Predicting Students' Wellbeing from Physiology, Phone, Mobility, and Behavioral Data

The goal of this project is to apply machine learning methods to model the wellbeing of MIT undergraduate students. Extensive data is obtained from the SNAPSHOT study, which monitors students on a 24/7 basis, collecting their location, smartphone logs, sleep schedule, phone and SMS communications, academics, social networks, and even physiological markers like skin conductance, skin temperature, and acceleration. We extract features from this data and apply a variety of machine learning algorithms including Multiple Kernel Learning, Gaussian Mixture Models, and Transfer Learning, among others. Interesting findings include: when participants visit novel locations they tend to be happier; when they use their phones or stay indoors for long periods they tend to be unhappy; and when several dimensions of wellbeing (including stress, happiness, health, and energy) are learned together, classification accuracy improves.