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P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing
Karolinska Inst, Inst Environm Med, Solnavagen 1, S-17177 Solna, Sweden.ORCID iD: 0000-0003-4506-6284
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Ergonomics.
Karolinska Inst, Inst Environm Med, Solnavagen 1, S-17177 Solna, Sweden..ORCID iD: 0000-0002-3256-9029
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Ergonomics. Karolinska Inst, Inst Environm Med, Solnavagen 1, S-17177 Solna, Sweden.ORCID iD: 0000-0001-5777-4232
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2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 5, article id 1225Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2019. Vol. 19, no 5, article id 1225
Keywords [en]
disease prevention, occupational healthcare, P-Ergonomics, precision ergonomics, musculoskeletal disorders, smart textiles, wearable sensors, wellbeing at work
National Category
Occupational Health and Environmental Health
Identifiers
URN: urn:nbn:se:kth:diva-249891DOI: 10.3390/s19051225ISI: 000462540400244PubMedID: 30862019Scopus ID: 2-s2.0-85062856566OAI: oai:DiVA.org:kth-249891DiVA, id: diva2:1307268
Available from: 2019-04-26 Created: 2019-04-26 Last updated: 2019-04-26Bibliographically approved

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Diaz-Olivares, Jose A.Forsman, MikaelSeoane, FernandoAbtahi, Farhad

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