This work presents a novel architecture for context-aware interactions within smart environments, leveraging Large Language Models (LLMs) to enhance user experiences. Our system integrates user location data obtained through UWB tags and sensor-equipped smart homes with real-time human activity recognition (HAR) to provide a comprehensive understanding of user context. This contextual information is then fed to an LLM-powered chatbot, enabling it to generate personalised interactions and recommendations based on the user's current activity and environment. This approach moves beyond traditional static chatbot interactions by dynamically adapting to the user's real-time situation. A case study conducted from a real-world dataset demonstrates the feasibility and effectiveness of our proposed architecture, showcasing its potential to create more intuitive and helpful interactions within smart homes. The results highlight the significant benefits of integrating LLM with real-time activity and location data to deliver personalised and contextually relevant user experiences.

本研究解决了智能环境中聊天机器人缺乏实时上下文感知的问题，通过结合用户位置数据和人类活动识别，提出了一种新的架构。研究表明，这种集成方法可显著提升用户体验，使聊天机器人能够根据用户的当前活动和环境生成个性化反馈，展示了其在智能家居中的广阔应用前景。

利用大型语言模型增强智能环境中的上下文感知聊天机器人