Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy.
Department of Occupational and Environmental Medicine, Epidemiology, and Hygiene, Istituto Nazionale Assicurazione Infortuni sul Lavoro, Monte Porzio Catone, 00144 Rome, Italy.
Sensors (Basel). 2023 Feb 23;23(5):2457. doi: 10.3390/s23052457.
Indoor locations with limited air exchange can easily be contaminated by harmful volatile compounds. Thus, is of great interest to monitor the distribution of chemicals indoors to reduce associated risks. To this end, we introduce a monitoring system based on a Machine Learning approach that processes the information delivered by a low-cost wearable VOC sensor incorporated in a Wireless Sensor Network (WSN). The WSN includes fixed anchor nodes necessary for the localization of mobile devices. The localization of mobile sensor units is the main challenge for indoor applications. Yes. The localization of mobile devices was performed by analyzing the with machine learning algorithms aimed at localizing the emitting source in a predefined map. Tests performed on a 120 m meandered indoor location showed a localization accuracy greater than 99%. The WSN, equipped with a commercial metal oxide semiconductor gas sensor, was used to map the distribution of ethanol from a point-like source. The sensor signal correlated with the actual ethanol concentration as measured by a PhotoIonization Detector (PID), demonstrating the simultaneous detection and localization of the VOC source.
室内空气交换有限的场所很容易受到有害挥发性化合物的污染。因此，监测室内化学物质的分布以降低相关风险具有重要意义。为此，我们引入了一种基于机器学习方法的监测系统，该系统处理由集成在无线传感器网络 (WSN) 中的低成本可穿戴挥发性有机化合物 (VOC) 传感器提供的信息。WSN 包括用于移动设备定位的固定锚节点。室内应用的主要挑战是移动传感器单元的定位。是的。通过分析旨在将发射源定位在预定义地图中的机器学习算法来执行移动设备的定位。在 120 米蜿蜒的室内位置进行的测试显示，定位精度大于 99%。配备商业金属氧化物半导体气体传感器的 WSN 用于绘制来自点状源的乙醇分布。传感器信号与光电离检测器 (PID) 测量的实际乙醇浓度相关，证明了 VOC 源的同时检测和定位。
