Most sports visualizations rely on a combination of spatial, highly temporal, and user-centric da... more Most sports visualizations rely on a combination of spatial, highly temporal, and user-centric data, making sports a challenging target for visualization. Emerging technologies, such as augmented and mixed reality (AR/XR), have brought exciting opportunities along with new challenges for sports visualization. We share our experience working with sports domain experts and present lessons learned from conducting visualization research in SportsXR. In our previous work, we have targeted different types of users in sports, including athletes, game analysts, and fans. Each user group has unique design constraints and requirements, such as obtaining real-time visual feedback in training, automating the low-level video analysis workflow, or personalizing embedded visualizations for live game data analysis. In this paper, we synthesize our best practices and pitfalls we identified while working on SportsXR. We highlight lessons learned in working with sports domain experts in designing and evaluating sports visualizations and in working with emerging AR/XR technologies. We envision that sports visualization research will benefit the larger visualization community through its unique challenges and opportunities for immersive and situated analytics.
Fig. 1: Our Omnioculars prototype supports a personalized and interactive game viewing experience... more Fig. 1: Our Omnioculars prototype supports a personalized and interactive game viewing experience with embedded visualizations for in-game analysis. We created a simulated basketball game environment to support our design probe of embedded visualizations.
Figure 1: Visualization design for real-time visual feedback during basketball free-throw shot pr... more Figure 1: Visualization design for real-time visual feedback during basketball free-throw shot practice. (a) A co-located 2D visualization on a desktop display (yellow arrow). (b) First-person view of a situated 3D visualization on an AR HMD.
IEEE Transactions on Visualization and Computer Graphics, 2021
Augmented Reality (AR) embeds digital information into objects of the physical world. Data can be... more Augmented Reality (AR) embeds digital information into objects of the physical world. Data can be shown in-situ, thereby enabling real-time visual comparisons and object search in real-life user tasks, such as comparing products and looking up scores in a sports game. While there have been studies on designing AR interfaces for situated information retrieval, there has only been limited research on AR object labeling for visual search tasks in the spatial environment. In this paper, we identify and categorize different design aspects in AR label design and report on a formal user study on labels for out-of-view objects to support visual search tasks in AR. We design three visualization techniques for out-of-view object labeling in AR, which respectively encode the relative physical position (height-encoded), the rotational direction (angle-encoded), and the label values (value-encoded) of the objects. We further implement two traditional in-view object labeling techniques, where labels are placed either next to the respective objects (situated) or at the edge of the AR FoV (boundary). We evaluate these five different label conditions in three visual search tasks for static objects. Our study shows that out-of-view object labels are beneficial when searching for objects outside the FoV, spatial orientation, and when comparing multiple spatially sparse objects. Angle-encoded labels with directional cues of the surrounding objects have the overall best performance with the highest user satisfaction. We discuss the implications of our findings for future immersive AR interface design.
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 2021
Figure 1: Visualization design for real-time visual feedback during basketball free-throw shot pr... more Figure 1: Visualization design for real-time visual feedback during basketball free-throw shot practice. (a) A co-located 2D visualization on a desktop display (yellow arrow). (b) First-person view of a situated 3D visualization on an AR HMD.
We wish to thank Coach Kathy Delaney-Smith, Mike Roux, Mark Kaliris, and Lindsay Werner at Harvar... more We wish to thank Coach Kathy Delaney-Smith, Mike Roux, Mark Kaliris, and Lindsay Werner at Harvard Women’s Basketball, and Mike Sotsky and Casey Brinn at Harvard Men’s Basketball for their time and expertise. This research is supported in part by King Abdullah University of Science and Technology (KAUST) and the KAUST Office of Sponsored Research (OSR) award OSR-2015-CCF-2533-01.
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Immersive Analytics is a quickly evolving field that unites several areas such as visualisation, ... more Immersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and humancomputer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publications, spanning several conferences. Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption. We present 17 key research challenges developed over multiple sessions by a diverse group of 24 international experts, initiated from a virtual scientific workshop at ACM CHI 2020. These challenges aim to coordinate future work by providing a systematic roadmap of current directions and impending hurdles to facilitate productive and effective applications for Immersive Analytics.
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Papers by Tica Lin