Analysis of Body Segment Parameter Differences Between Four Human Populations and the Estimation Errors of Four Popular Mathematical Models

Abstract

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.

Body Segment Parameter Estimation of the Human Lower Leg Using an Elliptical Model with Validation from DEXA

“…This procedure has proven to improve BSP estimations for body segments such as the thigh, lower leg, and forearm. 9,10,11 Two images were created from each scan to enable sectioning of the lower leg segment. A density image ( Fig.…”

“…5,9,23,26 Regression equations allow the rapid estimation of human BSPs using specific parameters such as whole body mass and height, and may include a number of anthropometric parameters such as limb Females (19- circumferences and breadths. While most regression equations available have been generated on one population such as young Caucasian males, 25 Durkin and Dowling 9 developed population-specific regression equations for four groups of individuals separated by age (19-30-years-old) and gender (male/female).…”

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

“…Various techniques available for estimating the inertial properties of a fully intact human body segment include: 1) regression equations based on cadaver data [1][2][3][4][5] , 2) mathematical models (i.e., geometric models) 6,7 , and 3) scanning & imaging techniques [8][9][10][11][12][13][14][15] . Many of these techniques require direct measurements from the body, but it has previously been shown that regardless of the estimation method being used, the precision of body segment inertial estimates based on these methods is high 16 .…”

Dynamic Analysis of Human Gait Disorder and Metabolical Cost Estimation

“…This occurs mainly due to the different segmentations of the body used, refer to Zatsiorsky [61]. Other sources of errors are the different methods used to measure the body segment parameters and the considerable variability among populations that differ for example in gender, age, race or body type (Shan and Bohn [43], Durkin and Dowling [14]). …”