Domain Generalization for Sensor-Based Activity Recognition via Decomposed Feature Representation and Semantic Augmentation

Domain generalization (DG) has recently offered a promising solution to tackle distribution-shift problem in sensor-based human activity recognition (HAR) scenario. Particularly, the sensor data augmentation has gained a lot of attention, which aims to generate more diverse representations to learn model invariance across different domains. However, most existing methods seek to directly perform augmentation in the sensor input space, which result in a limited variety of augmented features. Intuitively, performing augmentation in feature space would be more versatile and diverse, which has been seldom considered in sensor-based cross-domain activity recognition. In this paper, we propose a new feature space augmentation method by decomposing the obtained features into four main components, i.e., category-invariant domain-invariant, category-invariant domain-specific, category-specific domain-invariant, and category-specific domain-specific, which enable us to increase sensor sample diversity while emphasizing invariance learning to enhance domain generalization for activity recognition. Extensive experiments on four public HAR benchmarks including DSADS, USC-HAD, PAMAP2 and UCI-HAR demonstrate that our proposed feature space augmentation approach is able to achieve state-of-the-art performance under Cross-person, Cross-dataset, Cross-position and One-person-to-another settings. Detailed ablation and visualizations analyses verify its effectiveness, efficiency, and universality under cross-domain activity recognition scenario.