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.

本文提出了一种适应性特征融合的领域泛化方法（AFFAR），该方法通过将领域不变和领域特定表示相结合来提高模型的泛化能力，在三个公共 HAR 数据集上展开了广泛的实验，并将其应用于儿童注意缺陷多动障碍（ADHD）的诊断，证明了其优越性。