "take us elsewhere." However, an increasingly common design goal for VR experiences is to develop a deeper awareness of the internal landscape of one's own body (i.e., interoceptive awareness); here, design guidelines and evaluative techniques are less clear. To address this, we present a methodology, including a reusable codebook, for adapting the five dimensions of the Multidimensional Assessment of Interoceptive Awareness (MAIA) conceptual framework to explore interoceptive awareness in VR experiences via qualitative interviews. We report results from a first exploratory study (n=21) applying this method to understand the interoceptive experiences of users in a VR environment. The environment includes a guided body scan exercise with a motion-tracked avatar visible in a virtual mirror and an interactive visualization of a biometric signal detected via a heartbeat sensor. The results provide new insights on how this example VR experience might be refined to better support interoceptive awareness and how the methodology might continue to be refined for understanding other "inward-facing" VR experiences.

Legal Early Warning of Public Crisis in Network Public Opinion Events Based on Emotional Tendency

At present, China is in the period of social transformation, and social contradictions are gradually prominent. The research on NPO (network public opinion) emergency warning methods is gradually increasing. Some existing laws and regulations are abstracted and principled in content, lacking specific implementation rules and corresponding supporting measures, especially the legal rules of emergency administrative procedures. Therefore, the legal early warning model of NPO public crisis is based on emotional dimension content, NPO emotional characteristics, emotional dimension elements, and machine learning classification algorithm to construct text ET (emotional tendencies) classifier, which can be used to make ET judgment on text data. The results show that after PSO (particle swarm optimization) algorithm optimization, the precision, recall rate, and micro-average are significantly improved, and the precision is increased by nearly 14% and 80%. The conclusion shows that using PSO optimization parameters improves the classification effect of the classifier, and a better NPO crisis early warning model can be obtained.

Individual differences in the temporal progression of motion sickness and anxiety: the role of passengers' trait anxiety and motion sickness history

The objective of the study is to show that trait anxiety and motion sickness history are responsible for different temporal progressions of sickness in passengers. The level of inflight anxiety and inflight sickness severity was monitored for 124 passengers in a full-motion cabin simulator during a short-haul flight with four different flight segments. Four groups with different characteristics in trait anxiety and motion sickness susceptibility showed different profiles of inflight sickness development. High trait anxiety was responsible for high inflight anxiety and a constantly high level of motion sickness, while passengers with just a motion sickness history showed an increase in motion sickness severity over time. We suggest that trait anxiety and motion sickness susceptibility interact and have an impact on the temporal progression of inflight sickness severity. The analysis of temporal developments of anxiety and sickness are fruitful for understanding the origins of motion sickness, research and individual treatments. Practitioner summary: In a full-motion cabin simulator study with 124 passengers the level of inflight anxiety and inflight sickness severity was monitored. Trait anxiety and motion sickness history were found to have different impacts on the temporal progression of individual sickness severity. Abbreviations: ANOVA: analysis of variance; AVES: air vehicle simulator; hiA/hiM: group with high anxiety and high motion sickness susceptibility; hiA/loM: group with high anxiety and low motion sickness susceptibility;MSSQ: motion sickness susceptibility scale; loA/hiM: group with low anxiety and high motion sickness susceptibility; loA/loM: group with low anxiety and low motion sickness susceptibility; SPSS: statistical package for the social sciences; SSQ-TS: total score from the simulator sickness questionaire; STAI: state trait anxiety inventory.

Clinical predictors of cybersickness in virtual reality (VR) among highly stressed people

The use of virtual reality (VR) in the treatment of psychiatric disorders is increasing, and cybersickness has emerged as an important obstacle to overcome. However, the clinical factors affecting cybersickness are still not well understood. In this study, we investigated clinical predictors and adaptation effect of cybersickness during VR application in highly stressed people. Eighty-three healthy adult participants with high stress level were recruited. At baseline, we conducted psychiatric, ophthalmologic, and otologic evaluations and extracted physiological parameters. We divided the participants into two groups according to the order of exposure to VR videos with different degrees of shaking and repetitively administered the Simulator Sickness Questionnaire (SSQ) and the Fast Motion sickness Scale (FMS). There was no significant difference in changes in the SSQ or the FMS between groups. The 40–59 years age group showed a greater increase in FMS compared to the 19–39 years age group. Smoking was negatively associated with cybersickness, and a high Positive Affect and Negative Affect Schedule score was positively associated with cybersickness. In conclusion, changing the intensity of shaking in VR did not affect cybersickness. While smoking was a protective factor, more expression of affect was a risk factor for cybersickness.

Exploring the Participant-Related Determinants of Simulator Sickness in a Physical Motion Car Rollover Simulation as Measured by the Simulator Sickness Questionnaire

Physical motion driving simulators serve as a valuable research and training tool. Since many simulator participants suffer from simulator sickness (SS), we aimed to gain a better understanding of participant-related variables that may influence its incidence and severity. The study involved a 2-min mobile-platform car rollover simulation conducted in a group of 100 healthy adult participants. SS was measured with the Simulator Sickness Questionnaire immediately before and after the simulation. We investigated how the symptomatology of SS varies with gender, as well as with participants’ previous experiences such as extra driving training or car accidents. Although many SS symptoms occurred already before the simulation, all the symptoms except burping had a significantly greater incidence and severity after the simulation. Before the simulation, men reported disorientation symptoms more often than women, while participants with prior experiences of extra driving training or car accidents scored significantly higher in three out of four Questionnaire components: nausea symptoms, oculomotor symptoms, and the total score. The study offers interesting insights into associations between SS and prior experiences observed by means of high-fidelity real-motion simulations. More research is needed to determine the nature of these associations and their potential usefulness, for example, in helping accident survivors to cope with the distressing or even potentially disabling psychological consequences of accidents.