Evaluation of Physical Workload Using Simulation Versus Motion Capturing Data for Biomechanical Models

Throughout Europe, work-related musculoskeletal disorders (WRMSD) are still a major problem as they are most often associated with lower back pain or other physical complaints. In order to improve workplace safety and avoid WRMSD, it is therefore essential to analyse physical risk factors. Digital Human Models (DHM) can help to estimate ergonomic risks in working conditions. Traffic light schemes are commonly used to quantify the physical strain, while biomechanical loads are often neglected. The limitation of biomechanical models is, however, that they require motion data for their predictions. These data are time-consuming to capture and require camera-based or sensor-based methods. For this reason, a coupling between the "AnyBody Modeling System" and the "Editor for manual work activities" (ema) was developed to expand ergonomic evaluation with biomechanical parameters, without using motion capturing data. The findings indicate that the computer-generated motions of the interface rarely match real motions, therefore, the results of biomechanical loads have to be interpreted with caution. However, the coupling between AnyBody and ema allows to estimate the physical stress of specific activities. For a better understanding of physical workload another research approach is proposed taking kinematic and kinetic real-time data of workers into account to improve physical risk assessment in future.

The complete article is published in "DHM 2020. Proceedings of the 6th International Digital Human Modeling Symposium, August 31 - September 2, 2020" (2020).

Bibliographic information

Title:  Evaluation of Physical Workload Using Simulation Versus Motion Capturing Data for Biomechanical Models. 

Written by:  M. Peters, S. Wischniewski

in: DHM 2020. Proceedings of the 6th International Digital Human Modeling Symposium, August 31 - September 2, 2020 / L. Hanson, D. Högberg, E. Brolin (Eds.) IOS Press, 2020.  pages: 380-387, Project number: F 2494, PDF file, DOI: 10.3233/ATDE200046

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Further Information

Research Project

Project numberF 2494 StatusCompleted Project Personalised Body Sensor Networks with Built-In Intelligence for Real-Time Risk Assessment and Coaching of Ageing workers, in all types of working and living environments (BIONIC)

To the Project

Research completed