Evidence map›Paper›PMID 36679383›Full record

ArticleSensors (Basel, Switzerland)2023

Estimation of Occupancy Using IoT Sensors and a Carbon Dioxide-Based Machine Learning Model with Ventilation System and Differential Pressure Data.

Jehyun Kim, JongIl Bang, Anseop Choi, Hyeun Jun Moon, Minki Sung

Abstract read
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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. 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

5 authors.

Jehyun KimDepartment of Architectural Engineering, Sejong University, 209 Neungdong-Ro, Gwangjin-Gu, Seoul 05006, Republic of Korea.
JongIl BangDepartment of Architectural Engineering, Sejong University, 209 Neungdong-Ro, Gwangjin-Gu, Seoul 05006, Republic of Korea.ORCID 0000-0003-1304-7257
Anseop ChoiDepartment of Architectural Engineering, Sejong University, 209 Neungdong-Ro, Gwangjin-Gu, Seoul 05006, Republic of Korea.
Hyeun Jun MoonDepartment of Architectural Engineering, Dankook University, Youngin 16890, Republic of Korea.ORCID 0000-0002-4414-188X
Minki SungDepartment of Architectural Engineering, Sejong University, 209 Neungdong-Ro, Gwangjin-Gu, Seoul 05006, Republic of Korea.ORCID 0000-0001-8915-2710

Funding

the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (No. 20212020800120)
6 · The paper itself

Abstract

Infectious diseases such as the COVID-19 pandemic have necessitated preventive measures against the spread of indoor infections. There has been increasing interest in indoor air quality (IAQ) management. Air quality can be managed simply by alleviating the source of infection or pollution, but the person within a space can be the source of infection or pollution, thus necessitating an estimation of the exact number of people occupying the space. Generally, management plans for mitigating the spread of infections and maintaining the IAQ, such as ventilation, are based on the number of people occupying the space. In this study, carbon dioxide (CO

Indexed as

Air Pollution, IndoorCOVID-19Carbon DioxideEnvironmental MonitoringHumansPandemicsVentilationCarbon Dioxidecarbon dioxidedifferential pressureinternet of things sensormachine learningoccupancyventilation system

Identifiers

PMID36679383
PMCPMC9860618

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.