Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices
Solving the challenge of occupancy prediction is crucial in order to design efficient and sustainable office spaces and automate lighting, heating, and air circulation in these facilities. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/19/5497 |
| _version_ | 1797552592733077504 |
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| author | Beril Sirmacek Maria Riveiro |
| author_facet | Beril Sirmacek Maria Riveiro |
| author_sort | Beril Sirmacek |
| collection | DOAJ |
| description | Solving the challenge of occupancy prediction is crucial in order to design efficient and sustainable office spaces and automate lighting, heating, and air circulation in these facilities. 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 compensated for such heat artifacts. Therefore, in this paper, we presented 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 used a low-resolution <inline-formula><math display="inline"><semantics><mrow><mo>(</mo><mn>8</mn><mo>×</mo><mn>8</mn><mo>)</mo></mrow></semantics></math></inline-formula> and non-intrusive heat sensor to collect data from an actual meeting room. We proposed two novel workflows to predict the occupancy; one that is based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we used 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 analyzed noise resources that affect the heat sensor data. The experiments show that the feature classification based method gives high accuracy when the data are clean from noise artifacts. However, when there are noise artifacts, the computer vision based method can compensate for those artifacts providing robust results. Because the computer vision based method requires an empty room recording, the feature classification based method should be chosen either when there is no expectancy of seeing noise artifacts in the data or when there is no empty recording available. 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. |
| first_indexed | 2024-03-10T16:03:18Z |
| format | Article |
| id | doaj.art-00013831f2754d1eae195f60ab31f584 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:03:18Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-00013831f2754d1eae195f60ab31f5842023-11-20T15:04:30ZengMDPI AGSensors1424-82202020-09-012019549710.3390/s20195497Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart OfficesBeril Sirmacek0Maria Riveiro1Jönköping AI Lab (JAIL), Department of Computer Science and Informatics, School of Engineering, Jönköping University, 553 18 Jönköping, SwedenJönköping AI Lab (JAIL), Department of Computer Science and Informatics, School of Engineering, Jönköping University, 553 18 Jönköping, SwedenSolving the challenge of occupancy prediction is crucial in order to design efficient and sustainable office spaces and automate lighting, heating, and air circulation in these facilities. 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 compensated for such heat artifacts. Therefore, in this paper, we presented 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 used a low-resolution <inline-formula><math display="inline"><semantics><mrow><mo>(</mo><mn>8</mn><mo>×</mo><mn>8</mn><mo>)</mo></mrow></semantics></math></inline-formula> and non-intrusive heat sensor to collect data from an actual meeting room. We proposed two novel workflows to predict the occupancy; one that is based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we used 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 analyzed noise resources that affect the heat sensor data. The experiments show that the feature classification based method gives high accuracy when the data are clean from noise artifacts. However, when there are noise artifacts, the computer vision based method can compensate for those artifacts providing robust results. Because the computer vision based method requires an empty room recording, the feature classification based method should be chosen either when there is no expectancy of seeing noise artifacts in the data or when there is no empty recording available. 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.https://www.mdpi.com/1424-8220/20/19/5497heat sensorssmart officesoccupancy predictionmachine learningcomputer visionfeature engineering |
| spellingShingle | Beril Sirmacek Maria Riveiro Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices Sensors heat sensors smart offices occupancy prediction machine learning computer vision feature engineering |
| title | Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices |
| title_full | Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices |
| title_fullStr | Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices |
| title_full_unstemmed | Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices |
| title_short | Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices |
| title_sort | occupancy prediction using low cost and low resolution heat sensors for smart offices |
| topic | heat sensors smart offices occupancy prediction machine learning computer vision feature engineering |
| url | https://www.mdpi.com/1424-8220/20/19/5497 |
| work_keys_str_mv | AT berilsirmacek occupancypredictionusinglowcostandlowresolutionheatsensorsforsmartoffices AT mariariveiro occupancypredictionusinglowcostandlowresolutionheatsensorsforsmartoffices |