DA-HAR: Dual adversarial network for environment-independent WiFi human activity recognition

As the cornerstone of the development of emerging integrated sensing and communication, human activity recognition technology based on WiFi signals has been extensively studied. However, the existing activity sensing models will suffer serious performance degradation when applied to new scenarios due to the influence of environmental dynamics. To address this issue, we present an environment-independent activity recognition model named DA-HAR, which utilizes dual adversarial network. The framework exploits adversarial training among source domain classifiers and source–target domain discriminators to extract environment-independent activity features. To improve the performance of the model, a pseudo-label prediction based approach is introduced to assign labels to the target domain samples that closely resemble the source domain samples, thus mitigating the distribution deviation of activity features between source domain and target domain. Experimental results show that our proposed model has better cross-domain recognition performance compared to state-of-the-art recognition systems, especially when the distribution of activity features in the source domain and the target domain is significantly different, the accuracy is improved by 6.96% $\sim$ 11.22%.