Towards Position-independent Gesture Recognition Based on WiFi by Subcarrier Selection and Gesture Code

Gesture recognition based on WiFi has recently attracted wide attention from academia and industry. However, the position-independent sensing is still a challenging problem. Existing work has made a breakthrough by extracting position-independent features through multiple transceiver pairs. We explore the position-independent gesture recognition methods that maintain accuracy and robustness while providing only one transceiver pair. Due to the limited information access and spatial resolution in that scenarios, noise cannot be effectively eliminated and gesture features are easily confused. Therefore, we propose a subcarrier selection method to select the subcarrier with less interference by noise. We extract dynamic phase as features for gesture recognition, which is position-independent. In addition, we split the dynamic phase variations of different gestures into a series of segments code based on the actions (traverse, approach and away). The easily confused gesture features are transformed into distinguishable gesture code. We developed a prototype on a Commercial Off-The-Shelf WiFi device. Extensive experimental results show that our system achieves position-independent gesture recognition using only one transceiver pair within an acceptable error range, achieving a maximum recognition accuracy of 94.33% and an average recognition accuracy of 87.25% in different positions.