A System of Monitoring and Analyzing Human Indoor Mobility and Air Quality

TL;DR

This paper presents a sensor-based system that monitors indoor human mobility and air quality, analyzing their interrelationship to understand how human movement impacts indoor air pollution levels.

Contribution

It introduces an integrated sensor network and data analysis framework to simultaneously track indoor movement and air quality, revealing their correlation.

Findings

Human movements significantly affect air quality during busy hours.

The system effectively reconstructs participant trajectories using sensor data.

Indoor mobility patterns correlate with fluctuations in air quality parameters.

Abstract

Human movements in the workspace usually have non-negligible relations with air quality parameters (e.g., CO$_2$, 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.