Abstract

Radar-based Human Activity Recognition (HAR) is popular because of its privacy and contactless sensing capabilities. However, a major challenge in this area is the lack of large and diverse datasets. In response, we present a novel framework that uses generative models to transform textual descriptions into motion data, thereby simulating radar signals. This approach significantly enriches the realism and diversity of the dataset, especially for infrequent but critical activities such as falls and abnormal walking. Textual descriptions capture the semantic complexity and ambiguity of actions, thereby improving intraclass diversity. Our framework scales the data generation process and improves simulation fidelity by controlling gait variation, multi-viewpoint adaptation and background noise modelling. The simulated micro-Doppler dataset can be used for model comparison and transfer learning to improve recognition in realworld data, even when available data samples are scarce. Our approach significantly mitigates the challenge of data shortages, enabling significant advances in activity recognition with limited samples.