by Miguel Diaz-Rodriguez, Angel Valera, Alvaro Page, Antonio Besa, Vicente Mata
Abstract:
Accurate knowledge of Body Segment Inertia Parameters (BSIP) improves the assessment of dynamic analysis based on biomechanical models, which is of paramount importance in fields such as: sport activities or impact crash test. Early approaches for BSIP identification rely on experiments conducted on cadavers or through imaging techniques conducted on living subjects. Recent approaches rely on inverse dynamic modeling; however, most of the approaches are focused on the entire body, and verification of BSIP for dynamic analysis for distal segment or a chain of segments, which has proven to be of significant importance in impact test studies, is rarely established. Previous studies have suggested that BSIP should be obtained by using subject-specific identification techniques. To this end, our paper develops a novel approach for estimating subject-specific BSIP based on Static and Dynamic Identification Models (SIM, DIM). We test the validity of SIM and DIM by comparing the results using parameters obtained from a regression model proposed in DeLeva (1996). Both SIM and DIM are developed considering robotics formalism. First, the static model allows the mass and center of gravity (COG) to be estimated. Second, the results from the static model are included in the dynamics equation allowing us to estimate the moment of inertia (MOI). As a case study, we applied the approach to evaluate the dynamics modeling of the head complex. Findings provide some insight into the validity not only of the proposed method, but also the application proposed in DeLeva for dynamic modeling of body segments.
Reference:
Dynamic parameter identification of subject-specific body segment parameters using robotics formalism: case study head complex (Miguel Diaz-Rodriguez, Angel Valera, Alvaro Page, Antonio Besa, Vicente Mata), In Journal of Biomechanical Engineering, ASME DC, volume 138, 2016.
Bibtex Entry:
@article{diaz2016b,
title={Dynamic parameter identification of subject-specific body segment parameters using robotics formalism: case study head complex},
author={Diaz-Rodriguez, Miguel and Valera, Angel and Page, Alvaro and Besa, Antonio and Mata, Vicente},
year={2016},
journal={Journal of Biomechanical Engineering},
publisher={ASME DC},
volume={138},
number={5},
pages={051009},
url={http://biomechanical.asmedigitalcollection.asme.org/article.aspx?articleid=2503844},
doi={10.1115/1.4032997},
gsid={https://scholar.google.com/scholar?oi=bibs&hl=es&cites=13314640223419701846},
abstract={Accurate knowledge of Body Segment Inertia Parameters (BSIP) improves the assessment of dynamic analysis based on biomechanical models, which is of paramount importance in fields such as: sport activities or impact crash test. Early approaches for BSIP identification rely on experiments conducted on cadavers or through imaging techniques conducted on living subjects. Recent approaches rely on inverse dynamic modeling; however, most of the approaches are focused on the entire body, and verification of BSIP for dynamic analysis for distal segment or a chain of segments, which has proven to be of significant importance in impact test studies, is rarely established. Previous studies have suggested that BSIP should be obtained by using subject-specific identification techniques. To this end, our paper develops a novel approach for estimating subject-specific BSIP based on Static and Dynamic Identification Models (SIM, DIM). We test the validity of SIM and DIM by comparing the results using parameters obtained from a regression model proposed in DeLeva (1996). Both SIM and DIM are developed considering robotics formalism. First, the static model allows the mass and center of gravity (COG) to be estimated. Second, the results from the static model are included in the dynamics equation allowing us to estimate the moment of inertia (MOI). As a case study, we applied the approach to evaluate the dynamics modeling of the head complex. Findings provide some insight into the validity not only of the proposed method, but also the application proposed in DeLeva for dynamic modeling of body segments.},
keywords={body segment inertia parameters (BSIP), estimation techniques, motion capture,
biomechanics modeling, force platform},
}