Radar-Based Human Activity Recognition: A Study on Cross-Environment Robustness

Indoor radar-based human activity recognition (HAR) using machine learning has shown promising results. However, deploying an HAR model in unseen environments remains challenging due to a potential mismatch between training and operational conditions. Such mismatch can be reduced by acquiring annotated training data in more diverse situations. However, since this is time intensive, this paper explores the application of data augmentation and unsupervised domain adaptation (UDA) to enhance the robustness of HAR models, even when they are trained using a very limited amount of annotated data. In the initial analysis, a baseline HAR model was evaluated using a validation set (a) from the same environment as the training data and (b) from a different environment. The results showed a 29.6% decrease in the F1-score when tested on data from the different environment. Implementing data augmentation techniques—specifically, time–frequency warping—reduced this performance gap to 17.8%. Further improvements were achieved by applying an unsupervised domain adaptation strategy, which brought the performance gap drop down to 13.2%. Furthermore, an ablation study examining various augmentation methods and synthetic sample quantities demonstrates the superior performance of our proposed augmentation approach. The paper concludes with a discussion on how environmental variations, such as changes in aspect angle, occlusion and layout, can affect the time-Doppler radar representation and, consequently, HAR performance.