A sensitivity analysis method for the body segment inertial parameters based on ground reaction and joint moment regressor matrices

Copyright © 2017 Elsevier Ltd. All rights reserved..

This paper presents a method allowing a simple and efficient sensitivity analysis of dynamics parameters of complex whole-body human model. The proposed method is based on the ground reaction and joint moment regressor matrices, developed initially in robotics system identification theory, and involved in the equations of motion of the human body. The regressor matrices are linear relatively to the segment inertial parameters allowing us to use simple sensitivity analysis methods. The sensitivity analysis method was applied over gait dynamics and kinematics data of nine subjects and with a 15 segments 3D model of the locomotor apparatus. According to the proposed sensitivity indices, 76 segments inertial parameters out the 150 of the mechanical model were considered as not influent for gait. The main findings were that the segment masses were influent and that, at the exception of the trunk, moment of inertia were not influent for the computation of the ground reaction forces and moments and the joint moments. The same method also shows numerically that at least 90% of the lower-limb joint moments during the stance phase can be estimated only from a force-plate and kinematics data without knowing any of the segment inertial parameters.

Medienart:

E-Artikel

Erscheinungsjahr:

2017

Erschienen:

2017

Enthalten in:

Zur Gesamtaufnahme - volume:64

Enthalten in:

Journal of biomechanics - 64(2017) vom: 07. Nov., Seite 85-92

Sprache:

Englisch

Beteiligte Personen:

Futamure, Sumire [VerfasserIn]
Bonnet, Vincent [VerfasserIn]
Dumas, Raphael [VerfasserIn]
Venture, Gentiane [VerfasserIn]

Links:

Volltext

Themen:

BSIP estimation
Dynamics sensitivity
Gait
Inverse dynamics
Journal Article

Anmerkungen:

Date Completed 27.04.2018

Date Revised 02.12.2018

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1016/j.jbiomech.2017.09.005

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM276173473