WISDOM: Wi-Fi Based Contactless Multi-User Activity Recognition

Wi-Fi-based contactless activity recognition is of great importance to computer-human interaction, accounting for convenience concerns. However, it remains challenging to recognize activities from multiple users due to the multipath distortion and disruption of Wi-Fi signals. In this article, we propose a highly universal framework, namely, WISDOM, for Wi-Fi-based multiuser activity recognition. Specifically, we first leverage an existing model to identify the number of users from the input Wi-Fi signals. Then, we develop a subcarrier correlation and inversion-based sorting algorithm to extract the signal for each user. Finally, we design a neural network, i.e., WISDOM-Net, which is built on a bidirectional gated recurrent unit network incorporated with the attention mechanism and the one dimension convolutional neural network, to recognize the corresponding user activities. Experimental results show that our proposed WISDOM-Net outperforms the existing baselines on both the public and our own data sets. In particular, WISDOM-Net can reach an average recognition accuracy of up to 98.19% and 90.77% in 2-user and 3-user scenarios, respectively.