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Computer Science > Human-Computer Interaction

arXiv:2407.02606 (cs)
[Submitted on 2 Jul 2024]

Title:An AI-Based System Utilizing IoT-Enabled Ambient Sensors and LLMs for Complex Activity Tracking

Authors:Yuan Sun, Jorge Ortiz
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Abstract: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
Subjects: Human-Computer Interaction (cs.HC)
Cite as: arXiv:2407.02606 [cs.HC]
  (or arXiv:2407.02606v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2407.02606
Related DOI: https://doi.org/10.54097/dj2pt496

Submission history

From: Yuan Sun [view email]
[v1] Tue, 2 Jul 2024 18:46:05 UTC (1,635 KB)
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