LAUSR

Despite the intense effort from the research community, state-of-the-art WiFi-based human activity recognition (HAR) performance remains unsatisfactory. Current approaches usually use individual characteristics of CSI, i.e., amplitude or phase measurements, to model the relationship between the changes of channel state information (CSI) and human activities, which lead to their failure to achieve satisfactory accuracy due to information loss. To deal with this issue, this article proposes CeHAR, a CSI-based HAR using a channel-exchanging fusion network to deep fuse the CSI amplitude and phase features to obtain the informative features for HAR. The proposed CeHAR is a parameter-free dual-characteristic fusion framework that dynamically exchanges channels between subnetworks of two kinds of characteristics to comprehensively learn informative features from both. Specifically, the proposed approach employs two subnetworks using convolutional neural networks to learn features from each characteristic of CSI. The magnitude of the batch-normalization (BN) scaling factor is used to determine the channel importance of each characteristic, and then guides the exchange process. The proposed CeHAR also shares convolutional filters, but keeps private BNs layers in different characteristics, which, as an added benefit, allows our characteristic fusion network to be nearly as compact as a single-characteristic network. Extensive real-world experiments have been conducted to evaluate the performance of our proposed CeHAR, and the experimental results illustrate that our proposed approach outperforms baselines.