Tuberculosis and Respiratory Diseases

The Korean Academy of Tuberculosis and Respiratory Diseases (대한결핵및호흡기학회)
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Review of Internet of Things-Based Artificial Intelligence Analysis Method through Real-Time Indoor Air Quality and Health Effect Monitoring: Focusing on Indoor Air Pollution That Are Harmful to the Respiratory Organ

  • Eunmi Mun (Department of Software Engineering, Jeonbuk National University) ;
  • Jaehyuk Cho (Department of Software Engineering, Jeonbuk National University)
  • Received : 2022.06.07
  • Accepted : 2022.10.25
  • Published : 2023.01.31
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Abstract

Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed.

Keywords

Acknowledgement

This work was supported Korea Environmental Industry & Technology Institute (KEITI) grant funded by the Korean government (Ministry of Environment). Project No. RE202101551, the development of IoT-based technology for collecting and managing big data on environmental hazards and health effects. This research was also supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MIST) (No. 2019-0-00135, Implementation of 5G-based Smart Sensor Verification Platform). This paper was supported by research funds from Jeonbuk National University.

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