Virtual Reality (VR) provides new possibilities for modern knowledge work. However, the potential advantages of virtual work environments can only be used if it is feasible to work in them for an extended period of time. Until now, there are limited studies of long-term effects when working in VR. This paper addresses the need for understanding such long-term effects. Specifically, we report on a comparative study ( n = 16), in which participants were working in VR for an entire week—for five days, eight hours each day—as well as in a baseline physical desktop environment. This study aims to quantify the effects of exchanging a desktop-based work environment with a VR-based environment. Hence, during this study, we do not present the participants with the best possible VR system but rather a setup delivering a comparable experience to working in the physical desktop environment. The study reveals that, as expected, VR results in significantly worse ratings across most measures. Among other results, we found concerning levels of simulator sickness, below average usability ratings and two participants dropped out on the first day using VR, due to migraine, nausea and anxiety. Nevertheless, there is some indication that participants gradually overcame negative first impressions and initial discomfort. Overall, this study helps lay the groundwork for subsequent research, by clearly highlighting current shortcomings and identifying opportunities for improving the experience of working in VR.

This paper describes a user study conducted to evaluate the use of smooth animated transitions between directories in a three-dimensional, tree-map visualization. We looked specifically at the task of returning to a previously visited directory after either an animated or instantaneous return to the root location. The results of the study show that animation is a double-edged sword. Even though users take more shortcuts, they also make more severe navigational errors. It seems as though the promise of a more direct route to the target directory, which animation provides, somehow precludes users who navigate incorrectly from applying a successful recovery strategy.Godkänd; 2005; 20061105 (ysko

Humans have been developing and playing musical instruments for millennia. With technological advancements, instruments were becoming ever more sophisticated. In recent decades computer-supported innovations have also been introduced in hardware design, usability, and aesthetics. One of the most commonly digitally augmented instruments is the piano. Besides electronic keyboards, several prototypes augmenting pianos with different projections providing various levels of interactivity on and around the keyboard have been implemented in order to support piano players. However, it is still unclear whether these solutions support the learning process. In this paper, we present a systematic review of augmented piano prototypes focusing on instrument learning based on the four themes derived from interviews with piano experts to understand better the problems of teaching the piano. These themes are (i) synchronised movement and body posture, (ii) sight-reading, (iii) ensuring motivation, and (iv) encouraging improvisation. We found that prototypes are saturated on the synchronisation themes, and there are opportunities for sight-reading, motivation, and improvisation themes. We conclude by presenting recommendations on augmenting piano systems towards enriching the piano learning experience as well as on possible directions to expand knowledge in the area.

Physical objects are usually not designed with interaction capabilities to control digital content. Nevertheless, they provide an untapped source for interactions since every object could be used to control our digital lives. We call this the missing interface problem: Instead of embedding computational capacity into objects, we can simply detect users’ gestures on them. However, gesture detection on such unmodified objects has to date been limited in the spatial resolution and detection fidelity. To address this gap, we conducted research on micro-gesture detection on physical objects based on Google Soli’s radar sensor. We introduced two novel deep learning architectures to process range Doppler images, namely a three-dimensional convolutional neural network (Conv3D) and a spectrogram-based ConvNet. The results show that our architectures enable robust on-object gesture detection, achieving an accuracy of approximately 94% for a five-gesture set, surpassing previous state-of-the-art performance results by up to 39%. We also showed that the decibel (dB) Doppler range setting has a significant effect on system performance, as accuracy can vary up to 20% across the dB range. As a result, we provide guidelines on how to best calibrate the radar sensor.

In the last years, numerous papers were published comparing different learning management systems (LMS). Some of them dealt with only few comparison criteria, while others included almost every imaginable feature. When faced to do a comparison ourselves, we came across many of such papers and did a research of what authors considered relevant in an LMS. By comparing papers written in different years, we tried to find out if there is a pattern of features linked to a certain time period, how a demand for new features was evolving through time, and how did LMS developers respond to this demand. We also tried to figure out the present demands and which new features will be included in future versions of LMSs.