This week was mainly focused on bringing together the different stages of our project and transforming the raw tracking data into a more coherent visual experience. Building on the work from the previous week, we expanded our MediaPipe setup by introducing heatmaps to represent areas of high activity. Rather than simply showing tracked points and lines, the heatmaps allowed us to visualize where movement was concentrated, adding another layer to the idea of the AI observing and analysing the subject. We also spent time experimenting with motion trails and refining the way movement data was displayed to make the visuals feel more dynamic and informative.
As we developed these elements, we realised that having access to data was not enough; we needed to communicate it in a way that felt believable and visually engaging. We spent time researching references on data visualisation, interface design and motion graphics, looking at how graphs, labels and different visual elements could work together to create the impression of a sophisticated analytical system. We also began working on the final stages of the experience, including the completion screen. Inspired by some of the references we found, we designed a sequence that displayed messages such as “100% Complete” and “Subject Categorized”, helping create the impression that the AI had successfully processed the participant.
During our tutorial with Serra, she once again stressed the importance of clarity and encouraged us to think carefully about who the audience was and how the experience would be understood. Following her advice, we decided to fully embrace the 270-degree room for the final presentation and restructured the project around that environment. We also developed the analysis stage using the POPs plugin in TouchDesigner, creating abstract particle formations that represented the AI processing large amounts of human data. This became the most visually overwhelming section of the project and contrasted nicely with the final stage, where the visuals became much cleaner and more machine-like. By the end of the week, we had established a clear progression from data collection, to analysis, and finally to categorisation. For the first time, it felt like the individual pieces were coming together into a complete experience rather than separate experiments.