{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T07:00:52Z","timestamp":1762066852046,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T00:00:00Z","timestamp":1663286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Health of the State of Rhineland-Palatinate"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/make4030039","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T02:06:50Z","timestamp":1663553210000},"page":"803-813","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Sensor Fusion for Occupancy Estimation: A Study Using Multiple Lecture Rooms in a Complex Building"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7302-5431","authenticated-orcid":false,"given":"C\u00e9dric","family":"Roussel","sequence":"first","affiliation":[{"name":"i3mainz\u2014Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences, 55128 Mainz, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klaus","family":"B\u00f6hm","sequence":"additional","affiliation":[{"name":"i3mainz\u2014Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences, 55128 Mainz, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5158-796X","authenticated-orcid":false,"given":"Pascal","family":"Neis","sequence":"additional","affiliation":[{"name":"i3mainz\u2014Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences, 55128 Mainz, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.aci.2018.12.001","article-title":"Occupancy detection in non-residential buildings\u2014A survey and novel privacy preserved occupancy monitoring solution","volume":"17","author":"Ahmad","year":"2020","journal-title":"Appl. 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Proceedings of the 11th REHVA World Congress \u201cEnergy Efficient, Smart and Healthy Buildings\u201d, Prague, Czech Republic."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2573","DOI":"10.1007\/s12206-017-0455-z","article-title":"Uncertainties in neural network model based on carbon dioxide concentration for occupancy estimation","volume":"31","author":"Alam","year":"2016","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_6","unstructured":"Melfi, R., Rosenblum, B., Nordman, B., and Christensen, K. Measuring Building Occupancy Using Existing Network Infrastructure. International Green Computing Conference and Workshops, Orlando, FL, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1109\/TCE.2006.273150","article-title":"A pyroelectric infrared sensor-based indoor location-aware system for the smart home","volume":"52","author":"Lee","year":"2006","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1080\/0305569900160305","article-title":"The Hawthorne Effect: A fresh examination","volume":"16","author":"Diaper","year":"1990","journal-title":"Educ. Stud."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.apenergy.2018.11.078","article-title":"Carbon dioxide-based occupancy estimation using stochastic differential equations","volume":"236","author":"Wolf","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.procs.2019.08.069","article-title":"Real-Time Occupancy Estimation Using WiFi Network to Optimize HVAC Operation","volume":"155","author":"Simma","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_11","unstructured":"Yang, Z., Li, N., Becerik-Gerber, B., and Orosz, M. (2012, January 26\u201330). A Multi-Sensor Based Occupancy Estimation Model for Supporting Demand Driven HVAC Operations. Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design, Orlando, FL, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.enbuild.2010.09.014","article-title":"Towards a sensor for detecting human presence and characterizing activity","volume":"43","author":"Benezeth","year":"2011","journal-title":"Energy Build."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.jvcir.2009.03.005","article-title":"People detection and tracking with multiple stereo cameras using particle filters","volume":"20","year":"2009","journal-title":"J. Vis. Commun. Image Representat."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.enbuild.2017.04.014","article-title":"Predictive control of indoor environment using occupant number detected by video data and CO2 concentration","volume":"145","author":"Wang","year":"2017","journal-title":"Energy Build."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s12273-012-0075-6","article-title":"Information-theoretic environment features selection for occupancy detection in open office spaces","volume":"5","author":"Zhang","year":"2012","journal-title":"Build. Simul."},{"key":"ref_16","unstructured":"HPE\u2014Hewlett Packard Enterprise Development LP (2022, July 20). Aruba 7000 Series Mobility Controllers. Available online: https:\/\/www.arubanetworks.com\/assets\/ds\/DS_7000Series.pdf."},{"key":"ref_17","unstructured":"Google LLC (2022, August 17). Flutter. Available online: https:\/\/flutter.dev\/."},{"key":"ref_18","unstructured":"Iotbymukund (2022, July 19). How to Calculate Distance from the RSSI value of the BLE Beacon. Available online: https:\/\/iotandelectronics.wordpress.com\/2016\/10\/07\/howto-calculate-distance-from-the-rssi-value-of-the-ble-beacon\/."},{"key":"ref_19","first-page":"26","article-title":"Practical Indoor Navigation for Smartphones Based on a Metrological Investigation","volume":"7","author":"Roussel","year":"2021","journal-title":"AGIT J. Appl. Geoinform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8031","DOI":"10.1007\/s13762-019-02412-5","article-title":"The concentration of carbon dioxide in conference rooms: A simplified model and experimental verification","volume":"16","author":"Teleszewski","year":"2019","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_21","unstructured":"OGC (2022, August 17). OGC SensorThings API. Available online: https:\/\/www.ogc.org\/standards\/sensorthings."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/3\/39\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:33:02Z","timestamp":1760142782000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/3\/39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,16]]},"references-count":21,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["make4030039"],"URL":"https:\/\/doi.org\/10.3390\/make4030039","relation":{},"ISSN":["2504-4990"],"issn-type":[{"type":"electronic","value":"2504-4990"}],"subject":[],"published":{"date-parts":[[2022,9,16]]}}}