Medical Digital Twin

The researcher Gunnar Cedersund is, together with his team, creating digital twins based on data models of cells, organs and the body as a whole that, when combined with patient specific data, can provide predictions for the patient’s future health based on medications, diets, exercises, etc. 

The data that they produce through machine learning algorithms is in the form of graphs that can show how your body changes over time based on treatment and behavior. The goal is for the patient to engage with the prediction and potentially change behavior to change the health outcome. However, the researchers believe that graphs alone are too abstract and not efficient for driving engagement in the patient. They felt that a more personalized way of presenting the data was needed.  

Using a technology called Metahuman, InfraVis created an application with personalized 3D avatars, based on 3D scanned data of patients, and customized to look like them. The goal is to provide the patient with a more accessible interface for them to explore their personal data – their own body. 

Extended Rephotography

Extended Rephotography is a three-year interdisciplinary project on climate change that is developing uses of Extended Reality technologies for artistic research based on 360° video. The aim of the project is to develop methods for using immersive passages of time to observe the effects of a climate in transition. As their data spans over 400 years, and consists of photographs, 360 video, maps, and observations related to vast areas in Svalbard, the research group needed to collaboratively organize their data in time and space. 

What was required of Infravis was expertise in web development and UX design to create an online tool to register, explore and compare the data. This also included assistance for the project with data modelling and backend development to help the research team structure the material they brought back from Svalbard. The solution will help the research team compare their data to the historical material and plan future expeditions in terms of data acquisition. 

In-situ Visualization Support for AMR Meshes in NEK5000

Researchers at KTH wanted to gain a better understanding of how turbulence behaves around airplane wings, which could aid engineers in designing more efficient aircraft. The intermediate data produced by related simulations is typically discarded because it is too large to save. InfraVis helped get a deeper understanding of the data by analyzing and visualizing it while the simulation was running on a supercomputer, before the data was discarded. 

As part of this project, the InfraVis team visualized how the Magnus effect is employed in the Flettner rotor wherein the rotor deflects into a low-pressure zone caused by its rotation and allows for harvesting the wind energy. Given that around 80% of all transport occurs via ships, resulting in more than 15% of all carbon emissions, harvesting the wind energy using e.g. the Flettner rotor could save up to 15% of fuel consumption and be a step in the direction for a more sustainable form of transport.