Interaction Design of Wellness Building Space by Deep Learning and VR Technology in the Context of Internet of Things

Abstract

This study explores the application of deep learning theory and virtual reality (VR) technology to the interactive behavior design of building space according to the interaction behavior of wellness building space in the context of the Internet of Things (IoT). Firstly, VR theory is made into an image-based 3-dimensional (3D) modeling process. Secondly, the interactive behavior information data is analyzed according to the theory of deep learning and edge computing. Finally, the particle swarm optimization (PSO) is used to analyze the predicted temperature with the wellness building space model, as well as to make a study based on the changes in the user’s psychological indicators. The results show that the model predictions of deep learning-edge computing are most like the actual environmental settings. Both PSO and deep learning algorithms have varying degrees of influence on the final prediction results. The average temperature of the wellness building space established by deep learning and edge computing is 24.58 degrees, the average measured value of the actual environment is 24.49 degrees, and the predicted values of the two are similar. While users experienced the interactive design of the health building, their heart rate dropped from 73.17 to 68.79 and gradually became stable. There is no obvious change in the user’s heart rate, which reflects the comfort of the wellness building space designed based on deep learning and VR interaction.