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Abstract: Einleitung Die multimodale Adipositastherapie besteht aus Elementen der Ernährung, der Bewegung und des Verhaltens bzw. des Körperbildes. Anwendungen der virtuellen Realität (VR) können das Methodenspektrum der Körperbildtherapie erweitern. Im vorliegenden Projekt wurde ein VR-System (bestehend aus einem Avatar und einem virtuellen Spiegel) zur Verbesserung von Körperwahrnehmung und Körperbild bei Personen mit Adipositas entwickelt. Methoden Im Rahmen der multizentrischen kontrollierten ViTraS-Pilotstudie (Registrierungsnummer: DRKS00027906) wurden bei Personen mit Adipositas virtuelle bzw. traditionelle Körperbildübungen angewendet. Es fanden drei Sitzungen im Abstand von ca. zwei Wochen statt. Über die Zeit wurden anthropometrische Daten sowie Daten zum Ernährungsverhalten (Dutch Eating Behavior Questionnaire (DEBQ) und eine Auswahl von Fragen des The Eating Motivation Survey (TEMS)) erfasst. In zwei Sitzungen wurden Körperbildübungen virtuell (VR-Gruppe) oder nicht-virtuell (Kontrollgruppe) durchgeführt. Eine online Follow-Up-Datenerhebung fand 6 Wochen nach der letzten Sitzung statt. Ergebnisse Insgesamt wurden 66 Personen (VR-Gruppe: 31, Kontrollgruppe: 35) in die Studie eingeschlossen. Die Personen waren zu 79% (52/66) weiblich, 45±13 Jahre alt und der mittlere BMI lag bei 37±4 kg/m2. Die Auswertung des DEBQ Fragebogens hat ergeben, dass in der VR-Gruppe gezügeltes Essverhalten von Anfang bis Ende der Studie signifikant (p<0,05) zugenommen hat. Die Ergebnisse der Auswahl von Fragen des TEMS hat keine signifikanten (p≥0,05) Unterschiede innerhalb oder zwischen den Gruppen ergeben. Schlussfolgerung Die Studie hat ergeben, dass die Durchführung von virtuellen Körperbildübungen eine Auswirkung auf das Ernährungsverhalten von Personen mit Adipositas haben kann.

Abstract: Motion tracking "telemetry" data lies at the core of nearly all modern virtual reality (VR) and metaverse experiences. While generally presumed innocuous, recent studies have demonstrated that motion data actually has the potential to uniquely identify VR users. In this study, we go a step further, showing that a variety of private user information can be inferred just by analyzing motion data recorded from VR devices. We conducted a large-scale survey of VR users (N=1,006) with dozens of questions ranging from background and demographics to behavioral patterns and health information. We then obtained VR motion samples of each user playing the game "Beat Saber," and attempted to infer their survey responses using just their head and hand motion patterns. Using simple machine learning models, over 40 personal attributes could be accurately and consistently inferred from VR motion data alone. Despite this significant observed leakage, there remains limited awareness of the privacy implications of VR motion data, highlighting the pressing need for privacy-preserving mechanisms in multi-user VR applications.

Abstract: The Motion Learning Toolbox is a Python library designed to facilitate the preprocessing of motion tracking data in extended reality (XR) setups. It's particularly useful for researchers and engineers wanting to use XR tracking data as input for machine learning models. Originally developed for academic research targeting the identification of XR users by their motions, this toolbox includes a variety of data encoding methods that enhance machine learning model performance.

Abstract: When blending virtual and physical content, certain incongruencies emerge from hardware limitations, inaccurate tracking, or different appearances of virtual and physical content. They restrain us from perceiving virtual and physical content as one experience. Hence, it is crucial to investigate these issues to determine how they influence our experience. We present a virtualized augmented reality simulation that can systematically examine single incongruencies or different configurations.

Abstract: We present two help facilities aimed at addressing the challenges faced by users new to a software. Software documentation should ideally familiarise with the overall functionality, navigate through the concrete workflows, and opportunities for customisation. Large degrees of freedom in the use and vast basic scopes, however, often make it infeasible to convey this information upfront. Rather, it needs to be broken into helpful bits and pieces which are delivered at appropriate times. In order to address this challenge, we have designed two concrete help facilities, i.e. tooltips and tips-of-the-day, to feature crowdsourced information. In this paper, we present the results of a formative study to implement early proofs-of-concept for the open-source game authoring platform Godot. To support further research, we have made the code base publicly available on GitHub.

Abstract: Body awareness is relevant for the efficacy of psychotherapy. However, previous work on virtual reality (VR) and avatar-assisted therapy has often overlooked it. We investigated the effect of avatar individualization on body awareness in the context of VR-specific user experience, including sense of embodiment (SoE), plausibility, and sense of presence (SoP). In a between-subject design, 86 participants embodied three avatar types and engaged in VR movement exercises. The avatars were (1) generic and gender-matched, (2) customized from a set of pre-existing options, or (3) personalized photorealistic scans. Compared to the other conditions, participants with personalized avatars reported increased SoE, yet higher eeriness and reduced body awareness. Further, SoE and SoP positively correlated with body awareness across conditions. Our results indicate that VR user experience and body awareness do not always dovetail and do not necessarily predict each other. Future research should work towards a balance between body awareness and SoE.

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