Abstract

Continuously-worn wearable sensors produce copious amounts of rich bio-behavioral time series recordings. Exploring recurring patterns, often known as motifs, in wearable time series offers critical insights into understanding the nature of human behavior. Challenges in discovering motifs from wearable recordings include noise removal, pattern generalization, and accounting for subtle variations between subsequences in one motif set. In this work, we introduce a time series processing pipeline to summarize an optimal set of variable-length motifs in a real-world wearable recording data-set collected in a hospital workplace setting. We propose the use of the Savitzky-Golay filter for noise removal without significant data distortion. We then combine the previously developed HierarchIcal based Motif Enumeration (HIME) algorithm with a principled optimization approach to obtain the most repetitive patterns in long …

Date

2019

Authors

Tiantian Feng, Shrikanth S Narayanan

Conference

ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

7615-7619

Publisher

IEEE