Mirko Fetter

Supporting social protocols in tabletop interaction through visual cues

Multi-touch tabletops provide new means for co-located people to work together on a task by directly manipulating objects and tools on a single display in unison. Despite their benefits they also entail new challenges. One major concern is how to help users avoid conflicting actions. Previous work discusses if social protocols are sufficient to regulate coordination, and if policies are needed to enforce specific behaviours. Our study on different variants of a tabletop game shows that providing visual cues on ownership can help to follow social protocols and therefore reduce the need for policies.

TUIOFX—Toolkit Support for the Development of JavaFX Applications for Interactive Tabletops

TUIOFX is a novel toolkit for developing multi-touch, multi-user applications for interactive tabletops and surfaces. By seamlessly integrating with JavaFX, TUIOFX provides a low entry barrier for developing state-of-the-art applications with multi-user, multi-touch capabilities and allows the cross-platform deployment on various interactive tabletop and surface hardware.

MagnetiCode: physical mobile interaction through time-encoded magnetic identification tags

We present MagnetiCode, a new tagging mechanism that allows for physical mobile interaction. MagnetiCode tags can be captured and decoded by every compass-equipped mobile phone. They rely on a novel approach of transmitting binary IDs in form of a pulsed magnetic field. MagnetiCode therefore is able to substitute static tagging mechanisms like QR codes or RFID tags, in situations where visual tags are not appropriate or the expected number of users with NFC-enabled devices is poor. We confirm the general feasibility of our approach in a study.

CAESSA: visual authoring of context-aware experience sampling studies

In this paper we present a toolkit that enables HCI practitioners to visually author and setup Context-Aware Experience Sampling studies - CAESSA (Context-Aware Ex-perience Sampling Study Authoring).

Predicting selective availability for instant messaging

Instant messaging (IM) systems allow users to spontaneously communicate over distance, yet they bear the risk for disruption of the recipient. In order to reduce disruption, novel approaches for detecting and presenting mutual availability are needed. In this paper we show how fine-grained IM availability predictions can be made for nomadic users solely based on sensors installed on a laptop computer. Our approach provides comparable accuracies to previous work, while it eliminates the need for augmenting the offices or the users with further sensors. We performed a user study to collect sensor data. Alongside with labels collected by means of Experience Sampling, the data allow for creating probabilistic models for predicting selective availability. This way, we demonstrate how the required effort involved in proactively managing ones availability selectively towards a variety of recipients can be reduced by automatic adaptation, and give insights in the lessons learned.

Task-Based Focus and AdHoc-Focus-Territory: Novel Concepts for Shared Interactive Surfaces

Shared Interactive Surfaces allow co-located users to collaboratively work on a task. As current technology often is not able to distinguish between different users, there is a potential for concurrent conflicting actions of multiple users, leading to unwanted results and accordingly frustration. With our concepts for Task-Based Focus and AdHoc-Focus-Territory we provide light-weight solutions Integrated in our toolkit TUIOFX - for designers of multi-user, multi-touch applications. Our solution helps to overcome some of the problems of anonymous touch input, without an immediate need for more heavy-weight mechanisms like user identification.

TUIOFX: A JavaFX Toolkit for Shared Interactive Surfaces

Building multi-touch multi-user applications for Shared Interactive Surfaces is a complex endeavour that requires fundamental knowledge in touch enabling hardware, gesture recognition, graphical representation of digital information and multi-user interaction. While several specialised toolkits help developers in this effort, we identified a variety of challenges with these toolkits, as for example the lack of cross-platform support, the limited number of touch-enabled multi-user widgets, missing documentation, and lacking community support -- all raising the barriers to entry. In this paper, we present TUIOFX, a toolkit for developing multi-touch, multi-user applications for Shared Interactive Surfaces in Java, which tackles all of the identified problems. The sophisticated implementation of TUIOFX adds support for TUIO-enabled hardware and multi-user interaction under the hood of JavaFX, and leaves the well-learned JavaFX API for the developers fully intact -- thus allowing particularly novices a very quick start. In this paper we provide the technical insights, in the concepts and their elegant implementation.

ENGAGE! EMPOWER! ENCOURAGE!—Supporting Mundane Group Decisions on Tabletops

This paper presents an interaction model to support groups making decisions, aiming to Engage the group in the interaction, Empower all users to put forth their opinion, and Encourage the group to discuss the options. Based on the tabletop application MTEatsplore, we show how the interaction model helps to design a system to structure the decision making process and thus can lead to an effective and efficient yet inclusive support for mundane decisions.

Lightweight support for collaborative web browsing through spreadvector

We present SpreadVector, a prototype providing light-weight support for collaborative Web browsing. While a broad need for co-browsing tools has been repeatedly identified, the rate of mainstream adoption for existing solutions is still marginal. We argue that one reason is the lack of tools that offer lightweight collaborative browsing support. With SpreadVector we provide a con-cept and prototype for lightweight co-browsing.

LiLoLe--A Framework for Lifelong Learning from Sensor Data Streams for Predictive User Modelling

Adaptation in context-aware ubiquitous environments and adaptive systems is becoming more and more complex. Adaptations need to take into account information from a plethora of heterogeneous sensors, while the adaptation decisions often imply personalised aspects and individual preferences, which are likely to change over time. We present a novel concept for lifelong learning from sensor data streams for predictive user modelling that is applicable in scenarios where simpler mechanisms that rely on pre-trained general models fall short. With the LiLoLe-Framework, we pursue an approach that allows ubiquitous systems to continuously learn from their users and adapt the system at the same time through stream-based active learning. This Framework can guide the development of context-aware or adaptive systems in form of an overall architecture.