Hybrid domain adaptation with deep network architecture for end-to-end cross-domain human activity recognition

Abstract

Abstract Machine learning-based human activity recognition (HAR) as the means of human–computer interaction is important to empower the existing systems in the industry such as manufacturing and logistics to be more autonomous. However, it is often difficult to build HAR models due to the limitation of annotated samples. Domain adaptation has emerged to address such limitation (or absence) of labeled samples in the domain of interest (i.e., target domain) by using abundant amount of labeled samples in the other domain (i.e., source domain). Domain adaptation aims to solve learning problems where a source domain and a target domain are different but still related. With the use of homogeneous feature space, the existing approaches on homogeneous domain adaption is prohibitive for the real-life scenario, in which two different domains could be possibly of heterogeneous feature space. Although heterogeneous domain adaptation approaches exist, it still requires additional information (i.e., instance correspondence) which is difficult to satisfy when we deal with sensor data. Hybrid domain adaptation, a special case of heterogeneous domain adaptation where common feature between domain exists, is more realistic as the feature commonality is easier to satisfy. However, the existing approach operates using common features in the original feature space, which in fact, may still have distribution difference. In addition, the existing one requires hand-crafted feature extractions for more informative descriptors to classify human activities. In this work, we propose a deep architecture for hybrid domain adaptation, to enable end-to-end learning for human activity classification. The architecture is tested on sensor-based human activity recognition dataset. The experimental result shows that our approach yields better result compared to the existing human activity recognition approaches, both the deep and shallow approaches.