High-Efficiency Multi-Sensor System for Chair Usage Detection
Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such act...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/22/7580 |
| _version_ | 1797508524338577408 |
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| author | Alessandro Baserga Federico Grandi Andrea Masciadri Sara Comai Fabio Salice |
| author_facet | Alessandro Baserga Federico Grandi Andrea Masciadri Sara Comai Fabio Salice |
| author_sort | Alessandro Baserga |
| collection | DOAJ |
| description | Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%. |
| first_indexed | 2024-03-10T05:05:08Z |
| format | Article |
| id | doaj.art-9055fbb4a2b843d2b3639696934d7d09 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:05:08Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9055fbb4a2b843d2b3639696934d7d092023-11-23T01:26:01ZengMDPI AGSensors1424-82202021-11-012122758010.3390/s21227580High-Efficiency Multi-Sensor System for Chair Usage DetectionAlessandro Baserga0Federico Grandi1Andrea Masciadri2Sara Comai3Fabio Salice4Department of Physics, Politecnico di Milano, 20133 Milan, ItalyDepartment of Physics, Politecnico di Milano, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, ItalyRecognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%.https://www.mdpi.com/1424-8220/21/22/7580fall detectionchair usageambient assisted livingcapacitive coupling sensoraccelerometer sensorActivities of Daily Living |
| spellingShingle | Alessandro Baserga Federico Grandi Andrea Masciadri Sara Comai Fabio Salice High-Efficiency Multi-Sensor System for Chair Usage Detection Sensors fall detection chair usage ambient assisted living capacitive coupling sensor accelerometer sensor Activities of Daily Living |
| title | High-Efficiency Multi-Sensor System for Chair Usage Detection |
| title_full | High-Efficiency Multi-Sensor System for Chair Usage Detection |
| title_fullStr | High-Efficiency Multi-Sensor System for Chair Usage Detection |
| title_full_unstemmed | High-Efficiency Multi-Sensor System for Chair Usage Detection |
| title_short | High-Efficiency Multi-Sensor System for Chair Usage Detection |
| title_sort | high efficiency multi sensor system for chair usage detection |
| topic | fall detection chair usage ambient assisted living capacitive coupling sensor accelerometer sensor Activities of Daily Living |
| url | https://www.mdpi.com/1424-8220/21/22/7580 |
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