Evidence map›Paper›PMID 30862019›Full record

ArticleSensors (Basel, Switzerland)2019

P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing.

Mario Vega-Barbas, Jose A Diaz-Olivares, Ke Lu, Mikael Forsman, Fernando Seoane, Farhad Abtahi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mario Vega-BarbasInstitute of Environmental Medicine, Karolinska Institutet, Solnavägen 1, 17177 Solna, Sweden. mario.vega.barbas@ki.se.ORCID 0000-0003-4506-6284
Jose A Diaz-OlivaresSchool of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Hälsovägen 11C, 14157 Huddinge, Sweden. jadiaz@kth.se.
Ke LuInstitute of Environmental Medicine, Karolinska Institutet, Solnavägen 1, 17177 Solna, Sweden. ke.lu@ki.se.ORCID 0000-0002-3256-9029
Mikael ForsmanInstitute of Environmental Medicine, Karolinska Institutet, Solnavägen 1, 17177 Solna, Sweden. mikael.forsman@ki.se.ORCID 0000-0001-5777-4232
Fernando SeoaneDepartment of Clinical Science, Intervention and Technology, Karolinska Institutet, Hälsovägen 7, 14157 Huddinge, Sweden. fernando.seoane@ki.se.ORCID 0000-0002-6995-967X
Farhad AbtahiInstitute of Environmental Medicine, Karolinska Institutet, Solnavägen 1, 17177 Solna, Sweden. farhad.abtahi@ki.se.ORCID 0000-0001-7807-8682

Funding

EIT Health 18454
6 · The paper itself

Abstract

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.

Indexed as

Biosensing TechniquesWearable Electronic DevicesErgonomicsHumansMusculoskeletal DiseasesOccupational Diseasesdisease preventionmusculoskeletal disordersoccupational healthcareP-Ergonomicsprecision ergonomicssmart textileswearable sensorswellbeing at work

Identifiers

PMID30862019
PMCPMC6427483

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.