  Health management is getting increasing attention all over the world.
However, existing health management mainly relies on hospital examination and
treatment, which are complicated and untimely. The emerging of mobile devices
provides the possibility to manage people's health status in a convenient and
instant way. Estimation of health status can be achieved with various kinds of
data streams continuously collected from wearable sensors. However, these data
streams are multi-source and heterogeneous, containing complex temporal
structures with local contextual and global temporal aspects, which makes the
feature learning and data joint utilization challenging. We propose to model
the behavior-related multi-source data streams with a local-global graph, which
contains multiple local context sub-graphs to learn short term local context
information with heterogeneous graph neural networks and a global temporal
sub-graph to learn long term dependency with self-attention networks. Then
health status is predicted based on the structure-aware representation learned
from the local-global behavior graph. We take experiments on StudentLife
dataset, and extensive results demonstrate the effectiveness of our proposed
model.
