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BACKGROUND: A large number of different methods are available to identify and assess working postures. Although observation-based methods are most commonly used in practise, investigations showed different results regarding validity of such methods. OBJECTIVE: To investigate validity of one of the most commonly used observation-based assessment method in ergonomics, the Ovako Working Posture Analysing System (OWAS) and the European standard EN 1005-4 for evaluation of working postures, an experimental laboratory study was conducted. METHODS: Muscle activity was measured under combinations of static working postures of trunk inclination and shoulder flexion to compare these measurements and observation-based assessments according to OWAS and EN 1005-4. In order to investigate the magnitude of correspondence between muscle activity and observation-based assessments, Spearman rank correlation coefficients (r s ) were calculated. RESULTS: Significant correlations were found between OWAS and muscle activity (range from r s 2 = 0.17 r s 2 = 0.55). Significant correlations were found between EN 1005-4 and muscle activity (range from r s 2 = 0.34 to r s 2 = 0.74). CONCLUSIONS: Results emphasise a need for further developments of observation-based methods, since the two investigated methods showed a variance of validity ranging from small to large. Such improvements may also form a better basis for the ergonomic improvement of working conditions in practise, which is highly necessary due to a constantly high prevalence of MSDs in the last decades.

Musculoskeletal disorders are a prevalent disease in many Western countries. While a large number of ergonomic analyses and assessment methods are nowadays available, most current methods that assess exposure calculate overall risk scores of individual body segments without considering interaction effects of exposure variables. Therefore, a study was conducted that aimed at investigating and quantifying interaction effects of trunk inclination and arm lifting on ratings of perceived exertion (RPE) and muscle activity. A multiple regression model to predict musculoskeletal load under consideration of interaction effects was derived. The study revealed that there is a significant interaction effect of trunk inclination and arm lifting. Furthermore, final regression models explained variance in exposure variables in a range of R 2 ¼ 0.68 to R 2 ¼ 0.147 with a subset of two to three inputs. The predicative equations support the computerbased post-processing of sensor data.Practitioner summary: This article elaborates on the importance of interaction effects of working postures on assessment results of load. In practise, easy to-use-methods for an assessment of working postures are needed. Therefore, a regression model is derived, which facilitates the quantification of work load under consideration of interaction effects. The use of this regression model for the assessment of posture data gathered by range sensors is recommended.

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