Research on Optimization of User Behavior Perception and Interaction Feedback Mechanism in Ground Screen Projection

Abstract

Ground projection systems offer immersive, interactive experiences by projecting digital content onto physical surfaces and responding to user movements. However, these systems often struggle with accurately perceiving subtle user behaviors and delivering timely,
adaptive feedback. This paper explores key strategies to optimize such systems by integrating advanced sensor technologies, applying machine learning for behavior recognition, and developing context-aware, multimodal feedback mechanisms. Combining depth cameras, infrared
sensors, and pressure-sensitive surfaces improves motion detection across varied environments. Meanwhile, deep learning models such as
CNNs and RNNs enhance adaptability by recognizing complex, real-time behavior patterns. Additionally, multimodal feedbackvisual, auditory, and hapticensures more responsive and engaging user interactions. The use of edge computing and adaptive algorithms also supports
real-time processing and low-latency feedback delivery. Together, these strategies contribute to building more accurate, responsive, and immersive ground projection systems capable of meeting diverse user expectations in dynamic settings.