Body Segment Parameter Estimation of the Human Lower Leg Using an Elliptical Model with Validation from DEXA
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Accurate estimates of human body segment parameters (BSPs) are required for kinetic analyses of motion. The purpose of this study was to develop a geometric model of the human lower leg based on the mass distribution properties of the segment. Forty subjects were recruited from 4 human populations. Each population was randomly divided equally into model development (MD) and model validation (MV) groups. Participants underwent frontal and sagittal plane dual energy X-ray absorptiometry (DEXA) scans and anthropometric measurements. Leg BSPs were calculated from the scan information and mass distribution properties in the two planes were determined. Further, a geometric model was developed based on the ensemble averages of the mass distribution information from the MD groups. The model was applied to the MV groups and mean absolute errors were calculated for each BSP and each population. Finally, BSP estimates from literature sources were also determined and compared against DEXA. The model developed produced the lowest errors overall. Additionally, the results showed that the model developed estimated BSPs for all four populations with consistent accuracy whereas the other 4 models tested provided different levels of accuracy depending on the age and gender categories of the group tested. The results of this study present a model that accurately estimates BSPs of the lower leg for individuals varying in age, gender, race, and morphology. This study also presents a modelling technique that may successfully provide similar results for other body segments.
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