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由人工智能驱动的用于辅助生活的透明射频识别标签墙

Transparent RFID tag wall enabled by artificial intelligence for assisted living.

作者信息

Khan Muhammad Zakir, Usman Muhammad, Tahir Ahsen, Farooq Muhammad, Qayyum Adnan, Ahmad Jawad, Abbas Hasan, Imran Muhammad, Abbasi Qammer H

机构信息

James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.

School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, G4 0BA, UK.

出版信息

Sci Rep. 2024 Sep 16;14(1):18896. doi: 10.1038/s41598-024-64411-y.

DOI:10.1038/s41598-024-64411-y
PMID:39284809
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11405864/
Abstract

Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new opportunities to enhance AAL systems. These improvements could potentially lower healthcare costs and reduce hospitalisations by enabling more effective identification, monitoring, and localisation of hazardous activities, ensuring rapid response to emergencies. In response to these challenges, this paper introduces the Transparent RFID Tag Wall (TRT-Wall), a novel system taht utilises a passive ultra-high frequency (UHF) radio-frequency identification (RFID) tag array combined with deep learning for contactless human activity monitoring. The TRT-Wall is tested on five distinct activities: sitting, standing, walking (in both directions), and no-activity. Experimental results demonstrate that the TRT-Wall distinguishes these activities with an impressive average accuracy of under four distinct distances (2, 2.5, 3.5 and 4.5 m) by capturing the RSSI and phase information. This suggests that our proposed contactless AAL system possesses significant potential to enhance elderly patient-assisted living.

摘要

当前的活动辅助生活（AAL）方法复杂、昂贵且具有侵入性，这降低了它们的实用性和终端用户的接受度。然而，诸如人工智能和无线通信等新兴技术为增强AAL系统提供了新机遇。这些改进有可能通过更有效地识别、监测危险活动并对其进行定位，确保对紧急情况做出快速响应，从而降低医疗成本并减少住院次数。针对这些挑战，本文介绍了透明射频识别标签墙（TRT-Wall），这是一种新颖的系统，它利用无源超高频（UHF）射频识别（RFID）标签阵列结合深度学习来进行非接触式人体活动监测。TRT-Wall针对五种不同活动进行了测试：坐着、站立、行走（两个方向）以及静止不动。实验结果表明，TRT-Wall通过捕获接收信号强度指示（RSSI）和相位信息，在四个不同距离（2米、2.5米、3.5米和4.5米）下以令人印象深刻的平均准确率区分这些活动。这表明我们提出的非接触式AAL系统在增强老年患者辅助生活方面具有巨大潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/f210cc29f9f3/41598_2024_64411_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/0bb87f7728b6/41598_2024_64411_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/3abbc84d24bf/41598_2024_64411_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/b161e8f61a9f/41598_2024_64411_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/e662ad4c8f6b/41598_2024_64411_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/769393d21482/41598_2024_64411_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/8beb8f004230/41598_2024_64411_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/65975a0702b3/41598_2024_64411_Figb_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/81b63c983329/41598_2024_64411_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/8560673c34c0/41598_2024_64411_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/f80e192264b9/41598_2024_64411_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/f210cc29f9f3/41598_2024_64411_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/0bb87f7728b6/41598_2024_64411_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/1e0fb816a830/41598_2024_64411_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/23a5b94e94e5/41598_2024_64411_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/3abbc84d24bf/41598_2024_64411_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/b161e8f61a9f/41598_2024_64411_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/e662ad4c8f6b/41598_2024_64411_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/769393d21482/41598_2024_64411_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/8beb8f004230/41598_2024_64411_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/65975a0702b3/41598_2024_64411_Figb_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/81b63c983329/41598_2024_64411_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/8560673c34c0/41598_2024_64411_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/f80e192264b9/41598_2024_64411_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da26/11405864/f210cc29f9f3/41598_2024_64411_Fig11_HTML.jpg

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Privacy versus autonomy: a tradeoff model for smart home monitoring technologies.隐私与自主性：智能家居监控技术的权衡模型
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