Hand Gesture Detection Based on Non-audible Sound

With the rapid development and integration of the Internet of Things into every aspect of daily life, finding more intuitive and natural methods of human-computer interaction has become increasingly important. Hand gesture detection-based systems are garnering significant attention in this field, as they offer a natural and simpler way of communicating with various devices compared to traditional input methods like keyboards and mice. One approach to detecting user hand gestures involves generating a non-audible sound through speakers and analyzing the reflected signal using microphones. This paper proposes a solution for detecting 11 distinct hand gestures based on deep learning and the Doppler effect of sound waves. The system utilizes commercial speakers and microphones to generate a 20 kHz audio signal and analyze its reflection. The detection model is a Convolutional Neural Network (CNN), trained on a dataset of 2100 samples and tested on an additional 550 samples. The model achieves 91.43% accuracy and demonstrates high performance when evaluated on previously established datasets.