Dear Younger Me Dear Younger Me Dear Younger Me Dear Younger Me

Dear Younger Me is an ICAT strategic-initiative project that creates healing spaces for Black women through psychology, technology, and performance. It begins with a workshop in which participants (referred to as performers) write a letter to their younger self to come to terms with personal growth and past trauma. Those same performers are then recorded reading their notes aloud while wearing an Empatica E4 wristband, capturing heart rate and electrodermal activity (EDA) to register their emotional response in the moment. The project’s current output is a video of three performers reading their letters one by one, set to background music partially driven by their own EDA data.

My role was building the biosignal data-processing pipeline behind that video’s music. Using each performer’s Empatica E4 recording-session data, I extracted the raw EDA signal and decomposed it into its phasic and tonic components, exporting those alongside the heart-rate data. I then synchronized those data timestamps with the timecode of the spliced performance video, so every physiological data point lined up with an exact moment in a performer’s reading. A composer used those two EDA components directly as compositional material — for one performer, an EDA peak triggered the drum track; for another, the EDA response drove the pitch and amplitude of parts of the piece.

To validate and communicate that correspondence, I built a visualization pipeline in Jupyter using pandas and Matplotlib, charting each performer’s EDA response over time with peaks highlighted, then generated a sequence of those charts and composited them over the recorded video using ffmpeg and imagemagick. That gave the team a way to see, side by side, how each performer’s EDA response lined up with the music the composer built from that same data.