Poor indoor air quality is a significant burden to society that can cause health issues and decrease productivity. According to research, indoor air quality is intrinsically linked with human activity and mobility. Indeed, mobility is directly linked with transfer of small particles (e.g. PM2.5) and extent of activity affects production of CO2. Currently, however, estimation of indoor quality is difficult, requiring deployment of highly specialized sensing devices which need to be carefully placed and maintained. In this paper, we contribute by examining the suitability of infrastructure-based motion detectors for indoor air quality estimation. Such sensors are increasingly being deployed into smart environments, e.g., to control lighting and ventilation for energy management purposes. Being able to take advantage of these sensors would thus provide a cost-effective solution for indoor quality monitoring without need for deploying additional sensors. We perform a feasibility study considering measurements collected from a smart office environment having a dense deployment of motion detectors and correlating measurements obtained from motion detectors against air quality values. We consider two main pollutants,PM2.5 and CO2, and demonstrate that there indeed is a connection between extent of movement and PM2.5concentration. However, for CO2, no relationship can be established, mostly due to difficulties in separating between people passing by and those residing long-term in the environment.
Original languageEnglish
Title of host publicationProceedings : 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
Number of pages6
Publication date2019
ISBN (Print)978-1-7281-2927-3
Publication statusPublished - 2019
MoE publication typeA4 Article in conference proceedings
Event17th International Conference on Industrial Informatics (INDIN) - Helsinki, Finland
Duration: 22 Jul 201925 Jul 2019
Conference number: 17

Publication series

Name IEEE International Conference on Industrial Informatics (INDIN)
ISSN (Print)2378-363X
ISSN (Electronic)1935-4576

Fields of Science

  • 113 Computer and information sciences

Cite this