Wearable Interactive System with Uncoded Gesture Recognition Logic Enabled by Deep Learning
Gesture interaction has emerged as a highly effective interface for intelligent human-computer interaction, attributed to its intuitive interaction modality and multi-dimensional control capabilities. However, traditional gesture interaction devices often depend on predefined encoding rules, which substantially limit interaction efficiency and degrade user experience. This study introduces an innovative intelligent finger ring interaction system based on a triboelectric nanogenerator utilizing PDMS/SrTiO3 composite thin film (PS-TENG). The system maps freehand writing gestures directly to textual information input, thereby eliminating the need for complex gesture encoding schemes and offering a user-friendly, low-learning-curve input method. By integrating a deep learning model, the system achieves recognition accuracies of 98.21% for English letters, 96.87% for Arabic numerals, and 96.44% for Chinese characters. Furthermore, it supports secure and encrypted data transmission and enables wireless interaction for gaming control. These findings indicate that the intelligent finger ring interaction system possesses significant potential for practical applications in information input and wireless control.