Data-Driven AI

Data-Driven Immersive Analytics (DDIA)

Digital representations of real objects (digital twins) are becoming increasingly important, and not just since the Corona pandemic, where the benefits of “immersive” work environments were first demonstrated on a global scale. Immersive is the term used to describe immersion in digitally visualized worlds in which it is possible to access and interact with data or digital twins of objects.

Nevertheless, the usability of these immersive analysis and interaction methods is currently not always optimal because the digital methods follow different logics than the physical, familiar environment.

In this research project, we are working to improve such experiences and provide personalized and user-friendly digital experiences with data and analytics derived from a real-world environment. Both for individual users and for entire teams in dislocated collaboration.

Vision and strategy

Digital twins are representations of real objects of different complexity. However, because remote users:ins have a limited sense of space, and the physical world and the language of programmed interaction and analysis metaphors do not match well, these digital twins often appear insufficiently visualized.

However, a consistent experience of data and analytics is critical to the utility of such digital objects.

This project aims to research and develop technological solutions to address these limitations and strengthen the ability to act from a safe distance.
The considerable sums being invested in digitization are leading to complex “cyberphysical systems”, i.e., physical systems monitored and controlled by networks of sensors and computational cores. Various data representing the physical entity are collected in digital twins that include 3D structural data, semantic information, and sensor data.

Still, despite recent advances, the sensory experience offered by immersive technologies is rather limited. Data is available, but access, interaction, and analysis are separate from the physical entity. These are tedious activities when done in the physical world. Visualization and interaction metaphors and analysis methods were simply not developed to represent a physical world.

However, in various disciplines, we need digital information about physical entities in real time. So, how should information about physically and digitally existing entities best be combined in a coherent presentation? And what are appropriate interaction paradigms for analysis of processes in immersive environments?

Targets

In this project, new computational methods for immersive analysis are developed based on
(1) Support data that originates from a physical entity via a digital twin,
(2) data originating from the user and collected with physiological sensors; and
(3) Data derived from the immersive experience, called traces, recorded with software sensors.

In short, it’s about how to present and interact with immersive data views, what physiological features lead to perceptual optimization and personalization, and how to use traces of interactions in the environment to create recommendations and guidance.

The goal is to provide personalized experiences with data and analytics anchored in the real world – for the individual participant or together with peers locally or remotely.

  • Exploring models of embodied interaction with immersive analytics.
  • Developing paradigms for collaborative immersive remote analysis.
  • Exploring personalization based on physiological sensing and social immersive training.

Project Structure

Funding

The COMET Module proposes research on groundbreaking topics aiming to enable immersive analytics (tele-) collaboration, bringing two new perspectives so far rarely considered around immersive computing: personalization and paradigms for social learning.

This is a 4-year project with a funding rate of 80% and requires a commitment from private-sector partners for the total amount of EUR 140,000 per year, distributed among five (5) partners. For each partner, this amounts to max 112,000 EUR distributed in four years (or 28,000 EUR per year, over 4 years).