  The ubiquitous availability of smartphones and smartwatches with integrated
inertial measurement units (IMUs) enables straightforward capturing of human
activities. For specific applications of sensor based human activity
recognition (HAR), however, logistical challenges and burgeoning costs render
especially the ground truth annotation of such data a difficult endeavor,
resulting in limited scale and diversity of datasets. Transfer learning, i.e.,
leveraging publicly available labeled datasets to first learn useful
representations that can then be fine-tuned using limited amounts of labeled
data from a target domain, can alleviate some of the performance issues of
contemporary HAR systems. Yet they can fail when the differences between source
and target conditions are too large and/ or only few samples from a target
application domain are available, each of which are typical challenges in
real-world human activity recognition scenarios. In this paper, we present an
approach for economic use of publicly available labeled HAR datasets for
effective transfer learning. We introduce a novel transfer learning framework,
Cross-Domain HAR, which follows the teacher-student self-training paradigm to
more effectively recognize activities with very limited label information. It
bridges conceptual gaps between source and target domains, including sensor
locations and type of activities. Through our extensive experimental evaluation
on a range of benchmark datasets, we demonstrate the effectiveness of our
approach for practically relevant few shot activity recognition scenarios. We
also present a detailed analysis into how the individual components of our
framework affect downstream performance.
