TY - GEN
T1 - Indoor air quality monitoring system for proactive control of respiratory infectious diseases
T2 - 18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020
AU - Ho, Yao Hua
AU - Li, Pei En
AU - Chen, Ling Jyh
AU - Liu, Yu Lun
N1 - Funding Information:
We present an Indoor Air Quality Monitoring System for Proactive Control of Respiratory Infectious Diseases. The system consists of Data Producers (i.e., IAQ devices), Data Broker (i.e., backend server), and Data Consumers (i.e., data analysis application, visualization interfaces, and chatbot applications). Working closely with Taiwan CDC, a real-world experiments is conducted with 15 locations with 144 IAQ devices including hospitals, long-term care centers, and schools. Based on the evaluation results and feedbacks received throughout this study, we evaluate the feasibility of the proposed system for disease surveillance, as well as make a concrete plan for large-scale deployment in the future. Acknowledgments. This project is supported by the Taiwan Centers for Disease Control (CDC) Technology Research Project [Grant number: MOHW109-CDC-C-114-123601].
Publisher Copyright:
© 2020 ACM.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - Disease surveillance is essential for the control of flu and respiratory infectious diseases including the novel coronavirus disease (COVID-19). Indoor air quality monitoring has been shown effective in understanding the effectiveness of airflow and circulation indoors to reduce the risk of infectious diseases. In this project, we developed low-cost indoor air quality monitoring devices and systems to tackle the disease surveillance problem. The monitoring device consists of a set of air quality sensors. By strategic deployment and real-time data analysis, the system is able to yield insightful air circulation information indoors. The real-time data analysis is performed on air quality for the indoor ventilation using Long Short-Term Memory (LSTM) on sensed data. A series of user-friendly visualization interfaces and chatbot applications are designed to interact with users and ensure the successful delivery of infection control information. Finally, we work closely with the Taiwan Centers for Disease Control (CDC) and conduct field experiments in 15 locations including hospitals, long-term care centers, schools with total of 144 IAQ devices.
AB - Disease surveillance is essential for the control of flu and respiratory infectious diseases including the novel coronavirus disease (COVID-19). Indoor air quality monitoring has been shown effective in understanding the effectiveness of airflow and circulation indoors to reduce the risk of infectious diseases. In this project, we developed low-cost indoor air quality monitoring devices and systems to tackle the disease surveillance problem. The monitoring device consists of a set of air quality sensors. By strategic deployment and real-time data analysis, the system is able to yield insightful air circulation information indoors. The real-time data analysis is performed on air quality for the indoor ventilation using Long Short-Term Memory (LSTM) on sensed data. A series of user-friendly visualization interfaces and chatbot applications are designed to interact with users and ensure the successful delivery of infection control information. Finally, we work closely with the Taiwan Centers for Disease Control (CDC) and conduct field experiments in 15 locations including hospitals, long-term care centers, schools with total of 144 IAQ devices.
KW - indoor air quality
KW - internet of thing
KW - respiratory disease
UR - http://www.scopus.com/inward/record.url?scp=85097548365&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097548365&partnerID=8YFLogxK
U2 - 10.1145/3384419.3430456
DO - 10.1145/3384419.3430456
M3 - Conference contribution
AN - SCOPUS:85097548365
T3 - SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems
SP - 693
EP - 694
BT - SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems
PB - Association for Computing Machinery, Inc
Y2 - 16 November 2020 through 19 November 2020
ER -
