ABSTRACT
Integrating taste in AR/VR applications has various promising use cases - from social eating to the treatment of disorders. Despite many successful AR/VR applications that alter the taste of beverages and food, the relationship between olfaction, gustation, and vision during the process of multisensory integration (MSI) has not been fully explored yet. Thus, we present the results of a study in which participants were confronted with congruent and incongruent visual and olfactory stimuli while eating a tasteless food product in VR. We were interested (1) if participants integrate bi-modal congruent stimuli and (2) if vision guides MSI during congruent/incongruent conditions. Our results contain three main findings: First, and surprisingly, participants were not always able to detect congruent visual-olfactory stimuli when eating a portion of tasteless food. Second, when confronted with tri-modal incongruent cues, a majority of participants did not rely on any of the presented cues when forced to identify what they eat; this includes vision which has previously been shown to dominate MSI. Third, although research has shown that basic taste qualities like sweetness, saltiness, or sourness can be influenced by congruent cues, doing so with more complex flavors (e.g., zucchini or carrot) proved to be harder to achieve. We discuss our results in the context of multimodal integration, and within the domain of multisensory AR/VR. Our results are a necessary building block for future human-food interaction in XR that relies on smell, taste, and vision and are foundational for applied applications such as affective AR/VR.
ABSTRACT
Augmented Reality (AR) and Virtual Reality (VR) are pushing from the labs towards consumers, especially with social applications. These applications require visual representations of humans and intelligent entities. However, displaying and animating photo-realistic models comes with a high technical cost while low-fidelity representations may evoke eeriness and overall could degrade an experience. Thus, it is important to carefully select what kind of avatar to display. This article investigates the effects of rendering style and visible body parts in AR and VR by adopting a systematic literature review. We analyzed 72 papers that compare various avatar representations. Our analysis includes an outline of the research published between 2015 and 2022 on the topic of avatars and agents in AR and VR displayed using head-mounted displays, covering aspects like visible body parts (e.g., hands only, hands and head, full-body) and rendering style (e.g., abstract, cartoon, realistic); an overview of collected objective and subjective measures (e.g., task performance, presence, user experience, body ownership); and a classification of tasks where avatars and agents were used into task domains (physical activity, hand interaction, communication, game-like scenarios, and education/training). We discuss and synthesize our results within the context of today's AR and VR ecosystem, provide guidelines for practitioners, and finally identify and present promising research opportunities to encourage future research of avatars and agents in AR/VR environments.
ABSTRACT
Multimodal medical image fusion combines information of one or more images in order to improve the diagnostic value. While previous applications mainly focus on merging images from computed tomography, magnetic resonance imaging (MRI), ultrasonic and single-photon emission computed tomography, we propose a novel approach for the registration and fusion of preoperative 3D MRI with intraoperative 2D infrared thermography. Image-guided neurosurgeries are based on neuronavigation systems, which further allow us track the position and orientation of arbitrary cameras. Hereby, we are able to relate the 2D coordinate system of the infrared camera with the 3D MRI coordinate system. The registered image data are now combined by calibration-based image fusion in order to map our intraoperative 2D thermographic images onto the respective brain surface recovered from preoperative MRI. In extensive accuracy measurements, we found that the proposed framework achieves a mean accuracy of 2.46 mm.
