Research Papers

Dynamic Parameter Identification of Subject-Specific Body Segment Parameters Using Robotics Formalism: Case Study Head Complex

[+] Author and Article Information
Miguel Díaz-Rodríguez

Departamento de Tecnología y Diseño,
Facultad de Ingeniería,
Universidad de los Andes,
Mérida 5101, Venezuela
e-mail: dmiguel@ula.ve

Angel Valera

Institute Universitario de Automática e
Informática Industrial,
Universitat Politècnica de Valencià,
Valencia 46022, Spain

Alvaro Page

Grupo de Tecnología Sanitaria del IBV,
CIBER de Bioingeniería,
Biomateriales y Nanomedicina (CIBER-BBN),
Valencia 46022, Spain

Antonio Besa, Vicente Mata

Centro de Investigación en Ingeniería Mecánica,
Universitat Politècnica de Valencià,
Valencia 46022, Spain

Manuscript received October 16, 2015; final manuscript received February 29, 2016; published online March 30, 2016. Assoc. Editor: Brian D. Stemper.

J Biomech Eng 138(5), 051009 (Mar 30, 2016) (8 pages) Paper No: BIO-15-1523; doi: 10.1115/1.4032997 History: Received October 16, 2015; Revised February 29, 2016

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 the experiments conducted on cadavers or through imaging techniques conducted on living subjects. Recent approaches for BSIP identification 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 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 dynamics identification models (SIM, DIM). We test the validity of SIM and DIM by comparing the results using parameters obtained from a regression model proposed by De Leva (1996, “Adjustments to Zatsiorsky-Seluyanov's Segment Inertia Parameters,” J. Biomech., 29(9), pp. 1223–1230). 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 of the application proposed by De Leva (1996, “Adjustments to Zatsiorsky-Seluyanov's Segment Inertia Parameters,” J. Biomech., 29(9), pp. 1223–1230) for dynamic modeling of body segments.

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Grahic Jump Location
Fig. 1

Free body diagram of forces acting on two consecutive body segments

Grahic Jump Location
Fig. 2

The movement of the head in sagittal plane which is the same as the moving axode (dotted line) rolling without sliding over the fixed axode (solid line). (a) Rolling-pair model corresponding to an actual flexion–extension movement [33]. (b) Simplified rolling pair model for simulating the motion of the head.

Grahic Jump Location
Fig. 3

Static model relative absolute error versus frequency

Grahic Jump Location
Fig. 4

Dynamic model relative absolute error versus frequency

Grahic Jump Location
Fig. 5

Experimental setup for in vivo identification of BSIP

Grahic Jump Location
Fig. 6

The two computed (SIM and De Leva) and the measured (My left, Mx right) moment at the force platform (FP). (a) Head moves in sagittal plane, My. (b) Head moves in frontal plane, Mx.

Grahic Jump Location
Fig. 7

The two computed (DIM and De Leva) and the measured moment at the FP. (a) Head moves in sagittal plane, My. (b) Head moves in frontal plane, Mx.



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