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

International Conference on Affective Computing and Intelligent Interaction and Workshops : [proceedings]
Natasha JaquesRosalind Picard

Abstract

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.

Citations

Sep 28, 2017·PloS One·Orianna DeMasiBenjamin Recht
Sep 26, 2019·General Psychiatry·Naixin ZhangGuang He
Aug 16, 2019·Journal of Medical Internet Research·Michael JonesNoah Zimmerman
Dec 5, 2019·Journal of Exposure Science & Environmental Epidemiology·Jose G Cedeno LaurentJohn D Spengler
Oct 14, 2016·Social Psychiatry and Psychiatric Epidemiology·William J Kowalczyk
Oct 7, 2017·JMIR MHealth and UHealth·Orianna DeMasiBenjamin Recht
Feb 17, 2021·Brain Informatics·Xiaohui TaoJiajin Huang

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