  Sensor-based human activity recognition has become a critical component of
many emerging applications ranging from behavioral medicine to gaming. However,
an unprecedented increase in the diversity of sensor devices in the
Internet-of-Things era has limited the adoption of activity recognition models
for use across different domains. We propose ActiLabel a combinatorial
framework that learns structural similarities among the events in an arbitrary
domain and those of a different domain. The structural similarities are
captured through a graph model, referred to as the it dependency graph, which
abstracts details of activity patterns in low-level signal and feature space.
The activity labels are then autonomously learned by finding an optimal tiered
mapping between the dependency graphs. Extensive experiments based on three
public datasets demonstrate the superiority of ActiLabel over state-of-the-art
transfer learning and deep learning methods.
