Abstract
BackgroundStatic 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.MethodsFor 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 (ρ = 0.1), medium (ρ = 0.5) or high (ρ = 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.ResultsC-EVA classified exposures more correctly than univariate 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.ConclusionWhile 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.
Highlights
Static posture, repetitive movements and lack of physical variation are known risk factors for workrelated musculoskeletal disorders, and needs to be properly assessed in occupational studies
Some approaches are based on an aggregation of exposure variation analysis (EVA) cells below or above a certain threshold in exposure level and/or sequence duration [12,14,17], while others derive variables describing the centroid or standard deviation of the EVA cells [13,16,18,19], or suggest statistical analysis procedures using principal component analysis of the EVA marginal distribution [15] and hierarchical regression of exposure level, frequency and duration simultaneously [20]
We developed a novel data-driven exposure analysis approach based on data clustering techniques (C-EVA), and investigated its ability to discriminate between different simulated time lines of exposure compared with a conventional EVA using both univariate and multivariate approaches
Summary
Repetitive movements and lack of physical variation are known risk factors for workrelated musculoskeletal disorders, and needs to be properly assessed in occupational studies. Biomechanical exposure in occupational settings has been described to comprise three basic, conceptual dimensions, i.e., level (amplitude), duration and repetitiveness (frequency), the latter being closely associated with velocity and acceleration when postural exposure is of interest [9,10]. A computational framework, the exposure variation analysis (EVA), has been suggested to quantify variation [11]. EVA quantifies the accumulated proportion of recorded time that the exposure level remains uninterruptedly within pre-determined limits (“exposure level” categories) for pre-determined periods of time (“sequence duration” categories). Some approaches are based on an aggregation of EVA cells below or above a certain threshold in exposure level and/or sequence duration [12,14,17], while others derive variables describing the centroid or standard deviation of the EVA cells [13,16,18,19], or suggest statistical analysis procedures using principal component analysis of the EVA marginal distribution [15] and hierarchical regression of exposure level, frequency and duration simultaneously [20]
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