Estimation of muscular activity during movement for ergonomic applications

This work deals with the use of Digital Human models (DHM) for ergonomic applications. More precisely, it focuses on the modelling of the musculoskeletal system and muscular forces developed during movement.In the first part, the development of numerical musculoskeletal models of the lower and upper limbs under the Matlab environment is presented. These models were evaluated by examination of the muscles’ lever arms (for geometry) and by experimental measurements of maximum voluntary force (MVF) (for predicted muscle forces).In a second part, the lower limb muscular model was used to evaluate the role of biarticular muscles during gait. To achieve this, two methods were used, allowing to highlight the functional specificities of these muscles. The results indicate in particular that taking into account biarticular muscles is necessary to a correct estimation of muscle forces and joint contact forces, which are potential discomfort sources.Finally, the maximal force capacity of isometric elbow flexion-extension has been studied. The goal was to set up scaling methods allowing to fit the muscular model to the studied subject’s capacities. The experiment took place at INRETS, and implicated 9 male voluntary subjects. This study allowed to evaluate the model in terms of maximal force capacity prediction, and openend the path to new scaling methods for musculoskeletal models.

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Source https://theses.hal.science/tel-00563203
Author Fraysse, François
Maintainer CCSD
Last Updated May 18, 2026, 23:21 (UTC)
Created May 18, 2026, 23:21 (UTC)
Identifier NNT: 2009LYO10338
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Laboratoire de Biomécanique et Mécanique des Chocs (LBMC UMR T9406) ; Université Claude Bernard Lyon 1 (UCBL) ; Université de Lyon-Université de Lyon-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)
creator Fraysse, François
date 2009-12-15T00:00:00
harvest_object_id 651d1ac9-10b1-49cc-807c-ad9dfc0fa610
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-03-30T00:00:00
set_spec type:THESE