I spent the last two week in planning and discovery phase of the phase 3 project. It started with identifying the best visualization form to extrapolate the study findings from AP-Multimer research study done by the community partner. (based on data collected)
I did the initial analysis of the three datasets received (EEG, heart rate, and motion) from the community partner. These datasets have been cleaned, parsed and analyzed using Python analysis libraries by the Multimer team. Initially, there were a copious amount of data and large file size when were broken down to the small chunk of files and optimize it.
I also worked on setting up the development workflow for the final project. I would be using Webpack and npm scripts and Node server to bundle the application code and related assets.
Based on the technology stack selected, D3 will be the choice of standard visualization library. I am planning on integrating this with the React ecosystem for several reasons.I picked up this approach because it supports component creation framework that lets you build self-contained elements (like div or svg:rect) that have custom rendering methods, properties, state, and lifecycle methods.
My approach to the visualization style would be a data dashboard that provides users with multiple perspectives into the data as well as the ability to filter between different categories and see individual data points. The major component would be a force directed graphs that show the correlation between different sentiments for each story type. I am currently exploring various features and limitations of the force directed graphs and what steps are required to transform the final project data suitable for this type of visualization. From an interaction standpoint, the dashboard would include category menu, form filters so that user can compare visualization for different story videos. It would also include tooltip overlays to provide additional information about the survey results.