THE SENSITIVITY OF MUSCLE FORCE PREDICTIONS TO CHANGES IN PHYSIOLOGICAL CROSS-SECTIONAL AREA
THE SENSITIVITY OF MUSCLE FORCE PREDICTIONS TO CHANGES IN PHYSIOLOGICAL CROSS-SECTIONAL AREA
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DOI：
10.1016/0021-9290(86)90164-8
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发表时间：
1986-01-01
影响因子：
2.4
通讯作者：
FRIEDERICH, JA
中科院分区：
文献类型：
--
作者：
BRAND, RA;PEDERSEN, DR;FRIEDERICH, JA
肌肉的机械作用部分与肌肉的大小和它相对于关节的位置有关。一个多世纪以来，研究人员一直用“生理横截面积”（PCSA）来表示肌肉的大小。研究人员在数学上计算肌肉和关节力时，通常使用肌肉的PCSA的一些表达来约束解决方案，使其成为一个合理的解决方案（即小肌肉可能没有大的力，而大肌肉在预期或有显著的肌电图活动时具有大的力）。很明显，即使在体重和身高相似的个体中，肌肉质量（以及任何PCSA的表达）也因人而异。由于预测活体受试者肢体或躯干中每块肌肉的PCSA是不现实的，因此了解肌肉力解对PCSA可能变化的敏感性是很重要的。我们使用非线性优化技术来预测47个肌肉力和髋关节接触力。将两具尸体标本中47个下肢肌肉单元的PCSA（体积/肌纤维长度）和Pierrynowski报告的47个PCSA输入到优化算法中，生成三个解集。这三种解决方案在质量上是相似的，但有时预测的肌肉力量可能相差2到8倍。相比之下，关节力解的误差在11%以内，因此，变量要小得多。当使用优化技术预测肌肉力时，必须认识到解决方案对许多假设和变量（如PCSA）很敏感。因此，肌肉力解最好用于确定参数研究中的相对值（即趋势）。另一方面，联合力解对这种变化不太敏感，绝对值更可靠。
The mechanical effects of a muscle are related in part of the size of the muscle and to its location relative to the joint it crosses. For more than a century, researchers have expressed muscle size by its ''physiological cross-sectional area'' (PCSA). Researchers mathematically calculating muscle and joint forces typically use some expression of a muscle''s PCSA to constrain the solution to one which is reasonable (i.e. a solution in which small muscles may not have large forces, and large muscles have large forces when expected or when there is significant electromyographic activity). It is obvious that muscle mass (and therefore any expression of PCSA) varies significantly from person to person, even in individuals of similar weight and height. Since it is not practical to predict the PCSA of each muscle in a living subject''s limb or trunk, it is important to generally understand the sensitivity of muscle force solutions to possible variations in PCSA. We used nonlinear optimization techniques to predict 47 muscle forces and hip contact forces in a living subject. The PCSA (volume/muscle fiber length) of each of 47 lower limb muscle elements from two cadaver specimens and the 47 PCSA''s reported by Pierrynowski were input into an optimization algorithm to create three solution sets. The three solutions were qualitatively similar but at times a predicted muscle force could vary as much as two to eight times. In contrast, the joint force solutions were within 11% of each other and, therefore, much less variable. When using optimization techniques to predict muscle forces, it must be recognized that the solution is sensitive to many assumptions and variables such as PCSA. The muscle force solutions are therefore best used to determine relative values (i.e. trends) in parametric studies. On the other hand, the joint force solutions are less sensitive to such variations, and the absolute values are more reliable.