Enhancing Smart Environments with Context-Aware Chatbots using Large Language Models

TL;DR

This paper introduces a new architecture that combines large language models with real-time sensor data to create context-aware chatbots that improve user interaction within smart environments, demonstrated through a real-world case study.

Contribution

It presents a novel system integrating location, activity recognition, and LLMs for dynamic, personalized interactions in smart homes, advancing beyond static chatbot models.

Findings

Effective integration of LLMs with real-time sensor data.

Enhanced personalization and contextual relevance in user interactions.

Demonstrated feasibility through a real-world case study.

Abstract

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.