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.

翻译：人类在工作空间内的移动通常与空气质量参数（如二氧化碳、PM2.5和PM10）存在不可忽略的关联。我们建立了一个系统，用于监测室内人员流动与空气质量，并评估这两类时间序列数据之间的相互关系。具体而言，我们在室内环境中设计了一个传感器网络以连续观测空气质量参数。与此同时，另一传感模块检测了研究区域内参与者的移动情况。在该模块中，我们运用了现代数据分析与机器学习技术，结合相关传感器信息重建了参与者的运动轨迹。最终，进一步研究揭示了室内人员流动模式与室内空气质量参数之间的相关性。实验结果表明，在高峰时段，不同环境下的人员移动会显著影响空气质量。基于这些结果，我们为未来研究提出了建议。