WiFi Signal-Based Gesture Recognition Using Federated Parameter-Matched Aggregation

Gesture recognition plays an important role in smart homes, such as human-computer interaction, identity authentication, etc. Most of the existing WiFi signal-based approaches exploit a large number of channel state information (CSI) datasets to train a gestures classification model; however, these models require a large number of human participants to train, and are not robust to the recognition environment. To address this problem, we propose a WiFi signal-based gesture recognition system with matched averaging federated learning (WiMA). Since there are differences in the distribution of WiFi signal changes caused by the same gesture in different environments, the traditional federated parameter average algorithm seriously affects the recognition accuracy of the model. In WiMA, we exploit the neuron arrangement invariance of neural networks in parameter aggregation, which can improve the robustness of the gesture recognition model with heterogeneous CSI data of different training environments. We carried out experiments with seven participant users in a distributed gesture recognition environment. Experimental results show that the average accuracy of our proposed system is up to 90.4%, which is very close to the accuracy of state-of-the-art approaches with centralized training models.

Automated summary

Gesture recognition is crucial for smart homes. Existing WiFi signal-based approaches require large amounts of data and human participants to train models, which lack robustness. To address this issue, we propose a WiFi signal-based gesture recognition system using matched averaging federated learning, which improves the accuracy and robustness of the model by considering differences in WiFi signal distribution caused by the same gesture in different environments.