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P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing
Preventive healthcare has attracted much attention recently. Improving people’s lifestyles and promoting a healthy diet and wellbeing are important, but the importance of work-related diseases should not be undermined. Musculoskeletal disorders (MSDs) are among the most common work-related health pr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427483/ https://www.ncbi.nlm.nih.gov/pubmed/30862019 http://dx.doi.org/10.3390/s19051225 |
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author | Vega-Barbas, Mario Diaz-Olivares, Jose A. Lu, Ke Forsman, Mikael Seoane, Fernando Abtahi, Farhad |
author_facet | Vega-Barbas, Mario Diaz-Olivares, Jose A. Lu, Ke Forsman, Mikael Seoane, Fernando Abtahi, Farhad |
author_sort | Vega-Barbas, Mario |
collection | PubMed |
description | Preventive healthcare has attracted much attention recently. Improving people’s lifestyles and promoting a healthy diet and wellbeing are important, but the importance of work-related diseases should not be undermined. Musculoskeletal disorders (MSDs) are among the most common work-related health problems. Ergonomists already assess MSD risk factors and suggest changes in workplaces. However, existing methods are mainly based on visual observations, which have a relatively low reliability and cover only part of the workday. These suggestions concern the overall workplace and the organization of work, but rarely includes individuals’ work techniques. In this work, we propose a precise and pervasive ergonomic platform for continuous risk assessment. The system collects data from wearable sensors, which are synchronized and processed by a mobile computing layer, from which exposure statistics and risk assessments may be drawn, and finally, are stored at the server layer for further analyses at both individual and group levels. The platform also enables continuous feedback to the worker to support behavioral changes. The deployed cloud platform in Amazon Web Services instances showed sufficient system flexibility to affordably fulfill requirements of small to medium enterprises, while it is expandable for larger corporations. The system usability scale of 76.6 indicates an acceptable grade of usability. |
format | Online Article Text |
id | pubmed-6427483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64274832019-04-15 P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing Vega-Barbas, Mario Diaz-Olivares, Jose A. Lu, Ke Forsman, Mikael Seoane, Fernando Abtahi, Farhad Sensors (Basel) Article Preventive healthcare has attracted much attention recently. Improving people’s lifestyles and promoting a healthy diet and wellbeing are important, but the importance of work-related diseases should not be undermined. Musculoskeletal disorders (MSDs) are among the most common work-related health problems. Ergonomists already assess MSD risk factors and suggest changes in workplaces. However, existing methods are mainly based on visual observations, which have a relatively low reliability and cover only part of the workday. These suggestions concern the overall workplace and the organization of work, but rarely includes individuals’ work techniques. In this work, we propose a precise and pervasive ergonomic platform for continuous risk assessment. The system collects data from wearable sensors, which are synchronized and processed by a mobile computing layer, from which exposure statistics and risk assessments may be drawn, and finally, are stored at the server layer for further analyses at both individual and group levels. The platform also enables continuous feedback to the worker to support behavioral changes. The deployed cloud platform in Amazon Web Services instances showed sufficient system flexibility to affordably fulfill requirements of small to medium enterprises, while it is expandable for larger corporations. The system usability scale of 76.6 indicates an acceptable grade of usability. MDPI 2019-03-11 /pmc/articles/PMC6427483/ /pubmed/30862019 http://dx.doi.org/10.3390/s19051225 Text en © 2019 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 Vega-Barbas, Mario Diaz-Olivares, Jose A. Lu, Ke Forsman, Mikael Seoane, Fernando Abtahi, Farhad P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing |
title | P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing |
title_full | P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing |
title_fullStr | P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing |
title_full_unstemmed | P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing |
title_short | P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing |
title_sort | p-ergonomics platform: toward precise, pervasive, and personalized ergonomics using wearable sensors and edge computing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427483/ https://www.ncbi.nlm.nih.gov/pubmed/30862019 http://dx.doi.org/10.3390/s19051225 |
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