Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building
This paper uses various machine learning methods which explore the combination of multiple sensors for quality improvement. It is known that a reliable occupancy estimation can help in many different cases and applications. For the containment of the SARS-CoV-2 virus, in particular, room occupancy i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
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| Series: | Machine Learning and Knowledge Extraction |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4990/4/3/39 |
| _version_ | 1797485468973006848 |
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| author | Cédric Roussel Klaus Böhm Pascal Neis |
| author_facet | Cédric Roussel Klaus Böhm Pascal Neis |
| author_sort | Cédric Roussel |
| collection | DOAJ |
| description | This paper uses various machine learning methods which explore the combination of multiple sensors for quality improvement. It is known that a reliable occupancy estimation can help in many different cases and applications. For the containment of the SARS-CoV-2 virus, in particular, room occupancy is a major factor. The estimation can benefit visitor management systems in real time, but can also be predictive of room reservation strategies. By using different terminal and non-terminal sensors in different premises of varying sizes, this paper aims to estimate room occupancy. In the process, the proposed models are trained with different combinations of rooms in training and testing datasets to examine distinctions in the infrastructure of the considered building. The results indicate that the estimation benefits from a combination of different sensors. Additionally, it is found that a model should be trained with data from every room in a building and cannot be transferred to other rooms. |
| first_indexed | 2024-03-09T23:20:11Z |
| format | Article |
| id | doaj.art-1b0d08ce5a8c44c886b8f6346f4c4f07 |
| institution | Directory Open Access Journal |
| issn | 2504-4990 |
| language | English |
| last_indexed | 2024-03-09T23:20:11Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj.art-1b0d08ce5a8c44c886b8f6346f4c4f072023-11-23T17:28:18ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902022-09-014380381310.3390/make4030039Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex BuildingCédric Roussel0Klaus Böhm1Pascal Neis2i3mainz—Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences, 55128 Mainz, Germanyi3mainz—Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences, 55128 Mainz, Germanyi3mainz—Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences, 55128 Mainz, GermanyThis paper uses various machine learning methods which explore the combination of multiple sensors for quality improvement. It is known that a reliable occupancy estimation can help in many different cases and applications. For the containment of the SARS-CoV-2 virus, in particular, room occupancy is a major factor. The estimation can benefit visitor management systems in real time, but can also be predictive of room reservation strategies. By using different terminal and non-terminal sensors in different premises of varying sizes, this paper aims to estimate room occupancy. In the process, the proposed models are trained with different combinations of rooms in training and testing datasets to examine distinctions in the infrastructure of the considered building. The results indicate that the estimation benefits from a combination of different sensors. Additionally, it is found that a model should be trained with data from every room in a building and cannot be transferred to other rooms.https://www.mdpi.com/2504-4990/4/3/39Wi-FiBluetoothair qualityregressionclassification |
| spellingShingle | Cédric Roussel Klaus Böhm Pascal Neis Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building Machine Learning and Knowledge Extraction Wi-Fi Bluetooth air quality regression classification |
| title | Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building |
| title_full | Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building |
| title_fullStr | Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building |
| title_full_unstemmed | Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building |
| title_short | Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building |
| title_sort | sensor fusion for occupancy estimation a study using multiple lecture rooms in a complex building |
| topic | Wi-Fi Bluetooth air quality regression classification |
| url | https://www.mdpi.com/2504-4990/4/3/39 |
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