Safeguard the operator health is nowadays a hot topic for most of the companies whose production process relies on manual manufacturing and assembly activities. European legislations, national regulations and international standards force the companies to assess the risk of musculoskeletal disorders of operators while they are performing manual tasks. Furthermore, international corporates typically require their partners to adopt and implement particular indices and procedures to assess the ergonomic risks specific of their industrial sector. The expertise and time required by the ergonomic assessment activity compels the companies to huge financial, human and technological investments. An original Motion Analysis System (MAS) is developed to facilitate the evaluation of most of the ergonomic indices traditionally adopted by manufacturing firms. The MAS exploits a network of marker-less depth cameras to track and record the operator movements and postures during the performed tasks. The big volume of data provided by this motion capture technology is employed by the MAS to automatically and quantitatively assesses the risk of musculoskeletal disorders over the entire task duration and for each body part. The developed hardware/software architecture is tested and validated with a real industrial case study of a car manufacturer which adopts the European Assembly Worksheet (EAWS) to assess the ergonomic risk of its assembly line operators. The results suggest how the MAS is a powerful architecture compared to other motion capture solutions. Indeed, this technology accurately assesses the operator movements and his joint absolute position in the assembly station 3D layout. Finally, the MAS automatically and quantitatively fill out the different EAWS sections, traditionally evaluated through time- and resource-consuming activities.

Automatic assessment of the ergonomic risk for manual manufacturing and assembly activities through optical motion capture technology

Bortolini, Marco;Gamberi, Mauro;Pilati, Francesco;Regattieri, Alberto
2018

Abstract

Safeguard the operator health is nowadays a hot topic for most of the companies whose production process relies on manual manufacturing and assembly activities. European legislations, national regulations and international standards force the companies to assess the risk of musculoskeletal disorders of operators while they are performing manual tasks. Furthermore, international corporates typically require their partners to adopt and implement particular indices and procedures to assess the ergonomic risks specific of their industrial sector. The expertise and time required by the ergonomic assessment activity compels the companies to huge financial, human and technological investments. An original Motion Analysis System (MAS) is developed to facilitate the evaluation of most of the ergonomic indices traditionally adopted by manufacturing firms. The MAS exploits a network of marker-less depth cameras to track and record the operator movements and postures during the performed tasks. The big volume of data provided by this motion capture technology is employed by the MAS to automatically and quantitatively assesses the risk of musculoskeletal disorders over the entire task duration and for each body part. The developed hardware/software architecture is tested and validated with a real industrial case study of a car manufacturer which adopts the European Assembly Worksheet (EAWS) to assess the ergonomic risk of its assembly line operators. The results suggest how the MAS is a powerful architecture compared to other motion capture solutions. Indeed, this technology accurately assesses the operator movements and his joint absolute position in the assembly station 3D layout. Finally, the MAS automatically and quantitatively fill out the different EAWS sections, traditionally evaluated through time- and resource-consuming activities.
51st CIRP Conference on Manufacturing Systems
81
86
Bortolini, Marco; Gamberi, Mauro; Pilati, Francesco*; Regattieri, Alberto
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/657660
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