Prediction model of the effect of postural interactions on muscular activity and perceived exertion

• Published in: Ergonomics Vol. 63; no. 5; pp. 593 - 606
• Main Authors: Hellig, Tobias, Johnen, Laura, Mertens, Alexander, Nitsch, Verena, Brandl, Christopher
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 03-05-2020; Taylor & Francis LLC
• Subjects: Abbreviations; Analysis of covariance; Arm; Contraction; Covariance; Electromyography; Exposure; Inclination; multiple regression; Multiple regression models; Multivariate analysis; Muscle contraction; Muscles; Musculoskeletal diseases; Musculoskeletal load; Post-production processing; Postural load; Posture; Prediction models; Regression analysis; Space life sciences; Variance analysis; working postures; workplace design; Postural load; working postures; multiple regression; workplace design
• ISSN: 0014-0139; 1366-5847
• DOI: 10.1080/00140139.2020.1740333

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
= 0.68 to R
2
= 0.147 with a subset of two to three inputs. The predicative equations support the computer-based 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.
Abbreviations: RPE: rating of perceived exertion; MSD: musculoskeletal disorder; OWAS: ovako working posture analysing system; RULA: rapid upper limb assessment; LUBA: postural loading on the upper body assessment; REBA: rapid entire body assessment; OCRA: occupational repetitive action;S D: standard deviation; EMG: surface electromyography; LUT: left upper trapezius pars descendens; RUT: right upper trapezius pars descendens; LLT: left trapezius pars ascendens; RLT: right trapezius pars ascendens; LAD: left anterior deltoideus; RAD: right anterior deltoideus; LES: left erector spinae longissimus; RES: right erector spinae longissimus; SENIAM: surface electroMyoGraphy for the non-invasive assessment of muscles; MVC: maximum voluntary contraction; MANOVA: multivariate analysis of variance; ANOVA: analysis of variance; OLS: ordinary least squares; MANCOVA: multivariate analysis of covariance