  Rare life events significantly impact mental health, and their detection in
behavioral studies is a crucial step towards health-based interventions. We
envision that mobile sensing data can be used to detect these anomalies.
However, the human-centered nature of the problem, combined with the
infrequency and uniqueness of these events makes it challenging for
unsupervised machine learning methods. In this paper, we first investigate
granger-causality between life events and human behavior using sensing data.
Next, we propose a multi-task framework with an unsupervised autoencoder to
capture irregular behavior, and an auxiliary sequence predictor that identifies
transitions in workplace performance to contextualize events. We perform
experiments using data from a mobile sensing study comprising N=126 information
workers from multiple industries, spanning 10106 days with 198 rare events
(<2%). Through personalized inference, we detect the exact day of a rare event
with an F1 of 0.34, demonstrating that our method outperforms several
baselines. Finally, we discuss the implications of our work from the context of
real-world deployment.
