Andreas Halbig, M.Sc.

Abstract:

Cybersickness is still a prominent risk factor potentially affecting the usability of virtual reality applications. Automated real-time detection of cybersickness promises to support a better general understanding of the phenomena and to avoid and counteract its occurrence. It could be used to facilitate application optimization, that is, to systematically link potential causes (technical development and conceptual design decisions) to cybersickness in closed-loop user-centered development cycles. In addition, it could be used to monitor, warn, and hence safeguard users against any onset of cybersickness during a virtual reality exposure, especially in healthcare applications. This article presents a novel real-time-capable cybersickness detection method by deep learning of augmented physiological data. In contrast to related preliminary work, we are exploring a unique combination of mid-immersion ground truth elicitation, an unobtrusive wireless setup, and moderate training performance requirements. We developed a proof-of-concept prototype to compare (combinations of) convolutional neural networks, long short-term memory, and support vector machines with respect to detection performance. We demonstrate that the use of a conditional generative adversarial network-based data augmentation technique increases detection performance significantly and showcase the feasibility of real-time cybersickness detection in a genuine application example. Finally, a comprehensive performance analysis demonstrates that a four-layered bidirectional long short-term memory network with the developed data augmentation delivers superior performance (91.1% F1-score) for real-time cybersickness detection. To encourage replicability and reuse in future cybersickness studies, we released the code and the dataset as publicly available.

Abstract: The sense of presence and the sense of embodiment are two fundamental qualia, pivotal to many virtual reality experiences. Empirical research indicates a notable interdependence between these two qualia, where manipulations designed to affect one often exhibit a concurrent influence on the other. Existing theories on the development of qualia in virtual reality make no or only insufficient statements on this deep interdependence. In this work, we present a novel theoretical perspective on this connection. Based on existing theories, we argue that all the fundamental cues influencing one quale have the potential to impact the other one too. We present three studies ($n=42, n=42, n=32$) that generally support this novel perspective. Among other things, they show that traditional spatial presence cues such as head-tracking and passive depth cues (stereoscopy, linear perspective, etc.) can potentially also serve as embodiment cues. Conversely, they show that typical embodiment cues such as the visuotactile and visuoproprioceptive synchrony of a virtual hand are also spatial presence cues. The cues only differ in terms of how strongly they influence the respective quale. This novel perspective not only enhances our understanding of fundamental mechanics of virtual reality but it can also guide the development of more effective measurement instruments.

Abstract: In recent years, the applications and accessibility of Virtual Reality (VR) for the healthcare sector have continued to grow. However, so far, most VR applications are only relevant in research settings. Information about what healthcare professionals would need to independently integrate VR applications into their daily working routines is missing. The actual needs and concerns of the people who work in the healthcare sector are often disregarded in the development of VR applications, even though they are the ones who are supposed to use them in practice. By means of this study, we systematically involve health professionals in the development process of VR applications. In particular, we conducted an online survey with 102 healthcare professionals based on a video prototype which demonstrates a software platform that allows them to create and utilise VR experiences on their own. For this study, we adapted and extended the Technology Acceptance Model (TAM). The survey focused on the perceived usefulness and the ease of use of such a platform, as well as the attitude and ethical concerns the users might have. The results show a generally positive attitude toward such a software platform. The users can imagine various use cases in different health domains. However, the perceived usefulness is tied to the actual ease of use of the platform and sufficient support for learning and working with the platform. In the discussion, we explain how these results can be generalized to facilitate the integration of VR in healthcare practice.

Abstract: Measurements of physiological parameters provide an objective, often non-intrusive, and (at least semi-)automatic evaluation and utilization of user behavior. In addition, specific hardware devices of Virtual Reality (VR) often ship with built-in sensors, i.e. eye-tracking and movements sensors. Hence, the combination of physiological measurements and VR applications seems promising. Several approaches have investigated the applicability and benefits of this combination for various fields of applications. However, the range of possible application fields, coupled with potentially useful and beneficial physiological parameters, types of sensor, target variables and factors, and analysis approaches and techniques is manifold. This article provides a systematic overview and an extensive state-of-the-art review of the usage of physiological measurements in VR. We identified 1,119 works that make use of physiological measurements in VR. Within these, we identified 32 approaches that focus on the classification of characteristics of experience, common in VR applications. The first part of this review categorizes the 1,119 works by field of application, i.e. therapy, training, entertainment, and communication and interaction, as well as by the specific target factors and variables measured by the physiological parameters. An additional category summarizes general VR approaches applicable to all specific fields of application since they target typical VR qualities. In the second part of this review, we analyze the target factors and variables regarding the respective methods used for an automatic analysis and, potentially, classification. For example, we highlight which measurement setups have been proven to be sensitive enough to distinguish different levels of arousal, valence, anxiety, stress, or cognitive workload in the virtual realm. This work may prove useful for all researchers wanting to use physiological data in VR and who want to have a good overview of prior approaches taken, their benefits and potential drawbacks.