Learning Behavioral Representations from Wearable Sensors
This work addresses the challenge of interpreting physiological data for applications like improving quality of life, though it appears incremental as it builds on existing methods for sensor data analysis.
The paper tackled the problem of extracting behavioral states from wearable sensor data by using a non-parametric Bayesian approach, showing that the learned states can cluster participants into meaningful groups and better predict cognitive and psychological states.
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.