Projekte

Recent research in the area of immersive analytics demonstrated the utility of head-mounted augmented reality devices for visual data analysis. However, it can be challenging to use the by default supported mid-air gestures to interact with visualizations in augmented reality (e.g. due to limited precision). Touch-based interaction (e.g. via mobile devices) can compensate for these drawbacks, but is limited to two-dimensional input. In this work we present STREAM: Spatially-aware Tablets combined with Augmented Reality Head-Mounted Displays for the multimodal interaction with 3D visualizations. We developed a novel eyes-free interaction concept for the seamless transition between the tablet and the augmented reality environment. A user study reveals that participants appreciated the novel interaction concept, indicating the potential for spatially-aware tablets in augmented reality. Based on our findings, we provide design insights to foster the application of spatially-aware touch devices in augmented reality and research implications indicating areas that need further investigation.

Mobile intervention studies employ mobile devices to observe participants’ behavior change over several weeks. Researchers regularly monitor high-dimensional data streams to ensure data quality and prevent data loss (e.g., missing engagement or malfunctions). The multitude of problem sources hampers possible automated detection of such irregularities – providing a use case for interactive dashboards. With the advent of untethered head-mounted AR devices, these dashboards can be placed anywhere in the user's physical environment, leveraging the available space and allowing for flexible information arrangement and natural navigation. In this work, we present the user-centered design and the evaluation of IDIAR: Interactive Dashboards in AR, combining a head-mounted display with the familiar interaction of a smartphone. A user study with 15 domain experts for mobile intervention studies shows that participants appreciated the multimodal interaction approach. Based on our findings, we provide implications for research and design of interactive dashboards in AR.

Prolonged sedentary behavior contributes to many chronic diseases. An appropriate reminder could help screen-based workers to reduce their prolonged sedentary behavior. The fixed-duration point-of-choice prompt has been frequently used in related work. However, this prompting system has several drawbacks. In this paper, we propose the SedentaryBar, a context-aware reminding system using an always-on progress bar to show the duration of a working session, as an alternative to the prompt. The new reminding system uses both users' keyboard/mouse events on the computer and the state-of-the-art computer vision algorithm with the webcam to detect users' presence, which makes the system more accurate and intelligent. Our evaluation study compared the SedentaryBar and the prompt using subjective and objective measurements. After using each method for a week respectively, more participants preferred the SedentaryBar. The participants' perceived interruption and usefulness also suggested the SedentaryBar was more popular during the study. However, the logged data of the participants' working durations indicated the prompt was more effective in reducing their sedentary behavior.

Scientific evidence has shown that long-term sedentary behaviour is detrimental to human health. Therefore, a trend appears in the field of healthy lifestyle promotion that more attention is drawn to sedentary behaviour rather than only physical activity. However, technology-based mobile health intervention tools targeting reducing sedentary behaviour are still lacking. This paper aims to explore a solution for sedentary behaviour change through supporting action planning. Action planning can not only bridge the intention-behavior gap in controlled motivation processes, but also enforce the cue-behavior association in unconscious processes. We present a smartphone-based personal mobility pattern visualization, with which we expect the users can make better action plans. The interactive visualization integrates temporal and spatial patterns of personal sedentary and walking behaviour, to provide explicit hints on when, where, and how to reduce sedentary behaviour and increase daily steps. We also present our experimental design to evaluate the visualization- based intervention tool.

Immersive technologies such as augmented reality devices are opening up a new design space for the visual analysis of data. This paper studies the potential of an augmented reality environment for the purpose of collaborative analysis of multidimensional, abstract data. We present ART, a collaborative analysis tool to visualize multidimensional data in augmented reality using an interactive, 3D parallel coordinates visualization. The visualization is anchored to a touch-sensitive tabletop, benefiting from well-established interaction techniques. The results of group-based, expert walkthroughs show that ART can facilitate immersion in the data, a fluid analysis process, and collaboration. Based on the results, we provide a set of guidelines and discuss future research areas to foster the development of immersive technologies as tools for the collaborative analysis of multidimensional data.

In the domain of human behavior prediction, next-place prediction is an active research field. While prior work has applied sequential and temporal patterns for next-place prediction, no work has yet studied the prediction performance of combining sequential with temporal patterns compared to using them separately. In this paper, we address next-place prediction using the sequential and temporal patterns embedded in human mobility data that has been collected using the GPS sensor of smartphones. We test five next-place prediction methods, including single pattern-based methods and hybrid methods that combine temporal and sequential patterns. Instead of only examining average accuracy as in related work, we additionally evaluate the selected methods using incremental-prediction accuracy on two publicly available datasets (the MDC dataset and the StudentLife dataset). Our results suggest that (1) integrating multiple patterns is not necessarily more effective than using single patterns in average prediction accuracy, (2) most of the tested methods can outperform others for a certain time period (either for the prediction of all places or each place individually), and (3) average prediction accuracies of the top-three candidates using sequential patterns are relatively high (up to 0.77 and 0.91 in the median for both datasets). For real-time applications, we recommend applying multiple methods in parallel and choosing the prediction of the best method according to incremental-prediction accuracy. Lastly, we present an expert tool for visualizing the prediction results.

HCI research has demonstrated Mixed Reality (MR) as being beneficial for co-located collaborative work. For remote collaboration, however, the collaborators' visual contexts do not coincide due to their individual physical environments. The problem becomes apparent when collaborators refer to physical landmarks in their individual environments to guide each other's attention. In an experimental study with 16 dyads, we investigated how the provisioning of shared virtual landmarks (SVLs) influences communication behavior and user experience. A quantitative analysis revealed that participants used significantly less ambiguous spatial expressions and reported an improved user experience when SVLs were provided. Based on these findings and a qualitative video analysis we provide implications for the design of MRs to facilitate remote collaboration.

In this paper, we propose the idea of examining the effect of self-assembly on the success of mobile health persuasive technology. The IKEA effect shows that individuals evaluate products assembled by themselves more positively than pre-assembled products. The IKEA effect has been proven in several domains, e.g., in human robot interaction, where participants who assembled the robot evaluated the robot and the interaction with the robot more favorably than participants who did not assemble the robot themselves. We propose that the IKEA effect exists in the context of mobile health persuasive technology and has high potential for improving users' engagement and long-term user experience of mobile health persuasive applications. In this paper, we describe the IKEA effect and its potential for mobile health applications. In addition, we propose an experimental design to analyze the effect of self-assembly on user engagement and satisfaction.

In this paper we propose Mixed Reality (MR) interfaces as tools for the analysis and exploration of health-related data. Reported findings originate from the research project "SMARTACT" in which several intervention studies are conducted to investigate how participants' long-term health behavior can be improved. We conducted a focus group to identify limitations of current data analysis technologies and practices, possible uses of MR interfaces and associated open questions to leverage their potentials in the given domain.

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dc.contributor.author: Butscher, Simon; Wang, Yunlong; Ziesemer, Katrin; Villinger, Karoline; Wahl, Deborah R.; König, Laura M.; Sproesser, Gudrun; Renner, Britta; Schupp, Harald T.; Reiterer, Harald

This is the demo version of our workshop paper (Supporting Self-Assembly: The IKEA Effect on Mobile Health Persuasive Technology). We describe our proposal and experimental plan in this demo.

Personalized and contextual interventions are promising techniques for mobile persuasive technologies in mobile health. In this paper, we propose the "fingerprints" technique to analyze the users' daily behavior patterns to find the meaningful moments to better support mobile persuasive technologies, especially mobile health interventions. We assume that for many persons, their behaviors have patterns and can be detected through the sensor data from smartphones. We develop a three-step interactive machine learning workflow to describe the concept and approach of the "fingerprints" technique. By this we aim to implement a practical and light-weight mobile intervention system without burdening the users with manual logging. In our feasibility study, we show results that provide first insights into the design of the "fingerprints" technique.

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dc.contributor.author: Butscher, Simon; Reiterer, Harald

In Mixed Reality (MR) environments, virtual objects can be represented as if they were situated in the viewer’s physical environment. While the potentials of MR have been recognized and extensively researched for single user scenarios (e.g., in perceptual studies), MR for collaborative scenarios has not been widely investigated. In this paper we propose MR environments as ecologies for collaborative, cross-device interaction. We provide a scenario that illustrates its potentials and discuss possible research directions. We then present intermediate results of our research.