Publication | ACM SIGCHI Conference on Human Factors in Computing Systems 2022

AvatAR

An Immersive Analysis Environment for Human Motion Data Combining Interactive 3D Avatars and Trajectories

This paper presents AvatAR, an augmented reality (AR) environment for analyzing recordings of human motion data to gain insights into how humans utilize and interact with their surrounding environment. Using avatars, trajectories, and environmental visualizations (footprints, touchpoints, etc.), we make data not only explorable, but experienceable.

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Abstract

AvatAR: An Immersive Analysis Environment for Human Motion Data Combining Interactive 3D Avatars and Trajectories

Patrick Reipschläger, Frederik Brudy, Raimund Dachselt, Justin Matejka, George Fitzmaurice, Fraser Anderson

ACM SIGCHI Conference on Human Factors in Computing Systems 2022

Analysis of human motion data can reveal valuable insights about the utilization of space and interaction of humans with their environment. To support this, we present AvatAR, an immersive analysis environment for the in-situ visualization of human motion data, that combines 3D trajectories with virtual avatars showing people’s detailed movement and posture. Additionally, we describe how visualizations can be embedded directly into the environment, showing what a person looked at or what surfaces they touched, and how the avatar’s body parts can be used to access and manipulate those visualizations. AvatAR combines an AR HMD with a tablet to provide both mid-air and touch interaction for system control, as well as an additional overview device to help users navigate the environment.We implemented a prototype and present several scenarios to show that AvatAR can enhance the analysis of human motion data by making data not only explorable, but experienceable

AvatAR is an augmented reality environment for analyzing recordings of human motion data, that serves to gain insights into how human utilize and interact with their surrounding environment. We combine head-mounted Augmented Reality with a tablet device to display and manipulate in-situ visualizations of time-varying motion data directly in the environment in which is was recorded.

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