A Method of Ultrasonic Gesture Recognition Based on Attention Mechanism

The focus of this research lies in wireless gesture recognition, a prominent model of human-computer interaction that has garnered significant attention in recent years. This study aims to achieve efficient gesture recognition on smartphones using ultrasonic signals via speakers and microphones. Firstly, the original one-dimensional audio sequence is transformed into a two-dimensional spectrogram through the data preprocessing module, which facilitates capturing gesture patterns and changes while unifying the data format to enhance subsequent processing operability. Secondly, an algorithm for contour extraction is devised to mitigate signal interference caused by multipath effects. This algorithm enhances feature representation while reducing feature dimensionality and improving model robustness in adapting to interference and changes in various environments. By incorporating the spatial attention mechanism into the CNN model, it allows the model to focus on key areas, eliminate distracting information more effectively, better understand gesture features and shapes, and achieve more accurate recognition results. In testing basic number gestures, our method achieved an accuracy rate of 94%-96%, demonstrating its effectiveness. This would further enrich the spectrum of human-computer interaction, enhance user experiences, and drive the advancement of technological innovation.