TY - GEN
T1 - A System of Monitoring and Analyzing Human Indoor Mobility and Air Quality
AU - Qin, Kyle K.
AU - Rahaman, Mohammad Saiedur
AU - Ren, Yongli
AU - Cheng, Chi Tsun
AU - Cole, Ivan
AU - Salim, Flora D.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Human movements in the workspace usually have non-negligible relations with air quality parameters (e.g., CO2, PM2.5, and PM10). We establish a system to monitor indoor human mobility with air quality and assess the interrelationship between these two types of time series data. More specifically, a sensor network was designed in indoor environments to observe air quality parameters continuously. Simultaneously, another sensing module detected participants' movements around the study areas. In this module, modern data analysis and machine learning techniques have been applied to reconstruct the trajectories of participants with relevant sensor information. Finally, a further study revealed the correlation between human indoor mobility patterns and indoor air quality parameters. Our experimental results demonstrate that human movements in different environments can significantly impact air quality during busy hours. With the results, we propose recommendations for future studies.
AB - Human movements in the workspace usually have non-negligible relations with air quality parameters (e.g., CO2, PM2.5, and PM10). We establish a system to monitor indoor human mobility with air quality and assess the interrelationship between these two types of time series data. More specifically, a sensor network was designed in indoor environments to observe air quality parameters continuously. Simultaneously, another sensing module detected participants' movements around the study areas. In this module, modern data analysis and machine learning techniques have been applied to reconstruct the trajectories of participants with relevant sensor information. Finally, a further study revealed the correlation between human indoor mobility patterns and indoor air quality parameters. Our experimental results demonstrate that human movements in different environments can significantly impact air quality during busy hours. With the results, we propose recommendations for future studies.
KW - air quality monitoring
KW - human trajectory analysis
KW - indoor human mobility
UR - https://www.scopus.com/pages/publications/85171191144
U2 - 10.1109/MDM58254.2023.00025
DO - 10.1109/MDM58254.2023.00025
M3 - Conference Paper
AN - SCOPUS:85171191144
T3 - Proceedings - IEEE International Conference on Mobile Data Management
SP - 89
EP - 95
BT - Proceedings - 2023 24th IEEE International Conference on Mobile Data Management, MDM 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Conference on Mobile Data Management, MDM 2023
Y2 - 3 July 2023 through 6 July 2023
ER -