Deep Learning-Assisted Triboelectric Sensor for Complex Gesture Recognition

With the rapid development of the Internet of Things (IoT) and 5G technology, there has been a considerable increase in demand for self-powered and flexible sensors. However, existing solutions frequently prove inadequate regarding flexibility, energy efficiency, and the accuracy with which gestures can be recognized, particularly in noncontact operation scenarios. As a result, there is a need for innovative developments in sensor technology. This study proposes an artificial intelligence-based gesture recognition system comprising a triboelectric sensor ring, an Arduino signal processing module, and a deep learning module. Our approach enables the direct reading of triboelectric signals by Arduino through integrated circuits, thereby maintaining the output voltage of triboelectric signals within the input range of commonly used microcontrollers. The integration of triboelectric technology with sophisticated deep learning methodologies, notably the utilization of a one-dimensional convolutional neural network (CNN), has enabled the development of a system that exhibits an accuracy rate exceeding 95% in the recognition of 12 distinct gestures. This study demonstrates the prospective utility of triboelectric sensors in the realms of gesture recognition, wearable technology, and human-machine interaction.