Figure 1: Examples of data physicalizations: (left) population density map of Mexico City co-created by Richard Burdett and exhibited at the Tate Modern (photo by Stefan Geens), (center) similar data shown on an actuated display from the MIT Media Lab [70], and (right) spherical particles suspended by acoustic levitation [61]. All images are copyright to their respective owners. ABSTRACTPhysical representations of data have existed for thousands of years. Yet it is now that advances in digital fabrication, actuated tangible interfaces, and shape-changing displays are spurring an emerging area of research that we call Data Physicalization. It aims to help people explore, understand, and communicate data using computer-supported physical data representations. We call these representations physicalizations, analogously to visualizations -their purely visual counterpart. In this article, we go beyond the focused research questions addressed so far by delineating the research area, synthesizing its open challenges, and laying out a research agenda.

Human-robot collaboration is a key factor for the development of factories of the future, a space in which humans and robots can work and carry out tasks together. Safety is one of the most critical aspects in this collaborative human-robot paradigm. This article describes the experiments done and results achieved by the authors in the context of the Four-ByThree project, aiming to measure the trust of workers on fenceless human-robot collaboration in industrial robotic applications as well as to gauge the acceptance of different interaction mechanisms between robots and human beings.

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This article presents a semantic approach for multimodal interaction between humans and industrial robots to enhance the dependability and naturalness of the collaboration between them in real industrial settings. The fusion of several interaction mechanisms is particularly relevant in industrial applications in which adverse environmental conditions might affect the performance of vision-based interaction (e.g. poor or changing lighting) or voice-based interaction (e.g. environmental noise). Our approach relies on the recognition of speech and gestures for the processing of requests, dealing with information that can potentially be contradictory or complementary. For disambiguation, it uses semantic technologies that describe the robot characteristics and capabilities as well as the context of the scenario. Although the proposed approach is generic and applicable in different scenarios, this article explains in detail how it has been implemented in two real industrial cases in which a robot and a worker collaborate in assembly and deburring operations.

Device deformation allows new types of gestures to be used in interaction. We identify that the gesture/use-case pairings proposed by interaction designers are often driven by factors relating improved tangibility, spatial directionality and strong metaphorical bonds. With this starting point, we argue that some of the designs may not make use of the full potential of deformation gestures as continuous, bipolar input techniques. In two user studies, we revisited the basics of deformation input by taking a new systematic look at the question of matching gestures with use cases. We observed comparable levels of UX when using bend input in different continuous bipolar interactions, irrespective of the choice of tangibility, directionality and metaphor. We concluded that device bend gestures use their full potential when used to control continuous bipolar parameters, and when quick reactions are needed. From our studies, we also identify relative strengths of absolute and relative mappings, and report a Fitts' law study for device bending input.