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Multi-User Low Intrusive Occupancy Detection
Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876590/ https://www.ncbi.nlm.nih.gov/pubmed/29509693 http://dx.doi.org/10.3390/s18030796 |
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author | Pratama, Azkario Rizky Widyawan, Widyawan Lazovik, Alexander Aiello, Marco |
author_facet | Pratama, Azkario Rizky Widyawan, Widyawan Lazovik, Alexander Aiello, Marco |
author_sort | Pratama, Azkario Rizky |
collection | PubMed |
description | Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers’ mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS) of BLE (Bluetooth Low Energy) nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87–90% accuracy, demonstrating the effectiveness of the proposed approach. |
format | Online Article Text |
id | pubmed-5876590 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-58765902018-04-09 Multi-User Low Intrusive Occupancy Detection Pratama, Azkario Rizky Widyawan, Widyawan Lazovik, Alexander Aiello, Marco Sensors (Basel) Article Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers’ mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS) of BLE (Bluetooth Low Energy) nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87–90% accuracy, demonstrating the effectiveness of the proposed approach. MDPI 2018-03-06 /pmc/articles/PMC5876590/ /pubmed/29509693 http://dx.doi.org/10.3390/s18030796 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pratama, Azkario Rizky Widyawan, Widyawan Lazovik, Alexander Aiello, Marco Multi-User Low Intrusive Occupancy Detection |
title | Multi-User Low Intrusive Occupancy Detection |
title_full | Multi-User Low Intrusive Occupancy Detection |
title_fullStr | Multi-User Low Intrusive Occupancy Detection |
title_full_unstemmed | Multi-User Low Intrusive Occupancy Detection |
title_short | Multi-User Low Intrusive Occupancy Detection |
title_sort | multi-user low intrusive occupancy detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876590/ https://www.ncbi.nlm.nih.gov/pubmed/29509693 http://dx.doi.org/10.3390/s18030796 |
work_keys_str_mv | AT pratamaazkariorizky multiuserlowintrusiveoccupancydetection AT widyawanwidyawan multiuserlowintrusiveoccupancydetection AT lazovikalexander multiuserlowintrusiveoccupancydetection AT aiellomarco multiuserlowintrusiveoccupancydetection |