Crucial Region Search and Feature Discrimination for Radar-Based Human Activity Recognition

Radar, as a contactless, nonintrusive, weather- and light-independent sensor, works in complementary with other sensors. Radar-based human activity recognition (HAR) has the advantages of privacy preservation and noise robustness. Therefore, it has a tremendous potential for IoT applications. Existing methods are dedicate to finding best radar representations for HAR. However, research on the elimination of nonactivity information has not receive sufficient attention. In response to this question, the crucial region search (CRS) and feature discrimination (CRSFD) network has been proposed. It aims to automatically separate features and explicitly establish feature distribution, to accomplish nonactivity feature elimination. The CRSFD consists of the CRS module and the activity-associated feature discrimination (AAFD) module. The CRS module, with edge protection and importance assessment capabilities, is more suitable for irregular changes in activity features. The AAFD module is customized to automatically and explicitly analyze the distributions of activity and nonactivity features. Eventually, activity features are maintained for HAR. Experimental results on a human activity dataset and a human gesture dataset show that the proposed method has superior performance and is robust to noise.