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Modeling Behavioral Consistency in Large-Scale Wearable Recordings of Human Bio-Behavioral Signals

Continuously-worn wearable sensors provide an unprecedented opportunity to unobtrusively measure rich bio-behavioral time-series recordings in natural settings such as the workplace. These time-series data can be helpful in inferring broad patterns of behavior such as common routines and daily stress. Many existing approaches either rely on rigid pre-defined notions of activities or use sensitive contextual measurements, such as GPS location or localization within the home, that present privacy concerns and measurement challenges. In this work, we introduce a novel data processing pipeline to model behavioral consistency in a large real-world wearable recording data-set collected in a hospital workplace setting from nurses and direct clinical providers for a period of ten weeks. We use a non-parametric clustering method to generate time series clusters and capture behavioral consistency via the activity curve model. We evaluate the behavioral consistency model under different work roles and conditions such as between different groups of nursing professions and day versus night shift individuals. We also demonstrate that the learned behavioral consistency feature can assist in predicting self-reported work behaviors and anxiety levels.