AvatAR: An immersive analysis environment for human motion data combining interactive 3D avatars and trajectories
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed
Contributors
Abstract
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.
Details
Original language | English |
---|---|
Title of host publication | Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI) |
Editors | Simone Barbosa, Cliff Lampe, Caroline Appert, David A. Shamma, Steven Drucker, Julie Williamson, Koji Yatani |
Pages | 23:1-23:15 |
Number of pages | 15 |
ISBN (electronic) | 978-1-4503-9157-3 |
Publication status | Published - 2 May 2022 |
Peer-reviewed | No |
External IDs
Scopus | 85130554142 |
---|---|
Mendeley | 9400561e-4605-342e-a42d-fc3fa9f3d89d |
dblp | conf/chi/ReipschlagerBDM22 |
unpaywall | 10.1145/3491102.3517676 |
ORCID | /0000-0002-2176-876X/work/151435427 |
Keywords
ASJC Scopus subject areas
Keywords
- Immersive Analytics, In-situ visualisation, analysing space utilization, augmented/mixed reality, human motion data, motion analysis