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A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor
Bedsores are one of the severe problems which could affect a long-term lying subject in the hospitals or the hospice. To prevent lying bedsores, we present a smart Internet of Things (IoT) system for detecting the position of a lying person using novel textile pressure sensors. To build such a syste...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795588/ https://www.ncbi.nlm.nih.gov/pubmed/33396203 http://dx.doi.org/10.3390/s21010206 |
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author | Hudec, Robert Matúška, Slavomír Kamencay, Patrik Benco, Miroslav |
author_facet | Hudec, Robert Matúška, Slavomír Kamencay, Patrik Benco, Miroslav |
author_sort | Hudec, Robert |
collection | PubMed |
description | Bedsores are one of the severe problems which could affect a long-term lying subject in the hospitals or the hospice. To prevent lying bedsores, we present a smart Internet of Things (IoT) system for detecting the position of a lying person using novel textile pressure sensors. To build such a system, it is necessary to use different technologies and techniques. We used sixty-four of our novel textile pressure sensors based on electrically conductive yarn and the Velostat to collect the information about the pressure distribution of the lying person. Using Message Queuing Telemetry Transport (MQTT) protocol and Arduino-based hardware, we send measured data to the server. On the server side, there is a Node-RED application responsible for data collection, evaluation, and provisioning. We are using a neural network to classify the subject lying posture on the separate device because of the computation complexity. We created the challenging dataset from the observation of twenty-one people in four lying positions. We achieved a best classification precision of 92% for fourth class (right side posture type). On the other hand, the best recall (91%) for first class (supine posture type) was obtained. The best F1 score (84%) was achieved for first class (supine posture type). After the classification, we send the information to the staff desktop application. The application reminds employees when it is necessary to change the lying position of individual subjects and thus prevent bedsores. |
format | Online Article Text |
id | pubmed-7795588 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77955882021-01-10 A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor Hudec, Robert Matúška, Slavomír Kamencay, Patrik Benco, Miroslav Sensors (Basel) Article Bedsores are one of the severe problems which could affect a long-term lying subject in the hospitals or the hospice. To prevent lying bedsores, we present a smart Internet of Things (IoT) system for detecting the position of a lying person using novel textile pressure sensors. To build such a system, it is necessary to use different technologies and techniques. We used sixty-four of our novel textile pressure sensors based on electrically conductive yarn and the Velostat to collect the information about the pressure distribution of the lying person. Using Message Queuing Telemetry Transport (MQTT) protocol and Arduino-based hardware, we send measured data to the server. On the server side, there is a Node-RED application responsible for data collection, evaluation, and provisioning. We are using a neural network to classify the subject lying posture on the separate device because of the computation complexity. We created the challenging dataset from the observation of twenty-one people in four lying positions. We achieved a best classification precision of 92% for fourth class (right side posture type). On the other hand, the best recall (91%) for first class (supine posture type) was obtained. The best F1 score (84%) was achieved for first class (supine posture type). After the classification, we send the information to the staff desktop application. The application reminds employees when it is necessary to change the lying position of individual subjects and thus prevent bedsores. MDPI 2020-12-31 /pmc/articles/PMC7795588/ /pubmed/33396203 http://dx.doi.org/10.3390/s21010206 Text en © 2020 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 Hudec, Robert Matúška, Slavomír Kamencay, Patrik Benco, Miroslav A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor |
title | A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor |
title_full | A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor |
title_fullStr | A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor |
title_full_unstemmed | A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor |
title_short | A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor |
title_sort | smart iot system for detecting the position of a lying person using a novel textile pressure sensor |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795588/ https://www.ncbi.nlm.nih.gov/pubmed/33396203 http://dx.doi.org/10.3390/s21010206 |
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