Occupancy Detection using Gas Sensors

Andrzej Szczurek, Monika Maciejewska, Tomasz Pietrucha

2017

Abstract

Room occupancy is an important variable in high performance building management. Presence of people is usually detected by dedicated sensing systems. The most popular ones exploit physical phenomena. Such sensing solutions include passive infrared motion detectors, magnetic reed switches, ultrasonic, microwave and audible sensors, video cameras and radio-frequency identification. However, in most cases either human movement is needed to succeed in detection or privacy issues are involved. In this work, we studied occupancy detection using chemical sensors. In this case, the basis for detecting human presence indoors is their influence of chemical composition of air. Movement of people is not needed to succeed and privacy of occupants is secured. The approach was reported effective when using carbon dioxide, which is one of major human metabolites. We focused on volatile organic compounds (VOCs). Their consideration is justified because numerous human effluents belong to this group. The analysis showed that VOCs’ sensors, such as semiconductor gas sensors, offer comparable occupancy detection accuracy (97.16 %) as nondispersive infrared sensor (NDIR) (97.36 %), which is considered as the benchmark. In view of our results, semiconductor gas sensors are interesting candidates for nodes of sensor nets dedicated to detection of human presence indoors. They are smaller, cheaper and consume less energy.

Paper Citation

in Harvard Style

Szczurek A., Maciejewska M. and Pietrucha T. (2017). Occupancy Detection using Gas Sensors . In Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-211-0, pages 99-107. DOI: 10.5220/0006207100990107

in Bibtex Style

@conference{sensornets17,
author={Andrzej Szczurek and Monika Maciejewska and Tomasz Pietrucha},
title={Occupancy Detection using Gas Sensors},
booktitle={Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2017},
pages={99-107},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006207100990107},
isbn={978-989-758-211-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Occupancy Detection using Gas Sensors
SN - 978-989-758-211-0
AU - Szczurek A.
AU - Maciejewska M.
AU - Pietrucha T.
PY - 2017
SP - 99
EP - 107
DO - 10.5220/0006207100990107