OWAS inter-rater reliability

Lins, Christian; Fudickar, Sebastian; Hein, Andreas

doi:10.1016/j.apergo.2021.103357

Cited by 9 publications

(19 citation statements)

References 31 publications

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“…McAtamney and Corlett [ 8 ] claimed in the original study dealing with the RULA that there was a high consistency of scoring among 120 raters. Dockrell et al [ 77 ] reported the intra- and inter-rater reliabilities for six raters (three physiotherapists and three undergraduate physiotherapy students), based on the 24 children’s computing postures. The interclass correlation coefficients (ICCs) of the intra-rater reliability were 0.27–0.86 for the action levels and 0.47–0.84 for the grand scores depending upon the raters.…”

Section: Resultsmentioning

confidence: 99%

Systematic Comparison of OWAS, RULA, and REBA Based on a Literature Review

Kee

2022

IJERPH

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This study aimed to systematically compare three representative observational methods for assessing musculoskeletal loadings and their association with musculoskeletal disorders (MSDs): Ovako Working Posture Analysis System (OWAS), Rapid Upper Limb Assessment (RULA), and Rapid Entire Body Assessment (REBA). The comparison was based on a literature review without time limitations and was conducted on various factors related to observational methods. The comparisons showed that although it has a significant limitation of comprising only two classifications for the leg postures, (1) the RULA is the most frequently used method among the three techniques; (2) many studies adopted the RULA even in evaluation of unstable lower limb postures; (3) the RULA assessed postural loads as higher risk levels in most studies reviewed in this research; (4) the intra- and inter-reliabilities for the RULA were not low; and (5) the risk levels assessed by the RULA were more significantly associated with postural load criteria such as discomfort, MHTs and % capable at the trunk, and MSDs.

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Section: Resultsmentioning

confidence: 99%

Systematic Comparison of OWAS, RULA, and REBA Based on a Literature Review

Kee

2022

IJERPH

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“…Weir et al [33] were able to show that for video-based observations the classification accuracy of people with and without ergonomics training does not differ significantly. This has also been shown for live human observations, i.e., not video recordings [13]. Table 5: OWAS Risk Action Classes (as of [31]).…”

Section: Owas Methodsmentioning

confidence: 61%

“…The three raters were equipped with pens and structured sheets and viewed each participant from one viewing-angle. The raters sat next to each other at a distance of around 1 m among each other, and were requested to remain seated, to ensure the independent evaluation of the raters [13].…”

Section: Study Setupmentioning

confidence: 99%

“…With three observers, one cannot assume complete agreement between observers (inter-rater reliability [13]). Each OWAS category (back, arms, legs) is considered [6] See also https://mo.mathematik.uni-stuttgart.de/kurse/kurs10/seite111.html and http://peheko.…”

Section: Training Data From the Inertial Sensor Datamentioning

confidence: 99%

“…Today, checklists and more specific assessment methods, among others, are used to assess individual risks and should enable the person observing to assess activities and workplaces. The findings of these assessment methods can be used to derive appropriate, workplace-and person-specific prevention measures in the context of situational and behavioral prevention [13]. One of these assessment methods is the Ovako Working posture Analysing System (OWAS), which is later described in detail.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification Of Body Postures Using Smart Workwear

Lins

Hein

2022

Preprint

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Background: Despite advancing automation, employees in many industrial and service occupations still have to perform physically intensive work that may have negative effects on the health of the musculoskeletal system. For targeted preventive measures, precise knowledge of the work postures and movements performed is necessary. Methods: Prototype smart work clothes equipped with 15 inertial sensors were used to record reference body postures of 20 subjects. These reference postures were used to create a software-based posture classifier according to the Ovako Working Posture Analysing System (OWAS) by means of an evolutionary training algorithm. Results: A total of 111,275 posture shots were recorded and used for training the classifier. The results show that smart workwear, with the help of evolutionary trained software classifiers, is in principle capable of detecting harmful postures of its wearer. The detection rate of the evolutionary trained classifier (a_ccr = 0.35 for the postures of the back, a_ccr = 0.64 for the arms, and a_ccr = 0.25 for the legs) outperforms that of a TensorFlow trained classifying neural network. Conclusions: In principle, smart workwear – as prototypically shown in this paper – can be a helpful tool for assessing an individual’s risk for work-related musculoskeletal disorders. Numerous potential sources of error have been identified that can affect the detection accuracy of software classifiers required for this purpose.

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