Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series

• URL: http://arxiv.org/abs/2002.03595v3
• Date: Wed, 27 May 2020 21:14:48 GMT
• Title: Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series
• Authors: Xian Wu, Chao Huang, Pablo Roblesgranda, Nitesh Chawla
• Abstract summary: HeartSpace encodes time series data with variable-length and missing values via the integration of a time series encoding module and a pattern aggregation network. HeartSpace implements a Siamese-triplet network to optimize representations by jointly capturing intra- and inter-series correlations. The empirical evaluation over two different real-world data presents significant performance gains overstate-of-the-art baselines in a variety of applications.
• Score: 29.061466414756925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The prevalence of wearable sensors (e.g., smart wristband) is creating unprecedented opportunities to not only inform health and wellness states of individuals, but also assess and infer personal attributes, including demographic and personality attributes. However, the data captured from wearables, such as heart rate or number of steps, present two key challenges: 1) the time series is often of variable-length and incomplete due to different data collection periods (e.g., wearing behavior varies by person); and 2) inter-individual variability to external factors like stress and environment. This paper addresses these challenges and brings us closer to the potential of personalized insights about an individual, taking the leap from quantified self to qualified self. Specifically, HeartSpace proposed in this paper encodes time series data with variable-length and missing values via the integration of a time series encoding module and a pattern aggregation network. Additionally, HeartSpace implements a Siamese-triplet network to optimize representations by jointly capturing intra- and inter-series correlations during the embedding learning process. The empirical evaluation over two different real-world data presents significant performance gains overstate-of-the-art baselines in a variety of applications, including personality prediction, demographics inference, and user identification.

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