Spotify Mood
Engine.
Personal Spotify listening data clustered into mood profiles to identify behavioral listening patterns over time.
What can wearable data tell us about mental health, before it shows up in a survey?
[The gap: most mental health prediction relies on self-report. Wearables capture behavior continuously, but most analyses are correlational. Can we move from passive tracking to actionable early insight?]
From raw signal to causal claim, in four stages.
[The pipeline: pre-processing biometric streams, K-Means clustering on behavioral features, regression to depression outcomes, LiNGAM to test directionality.]
Sleep and steps — not heart rate — drive the signal.
[The key finding: sleep duration and daily step count emerged as the strongest behavioral predictors. LiNGAM revealed direct (not just correlational) paths to depression risk.]
Causal claims need humility, and structure.
[Reflection: where the method held up, where it didn't, and what it would take to take this from research to a real product.]