Abstract

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 …

Date

2020

Authors

Tiantian Feng, Shrikanth S Narayanan

Conference

ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

1011-1015

Publisher

IEEE