Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be better positioned to deliver targeted, just-in-time mental health interventions via mobile applications. This work attempts to personalize the passive recognition of negative affect states via group-based modeling of user behavior patterns captured from mobility, communication, and activity patterns. Results show that group models outperform generalized models in a dataset based on two weeks of users' daily lives.
@article{arxiv.1802.00029,
title = {Cluster-based Approach to Improve Affect Recognition from Passively Sensed Data},
author = {Mawulolo K. Ameko and Lihua Cai and Mehdi Boukhechba and Alexander Daros and Philip I. Chow and Bethany A. Teachman and Matthew S. Gerber and Laura E. Barnes},
journal= {arXiv preprint arXiv:1802.00029},
year = {2018}
}