Cybersickness, Cognition, & Motor Skills: The Effects of Music, Gender, and Gaming Experience

How different text display patterns affect cybersickness in augmented reality

Cybersickness remains a pivotal factor that impacts user experience in Augmented Reality (AR). Research probing into the relationship between AR reading tasks and cybersickness, particularly focusing on text display patterns and user characteristics, has been scant. Moreover, the influence of cybersickness on searching ability and the broader spectrum of user experience has not been rigorously tested. Recent investigations have aimed to pinpoint the variables that contribute to cybersickness during AR reading sessions. In one such study, 40 participants underwent a series of controlled experiments with randomized text display patterns, including variations in text speed and text movement modes. Post-experiment, participants completed a questionnaire that helped quantify their experiences and the degree of cybersickness encountered. The data highlighted that satiety, text speed, and text movement mode are significant contributors to cybersickness. When participants experienced higher levels of cybersickness, font color stood out as a particularly influential factor, whereas gender differences seemed to affect the onset of cybersickness more noticeably at lower levels. This study also drew attention to the impact of cybersickness on search ability within AR environments. It was noted that as cybersickness intensity increased, search ability was markedly compromised. In sum, the research underscores the importance of text display patterns and user characteristics, such as past AR experience, in understanding cybersickness and its detrimental effects on user experience and search ability, particularly under conditions of intense cybersickness.

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

Introduction

Novel technologies based on virtual reality (VR) are creating attractive virtual environments with high ecological value, used both in basic/clinical neuroscience and modern medical practice. The study aimed to evaluate the effects of VR-based training in an elderly population.

Materials and methods

The study included 36 women over the age of 60, who were randomly divided into two groups subjected to balance-strength and balance-cognitive training. The research applied both conventional clinical tests, such as (a) the Timed Up and Go test, (b) the five-times sit-to-stand test, and (c) the posturographic exam with the Romberg test with eyes open and closed. Training in both groups was conducted for 10 sessions and embraced exercises on a bicycle ergometer and exercises using non-immersive VR created by the ActivLife platform. Machine learning methods with a k-nearest neighbors classifier, which are very effective and popular, were proposed to statistically evaluate the differences in training effects in the two groups.

Results and conclusion

The study showed that training using VR brought beneficial improvement in clinical tests and changes in the pattern of posturographic trajectories were observed. An important finding of the research was a statistically significant reduction in the risk of falls in the study population. The use of virtual environments in exercise/training has great potential in promoting healthy aging and preventing balance loss and falls among seniors.

Investigating Personalization Techniques for Improved Cybersickness Prediction in Virtual Reality Environments

In recent cybersickness research, there has been a growing interest in predicting cybersickness using real-time physiological data such as heart rate, galvanic skin response, eye tracking, postural sway, and electroencephalogram. However, the impact of individual factors such as age and gender, which are pivotal in determining cybersickness susceptibility, remains unknown in predictive models. Our research seeks to address this gap, underscoring the necessity for a more personalized approach to cybersickness prediction to ensure a better, more inclusive virtual reality experience. We hypothesize that a personalized cybersickness prediction model would outperform non-personalized models in predicting cybersickness. Evaluating this, we explored four personalization techniques: 1) data grouping, 2) transfer learning, 3) early shaping, and 4) sample weighing using an open-source cybersickness dataset. Our empirical results indicate that personalized models significantly improve prediction accuracy. For instance, with early shaping, the Deep Temporal Convolutional Neural Network (DeepTCN) model achieved a 69.7% reduction in RMSE compared to its non-personalized version. Our study provides evidence of personalization techniques' benefits in improving cybersickness prediction. These findings have implications for developing personalized cybersickness prediction models tailored to individual differences, which can be used to develop personalized cybersickness reduction techniques in the future.

Influence of Normal Aging and Multisensory Data Fusion on Cybersickness and Postural Adaptation in Immersive Virtual Reality

Immersive Virtual Reality (VR) systems are expanding as sensorimotor readaptation tools for older adults. However, this purpose may be challenged by cybersickness occurrences possibly caused by sensory conflicts. This study aims to analyze the effects of aging and multisensory data fusion processes in the brain on cybersickness and the adaptation of postural responses when exposed to immersive VR.

METHODS

We repeatedly exposed 75 participants, aged 21 to 86, to immersive VR while recording the trajectory of their Center of Pressure (CoP). Participants rated their cybersickness after the first and fifth exposure.

RESULTS

The repeated exposures increased cybersickness and allowed for a decrease in postural responses from the second repetition, i.e., increased stability. We did not find any significant correlation between biological age and cybersickness scores. On the contrary, even if some postural responses are age-dependent, a significant postural adaptation occurred independently of age. The CoP trajectory length in the anteroposterior axis and mean velocity were the postural parameters the most affected by age and repetition.

CONCLUSIONS

This study suggests that cybersickness and postural adaptation to immersive VR are not age-dependent and that cybersickness is unrelated to a deficit in postural adaptation or age. Age does not seem to influence the properties of multisensory data fusion.