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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.