Design and Proposal of a Human-Machine Collaborative Interaction System with Semantic Recognition for Personalized Indoor Guided Tours

This experiment focuses on establishing a guided-tour system that enables human-machine collaboration through semantic recognition. The system is designed to provide dynamic and personally adaptable indoor guidance, which is relevant to complex industrial and engineering environments where technicians require real-time, context-aware instructions for equipment inspection, maintenance, and safety management. From the perspective of advanced electromagnetic systems, the same architecture can be connected with antenna-assisted positioning, radio-frequency identification, or wireless sensor data to support more accurate indoor localization and interaction. The research addresses shortcomings of current guided systems, including limited real-time response and insufficient personalization. It introduces a more interactive and user-friendly experience, identifies these deficiencies in existing studies, and illustrates how next-generation semantic recognition can make educational and industrial tours more detailed and adaptive. Future studies will focus on three areas: using augmented reality to enhance immersive learning, integrating emotion detection to adjust the tone and timing of guidance, and extending multi-device cooperation for group scenarios. The findings have implications for interactive educational technology, personalized learning environments, and indoor engineering guidance systems that depend on semantic interpretation and reliable wireless context awareness.