Deep Learning Super-Resolution Network Facilitating Fiducial Tangibles on Capacitive Touchscreens
Full-Paper: Over the last years, we have seen many approaches using tangibles to address the limited expressiveness of touchscreens. Mainstream tangible detection uses fiducial markers embedded in the tangibles. However, the coarse sensor size of capacitive touchscreens makes tangibles bulky, limiting their usefulness. We propose a novel deep-learning super-resolution network to facilitate fiducial tangibles on capacitive touchscreens better. In detail, our network super-resolves the markers enabling off-the-shelf detection algorithms to track tangibles reliably. Our network generalizes to unseen marker sets, such as AprilTag, ArUco, and ARToolKit. Therefore, we are not limited to a fixed number of distinguishable objects and do not require data collection and network training for new fiducial markers. With extensive evaluation including real-world users and five showcases, we demonstrate the applicability of our open-source approach on commodity mobile devices and further highlight the potential of tangibles on capacitive touchscreens.
Understanding and Mitigating Technology-Facilitated Privacy Violations in the Physical World
Full-Paper: We are constantly surrounded by technology that collects and processes sensitive data, paving the way for privacy violations. Yet, current research investigating technology-facilitated privacy violations in the physical world is scattered and focused on specific scenarios or investigates such violations purely from an expert’s perspective. Informed through a large-scale online survey, we first construct a scenario taxonomy based on user-experienced privacy violations in the physical world through technology. We then validate our taxonomy and establish mitigation strategies using interviews and co-design sessions with privacy and security experts. In summary, this work contributes (1) a refined scenario taxonomy for technology-facilitated privacy violations in the physical world, (2) an understanding of how privacy violations manifest in the physical world, (3) a decision tree on how to inform users, and (4) a design space to create notices whenever adequate. With this, we contribute a conceptual framework to enable a privacy-preserving technology-connected world.
Using Pseudo-Stiffness to Enrich the Haptic Experience in Virtual Reality
Full-Paper: Providing users with a haptic sensation of the hardness and softness of objects in virtual reality is an open challenge. While physical props and haptic devices help, their haptic properties do not allow for dynamic adjustments. To overcome this limitation, we present a novel technique for changing the perceived stiffness of objects based on a visuo-haptic illusion. We achieved this by manipulating the hands’ Control-to-Display (C/D) ratio in virtual reality while pressing down on an object with fixed stiffness. In the first study (N=12), we determine the detection thresholds of the illusion. Our results show that we can exploit a C/D ratio from 0.7 to 3.5 without user detection. In the second study (N=12), we analyze the illusion’s impact on the perceived stiffness. Our results show that participants perceive the objects to be up to 28.1% softer and 8.9% stiffer, allowing for various haptic applications in virtual reality.
A Database for Kitchen Objects: Investigating Danger Perception in the Context of Human-Robot Interaction
Late-Braking Work: In the future, humans collaborating closely with cobots in everyday tasks will require handing each other objects. So far, researchers have optimized human-robot collaboration concerning measures such as trust, safety, and enjoyment. However, as the objects themselves influence these measures, we need to investigate how humans perceive the danger level of objects. Thus, we created a database of 153 kitchen objects and conducted an online survey (N=300) investigating their perceived danger level. We found that (1) humans perceive kitchen objects vastly differently, (2) the object-holder has a strong effect on the danger perception, and (3) prior user knowledge increases the perceived danger of robots handling those objects. This shows that future human-robot collaboration studies must investigate different objects for a holistic image. We contribute a wiki-like open-source database to allow others to study predefined danger scenarios and eventually build object-aware systems: https://hri-objects.leusmann.io/.
Exploring Physiological Correlates of Visual Complexity Adaptation: Insights from EDA, ECG, and EEG Data for Adaptation Evaluation in VR Adaptive Systems
Physiologically-adaptive Virtual Reality can drive interactions and adjust virtual content to better fit users’ needs and support specific goals. However, the complexity of psychophysiological inference hinders efficient adaptation as the relationship between cognitive and physiological features rarely show one-to-one correspondence. Therefore, it is necessary to employ multimodal approaches to evaluate the effect of adaptations. In this work, we analyzed a multimodal dataset (EEG, ECG, and EDA) acquired during interaction with a VR-adaptive system that employed EDA as input for adaptation of secondary task difficulty. We evaluated the effect of dynamic adjustments on different physiological features and their correlation. Our results show that when the adaptive system increased the secondary task difficulty, theta, beta, and phasic EDA features increased. Moreover, we found a high correlation between theta, alpha, and beta oscillations during difficulty adjustments. Our results show how specific EEG and EDA features can be employed for evaluating VR adaptive systems.