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Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites
Continuous, real-time monitoring of occupational health and safety in high-risk workplaces such as construction sites can substantially improve the safety of workers. However, introducing such systems in practice is associated with a number of challenges, such as scaling up the solution while keepin...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385062/ https://www.ncbi.nlm.nih.gov/pubmed/37514757 http://dx.doi.org/10.3390/s23146464 |
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author | Sowiński, Piotr Rachwał, Kajetan Danilenka, Anastasiya Bogacka, Karolina Kobus, Monika Dąbrowska, Anna Paszkiewicz, Andrzej Bolanowski, Marek Ganzha, Maria Paprzycki, Marcin |
author_facet | Sowiński, Piotr Rachwał, Kajetan Danilenka, Anastasiya Bogacka, Karolina Kobus, Monika Dąbrowska, Anna Paszkiewicz, Andrzej Bolanowski, Marek Ganzha, Maria Paprzycki, Marcin |
author_sort | Sowiński, Piotr |
collection | PubMed |
description | Continuous, real-time monitoring of occupational health and safety in high-risk workplaces such as construction sites can substantially improve the safety of workers. However, introducing such systems in practice is associated with a number of challenges, such as scaling up the solution while keeping its cost low. In this context, this work investigates the use of an off-the-shelf, low-cost smartwatch to detect health issues based on heart rate monitoring in a privacy-preserving manner. To improve the smartwatch’s low measurement quality, a novel, frugal machine learning method is proposed that corrects measurement errors, along with a new dataset for this task. This method’s integration with the smartwatch and the remaining parts of the health and safety monitoring system (built on the ASSIST-IoT reference architecture) are presented. This method was evaluated in a laboratory environment in terms of its accuracy, computational requirements, and frugality. With an experimentally established mean absolute error of 8.19 BPM, only 880 bytes of required memory, and a negligible impact on the performance of the device, this method meets all relevant requirements and is expected to be field-tested in the coming months. To support reproducibility and to encourage alternative approaches, the dataset, the trained model, and its implementation on the smartwatch were published under free licenses. |
format | Online Article Text |
id | pubmed-10385062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103850622023-07-30 Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites Sowiński, Piotr Rachwał, Kajetan Danilenka, Anastasiya Bogacka, Karolina Kobus, Monika Dąbrowska, Anna Paszkiewicz, Andrzej Bolanowski, Marek Ganzha, Maria Paprzycki, Marcin Sensors (Basel) Article Continuous, real-time monitoring of occupational health and safety in high-risk workplaces such as construction sites can substantially improve the safety of workers. However, introducing such systems in practice is associated with a number of challenges, such as scaling up the solution while keeping its cost low. In this context, this work investigates the use of an off-the-shelf, low-cost smartwatch to detect health issues based on heart rate monitoring in a privacy-preserving manner. To improve the smartwatch’s low measurement quality, a novel, frugal machine learning method is proposed that corrects measurement errors, along with a new dataset for this task. This method’s integration with the smartwatch and the remaining parts of the health and safety monitoring system (built on the ASSIST-IoT reference architecture) are presented. This method was evaluated in a laboratory environment in terms of its accuracy, computational requirements, and frugality. With an experimentally established mean absolute error of 8.19 BPM, only 880 bytes of required memory, and a negligible impact on the performance of the device, this method meets all relevant requirements and is expected to be field-tested in the coming months. To support reproducibility and to encourage alternative approaches, the dataset, the trained model, and its implementation on the smartwatch were published under free licenses. MDPI 2023-07-17 /pmc/articles/PMC10385062/ /pubmed/37514757 http://dx.doi.org/10.3390/s23146464 Text en © 2023 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 Sowiński, Piotr Rachwał, Kajetan Danilenka, Anastasiya Bogacka, Karolina Kobus, Monika Dąbrowska, Anna Paszkiewicz, Andrzej Bolanowski, Marek Ganzha, Maria Paprzycki, Marcin Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites |
title | Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites |
title_full | Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites |
title_fullStr | Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites |
title_full_unstemmed | Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites |
title_short | Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites |
title_sort | frugal heart rate correction method for scalable health and safety monitoring in construction sites |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385062/ https://www.ncbi.nlm.nih.gov/pubmed/37514757 http://dx.doi.org/10.3390/s23146464 |
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