The Correlations of Scene Complexity, Workload, Presence, and Cybersickness in a Task-Based VR Game

This investigation examined the relationships among scene complexity, workload, presence, and cybersickness in virtual reality (VR) environments. Numerous factors can influence the overall VR experience, and existing research on this matter is not yet conclusive, warranting further investigation. In this between-subjects experimental setup, 44 participants engaged in the Pendulum Chair game, with half exposed to a simple scene with lower optic flow and lower familiarity, and the remaining half to a complex scene characterized by higher optic flow and greater familiarity. The study measured the dependent variables workload, presence, and cybersickness and analyzed their correlations. Equivalence testing was also used to compare the simple and complex environments. Results revealed that despite the visible differences between the environments, within the 10% boundaries of the maximum possible value for workload and presence, and 13.6% of the maximum SSQ value, a statistically significant equivalence was observed between the simple and complex scenes. Additionally, a moderate, negative correlation emerged between workload and SSQ scores. The findings suggest two key points: (1) the nature of the task can mitigate the impact of scene complexity factors such as optic flow and familiarity, and (2) the correlation between workload and cybersickness may vary, showing either a positive or negative relationship

Innovative Cybersickness Detection: Exploring Head Movement Patterns in Virtual Reality

Despite the widespread adoption of Virtual Reality (VR) technology, cybersickness remains a barrier for some users. This research investigates head movement patterns as a novel physiological marker for cybersickness detection. Unlike traditional markers, head movements provide a continuous, non-invasive measure that can be easily captured through the sensors embedded in all commercial VR headsets. We used a publicly available dataset from a VR experiment involving 75 participants and analyzed head movements across six axes. An extensive feature extraction process was then performed on the head movement dataset and its derivatives, including velocity, acceleration, and jerk. Three categories of features were extracted, encompassing statistical, temporal, and spectral features. Subsequently, we employed the Recursive Feature Elimination method to select the most important and effective features. In a series of experiments, we trained a variety of machine learning algorithms. The results demonstrate a 76% accuracy and 83% precision in predicting cybersickness in the subjects based on the head movements. This study contribution to the cybersickness literature lies in offering a preliminary analysis of a new source of data and providing insight into the relationship of head movements and cybersickness.This is a preprint from Salehi, Masoud, Nikoo Javadpour, Brietta Beisner, Mohammadamin Sanaei, and Stephen B. Gilbert. "Innovative Cybersickness Detection: Exploring Head Movement Patterns in Virtual Reality." arXiv preprint arXiv:2402.02725 (2024). doi: https://doi.org/10.48550/arXiv.2402.02725. Copyright 2024 The Authors. CC BY

Apple's Knowledge Navigator: Why Doesn't that Conversational Agent Exist Yet?

Apple's 1987 Knowledge Navigator video contains a vision of a sophisticated digital personal assistant, but the natural human-agent conversational dialog shown does not currently exist. To investigate why, the authors analyzed the video using three theoretical frameworks: the DiCoT framework, the HAT Game Analysis framework, and the Flows of Power framework. These were used to codify the human-agent interactions and classify the agent's capabilities. While some barriers to creating such agents are technological, other barriers arise from privacy, social and situational factors, trust, and the financial business case. The social roles and asymmetric interactions of the human and agent are discussed in the broader context of HAT research, along with the need for a new term for these agents that does not rely on a human social relationship metaphor. This research offers designers of conversational agents a research roadmap to build more highly capable and trusted non-human teammates.This proceeding is published as Newendorp, Amanda K., Mohammadamin Sanaei, Arthur J. Perron, Hila Sabouni, Nikoo Javadpour, Maddie Sells, Katherine Nelson, Michael Dorneich, and Stephen B. Gilbert. "Apple's Knowledge Navigator: Why Doesn't that Conversational Agent Exist Yet?." In Proceedings of the CHI Conference on Human Factors in Computing Systems, pp. 1-14. 2024. doi: https://doi.org/10.1145/3613904.3642739. Copyright 2024, The Authors. This work is licensed under a Creative Commons Attribution-Share Alike International 4.0 License

Machine Learning Approaches Towards Cybersickness Prediction: An Updated Systematic Review

Cybersickness, a form of motion sickness experienced in virtual reality (VR), poses a significant challenge to the widespread adoption of VR technologies. This systematic review explores advancements in machine learning (ML) techniques to detect and predict cybersickness by analyzing physiological signals. This review spans January 2022 to June 2024, examining 42 papers, focusing on the ML models used, the types of data processed, and the performance of these models. The goal of a review like this is to comprehensively analyze and synthesize existing research on a specific topic—in this case, the use of machine learning (ML) techniques for predicting and mitigating cybersickness in virtual reality (VR) environments. This review aims to provide a consolidated overview of the current state of research, highlighting significant findings, trends, and advancements in ML techniques for cybersickness prediction and detection. Results show that around 40% of the papers focused on real-time prediction or detection. Deep learning approaches have more than doubled in two years, despite their need for large datasets and substantial computational resources. Though these papers typically showed high accuracy values, they had smaller population samples, suggesting that they might be overfit and not generalize to the entire population, which varies broadly in cybersickness susceptibility. Also, most papers did not describe their data labeling in detail, which results in difficulty for reproducibility, further weakening the contribution of those studies. Thirteen of the 42 papers used a single biosignal as the source of prediction or detection; six used two biosignals, and the remaining 23 studies used three or more biosignals. The majority of the papers used games as their VR content domain, with others using videos, scenes, or explorable spaces. The majority of papers used active tasks, while the users in some studies watched a VR experience passively. Three of the papers predicted or detected cybersickness using only the HMD sensors (head and/or eye-tracking), rather than adding additional sensors to a user. Among the other biosignals used, EDA, body movement, and heart rate were the most popular. Research populations were biased towards younger males; more diverse populations are needed to represent the full population. This review describes the ideal cybersickness study in terms of maximizing a study's contribution to the field. If future researchers offer more consistent and detailed research reporting with larger research populations, more robust systems for real-time cybersickness prediction and mitigation will be possible

2023 Analysis of Apple Knowledge Navigator

An analysis of the Apple Knowledge Navigator video, focusing particularly on the aspects of conversational dialogue between its participant