  The performance of Human Activity Recognition (HAR) models, particularly deep
neural networks, is highly contingent upon the availability of the massive
amount of annotated training data which should be sufficiently labeled. Though,
data acquisition and manual annotation in the HAR domain are prohibitively
expensive due to skilled human resource requirements in both steps. Hence,
domain adaptation techniques have been proposed to adapt the knowledge from the
existing source of data. More recently, adversarial transfer learning methods
have shown very promising results in image classification, yet limited for
sensor-based HAR problems, which are still prone to the unfavorable effects of
the imbalanced distribution of samples. This paper presents a novel generic and
robust approach for semi-supervised domain adaptation in HAR, which capitalizes
on the advantages of the adversarial framework to tackle the shortcomings, by
leveraging knowledge from annotated samples exclusively from the source subject
and unlabeled ones of the target subject. Extensive subject translation
experiments are conducted on three large, middle, and small-size datasets with
different levels of imbalance to assess the robustness and effectiveness of the
proposed model to the scale as well as imbalance in the data. The results
demonstrate the effectiveness of our proposed algorithms over state-of-the-art
methods, which led in up to 13%, 4%, and 13% improvement of our high-level
activities recognition metrics for Opportunity, LISSI, and PAMAP2 datasets,
respectively. The LISSI dataset is the most challenging one owing to its less
populated and imbalanced distribution. Compared to the SA-GAN adversarial
domain adaptation method, the proposed approach enhances the final
classification performance with an average of 7.5% for the three datasets,
which emphasizes the effectiveness of micro-mini-batch training.
