Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices

Version 1 : Received: 6 August 2020 / Approved: 8 August 2020 / Online: 8 August 2020 (04:07:31 CEST)

A peer-reviewed article of this Preprint also exists.

Sirmacek, B.; Riveiro, M. Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices. Sensors 2020, 20, 5497. Sirmacek, B.; Riveiro, M. Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices. Sensors 2020, 20, 5497.

Journal reference: Sensors 2020, 20, 5497
DOI: 10.3390/s20195497

Abstract

In order to design efficient and sustainable office spaces and to automate lighting, heating and air circulation in these facilities, solving the challenge of occupancy prediction is crucial. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors, however, they have not addressed nor compensate for such heat artifacts. Therefore, in this paper, we present a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We use a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We propose two novel workflows to predict the occupancy; one based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we use several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyze noise resources which affect the heat sensor data. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

Subject Areas

heat sensors; smart offices; occupancy prediction; machine learning; computer vision; feature engineering; explainability

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