  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.
