Andreas Halbig, M.Sc.

Andreas Halbig, M. Sc.

Wissenschaftlicher Mitarbeiter/PhD Student

andreas.halbig@uni-wuerzburg.de

Psychologie Intelligenter Interaktiver Systeme
Julius-Maximilians-Universität Würzburg
Institut für Mensch-Computer-Medien
Universität Würzburg
Emil-Fischer-Straße 50
97074 Würzburg
Deutschland

About me

Andreas studied Mensch-Computer-Systeme (B.Sc.) and Human-Computer Interaction (M.Sc.) at Julius-Maximilians-Universität Würzburg. In his master's thesis, he explored how virtual agents in virtual environments can create motivating and disciplining effects in learning and work tasks.

During his time at the Chair of Human-Computer Interaction, Andreas contributed to the VIA-VR project, where he focused on integrating physiological data into VR healthcare applications.

Currently, as part of the PIIS working group, he works on the AIL-AT Work project, with a primary focus on human-AI teaming and its simulation in virtual reality.

Beyond this, Andreas' research delves into fundamental VR qualia, particularly spatial presence and the sense of embodiment, as well as their interrelation.

Publications

2024[ to top ]

Halbig, Andreas, and Marc Erich Latoschik. “Common Cues? Toward the Relationship of Spatial Presence and the Sense of Embodiment”. In 23rd IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE Computer Society, 2024. https://downloads.hci.informatik.uni-wuerzburg.de/2024-ISMAR-halbig-common-cues.pdf.
- [ BibTeX ]
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@inproceedings{halbig2024common, author = {Halbig, Andreas and Latoschik, Marc Erich}, booktitle = {23rd IEEE International Symposium on Mixed and Augmented Reality (ISMAR)}, keywords = {xrhub}, note = {Accepted for Publication}, publisher = {IEEE Computer Society}, title = {Common Cues? Toward the Relationship of Spatial Presence and the Sense of Embodiment}, year = 2024 }
Yalcin, Murat, Andreas Halbig, Martin Fischbach, and Marc Erich Latoschik. “Automatic Cybersickness Detection by Deep Learning of Augmented Physiological Data from Off-the-Shelf Consumer-Grade Sensors”. Frontiers in Virtual Reality 5 (2024). https://doi.org/10.3389/frvir.2024.1364207.
- [ Abstract ]
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<p>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.</p>

@article{10.3389/frvir.2024.1364207, abstract = {<p>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.</p>}, author = {Yalcin, Murat and Halbig, Andreas and Fischbach, Martin and Latoschik, Marc Erich}, journal = {Frontiers in Virtual Reality}, keywords = {myalcin}, title = {Automatic Cybersickness Detection by Deep Learning of Augmented Physiological Data from Off-the-Shelf Consumer-Grade Sensors}, volume = 5, year = 2024 }

2022[ to top ]

Halbig, Andreas, Sooraj K. Babu, Shirin Gatter, Marc Erich Latoschik, Kirsten Brukamp, and Sebastian von Mammen. “Opportunities and Challenges of Virtual Reality in Healthcare -- A Domain Experts Inquiry”. Frontiers in Virtual Reality 3 (2022). https://doi.org/10.3389/frvir.2022.837616.
- [ Abstract ]
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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.

@article{Halbig:2022aa, 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.}, author = {Halbig, Andreas and Babu, Sooraj K. and Gatter, Shirin and Latoschik, Marc Erich and Brukamp, Kirsten and von Mammen, Sebastian}, journal = {Frontiers in Virtual Reality}, keywords = {via-vr}, title = {Opportunities and Challenges of Virtual Reality in Healthcare – A Domain Experts Inquiry}, volume = 3, year = 2022 }

2021[ to top ]

Halbig, Andreas, and Marc Erich Latoschik. “A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality”. Frontiers in Virtual Reality 2 (2021): 89. https://doi.org/10.3389/frvir.2021.694567.
- [ Abstract ]
- [ BibTeX ]
- [ URL ]
- [ DOI ]
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.

@article{10.3389/frvir.2021.694567, 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.}, author = {Halbig, Andreas and Latoschik, Marc Erich}, journal = {Frontiers in Virtual Reality}, keywords = {via-vr}, pages = 89, title = {A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality}, volume = 2, year = 2021 }

Hubland Nord, Gebäude 50

Wissenschaftlicher Mitarbeiter/PhD Student

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