%0 Conference Paper %1 1a074a859ba64189884619cd2e2e27e6 %A Luo, Weizhou %A Yu, Zhongyuan %A Rzayev, Rufat %A Satkowski, Marc %A Gumhold, Stefan %A McGinity, Matthew %A Dachselt, Raimund %B CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems %D 2023 %K topic_visualcomputing Analytics, FIS_scads Immersive In-situ affordance, analysis, augmented/mixed data movement physical reality, referents, visualization %R 10.1145/3544548.3580715 %T PEARL: Physical Environment based Augmented Reality Lenses for In-Situ Human Movement Analysis %X This paper presents Pearl, a mixed-reality approach for the analysis of human movement data in situ. As the physical environment shapes human motion and behavior, the analysis of such motion can benefit from the direct inclusion of the environment in the analytical process. We present methods for exploring movement data in relation to surrounding regions of interest, such as objects, furniture, and architectural elements. We introduce concepts for selecting and filtering data through direct interaction with the environment, and a suite of visualizations for revealing aggregated and emergent spatial and temporal relations. More sophisticated analysis is supported through complex queries comprising multiple regions of interest. To illustrate the potential of Pearl, we developed an Augmented Reality-based prototype and conducted expert review sessions and scenario walkthroughs in a simulated exhibition. Our contribution lays the foundation for leveraging the physical environment in the in-situ analysis of movement data.

@inproceedings{1a074a859ba64189884619cd2e2e27e6, abstract = {This paper presents Pearl, a mixed-reality approach for the analysis of human movement data in situ. As the physical environment shapes human motion and behavior, the analysis of such motion can benefit from the direct inclusion of the environment in the analytical process. We present methods for exploring movement data in relation to surrounding regions of interest, such as objects, furniture, and architectural elements. We introduce concepts for selecting and filtering data through direct interaction with the environment, and a suite of visualizations for revealing aggregated and emergent spatial and temporal relations. More sophisticated analysis is supported through complex queries comprising multiple regions of interest. To illustrate the potential of Pearl, we developed an Augmented Reality-based prototype and conducted expert review sessions and scenario walkthroughs in a simulated exhibition. Our contribution lays the foundation for leveraging the physical environment in the in-situ analysis of movement data. }, added-at = {2024-11-28T16:27:18.000+0100}, author = {Luo, Weizhou and Yu, Zhongyuan and Rzayev, Rufat and Satkowski, Marc and Gumhold, Stefan and McGinity, Matthew and Dachselt, Raimund}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/20623f502bba972220a90f07bd0837d17/scadsfct}, booktitle = {CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems}, day = 19, doi = {10.1145/3544548.3580715}, interhash = {2d5840caf57c13c9077acb45b196c25f}, intrahash = {0623f502bba972220a90f07bd0837d17}, keywords = {topic_visualcomputing Analytics, FIS_scads Immersive In-situ affordance, analysis, augmented/mixed data movement physical reality, referents, visualization}, language = {English}, month = apr, note = {Publisher Copyright: {\textcopyright} 2023 Owner/Author.}, timestamp = {2024-11-28T17:41:40.000+0100}, title = {PEARL: Physical Environment based Augmented Reality Lenses for In-Situ Human Movement Analysis}, year = 2023 }