  Human activity recognition requires the efforts to build a generalizable
model using the training datasets with the hope to achieve good performance in
test datasets. However, in real applications, the training and testing datasets
may have totally different distributions due to various reasons such as
different body shapes, acting styles, and habits, damaging the model's
generalization performance. While such a distribution gap can be reduced by
existing domain adaptation approaches, they typically assume that the test data
can be accessed in the training stage, which is not realistic. In this paper,
we consider a more practical and challenging scenario: domain-generalized
activity recognition (DGAR) where the test dataset \emph{cannot} be accessed
during training. To this end, we propose \emph{Adaptive Feature Fusion for
Activity Recognition~(AFFAR)}, a domain generalization approach that learns to
fuse the domain-invariant and domain-specific representations to improve the
model's generalization performance. AFFAR takes the best of both worlds where
domain-invariant representations enhance the transferability across domains and
domain-specific representations leverage the model discrimination power from
each domain. Extensive experiments on three public HAR datasets show its
effectiveness. Furthermore, we apply AFFAR to a real application, i.e., the
diagnosis of Children's Attention Deficit Hyperactivity Disorder~(ADHD), which
also demonstrates the superiority of our approach.
