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ACII
2015
Springer

Predicting students' happiness from physiology, phone, mobility, and behavioral data

3 years 8 days ago
Predicting students' happiness from physiology, phone, mobility, and behavioral data
—In order to model students’ happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness. Because of the relationship between happiness and depression, modeling happiness may help us to detect individuals who are at risk of depression and guide interventions to help them. We are also interested in how behavioral factors (such as sleep and social activity) affect happiness positively and negatively. A variety of machine learning and feature selection techniques are compared, including Gaussian Mixture Models and ensemble classification. We achieve 70% classification accuracy of self-reported happiness on held-out test data.
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACII
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