PEARL: Physical Environment based Augmented Reality Lenses for In-Situ Human Movement Analysis


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.

Weizhou Luo
Technische Universität Dresden, Dresden, Germany
Zhongyuan Yu
TU Dresden, Dresden, Germany
Rufat Rzayev
Technische Universität Dresden, Dresden, Germany
Marc Satkowski
Technische Universität Dresden, Dresden, Germany
Stefan Gumhold
TU Dresden, Dresden, Germany
Matthew McGinity
Technische Universität Dresden, Dresden, Germany
Raimund Dachselt
Technische Universität Dresden, Dresden, Germany


会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: Immersive & Ubiquitous Analytics

Hall D
6 件の発表
2023-04-25 18:00:00
2023-04-25 19:30:00