Complex activity recognition plays an important role in elderly care assistance. However, the reasoning ability of edge devices is constrained by the classic machine learning model capacity. In this paper, we present a non-invasive ambient sensing system that can detect multiple activities and apply large language models (LLMs) to reason the activity sequences. This method effectively combines edge devices and LLMs to help elderly people in their daily activities, such as reminding them to take pills or handling emergencies like falls. The LLM-based edge device can also serve as an interface to interact with elderly people, especially with memory issue, assisting them in their daily lives. By deploying such a system, we believe that the smart sensing system can improve the quality of life for older people and provide more efficient protection

翻译：复杂活动识别在老年人照护辅助中发挥着重要作用。然而，边缘设备的推理能力受限于传统机器学习模型的性能。本文提出一种非侵入式环境感知系统，能够检测多种活动并运用大型语言模型（LLMs）对活动序列进行推理。该方法有效结合边缘设备与LLMs，以协助老年人的日常活动，例如提醒服药或处理跌倒等紧急情况。基于LLM的边缘设备还可作为交互界面，特别适用于存在记忆障碍的老年人，辅助其日常生活。通过部署此类系统，我们相信智能感知系统能够提升老年人的生活质量并提供更高效的保护。