Presenter(s)

Colin R. Theis

Download

Download Project (613 KB)

Description

Computational simulations of human movement require precise knowledge of the location of the body's center of mass (CoM). The CoM is a quantity that is impossible to directly measure experimentally. The location varies on a subject-to-subject basis and is essential for the generation of accurate simulations. OpenSim, a musculoskeletal modeling software, utilizes a built-in software function to estimate the CoM based off the geometry of the model. However, this estimation technique can be imprecise because the estimation is based solely off generic mass and geometry distributions. Since every individual is different with respect to body segment length and mass distributions, it is likely this estimation is inaccurate for all individuals. Therefore, using a new technique developed in UD’s DIMLab, we can estimate an individual's CoM more accurately than OpenSim. This estimation technique uses the statically equivalent serial chain model (SESC). The technique is based on a virtual chain, identified from a minimal amount of experimental kinematic data to be accurate. The system does not require knowledge of the total mass, or any of the individual segment mass or length properties. The SESC model is a function of the anatomical joint angles measured experimentally from the subject and terminates at the CoM. This project explores the feasibility of combining experimental CoM estimation methods with simulation based estimates of CoM. We aim to find a method to validate CoM estimates applied in simulations and improve simulation accuracy. We aim to integrate the SESC model into the OpenSim software package as the main mechanism for locating the CoM.

Publication Date

4-22-2020

Project Designation

Independent Research

Primary Advisor

Allison L. Kinney

Primary Advisor's Department

Mechanical and Aerospace Engineering

Keywords

Stander Symposium project, School of Engineering

Validation of Center of Mass Estimation in Humans

Share

COinS