Authoring Support for Mobile Interaction with the Real World.

Mobile phones have been established as devices for the interaction with objects from the everyday world, such as posters, advertisements or points of interest. However, the usage of physical mobile applications is often still restricted by fixed content and behavior, whose authoring usually requires a considerable coding effort. This paper presents an approach to an authoring tool that separates the creative process of authoring content and behavior for mobile applications from its technical deployment. The tool supports non-technical users in the creation of content and behavior for the mobile guiding application MOPS that associates its content with points of interest in the real world through Physical Mobile Interaction

Privacy and Curiosity in Mobile Interactions with Public Displays.

Personal multimedia devices like mobile phones create new needs for larger displays distributed at specific points in the environment to look up information about the current place, playing games or exchanging multimedia data. The technical prerequisites are covered; however, using public displays always exposing information. In this paper we look at these issues from the privacy as well as from the curiosity perspective with several studies showing and confirming users’ reservations against public interactions. Interactive advertisements can exploit this best using specific types of interaction techniques

Comparing Techniques for Mobile Interaction with Objects from the Real World.

Mobile interaction with objects from the real world is gaining in popularity and importance as different mobile technologies increasingly provide the basis for the extraction and usage of information from physical objects. So far, Physical Mobile Interaction is used in rather simple ways. This paper presents a comparison and evaluation of more complex and sophisticated techniques for Physical Mobile Interaction. The results indicate the importance of usability guidelines that pay attention to these new interaction techniques

ACM Classification Keywords

In many contexts, pen and paper are the ideal option for collecting information despite the pervasiveness of mobile devices. Reasons include the unconstrained nature of sketching or handwriting, as well as the tactility of moving a pen over a paper that supports very fine granular control of the pen. In particular in the context of hospitals, many writing and note taking tasks are still performed using pen and paper. However, often this requires time-consuming transcription into digital form for the sake of documentation. We present Penbook – a system providing a touch screen together with a built-in projector integrated with a wireless pen and a projection screen augmented with Anoto paper. This allows using the pen to write or sketch digital information with light on the projection surface while having the distinct tactility of a pen moving over paper. The touch screen can be used in parallel with the projected information turning the tablet into a dual-display device. In this paper, we present the Penbook concept, detail specific applications in a hospital context, and present a prototype implementation of Penbook

AutoTherm: A Dataset and Ablation Study for Thermal Comfort Prediction in Vehicles

State recognition in well-known and customizable environments such as vehicles enables novel insights into users and potentially their intentions. Besides safety-relevant insights into, for example, fatigue, user experience-related assessments become increasingly relevant. As thermal comfort is vital for overall comfort, we introduce a dataset for its prediction in vehicles incorporating 31 input signals and self-labeled user ratings based on a 7-point Likert scale (-3 to +3) by 21 subjects. An importance ranking of such signals indicates higher impact on prediction for signals like ambient temperature, ambient humidity, radiation temperature, and skin temperature. Leveraging modern machine learning architectures enables us to not only automatically recognize human thermal comfort state but also predict future states. We provide details on how we train a recurrent network-based classifier and, thus, perform an initial performance benchmark of our proposed thermal comfort dataset. Ultimately, we compare our collected dataset to publicly available datasets