motif2vec: Semantic-aware Representation Learning for Wearables' Time Series Data

Abstract

The proliferation of wearable sensors allows for the continuous collection of temporal characterization of an individual's physical activity and physiological data. This is enabling an unprecedented opportunity to delve into a deeper analysis of the underlying patterns of such temporal data and to infer attributes associated with health, behaviors, and well-being. However, there remain several challenges to fully discover both structural and temporal patterns (motifs) in these data streams and to leverage the semantic relationship among these motifs. These include: i) the temporal data of variable length and high resolution leads to the motifs of various sizes; ii) periodic occurrences and hierarchical overlaps of these motifs further challenge the modeling of their complex structural and semantic relations. We propose a semantic-aware unsupervised representation learning model, motif2vec, to learn the latent representation of time series data collected from wearable sensors. The motif2vec consists of three major components: 1) transforming the time series into a set of variable-length motif sequences; 2) formalizing random walks to construct the neighborhood of motifs and thus to extract structural and semantic relationship among motifs; 3) learning time series latent features to capture the motif neighborhood structure with a skip-gram model. Experiments on two real-world datasets, derived from two different wearables and population groups, show motif2vec outperforms six state-of-the-art benchmarks on various tasks.