Design of Human–Computer Interaction Gesture Recognition System Based on a Flexible Biosensor

Abstract

Abstract The continuous development of high-speed Internet technology has made the application of robots increasingly widespread. Current robots and human–computer interaction systems mostly use rigid materials, such as metals and semiconductors, which have limitations in terms of deformability and flexibility. In addition, the biocompatibility and user comfort of these materials are also an issue. Therefore, research into new flexible biosensors is essential to improve the flexibility, comfort, and interactivity of these systems. This research will select polymer hydrogel as the electrode material of the sensor and polydimethylsiloxane as the base material of the sensor to design a resistance flexible biosensor to solve the poor flexibility. The research will use a template-matching method to verify the feasibility of gesture recognition of the flexible sensor. The remote control system of the robot finger is designed by a proportional-integral differential controller tuned by aradial basis function neural network. The feasibility of the research system is verified by simulation and scene experiments. The flexible sensor studied and prepared had a sensitivity of 0.7269, a tensile limit of 300%, and a thickness of 0.16 mm, showing good sensitivity and stability. The recognition accuracy of the sensor designed in the study was 92.8%, which was 8.1% higher than that of the data glove. Compared with traditional proportional-integral derivative (PID) controllers, the improved controller system error was within 10 to 3 rad, which had better adaptability and stability. Key information includes the design method of the flexible biosensor, its high sensitivity and stability under multiple stretches, and the proposal and validation of a new RBFNN–PID control model. These results showed that using this new sensor and control model significantly improved the control accuracy of mechanical fingers and the effect of gesture recognition. These results have important implications for the development of more advanced human–computer interaction systems. They not only improve the performance and reliability of the system, but also improve the user's interactive experience. These technologies are particularly promising in the fields of prosthetics for disabled people, advanced game controllers, and remotely controlled robots operating in hazardous environments. The research results are expected to lead to the development of advanced prosthetics, augmented reality devices, advanced game controllers, and automated robots. The main contribution of the research is to design a resistive flexible biosensor, which improves the traditional sensor's poor flexibility and large size and improves the sensor's ability to sense small changes. Future research may focus on further improving the sensor's long-term stability and performance under a variety of environmental conditions. In addition, commercializing these technologies and making them universal is also an important direction for the future.

Keywords

• Flexible sensor
• Human–computer interaction
• Gesture recognition system
• Template matching
• RBF neural network
• PID control