Body segment inertial parameters and low back load in individuals with central adiposity

There is a paucity of information regarding the impact of central adiposity on the inertial characteristics of body segments. Deriving low back loads during lifting requires accurate estimate of inertial parameters. The purpose was to determine the body segment inertial parameters of people with central adiposity using a photogrammetric technique, and then to evaluate the impact on lumbar spine loading. Five participants with central adiposity (waist:hip ratio>0.9, waist circumference>102 cm) were compared to a normal BMI group. A 3D wireframe model of the surface topography was constructed, partitioned into 8 body segments and then body segment inertial parameters were calculated using volumetric integration assuming uniform segment densities for the segments. Central adiposity dependent increases in body segment parameters ranged from 12 to 400%, varying across segments (greatest for trunk) and parameters. The increase in mass distribution to the trunk was accompanied by an anterior and inferior shift of the centre of mass. A proximal shift in centre of mass was detected for the extremities, along with a reduction in mass distribution to the lower extremity. L5/S1 torques (392 vs 263 Nm) and compressive forces (5918 vs 3986 N) were substantially elevated in comparison to the normal BMI group, as well as in comparison to torques and forces predicted using published BSIP equations. Central adiposity resulted in substantial but non-uniform increases in inertial parameters resulting in task specific increases in torque and compressive loads arising from different inertial and physical components.     

Analysis of body segment parameter differences between four human populations and the estimation errors of four popular mathematical models

Calculating the kinetics of motion using inverse or forward dynamics methods requires the use of accurate body segment inertial parameters. The methods available for calculating these body segment parameters (BSPs) have several limitations and a main concern is the applicability of predictive equations to several different populations. This study examined the differences in BSPs between 4 human populations using dual energy x-ray absorptiometry (DEXA), developed linear regression equations to predict mass, center of mass location (CM) and radius of gyration (K) in the frontal plane on 5 body segments and examined the errors produced by using several BSP sources in the literature. Significant population differences were seen in all segments for all populations and all BSPs except hand mass, indicating that population specific BSP predictors are needed. The linear regression equations developed performed best overall when compared to the other sources, yet no one set of predictors performed best for all segments, populations or BSPs. Large errors were seen with all models which were attributed to large individual differences within groups. Equations which account for these differences, including measurements of limb circumferences and breadths may provide better estimations. Geometric models use these parameters, however the models examined in this study did not perform well, possibly due to the assumption of constant density or the use of an overly simple shape. Creating solids which account for density changes or which mimic the mass distribution characteristics of the segment may solve this problem. Otherwise, regression equations specific for populations according to age, gender, race, and morphology may be required to provide accurate estimations of BSPs for use in kinetic equations of motion.     

Barbell kinematics should not be used to estimate power output applied to the Barbell-and-body system center of mass during lower-body resistance exercise

The aim of this study was to compare measures of power output applied to the center of mass of the barbell and body system (CM) obtained by multiplying ground reaction force (GRF) by (a) the velocity of the barbell; (b) the velocity of the CM derived from three-dimensional (3D) whole-body motion analysis, and (c) the velocity of the CM derived from GRF during lower-body resistance exercise. Ten resistance-trained men performed 3 maximal-effort single back squats with 60% 1 repetition maximum while GRF and whole-body motion were captured using synchronized Kistler force platforms and a Vicon Motus motion analysis system. Repeated measures analysis of variance of time-normalized kinematic and kinetic data obtained using the different methods showed that the barbell was displaced 13.4% (p < 0.05) more than the CM, the velocity of the barbell was 16.1% (p < 0.05) greater than the velocity of the CM, and power applied to the CM obtained by multiplying GRF by the velocity of the barbell was 18.7% (p < 0.05) greater than power applied to the CM obtained by multiplying the force applied to the CM by its resultant velocity. Further, the velocity of the barbell was significantly greater than the velocity of the trunk, upper leg, lower leg, and foot (p < 0.05), indicating that a failure to consider the kinematics of body segments during lower-body resistance exercise can lead to a significant overestimation of power applied to the CM. Strength and conditioning coaches and investigators are urged to obtain measures of power from the force applied to and the velocity of either the barbell (using inverse dynamics) or CM (GRF or 3D motion analysis). Failure to apply these suggestions could result in continued overestimation of CM power, compromising methodological integrity.     

A general model for estimating lower extremity inertial properties of individuals with transtibial amputation

Lower extremity joint moment magnitudes during swing are dependent on the inertial properties of the prosthesis and residual limb of individuals with transtibial amputation (TTA). Often, intact limb inertial properties (INTACT) are used for prosthetic limb values in an inverse dynamics model even though these values overestimate the amputated limb’s inertial properties. The purpose of this study was to use subject-specific (SPECIFIC) measures of prosthesis inertial properties to generate a general model (GENERAL) for estimating TTA prosthesis inertial properties. Subject-specific mass, center of mass, and moment of inertia were determined for the shank and foot segments of the prosthesis (n = 11) using an oscillation technique and reaction board. The GENERAL model was derived from the means of the SPECIFIC model. Mass and segment lengths are required GENERAL model inputs. Comparisons of segment inertial properties and joint moments during walking were made using three inertial models (unique sample; n = 9): (1) SPECIFIC, (2) GENERAL, and (3) INTACT. Prosthetic shank inertial properties were significantly smaller with the SPECIFIC and GENERAL model than the INTACT model, but the SPECIFIC and GENERAL model did not statistically differ. Peak knee and hip joint moments during swing were significantly smaller for the SPECIFIC and GENERAL model compared with the INTACT model and were not significantly different between SPECIFIC and GENERAL models. When subject-specific measures are unavailable, using the GENERAL model produces a better estimate of prosthetic side inertial properties resulting in more accurate joint moment measurements for individuals with TTA than the INTACT model.     

Changes in segmental inertial properties with age

The purpose of this study was to examine how the limb segment inertial parameters vary across the decades from the 1920s to the 1970s. Sixty-six males participated in this study, ranging in age from 20 to 79 years. Pre-screening ensured that all subjects were healthy. The inertial properties of the segments were determined by modeling each segment as series of geometric solids. A multivariate analysis of variance (ANOVA) revealed statistically significant differences between decade age groups for the upper arm, forearm, shank, and thigh (p<0.01). Subsequent ANOVAs revealed statistically significant differences for all the inertial properties for the upper arm, the center of mass location for the forearm, and segment mass for the thigh. Linear regression lines were fit to the data so that each inertial parameter for each segment could be predicted by subject's age, with the slope of this regression line indicating the trend in the data. These trends were statistically significant for all forearm inertial parameters, thigh mass and longitudinal moment of inertia, and forearm center of mass location. The changes for the thigh, upper arm, and forearm were consistent with the changes, which would accompany a change in muscle mass with aging. Resultant joint moments were computed for a set of gait data using inertial properties reflective of the subjects from the age extremes in the study. The resulting differences in the knee and hip moments, young versus old, were all less than 4.5%.     

Estimating segment inertial parameters using fan-beam DXA

Body segment inertial parameters are required as input parameters when the kinetics of human motion is to be analyzed. However, owing to interindividual differences in body composition, noninvasive inertial estimates are problematic. Dual-energy x-ray absorptiometry (DXA) is a relatively new imaging approach that can provide cost- and time-effective means for estimating these parameters with minimal exposure to radiation. With the introduction of a new generation of DXA machines, utilizing a fan-beam configuration, this study examined their accuracy as well as a new interpolative data-reduction process for estimating inertial parameters. Specifically, the inertial estimates of two objects (an ultra-high molecular density plastic rod and an animal specimen) and 50 participants were obtained. Results showed that the fan-beam DXA, along with the new interpolative data-reduction process, provided highly accurate estimates (0.10-0.39%). A greater variance was observed in the center of mass location and moment of inertia estimates, likely as a result of the course end-point location (1.31 cm). However, using a midpoint interpolation of the end-point locations, errors in the estimates were greatly reduced for the center of mass location (0.64-0.92%) and moments of inertia (-0.23 to -0.48%).     

Are fixed limb inertial models valid for dynamic simulations of human movement?

During human movement, muscle activation and limb movement result in subtle changes in muscle mass distribution. Muscle mass redistribution can affect limb inertial properties and limb dynamics, but it is not currently known to what extent. The objectives of this study were to investigate: (1) how physiological alterations of muscle and tendon length affect limb inertial characteristics, and (2) how such changes affect dynamic simulations of human movement. To achieve these objectives, a digital model of a human leg, custom software, and Software for interactive musculoskeletal modeling were used to simulate mass redistribution of muscle–tendon structures within a limb segment during muscle activation and joint movement. Thigh and shank center of mass and moments of inertia for different muscle activation and joint configurations were determined and compared. Limb inertial parameters representing relaxed muscles and fully active muscles were input into a simulated straight-leg movement to evaluate the effect inertial parameter variations could have on movement simulation results. Muscle activation and limb movement altered limb segment center of mass and moments of inertia by less than 0.04 cm and 1.2%, respectively. These variations in limb inertial properties resulted in less than 0.01% change in maximum angular velocity for a simulated straight-leg hip flexion task. These data demonstrate that, for the digital human leg model considered, assuming static quantities for segment center of masses and moments of inertia in movement simulations appear reasonable and induce minimal errors in simulated movement dynamics.     

Morphometry of Macaca mulatta forelimb. I. Shoulder and elbow muscles and segment inertial parameters

The present study examined the morphometric properties of the forelimb, including the inertial properties of the body segments and the morphometric parameters of 21 muscles spanning the shoulder and/or elbow joints of six Macaca mulatta and three M. fascicularis. Five muscle parameters are presented: optimal fascicle length (L(0)(M)), tendon slack length (L(S)(T)), physiological cross-sectional area (PCSA), pennation angle (alpha(0)), and muscle mass (m). Linear regressions indicate that muscle mass, and to a lesser extent PCSA, correlated with total body weight. Segment mass, center-of-mass, and the moment of inertia of the upper arm, forearm, and hand are also presented. Our data indicate that for some segments, radius of gyration (rho) predicts segment moment of inertia better than linear regressions based on total body weight. Key differences between the monkey and human forelimb are highlighted.     

Adipose tissue volume measured by magnetic resonance imaging and computerized tomography in rats

The primary purpose of this study was to investigate the viability of magnetic resonance imaging (MRI) as a means of measuring the body composition of rodents. To do so we compared adipose tissue (AT) volumes measured by MRI with those obtained by X-ray computerized tomography (CT) in a group of rats (n = 17) varying in weight (465-815 g) and percent body fat (5.4-31.1%), with the latter determined by chemical analysis. For both MRI and CT, AT volumes (cm3) per transverse slice (3-mm thickness, 21-mm centers) were determined using a computer-based image analysis system that permitted detailed comparisons of both visceral and subcutaneous AT depots. Total AT volumes were calculated using a linear interpolation of AT areas obtained on consecutive slices. Correlation coefficients between MRI and CT for visceral [r = 0.98, standard error of estimate (SEE) = 6.8 cm3], subcutaneous (r = 0.98, SEE = 6.5 cm3), and total AT volumes (r = 0.99, SEE = 9.0 cm3) were highly significant (P less than 0.001). Both MRI- and CT-predicted AT mass (assuming fat density = 0.90 g/ml) correlated strongly with chemically extracted lipid (grams) values (r = 0.98, SEE 9.6 g and r = 0.99, SEE = 6.9 g, respectively). Post hoc Scheffé contrasts demonstrated that the mean AT and lipid mass values derived by the three methods were not significantly different (P = 0.01). No systematic differences were observed because the regression lines derived for either MRI or CT vs. chemical analysis were not significantly different from the identity line.(ABSTRACT TRUNCATED AT 250 WORDS)     

Reexamination of a canonical model for plant organ biomass partitioning

A previously proposed "canonical" model for the scaling relations among leaf, stem, and root biomass (M(L), M(S), and M(R), respectively) asserts that the proportional relations M(L) ∝ M(S)(3/4) ∝ M(R)(3/4) and M(S) ∝ M(R) hold across seed plant species. This model is scrutinized by determining whether the scaling relations between M(L), M(S), and M(R) vs. basal stem diameter D(S) and between M(L), M(S), and M(R) vs. plant height h are logically consistent with previously predicted scaling exponents. For example, if M(L) is observed to scale as the 2-power of D(S) and the model asserts that M(L) ∝ M(S)(3/4), then M(S) must scale as the 8/3-power of D(S) if the model is valid. Using a large data base for species with self-supporting stems, statistical support was found for most such comparisons between predicted and observed scaling relationships. However, this judgement is predicated on (1) the assertion that the scaling exponents for M(R) with respect to D(S) (or h) are numerically "deflated" due to a systematic underestimate of fine and small root biomass and (2) the stringent protocol used to calculate the 95% confidence intervals of scaling exponents, which favors rejection of the model. In light of these features, the "canonical" model is logically consistent with the new scaling relations reported here. Therefore, the model is judged valid within the context of this evaluation.     

A sensitivity analysis of the calculation of mechanical output through inverse dynamics: a computer simulation study

The purpose of this study was to systematically determine the effect of experimental errors on the work output calculated using two different methods of inverse dynamics during vertical jumping: (a) the conventional (rotational) method and (b) the translational method. A two-dimensional musculoskeletal model was used to generate precisely known kinematics. Next, the location of each joint center (JC) and the location of each segment's center of mass (CM) were manipulated by +/-10% of segment length to simulate errors in the location of joint centers (delta JC) and errors in the location of segment's center of mass (delta CM), respectively. Work output was subsequently calculated by applying the two methods of inverse dynamics to the manipulated kinematic data. The results showed that the translational method of inverse dynamics was less sensitive (up to 13% error in total work output) to delta JC and delta CM than the rotational method (up to 28% error in total work output). The rotational method of inverse dynamics was particularly sensitive to simulated errors in JC.     

Agreement of Mammographic Measures of Volumetric Breast Density to MRI

Background

Clinical scores of mammographic breast density are highly subjective. Automated technologies for mammography exist to quantify breast density objectively, but the technique that most accurately measures the quantity of breast fibroglandular tissue is not known.

Purpose

To compare the agreement of three automated mammographic techniques for measuring volumetric breast density with a quantitative volumetric MRI-based technique in a screening population.

Materials and Methods

Women were selected from the UCSF Medical Center screening population that had received both a screening MRI and digital mammogram within one year of each other, had Breast Imaging Reporting and Data System (BI-RADS) assessments of normal or benign finding, and no history of breast cancer or surgery. Agreement was assessed of three mammographic techniques (Single-energy X-ray Absorptiometry [SXA], Quantra, and Volpara) with MRI for percent fibroglandular tissue volume, absolute fibroglandular tissue volume, and total breast volume.

Results

Among 99 women, the automated mammographic density techniques were correlated with MRI measures with R² values ranging from 0.40 (log fibroglandular volume) to 0.91 (total breast volume). Substantial agreement measured by kappa statistic was found between all percent fibroglandular tissue measures (0.72 to 0.63), but only moderate agreement for log fibroglandular volumes. The kappa statistics for all percent density measures were highest in the comparisons of the SXA and MRI results. The largest error source between MRI and the mammography techniques was found to be differences in measures of total breast volume.

Conclusion

Automated volumetric fibroglandular tissue measures from screening digital mammograms were in substantial agreement with MRI and if associated with breast cancer could be used in clinical practice to enhance risk assessment and prevention.     

Effect of race and resistance training status on the density of fat-free mass and percent fat estimates.

The impact of race and resistance training status on the assumed density of the fat-free mass (D(FFM)) and estimates of body fatness via hydrodensitometry (%Fat(D)) vs. a four-component model (density, water, mineral; %Fat(D,W,M)) were determined in 45 men: white controls (W; n = 15), black controls (B; n = 15), and resistance-trained blacks (B-RT; n = 15). Body density by hydrostatic weighing, body water by deuterium dilution, and bone mineral by dual-energy X-ray absorptiometry were used to estimate %Fat(D,W,M). D(FFM) was not different between B and W (or 1.1 g/ml); however, D(FFM) in B-RT was significantly lower (1.091 +/- 0.012 g/ml; P < 0.05). Therefore, %Fat(D) using the Siri equation was not different from %Fat(D,W,M) in W (17.5 +/- 5.0 vs. 18.3 +/- 5.4%) or B (14.9 +/- 5.6 vs. 15.7 +/- 5.7%) but significantly overestimated %Fat(D,W,M) in B-RT (14.0 +/- 5.9 vs. 10.4 +/- 6.0%; P < 0.05). The use of a race-specific equation (assuming D(FFM) = 1.113 g/ml) did not improve the agreement between %Fat(D) and %Fat(D,W,M), resulting in a significantly greater mean (+/-SD) discrepancy for B (1.7 +/- 1.8% fat) and B-RT (6.2 +/- 4.3% fat). Thus race per se does not affect D(FFM) or estimates of %Fat(D); however, B-RT have a D(FFM) lower than 1.1 g/ml, leading to an overestimation of %Fat(D).     

Using mass distribution information to model the human thigh for body segment parameter estimation

Accurate estimations of body segment inertial parameters (BSPs) are required to calculate the kinetics of motion. The purpose of this study was to develop a geometric model of the human thigh segment based on mass distribution properties determined from dual energy x ray absorptiometry (DEXA). One hundred subjects from four populations underwent a DEXA scan and anthropometric measurements were taken. The mass distribution properties of the thigh segment were determined for 20 subjects, a geometric model was developed, and the model was applied to the remaining 80 subjects. The model was validated by comparing to benchmark DEXA measurements. Four other popular models in the literature were also evaluated in the same manner No one set of predictors performed best for a particular group or BSP, however modeling the mass distribution properties of the segment allows the assumption of constant density while still accurately representing the inertial properties of the segment and provides promise for future development of BSP models.     

A 3D interactive method for estimating body segmental parameters in animals: application to the turning and running performance of Tyrannosaurus rex

We developed a method based on interactive B-spline solids for estimating and visualizing biomechanically important parameters for animal body segments. Although the method is most useful for assessing the importance of unknowns in extinct animals, such as body contours, muscle bulk, or inertial parameters, it is also useful for non-invasive measurement of segmental dimensions in extant animals. Points measured directly from bodies or skeletons are digitized and visualized on a computer, and then a B-spline solid is fitted to enclose these points, allowing quantification of segment dimensions. The method is computationally fast enough so that software implementations can interactively deform the shape of body segments (by warping the solid) or adjust the shape quantitatively (e.g., expanding the solid boundary by some percentage or a specific distance beyond measured skeletal coordinates). As the shape changes, the resulting changes in segment mass, center of mass (CM), and moments of inertia can be recomputed immediately. Volumes of reduced or increased density can be embedded to represent lungs, bones, or other structures within the body. The method was validated by reconstructing an ostrich body from a fleshed and defleshed carcass and comparing the estimated dimensions to empirically measured values from the original carcass. We then used the method to calculate the segmental masses, centers of mass, and moments of inertia for an adult Tyrannosaurus rex, with measurements taken directly from a complete skeleton. We compare these results to other estimates, using the model to compute the sensitivities of unknown parameter values based upon 30 different combinations of trunk, lung and air sac, and hindlimb dimensions. The conclusion that T. rex was not an exceptionally fast runner remains strongly supported by our models-the main area of ambiguity for estimating running ability seems to be estimating fascicle lengths, not body dimensions. Additionally, the craniad position of the CM in all of our models reinforces the notion that T. rex did not stand or move with extremely columnar, elephantine limbs. It required some flexion in the limbs to stand still, but how much flexion depends directly on where its CM is assumed to lie. Finally we used our model to test an unsolved problem in dinosaur biomechanics: how fast a huge biped like T. rex could turn. Depending on the assumptions, our whole body model integrated with a musculoskeletal model estimates that turning 45 degrees on one leg could be achieved slowly, in about 1-2s.     

Effects of Visual Cues of Object Density on Perception and Anticipatory Control of Dexterous Manipulation

Anticipatory force planning during grasping is based on visual cues about the object’s physical properties and sensorimotor memories of previous actions with grasped objects. Vision can be used to estimate object mass based on the object size to identify and recall sensorimotor memories of previously manipulated objects. It is not known whether subjects can use density cues to identify the object’s center of mass (CM) and create compensatory moments in an anticipatory fashion during initial object lifts to prevent tilt. We asked subjects (n = 8) to estimate CM location of visually symmetric objects of uniform densities (plastic or brass, symmetric CM) and non-uniform densities (mixture of plastic and brass, asymmetric CM). We then asked whether subjects can use density cues to scale fingertip forces when lifting the visually symmetric objects of uniform and non-uniform densities. Subjects were able to accurately estimate an object’s center of mass based on visual density cues. When the mass distribution was uniform, subjects could scale their fingertip forces in an anticipatory fashion based on the estimation. However, despite their ability to explicitly estimate CM location when object density was non-uniform, subjects were unable to scale their fingertip forces to create a compensatory moment and prevent tilt on initial lifts. Hefting object parts in the hand before the experiment did not affect this ability. This suggests a dichotomy between the ability to accurately identify the object’s CM location for objects with non-uniform density cues and the ability to utilize this information to correctly scale their fingertip forces. These results are discussed in the context of possible neural mechanisms underlying sensorimotor integration linking visual cues and anticipatory control of grasping.     

Body segment inertial parameter estimation for the general population of older adults

The practical determination of accurate body segment inertial parameters for the general older adult population remains a problem, especially in estimating these parameters for women and accounting for variations in body type. A method is presented for determining the mass and center of mass location of the body segments of individuals within the general population of older adults. Effects of sex and body type on older adult mass distribution are accounted for using 32 easily obtainable body measurements. The method is based on existing results from different data sources and employs a combination of validated estimation approaches, including: body mass and segment length proportions, linear and nonlinear regression equations, and a mathematical model of the trunk. The method was applied to a validation sample of healthy, community-dwelling older adults (29 men, 50 women). Predicted body mass was 96.7+/-4.8% and 95.7+/-3.7% of measured body mass in the men and women, respectively. The estimates of body segment mass and trunk center of mass location for the sample population approximate those reported in the literature, supporting the validity of the described method. By producing practical, subject-specific estimates of body segment inertial parameters, the method should allow more accurate biomechanical analyses of the older adult population.     

Volume of ankle dorsiflexors and plantar flexors determined with stereological techniques.

The validity of the methods used for determination of muscle mass has not been evaluated previously. We determined muscle mass by estimating muscle volume with assumption-free stereological techniques applied to magnetic resonance imaging (MRI) in 18 healthy untrained subjects (6 women, 12 men) aged 41 yr (29-64 yr; median, range). Muscle mass was also estimated by measuring leg circumference and cross-sectional muscle areas (CSA) from MRIs at three predetermined levels. Power [peak torque (PT)] of the ankle dorsiflexors and plantar flexors was estimated by using isokinetic dynamometry. Dorsiflexor volume (r2 = 0.76, P < 5 x 10(-6)) and CSA (r2 = 0.73, P < 5 x 10(-5)) were related to PT, whereas circumference was not (r2 = 0.17, not significant). Correspondingly, a relationship to plantar PT was established for plantar flexor volume (r2 = 0.69, P < 5 x 10(-5)) and CSA (r2 = 0.46, P < 5 x 10(-3)) but not leg circumference (r2 = 0.15, not significant). SDs of the residuals were smaller for the relationship between dorsiflexor PT and volume than between PT and CSA (0.42 vs. 0.45) for plantar flexors (1.5 vs. 2.0). By using the Cavalieri method, six MRI sections and 15 min of point counting are sufficient to obtain a valid estimate of the volume of the muscles of the lower leg.