Body segment parameters (BSPs), such as segment length, mass, center of mass (COM), and radius of gyration, are commonly used as inputs in ergonomic applications, 1 as well as biomechanical models used to estimate the risk of musculoskeletal injuries during lifting and gait. 2 – 5 Specific
Zachary Merrill, Grace Bova, April Chambers and Rakié Cham
Damien Moore, Tania Pizzari, Jodie McClelland and Adam I. Semciw
However, the GMed is structurally and functionally composed of 3 unique segments, 2 and a large proportion of the anterior and posterior segments are deep to the superficially located tensor fascia lata and gluteus maximus, respectively. 3 The aim of this study was to determine activity levels using
Damien Moore, Adam I. Semciw, Jodie McClelland, Henry Wajswelner and Tania Pizzari
muscle fibers 5 , 14 suggest a major role in hip joint stability. GMin is also comprised 2 uniquely oriented and structurally distinct segments (anterior and posterior) 2 , 15 that have potential for independent function. 16 Based on morphology 2 , 4 , 5 and previous gait studies, 13 the proposed
Robert K. Jensen, Tina Treitz and Han Sun
The purpose of the study was to use the elliptical cylinder model adapted for infants (Sun & Jensen, 1994) with a cross-sectional sample to select appropriate multiple linear regression equations for predicting masses and nonlinear regression equations for predicting principal moments of inertia (Yeadon & Morlock, 1989). The linear and nonlinear predictions were evaluated with an independent cross-validation sample of infants and a sample where inertias ranged below and above the cross-sectional sample. The cross-validation for masses was compared to a cross-validation of four linear regressions for masses developed by Schneider and Zernicke (1992). It is recommended that the linear regression equations developed in this study be used to predict infant segment masses. It is also recommended that the nonlinear regression equations developed in this study be used to predict the principal moments of inertia of all infant segments, other than head Ix and lower trunk Ix and Iy.
Samantha L. Winter, Sarah M. Forrest, Joanne Wallace and John H. Challis
In order to study, analyze, or optimize human movement, the mass, the center of mass location, and the body segment moments of inertia must be known. These body segment inertial parameters (BSIPs) affect the accuracy when calculating the resultant joint moments during activities which involve high
Alison Schinkel-Ivy, Vicki Komisar and Carolyn A. Duncan
individual segment COMs, representing an imaginary point at which the total body mass is assumed to be concentrated. 20 , 21 Estimated COM position provides information regarding the nature of balance control. For example, horizontal COM position and velocity relative to the base of support influence
Robert K. Jensen, Tina Treitz and Sylvie Doucet
The purpose of this study was to develop prediction equations to estimate mass, radius to the center of mass (CM), and principal moments of the segments during pregnancy. Nonlinear regression equations were determined for the lower trunk, upper trunk, and thigh. The third sampling month of a longitudinal study was used (Sample 1, n = 15). The nonlinear regressions were then used to predict segment inertias above and below the third sampling month (Sample 2, the remaining 74 measurements). For the remaining segments, body mass and segment lengths were used as predictor variables for mass, radius to CM, and radius of gyration about the centroidal axes. The remaining seven segments did not change substantially during pregnancy, and the means of the repeated measures were used for the simple linear regressions. Eighteen of the 28 regressions and all of the CM regressions were significant. With pregnant subjects it is recommended that these regressions be used if application of the elliptical cylinder model is not possible.
L.J. Richard Casius, Maarten F. Bobbert and Arthur J. van Soest
Mathematical modeling and computer simulation play an increasingly important role in the search for answers to questions that cannot be addressed experimentally. One of the biggest challenges in forward simulation of the movements of the musculoskeletal system is finding an optimal control strategy. It is not uncommon for this type of optimization problem that the segment dynamics need to be calculated millions of times. In addition, these calculations typically consume a large part of the CPU time during forward movement simulations. As numerous human movements are two-dimensional (2-D) to a reasonable approximation, it is extremely convenient to have a dedicated, computational efficient method for 2-D movements. In this paper we shall present such a method. The main goal is to show that a systematic approach can be adopted which allows for both automatic formulation and solution of the equations of kinematics and dynamics, and to provide some fundamental insight in the mechanical theory behind forward dynamics problems in general. To illustrate matters, we provide for download an example implementation of the main segment dynamics algorithm, as well as a complete implementation of a model of human sprint cycling.
Wing-Kai Lam, Winson Chiu-Chun Lee, Wei Min Lee, Christina Zong-Hao Ma and Pui Wah Kong
, moments, and powers were calculated as these variables are of direct relevance to athletic performances. 1 , 2 , 16 An inverse dynamic model in Visual 3D (C-Motion Inc, Germantown, USA), which comprised of shank, rearfoot, and forefoot segments, was used for calculation of joint moments and powers. The
Hans Kainz, Hoa X. Hoang, Chris Stockton, Roslyn R. Boyd, David G. Lloyd and Christopher P. Carty
the generic model to the individual’s anthropometry. Linear scaling methods use the ratios between the participant’s segment dimensions and that of the model to scale the generic model. 5 The participant’s segment dimensions are estimated from the three-dimensional (3D) location of experimental