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

@article{Jaques2015PredictingSH,
  title={Predicting students' happiness from physiology, phone, mobility, and behavioral data},
  author={Natasha Jaques and Sara Taylor and Asaph Azaria and Asma Ghandeharioun and Akane Sano and Rosalind W. Picard},
  journal={2015 International Conference on Affective Computing and Intelligent Interaction (ACII)},
  year={2015},
  pages={222-228}
}
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… Expand
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