  Continuous collection of physiological data from wearable sensors enables
temporal characterization of individual behaviors. Understanding the relation
between an individual's behavioral patterns and psychological states can help
identify strategies to improve quality of life. One challenge in analyzing
physiological data is extracting the underlying behavioral states from the
temporal sensor signals and interpreting them. Here, we use a non-parametric
Bayesian approach to model sensor data from multiple people and discover the
dynamic behaviors they share. We apply this method to data collected from
sensors worn by a population of hospital workers and show that the learned
states can cluster participants into meaningful groups and better predict their
cognitive and psychological states. This method offers a way to learn
interpretable compact behavioral representations from multivariate sensor
signals.
