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A Novel Smart Chair System for Posture Classification and Invisible ECG Monitoring
In recent years, employment in sedentary occupations has continuously risen. Office workers are more prone to prolonged static sitting, spending 65–80% of work hours sitting, increasing risks for multiple health problems, including cardiovascular diseases and musculoskeletal disorders. These adverse...
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/PMC9867523/ https://www.ncbi.nlm.nih.gov/pubmed/36679516 http://dx.doi.org/10.3390/s23020719 |
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author | Pereira, Leonor Plácido da Silva, Hugo |
author_facet | Pereira, Leonor Plácido da Silva, Hugo |
author_sort | Pereira, Leonor |
collection | PubMed |
description | In recent years, employment in sedentary occupations has continuously risen. Office workers are more prone to prolonged static sitting, spending 65–80% of work hours sitting, increasing risks for multiple health problems, including cardiovascular diseases and musculoskeletal disorders. These adverse health effects lead to decreased productivity, increased absenteeism and health care costs. However, lack of regulation targeting these issues has oftentimes left them unattended. This article proposes a smart chair system, with posture and electrocardiography (ECG) monitoring modules, using an “invisible” sensing approach, to optimize working conditions, without hindering everyday tasks. For posture classification, machine learning models were trained and tested with datasets composed by center of mass coordinates in the seat plane, computed from the weight measured by load cells fixed under the seat. Models were trained and evaluated in the classification of five and seven sitting positions, achieving high accuracy results for all five-class models (>97.4%), and good results for some seven-class models, particularly the best performing k-NN model (87.5%). For ECG monitoring, signals were acquired at the armrests covered with conductive nappa, connected to a single-lead sensor. Following signal filtering and segmentation, several outlier detection methods were applied to remove extremely noisy segments with mislabeled R-peaks, but only DBSCAN showed satisfactory results for the ECG segmentation performance (88.21%) and accuracy (90.50%). |
format | Online Article Text |
id | pubmed-9867523 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98675232023-01-22 A Novel Smart Chair System for Posture Classification and Invisible ECG Monitoring Pereira, Leonor Plácido da Silva, Hugo Sensors (Basel) Article In recent years, employment in sedentary occupations has continuously risen. Office workers are more prone to prolonged static sitting, spending 65–80% of work hours sitting, increasing risks for multiple health problems, including cardiovascular diseases and musculoskeletal disorders. These adverse health effects lead to decreased productivity, increased absenteeism and health care costs. However, lack of regulation targeting these issues has oftentimes left them unattended. This article proposes a smart chair system, with posture and electrocardiography (ECG) monitoring modules, using an “invisible” sensing approach, to optimize working conditions, without hindering everyday tasks. For posture classification, machine learning models were trained and tested with datasets composed by center of mass coordinates in the seat plane, computed from the weight measured by load cells fixed under the seat. Models were trained and evaluated in the classification of five and seven sitting positions, achieving high accuracy results for all five-class models (>97.4%), and good results for some seven-class models, particularly the best performing k-NN model (87.5%). For ECG monitoring, signals were acquired at the armrests covered with conductive nappa, connected to a single-lead sensor. Following signal filtering and segmentation, several outlier detection methods were applied to remove extremely noisy segments with mislabeled R-peaks, but only DBSCAN showed satisfactory results for the ECG segmentation performance (88.21%) and accuracy (90.50%). MDPI 2023-01-08 /pmc/articles/PMC9867523/ /pubmed/36679516 http://dx.doi.org/10.3390/s23020719 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 Pereira, Leonor Plácido da Silva, Hugo A Novel Smart Chair System for Posture Classification and Invisible ECG Monitoring |
title | A Novel Smart Chair System for Posture Classification and Invisible ECG Monitoring |
title_full | A Novel Smart Chair System for Posture Classification and Invisible ECG Monitoring |
title_fullStr | A Novel Smart Chair System for Posture Classification and Invisible ECG Monitoring |
title_full_unstemmed | A Novel Smart Chair System for Posture Classification and Invisible ECG Monitoring |
title_short | A Novel Smart Chair System for Posture Classification and Invisible ECG Monitoring |
title_sort | novel smart chair system for posture classification and invisible ecg monitoring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9867523/ https://www.ncbi.nlm.nih.gov/pubmed/36679516 http://dx.doi.org/10.3390/s23020719 |
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