Identification des paramètres inertiels segmentaires humains

Abstract : The aim of this thesis is the identification of body segment inertial parameters (BSIP), i.e. the segment mass, center of mass location and inertia tensor. Those ten parameters per segment are a mandatory input for inverse kinetics methods which are widely used in biomechanics studies. Despite the fact that methods exist to estimate them from anthropometric tables or segment volumes measurements, identification is useful when subjects are atypical (such as disabled people, pregnant women or athletes with muscular hypertrophies). The originality of this work is to use a mixed approach to write the identification problem, combining a vectorial and a matrix formulations of rigid multi-body motion equations, based on previous work did in the RoBioSS axis by Tony Monnet during his PhD. The first one permit to identify segmental masses and center of mass locations. The second one identifies segmental inertia tensors.Inputs of identification algorithm are rotation matrices, their second derivatives, segmental accelerations, and external torsor. Even though this external torsor is directly measured with a force plate, the others inputs are derived from kinematics measurements performed by an optoelectronical device. This device measures kinematics with skin mounted markers tracked by cameras, and the obtained kinematics deviate from the theoretical kinematics of rigid bodies, because of the soft tissues artefacts. In order to deal with these artefacts an optimal rotation matrix computation, based on material transformation, has been performed.Also, noise appears during measurement because of the soft tissues artefacts and the measure device. When double numerical derivatives are applied, this noise becomes greater than the carrier signal. In order to deal with it, five filters, i.e. Butterworth filter, Savitsky-Golay smoothing, sliding average window, spline smoothing and singular spectrum analysis, taken from literature have been implemented and compared. Results show that BSIP identify from vectorial formulation didn’t need any filtering. On the other hand, inertia tensors identification needed smoothed inputs and the best way to smooth them was the sliding average window.Finally, a kinematic chain model of the upper limb has been implemented to rigidify the kinematics. Preliminary results aren’t satisfying but the chain model can be improved before assuming kinematic chain aren’t well suited to enhance BSIP identification. Ultimately, the developed mixed approach has been validated by upper limb inertial parameters identification of eighteen subjects. Identified inertial parameters have also been compared with ones estimated with an anthropometric table. The conclusion is that the identified parameters were very close to the estimated ones, which shows that identification will be reliable to estimate inertial parameters of atypical subjects for whom anthropometric tables aren’t available.
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Marien Couvertier. Identification des paramètres inertiels segmentaires humains. Biomécanique [physics.med-ph]. Université de Poitiers, 2018. Français. ⟨NNT : 2018POIT2323⟩. ⟨tel-02269606⟩

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