Evidence map›Paper›PMID 36904660›Full record

ArticleSensors (Basel, Switzerland)2023

Personal VOCs Exposure with a Sensor Network Based on Low-Cost Gas Sensor, and Machine Learning Enabled Indoor Localization.

Leonardo Papale, Alexandro Catini, Rosamaria Capuano, Valerio Allegra, Eugenio Martinelli, Massimo Palmacci, Giovanna Tranfo, Corrado Di Natale

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In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Leonardo PapaleDepartment of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy.
Alexandro CatiniDepartment of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy.ORCID 0000-0001-6050-2908
Rosamaria CapuanoDepartment of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy.ORCID 0000-0002-7811-0862
Valerio AllegraDepartment of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy.
Eugenio MartinelliDepartment of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy.ORCID 0000-0002-6673-2066
Massimo PalmacciDepartment of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy.
Giovanna TranfoDepartment of Occupational and Environmental Medicine, Epidemiology, and Hygiene, Istituto Nazionale Assicurazione Infortuni sul Lavoro, Monte Porzio Catone, 00144 Rome, Italy.ORCID 0000-0001-8973-6046
Corrado Di NataleDepartment of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy.ORCID 0000-0002-0543-4348

Funding

Istituto Nazionale per l'Assicurazione Contro gli Infortuni sul Lavoro BRIC2019- ID 07
6 · The paper itself

Abstract

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

Indexed as

indoor localizationvolatile organic compoundsWireless Sensor Network

Identifiers

PMID36904660
PMCPMC10007132

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.