Smart Healthy Intelligent Room: Headcount through Air Quality Monitoring

Giovanni Cicceri, Carlo Scaffidi, Zakaria Benomar, S. Distefano, A. Puliafito, Giuseppe Tricomi, Giovanni Merlino
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引用次数: 3

Abstract

In this work, we propose a low-cost Smart and Healthy Intelligent Room System (SHIRS), able to monitor Indoor Air Quality (IAQ) by enhancing edge-based computation. SHIRS exploits the ability to run Machine Learning (ML) algorithms to infer humans presence (headcount) from environmental data analysis. Experimental results show the validity of the proposed approach, demonstrate the potential of edge-based computing and push towards the adoption of smart integrated Cloud-IoT frameworks for environmental monitoring and control.
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智能健康智能房间:通过空气质量监测统计人数
在这项工作中,我们提出了一种低成本的智能健康智能房间系统(SHIRS),能够通过增强边缘计算来监测室内空气质量(IAQ)。SHIRS利用运行机器学习(ML)算法的能力,从环境数据分析中推断人类的存在(人数)。实验结果表明了所提出方法的有效性,展示了基于边缘计算的潜力,并推动采用智能集成云-物联网框架进行环境监测和控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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