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The Risk Assessment of Work-related Musculoskeletal Disorders based on OpenSim

Jing Chang 1, 2
2 ReV - Robotique Et Vivant
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : Work-related musculoskeletal disorders cause physical and mental illnesses in workers, reduce their productivity and cause great losses to industries and society. This thesis focuses on the assessment of the physical risk of work-related musculoskeletal disorders in industry, for which four key points are identified: measuring workloads, assessing the effect of workload accumulation, quantifying individual characteristics and integrating the risk assessment into digital human modeling tools. In the state of the art, the epidemiologic studies of musculoskeletal disoders and the current methods used for its physical risk assessment are reviewed, as well as the studies concerning the four key points. The second part presents an experimental study involving 17 subjects to explore a new indicator to muscle fatigue with surface EMG. In the next part, efforts are made to integrate a muscle fatigue model into OpenSim, a digital human modeling software, with which the capacity decrease of each muscle is predictable for a given task. The predicted values could be applicable to the physical risk assessment. The fourth part introduce the work to build up a Fullchain musculoskeletal model in OpenSim in view that no current model covers muscles of the torso and all the limbs. Special attention is paid to the method used by OpenSim to adapt the model inertial properties to individuals. Errors of the method is evaluated with reference data coming from the whole-body 3D scan. In the last part, the newly built Full-chain model is applied on the posture analysis of an overhead drilling task. The muscle activition varies as a function of postures, which is suggested as the indicator of posture loads.
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Jing Chang. The Risk Assessment of Work-related Musculoskeletal Disorders based on OpenSim. Automatic. École centrale de Nantes, 2018. English. ⟨NNT : 2018ECDN0042⟩. ⟨tel-02395240⟩

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