Thesis subject

MSc thesis topic: Aurora digital twin - eye-tracking and movement data in virtual reality

Spatial data and its analysis can take many forms. Virtual environments, for example, are inherently spatial and allow for collecting an abundance of data such as eye-tracking and movement patterns of users. We can apply spatial data science and advanced visualization approaches to better understand and interpret this data.

Using this approach, we are in the process of building the capacity of developing a deeper understanding of human behavior and cognitive processing in different spaces such as the newly built living lab: the aurora cafeteria. The aurora canteen offers the opportunity to provide a better understanding of how food systems and food catering can be adjusted to better serve society by making it easier to make sustainable food choices.

Immersive technologies such as augmented and virtual reality, are entering a new era. Thanks to substantial investments by leading companies (e.g., Facebook(™), Microsoft(™), and Google(™)), immersive media are becoming mainstream, that is, they are affordable and rarely cause cybersickness anymore. At the same time, the creation and the design of immersive experiences are getting easier and easier and accessible to many more people. Tools such as Unity(™) or Unreal(™), that is, game engines, are sophisticated and well-supported development environments.

In this particular case, the virtual environment of the Aurora Canteen is already built and the focus of the project will be on the opportunities to collect time-series spatial data of people in a virtual environment, to analyze and visualize the data, and to develop an understanding of what we can learn from such data about human behavior and cognitive processes.

Relevance to research/projects

This project is part of an OneWageningen initiative as a collaboration between GRS and Marketing and Consumer Behavior. The project is additionally supported by a university-wide research effort (WANDER) seeking to establish immersive technologies firmly in the portfolio of research and education tools for environmental and societal challenges.

Objectives

  • Design different scenarios within the digital twin of the Aurora canteen
  • Think through a conceptual framework for what kind of spatial data can be collected in virtual environments and how we can apply spatial data science approaches to this data.
  • Analyze and visualize the data collected in this virtual environment.
  • Evaluate the effects of different scenarios

Literature

  • Bialkova, Svetlana; Grunert, Klaus G.; van Trijp, Hans (2020): From desktop to supermarket shelf: Eye-tracking exploration on consumer attention and choice. In Food Quality and Preference 81, p. 103839.
  • Taufik, Danny; Kunz, Marvin C.; Onwezen, Marleen C. (2021): Changing consumer behaviour in virtual reality: A systematic literature review. In Computers in Human Behavior Reports 3, p. 100093.
  • Huang, Jiawei; Klippel, Alexander (2020): The effects of visual realism on spatial memory and exploration patterns in virtual reality. In Robert J. Teather (Ed.): VRST '20: 26th ACM Symposium on Virtual Reality Software and Technology. New York, NY, United States: Association for Computing Machinery (ACM Digital Library), pp. 1–11.
  • Edwards, Caitlyn; Masterson, Travis D.; Sajjadi, Pejman; Fatemi, A.; Krieger, Erica; Klippel, Alexander (accepted): The Immersive Virtual Alimentation and Nutrition (IVAN) Application: An Interactive Digital Dietitian Journal of Nutrition Education and Behavior. In Journal of Nutrition Education and Behavior.
  • Wallgrün, Jan Oliver; Bagher, Mahda M.; Sajjadi, Pejman; Klippel, Alexander (2020): A comparison of visual attention guiding approaches for 360° image-based VR tours. In : 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). Piscataway, New Jersey: IEEE, pp. 83–91.

Requirements

  • An interest in technologies and gaming
  • Interest in working with immersive technologies
  • Interest in empirical studies and evaluations
  • Interest in data science and analytics

Theme(s): Modelling & visualisation; Human – space interaction