Collaborative LLM Agents for Daily Activity Summarization on Edge Devices

In recent years, with the increase in the number of elderly people living alone, the demand for monitoring systems has increased. There are two main requirements for a monitoring system: first, it must protect privacy, and second, it must be low-cost and easy to implement. This study proposes a daily activity summarization system for privacy-preserving monitoring of elderly individuals living alone, utilizing collaborative lightweight Large Language Model (LLM) agents deployed on edge devices. The system integrates non-invasive sensors with a novel three-agent architecture: an activity recognition agent processes sensor data by machine learning approach, an hourly summarization LLM agent generates intermediate reports in 3-hour segments, and a daily summarization LLM agent produces comprehensive summaries. The proposed multi-agent approach for summary generation was designed not only to achieve load balancing but also to enhance the quality of the outputs. By distributing tasks among three agents based on time intervals and context-specific activities, the system effectively captured both short-term and long-term activity patterns, preserving critical temporal dependencies. This division reduced variability and inconsistencies by enabling each agent to focus on a manageable data segment, ensuring accurate and context-aware processing.