Tangible user interfaces : past, present and future directions

In the last two decades, Tangible User Interfaces (TUIs) have emerged as a new interface type that interlinks the digital and physical worlds. Drawing upon users' knowledge and skills of interaction with the real non-digital world, TUIs show a potential to enhance the way in which people interact with and leverage digital information. However, TUI research is still in its infancy and extensive research is required in or- der to fully understand the implications of tangible user interfaces, to develop technologies that further bridge the digital and the physical, and to guide TUI design with empirical knowledge. This paper examines the existing body of work on Tangible User In- terfaces. We start by sketching the history of tangible user interfaces, examining the intellectual origins of this field. We then present TUIs in a broader context, survey application domains, and review frame- works and taxonomies. We also discuss conceptual foundations of TUIs including perspectives from cognitive sciences, phycology, and philoso- phy. Methods and technologies for designing, building, and evaluating TUIs are also addressed. Finally, we discuss the strengths and limita- tions of TUIs and chart directions for future research

Assessing the effectiveness of direct gesture interaction for a safety critical maritime application

Multi-touch interaction, in particular multi-touch gesture interaction, is widely believed to give a more natural interaction style. We investigated the utility of multi-touch interaction in the safety critical domain of maritime dynamic positioning (DP) vessels. We conducted initial paper prototyping with domain experts to gain an insight into natural gestures; we then conducted observational studies aboard a DP vessel during operational duties and two rounds of formal evaluation of prototypes - the second on a motion platform ship simulator. Despite following a careful user-centred design process, the final results show that traditional touch-screen button and menu interaction was quicker and less erroneous than gestures. Furthermore, the moving environment accentuated this difference and we observed initial use problems and handedness asymmetries on some multi-touch gestures. On the positive side, our results showed that users were able to suspend gestural interaction more naturally, thus improving situational awareness

RealTimeChess: Lessons from a Participatory Design Process for a Collaborative Multi-Touch, Multi-User Game

We report on a long-term participatory design process during which we designed and improved RealTimeChess, a collaborative but competitive game that is played using touch input by multiple people on a tabletop display. During the design process we integrated concurrent input from all players and pace control, allowing us to steer the interaction along a continuum between high-paced simultaneous and low-paced turn-based gameplay. In addition, we integrated tutorials for teaching interaction techniques, mechanisms to control territoriality, remote interaction, and alert feedback. Integrating these mechanism during the participatory design process allowed us to examine their effects in detail, revealing for instance effects of the competitive setting on the perception of awareness as well as territoriality. More generally, the resulting application provided us with a testbed to study interaction on shared tabletop surfaces and yielded insights important for other time-critical or attention-demanding applications.

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic