Wi-Fi Based Human Activity Recognition Using BiLSTM with Kernel Ridge Regression

In this paper, we propose a fusion network for human activity recognition based on the Wi-Fi Channel State Information (CSI) signals. The system employs a Bidirectional Long Short-Term Memory (BiLSTM) layer to extract action features from CSI data blocks and then trains them using the Kernel Ridge Regression (KRR). The trained block responses are subsequently fused based on the sum-rule to form the final decision. In contrast to deep learning, this process is computationally efficient because there is no need to train the BiLSTM. The proposed method has been tested on two publicly available databases to validate the accuracy performance.