This research presents a novel architecture that combines location tracking, sensor data, and human activity recognition with Large Language Models to create truly contextual AI assistants.

• System tracks users via UWB tags and smart home sensors
• Real-time activity recognition provides behavioral context
• LLM-powered chatbot generates personalized responses based on comprehensive user context
• Architecture enables more natural and relevant AI interactions in smart environments

Medical Impact: This technology offers significant potential for healthcare settings, enabling systems that can monitor patients, understand their activities, and provide timely, contextually-appropriate support or alerts to caregivers—particularly valuable for elderly care and assisted living environments.

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