Discovering Optimal Variable-length Time Series Motifs in Large-scale Wearable Recordings of Human Bio-behavioral Signals

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-term wearable time-series. We also describe challenges in using just a single method to detect motifs in wearable time series in our experiments. We demonstrate our pipeline can effectively identify meaningful variable-length motifs in large-scale heart rate signals collected continuously from over 100 individuals both at and outside their workplace over 10 weeks through two machine learning experiments.