Research on Intelligent Aging Furniture System Enabled by Information Interaction and Communication Technology

This study focuses on the application of information interaction and communication technology in intelligent furniture system, aiming to develop intelligent furniture solutions. Advanced deep learning techniques, especially the combination of convolutional neural networks (CNN) and recurrent neural networks (RNN), are used in this study to extract efficient features from furniture images in indoor environments. Through this method, the multi-label automatic labeling of furniture can be realized, and the key attribute information such as the type, location, color and material of furniture can be accurately identified. The research results show that the proposed method performs well in improving the accuracy and efficiency of furniture identification, and provides strong technical support for the design and implementation of intelligent aging furniture system. In addition, the system can also realize intelligent interaction between furniture and users through information interaction technology, such as the function of controlling furniture by voice or gesture, which greatly improves the living comfort and convenience of the elderly.