Wi-Fi sensing for gesture recognition systems is a fascinating and challenging research topic. We propose a multitask sign language recognition framework called Wi-SignFi, which accounts for gestures in the real world associated with various objects, actions, or scenes. The proposed framework comprises a convolutional neural network (CNN) and K-nearest neighbor (KNN) module. It is evaluated on the public SignFi dataset and achieves 98.91%, 86.67%, and 99.99% average gesture recognition accuracies on 276/150 activities, five users, and two environments, respectively. The experimental results show that the proposed gesture recognition method outperforms previous methods. Instead of converting the channel state information (CSI) data of multiple antennas into three-dimensional matrices (i.e., color images) as in the existing literature, we found that the CSI data can be converted into matrices (i.e., grayscale images) by concatenating different channels, allowing the Wi-SignFi model to balance between speed and accuracy. This finding facilitates deploying Wi-SignFi on Nvidia’s Jetson Nano edge embedded devices. We expect this work to promote the integration of Wi-Fi sensing and the Internet of Things (IoT) and improve the quality of life of the deaf community.

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