Enhancing Smart Environments with Context-Aware Chatbots using Large Language Models
This work addresses the need for more intuitive interactions in smart homes, though it appears incremental as it combines existing technologies like LLMs and sensors in a novel architecture.
The paper tackled the problem of static chatbot interactions in smart environments by integrating real-time location and activity data with Large Language Models, resulting in a system that demonstrated feasibility and effectiveness in providing personalized, context-aware user experiences.
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.