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Cluster-based exposure variation analysis
Center for Sensory Motor Interaction, Department of Health Science and Technology, Aalborg University.
University of Gävle, Faculty of Health and Occupational Studies, Department of Occupational and Public Health Sciences. University of Gävle, Centre for Musculoskeletal Research.ORCID iD: 0000-0003-1443-6211
Center for Sensory Motor Interaction, Department of Health Science and Technology, Aalborg University.
2013 (English)In: BMC Medical Research Methodology, ISSN 1471-2288, Vol. 13, 54-54 p.Article in journal (Refereed) Published
Abstract [en]

Background: Static posture, repetitive movements and lack of physical variation are known risk factors for work-related musculoskeletal disorders, and thus needs to be properly assessed in occupational studies. The aims of this study were (i) to investigate the effectiveness of a conventional exposure variation analysis (EVA) in discriminating exposure time lines and (ii) to compare it with a new cluster-based method for analysis of exposure variation.

Methods: For this purpose, we simulated a repeated cyclic exposure varying within each cycle between “low” and “high” exposure levels in a “near” or “far” range, and with  “low” or “high” velocities (exposure change rates). The duration of each cycle was also manipulated by selecting a “small” or “large” standard deviation of the cycle time. Theses parameters reflected three dimensions of exposure variation, i.e. range, frequency and temporal similarity. Each simulation trace included two realizations of 100 concatenated cycles with either low (r=0.1), medium (r=0.5) or high (r=0.9) correlation between the realizations. These traces were analyzed by conventional EVA, and a novel cluster-based EVA (C-EVA). Principal component analysis (PCA) was applied on the marginal distributions of 1) the EVA of each of the realizations (univariate approach), 2) a combination of the EVA of both realizations (multivariate approach) and 3) C-EVA. The least number of principal components describing more than 90% of variability in each case was selected and the projection of marginal distributions along the selected principal component was calculated. A linear classifier was then applied to these projections to discriminate between the simulated exposure patterns, and the accuracy of classified realizations was determined.

Results: C-EVA classified exposures more correctly than uni 1 variate and multivariate EVA approaches; classification accuracy was 49%, 47% and 52% for EVA (univariate and multivariate), and C-EVA, respectively (p<0.001). All three methods performed poorly in discriminating exposure patterns differing with respect to the variability in cycle time duration.

Conclusion: While C-EVA had a higher accuracy than conventional EVA, both failed to detect differences in temporal similarity. The data-driven optimality of data reduction and the capability of handling multiple exposure time lines in a single analysis are the advantages of the C-EVA.

Place, publisher, year, edition, pages
2013. Vol. 13, 54-54 p.
Keyword [en]
Ergonomics, linear discriminant analysis, neck-shoulder disorders, principle component analysis
National Category
Environmental Health and Occupational Health
Identifiers
URN: urn:nbn:se:hig:diva-11947DOI: 10.1186/1471-2288-13-54ISI: 000317475300001Scopus ID: 2-s2.0-84875686999OAI: oai:DiVA.org:hig-11947DiVA: diva2:530777
Available from: 2012-06-04 Created: 2012-06-04 Last updated: 2014-11-11Bibliographically approved

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Publisher's full textScopushttp://www.biomedcentral.com/1471-2288/13/54

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Mathiassen, Svend Erik
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CiteExportLink to record
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