Wicke Jason, Dumas Genevieve A
Department of Health & Human Performance, Texas A&M Commerce University, Commerce, TX, USA.
J Appl Biomech. 2010 Feb;26(1):26-31. doi: 10.1123/jab.26.1.26.
The geometric method combines a volume and a density function to estimate body segment parameters and has the best opportunity for developing the most accurate models. In the trunk, there are many different tissues that greatly differ in density (e.g., bone versus lung). Thus, the density function for the trunk must be particularly sensitive to capture this diversity, such that accurate inertial estimates are possible. Three different models were used to test this hypothesis by estimating trunk inertial parameters of 25 female and 24 male college-aged participants. The outcome of this study indicates that the inertial estimates for the upper and lower trunk are most sensitive to the volume function and not very sensitive to the density function. Although it appears that the uniform density function has a greater influence on inertial estimates in the lower trunk region than in the upper trunk region, this is likely due to the (overestimated) density value used. When geometric models are used to estimate body segment parameters, care must be taken in choosing a model that can accurately estimate segment volumes. Researchers wanting to develop accurate geometric models should focus on the volume function, especially in unique populations (e.g., pregnant or obese individuals).
几何方法结合了体积和密度函数来估计身体节段参数,并且最有机会开发出最准确的模型。在躯干中,有许多不同的组织,其密度差异很大(例如,骨骼与肺部)。因此,躯干的密度函数必须特别敏感才能捕捉到这种差异,以便能够进行准确的惯性估计。通过估计25名女性和24名男性大学生参与者的躯干惯性参数,使用了三种不同的模型来检验这一假设。这项研究的结果表明,上躯干和下躯干的惯性估计对体积函数最为敏感,而对密度函数不太敏感。尽管似乎均匀密度函数对下躯干区域惯性估计的影响比对上躯干区域更大,但这可能是由于所使用的(高估的)密度值。当使用几何模型来估计身体节段参数时,必须谨慎选择能够准确估计节段体积的模型。想要开发准确几何模型的研究人员应关注体积函数,尤其是在特殊人群(例如孕妇或肥胖个体)中。