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Human movements in the workspace usually have non-negligible relations withair quality parameters (e.g., CO$_2$, PM2.5, and PM10). We establish a systemto monitor indoor human mobility with air quality and assess theinterrelationship between these two types of time series data. Morespecifically, a sensor network was designed in indoor environments to observeair quality parameters continuously. Simultaneously, another sensing moduledetected participants' movements around the study areas. In this module, moderndata analysis and machine learning techniques have been applied to reconstructthe trajectories of participants with relevant sensor information. Finally, afurther study revealed the correlation between human indoor mobility patternsand indoor air quality parameters. Our experimental results demonstrate thathuman movements in different environments can significantly impact air qualityduring busy hours. With the results, we propose recommendations for futurestudies.

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