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Ergonomic risk assessment is now, due to an increased awareness, carried out more often than in the past. The conventional risk assessment evaluation, based on expert-assisted observation of the workplaces and manually filling in score tables, is still predominant. Data analysis is usually done with a focus on critical moments, although without the support of contextual information and changes over time. In this paper we introduce ErgoExplorer, a system for the interactive visual analysis of risk assessment data. In contrast to the current practice, we focus on data that span across multiple actions and multiple workers while keeping all contextual information. Data is automatically extracted from video streams. Based on carefully investigated analysis tasks, we introduce new views and their corresponding interactions. These views also incorporate domain-specific score tables to guarantee an easy adoption by domain experts. All views are integrated into ErgoExplorer, which relies on coordinated multiple views to facilitate analysis through interaction. ErgoExplorer makes it possible for the first time to examine complex relationships between risk assessments of individual body parts over long sessions that span multiple operations. The newly introduced approach supports analysis and exploration at several levels of detail, ranging from a general overview, down to inspecting individual frames in the video stream, if necessary. We illustrate the usefulness of the newly proposed approach applying it to several datasets., Comment: Accepted for IEEE VIS 2022 and IEEE TVCG

Route planning and navigation are common tasks that often require additional information on points of interest. Augmented Reality (AR) enables mobile users to utilize text labels, in order to provide a composite view associated with additional information in a real-world environment. Nonetheless, displaying all labels for points of interest on a mobile device will lead to unwanted overlaps between information, and thus a context-responsive strategy to properly arrange labels is expected. The technique should remove overlaps, show the right level-of-detail, and maintain label coherence. This is necessary as the viewing angle in an AR system may change rapidly due to users' behaviors. Coherence plays an essential role in retaining user experience and knowledge, as well as avoiding motion sickness. In this paper, we develop an approach that systematically manages label visibility and levels-of-detail, as well as eliminates unexpected incoherent movement. We introduce three label management strategies, including (1) occlusion management, (2) level-of-detail management, and (3) coherence management by balancing the usage of the mobile phone screen. A greedy approach is developed for fast occlusion handling in AR. A level-of-detail scheme is adopted to arrange various types of labels. A 3D scene manipulation is then built to simultaneously suppress the incoherent behaviors induced by viewing angle changes. Finally, we present the feasibility and applicability of our approach through one synthetic and two real-world scenarios, followed by a qualitative user study.

During the last decades, anatomy has become an interesting topic in education---even for laymen or schoolchildren. As medical imaging techniques become increasingly sophisticated, virtual anatomical education applications have emerged. Still, anatomical models are often preferred, as they facilitate 3D localization of anatomical structures. Recently, data physicalizations (i.e., physical visualizations) have proven to be effective and engaging---sometimes, even more than their virtual counterparts. So far, medical data physicalizations involve mainly 3D printing, which is still expensive and cumbersome. We investigate alternative forms of physicalizations, which use readily available technologies (home printers) and inexpensive materials (paper or semi-transparent films) to generate crafts for anatomical edutainment. To the best of our knowledge, this is the first computer-generated crafting approach within an anatomical edutainment context. Our approach follows a cost-effective, simple, and easy-to-employ workflow, resulting in assemblable data sculptures (i.e., semi-transparent sliceforms). It primarily supports volumetric data (such as CT or MRI), but mesh data can also be imported. An octree slices the imported volume and an optimization step simplifies the slice configuration, proposing the optimal order for easy assembly. A packing algorithm places the resulting slices with their labels, annotations, and assembly instructions on a paper or transparent film of user-selected size, to be printed, assembled into a sliceform, and explored. We conducted two user studies to assess our approach, demonstrating that it is an initial positive step towards the successful creation of interactive and engaging anatomical physicalizations.

We present a method for producing documentary-style content using real-time scientific visualization. We produce molecumentaries, i.e., molecular documentaries featuring structural models from molecular biology. We employ scalable methods instead of the rigid traditional production pipeline. Our method is motivated by the rapid evolution of interactive scientific visualization, which shows great potential in science dissemination. Without some form of explanation or guidance, however, novices and lay-persons often find it difficult to gain insights from the visualization itself. We integrate such knowledge using the verbal channel and provide it along an engaging visual presentation. To realize the synthesis of a molecumentary, we provide technical solutions along two major production steps: 1) preparing a story structure and 2) turning the story into a concrete narrative. In the first step, information about the model from heterogeneous sources is compiled into a story graph. Local knowledge is combined with remote sources to complete the story graph and enrich the final result. In the second step, a narrative, i.e., story elements presented in sequence, is synthesized using the story graph. We present a method for traversing the story graph and generating a virtual tour, using automated camera and visualization transitions. Texts written by domain experts are turned into verbal representations using text-to-speech functionality and provided as a commentary. Using the described framework we synthesize automatic fly-throughs with descriptions that mimic a manually authored documentary. Furthermore, we demonstrate a second scenario: guiding the documentary narrative by a textual input.

We present ScaleTrotter, a conceptual framework for an interactive, multi-scale visualization of biological mesoscale data and, specifically, genome data. ScaleTrotter allows viewers to smoothly transition from the nucleus of a cell to the atomistic composition of the DNA, while bridging several orders of magnitude in scale. The challenges in creating an interactive visualization of genome data are fundamentally different in several ways from those in other domains like astronomy that require a multi-scale representation as well. First, genome data has intertwined scale levels---the DNA is an extremely long, connected molecule that manifests itself at all scale levels. Second, elements of the DNA do not disappear as one zooms out---instead the scale levels at which they are observed group these elements differently. Third, we have detailed information and thus geometry for the entire dataset and for all scale levels, posing a challenge for interactive visual exploration. Finally, the conceptual scale levels for genome data are close in scale space, requiring us to find ways to visually embed a smaller scale into a coarser one. We address these challenges by creating a new multi-scale visualization concept. We use a scale-dependent camera model that controls the visual embedding of the scales into their respective parents, the rendering of a subset of the scale hierarchy, and the location, size, and scope of the view. In traversing the scales, ScaleTrotter is roaming between 2D and 3D visual representations that are depicted in integrated visuals. We discuss, specifically, how this form of multi-scale visualization follows from the specific characteristics of the genome data and describe its implementation. Finally, we discuss the implications of our work to the general illustrative depiction of multi-scale data.