We consider the problem of detecting sensor commissioning in the form of determining the sensor layout. We address this problem for single-pixel thermopile sensors, located at the ceiling, that provide remote temperature measurements for people counting applications and HVAC controls. We employ a random forest classifier to determine the deployed layout in an area. For this classifier, we propose spatio-temporal distance features using two-sided cumulative sum recursive least squares (CUSUM RLS) filtering of the thermopile temperature sensor signals. Using sensor data generated with simulated occupancy patterns and a thermopile signal model, we show that the proposed method achieves a true positive rate (determining the correct layout) of 90.2% and false positive rate of 1.3%.
|Title of host publication||28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings|
|Place of Publication||Amsterdam (Netherlands)|
|Number of pages||5|
|Publication status||Published - 2020|
|Event||EUSIPCO 2020: The 28th European Signal Processing Conference - Amsterdam, Netherlands|
Duration: 18 Jan 2021 → 22 Jan 2021
Conference number: 28th
|Name||European Signal Processing Conference|
|Period||18/01/21 → 22/01/21|
|Other||Date change due to COVID-19 (former date August 24-28 2020)|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.