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Open AccessArticle

Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data

Jönköping AI Lab (JAIL), Department of Computer Science and Informatics, School of Engineering, Jönköping University, 551 11 Jönköping, Sweden
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Academic Editors: Janos Abonyi and Anthony Fleury
Sensors 2021, 21(4), 1036; https://doi.org/10.3390/s21041036
Received: 29 December 2020 / Revised: 26 January 2021 / Accepted: 28 January 2021 / Published: 3 February 2021
(This article belongs to the Section Physical Sensors)
Indoor occupancy prediction is a prerequisite for the management of energy consumption, security, health, and other systems in smart buildings. Previous studies have shown that buildings that automatize their heating, lighting, air conditioning, and ventilation systems through considering the occupancy and activity information might reduce energy consumption by more than 50%. However, it is difficult to use high-resolution sensors and cameras for occupancy prediction due to privacy concerns. In this paper, we propose a novel solution for predicting occupancy using multiple low-cost and low-resolution heat sensors. We suggest two different methods for fusing and processing the data captured from multiple heat sensors and we use a Convolutional Neural Network for predicting occupancy. We conduct experiments to assess both the performance of the proposed solutions and analyze the impact of sensor field view overlaps on the prediction results. In summary, our experimental results show that the implemented solutions show high occupancy prediction accuracy and real-time processing capabilities.
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Keywords: heat sensors; multi-sensor; sensor fusion; occupancy prediction; machine learning; artificial intelligence (AI); neural networks; smart offices heat sensors; multi-sensor; sensor fusion; occupancy prediction; machine learning; artificial intelligence (AI); neural networks; smart offices
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MDPI and ACS Style

Arvidsson, S.; Gullstrand, M.; Sirmacek, B.; Riveiro, M. Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data. Sensors 2021, 21, 1036. https://doi.org/10.3390/s21041036

AMA Style

Arvidsson S, Gullstrand M, Sirmacek B, Riveiro M. Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data. Sensors. 2021; 21(4):1036. https://doi.org/10.3390/s21041036

Chicago/Turabian Style

Arvidsson, Simon; Gullstrand, Marcus; Sirmacek, Beril; Riveiro, Maria. 2021. "Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data" Sensors 21, no. 4: 1036. https://doi.org/10.3390/s21041036

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