EViMNet: An Efficient Visual Model for Radar-Based Human Activity Recognition

In recent years, Radar-based human action recognition has recently gained attention due to its high precision and robustness, making it well-suited for Internet of Things (IoT) applications. However, radar spectrograms often contain noise and redundant information, while conventional classification models exhibit limitations in capturing complex nonlinear patterns, which hinders recognition performance. To address these challenges, we propose EViMNet, an efficient vision-based model for robust Radar-based human behavior recognition. EViMNet integrates two core components. The first is a Stem Filtering Block (SFB) that applies Gaussian filtering to enhance target features and suppress source noise. The second is an improved Multi-Layer Perceptron (MLP) architecture, where traditional linear layers are replaced by Kolmogorov–Arnold Networks (KANs). These KANs utilize B-spline basis functions to enable flexible nonlinear modeling and increase sensitivity to subtle signal variations. Experimental results show that EViMNet achieves 95.20% accuracy on the Glasgow dataset. For generalization evaluation, it further attains 99.18% cross-validation accuracy on the Doppler Gesture dataset and achieves the best cross-dataset performance when trained on Glasgow and tested on the corresponding actions in CI4R-MIX77. These results verify the strong generalization and practical applicability of the proposed model in IoT scenarios.