Wi-Fi-Based Location-Independent Human Activity Recognition with Attention Mechanism Enhanced Method

Wi-Fi-based human activity recognition is emerging as a crucial supporting technology for various applications. Although great success has been achieved for location-dependent recognition tasks, it depends on adequate data collection, which is particularly laborious and time-consuming, being impractical for actual application scenarios. Therefore, mitigating the adverse impact on performance due to location variations with the restricted data samples is still a challenging issue. In this paper, we provide a location-independent human activity recognition approach. Specifically, aiming to adapt the model well across locations with quite limited samples, we propose a Channel-Time-Subcarrier Attention Mechanism (CTS-AM) enhanced few-shot learning method that fulfills the feature representation and recognition tasks. Consequently, the generalization capability of the model is significantly improved. Extensive experiments show that more than 90% average accuracy for location-independent human activity recognition can be achieved when very few samples are available.