Collaborative Lightweight LLM Agents for Daily Activity Summarization on Edge Devices

Abstract

This paper presents a privacy-preserving monitoring system for 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, an hourly summarization agent generates intermediate reports in 3-hour segments, and a daily summarization agent produces comprehensive summaries. Our evaluation on real-world data from two elderly households demonstrates that the system achieves 95.8% accuracy in activity recognition and generates natural language summaries comparable to GPT-4o, while maintaining privacy through local processing on affordable Raspberry Pi hardware. The results indicate that our approach effectively balances monitoring accuracy, summary quality, and practical deployment constraints, making it suitable for widespread adoption in elderly care applications.