Investigation of observational methods assessing workload of static working postures based on surface electromyography

Tobias Hellig; Vera Rick; Alexander Mertens; Verena Nitsch; Christopher Brandl

doi:10.3233/wor-192854

Investigation of observational methods assessing workload of static working postures based on surface electromyography

Work ◽

10.3233/wor-192854 ◽

2019 ◽

Vol 62 (2) ◽

pp. 185-195 ◽

Cited By ~ 3

Author(s):

Tobias Hellig ◽

Vera Rick ◽

Alexander Mertens ◽

Verena Nitsch ◽

Christopher Brandl

Keyword(s):

Surface Electromyography ◽

Observational Methods ◽

Working Postures

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Observational Methods for Analyzing Working Postures in Agriculture

Journal of Agricultural Safety and Health ◽

10.13031/2013.17753 ◽

1997 ◽

Vol 3 (3) ◽

Cited By ~ 6

Author(s):

S. Pinzke

Keyword(s):

Observational Methods ◽

Working Postures

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Investigation of Sensitivity of OWAS and European Standard 1005-4 to Assess Workload of Static Working Postures by Surface Electromyography

Advances in Intelligent Systems and Computing - Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) ◽

10.1007/978-3-319-96083-8_17 ◽

2018 ◽

pp. 138-147

Author(s):

Tobias Hellig ◽

Alexander Mertens ◽

Christopher Brandl

Keyword(s):

Surface Electromyography ◽

European Standard ◽

Working Postures

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A deep learning-based RULA method for working posture assessment

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181319631174 ◽

2019 ◽

Vol 63 (1) ◽

pp. 1090-1094

Author(s):

Li Li ◽

Xu Xu

Keyword(s):

Deep Learning ◽

Deep Neural Network ◽

Causal Relation ◽

Percentage Error ◽

Average Error ◽

Observational Methods ◽

Industrial Practice ◽

Working Posture ◽

Working Postures ◽

Posture Assessment

Musculoskeletal disorders (MSDs) represent one of the leading cause of injuries from modern industries. Previous research has identified a causal relation between MSDs and awkward working postures. Therefore, a robust tool for estimating and monitoring workers’ working posture is crucial to MSDs prevention. The Rapid Upper Limb Assessment (RULA) is one of the most adopted observational methods for assessing working posture and the associated MSDs risks in industrial practice. The manual application of RULA, however, can be time consuming. This research proposed a deep learning-based method for real-time estimating RULA from 2-D articulated pose using deep neural network. The method was trained and evaluated by 3-D pose data from Human 3.6, an open 3-D pose dataset, and achieved overall Marginal Average Error (MAE) of 0.15 in terms of RULA grand score (or 3.33% in terms of percentage error). All the data and code can be found at the first author’s GitHub ( https://github.com/LLDavid/RULA_machine ).

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