Research on the Design of a Human Gesture Recognition and Interaction System Based on Deep Learning

With the continuous advancement of human-computer interaction (HCI) technology, traditional interaction methods are increasingly unable to meet the growing complexity of application requirements. Human gesture recognition, as a natural and intuitive interaction method, has been widely applied in fields such as smart devices and virtual reality due to its convenience and flexibility. In recent years, the emergence of deep learning technology has provided new solutions for improving the performance of gesture recognition systems. This study designs a human gesture recognition and interaction system based on deep learning methods, aiming to enhance recognition accuracy and real-time responsiveness. First, the paper reviews the development of gesture recognition technology and provides a detailed analysis of deep learning-based gesture recognition methods. Subsequently, a gesture recognition model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) is proposed, along with the overall system architecture. Through the processes of gesture image data collection, preprocessing, and model training, experimental results demonstrate that the proposed system exhibits significant advantages in recognition accuracy, robustness, and response speed. Finally, the paper discusses optimization strategies for the system and envisions the broad application prospects of deep learning technology in future HCI systems.