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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...

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Autores principales: Baserga, Alessandro, Grandi, Federico, Masciadri, Andrea, Comai, Sara, Salice, Fabio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8620359/
https://www.ncbi.nlm.nih.gov/pubmed/34833654
http://dx.doi.org/10.3390/s21227580
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author Baserga, Alessandro
Grandi, Federico
Masciadri, Andrea
Comai, Sara
Salice, Fabio
author_facet Baserga, Alessandro
Grandi, Federico
Masciadri, Andrea
Comai, Sara
Salice, Fabio
author_sort Baserga, Alessandro
collection PubMed
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%.
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spelling pubmed-86203592021-11-27 High-Efficiency Multi-Sensor System for Chair Usage Detection Baserga, Alessandro Grandi, Federico Masciadri, Andrea Comai, Sara Salice, Fabio Sensors (Basel) Article 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%. MDPI 2021-11-15 /pmc/articles/PMC8620359/ /pubmed/34833654 http://dx.doi.org/10.3390/s21227580 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Baserga, Alessandro
Grandi, Federico
Masciadri, Andrea
Comai, Sara
Salice, Fabio
High-Efficiency Multi-Sensor System for Chair Usage Detection
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8620359/
https://www.ncbi.nlm.nih.gov/pubmed/34833654
http://dx.doi.org/10.3390/s21227580
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