In this paper an existing in vivo parameter identification method for arteries is extended to account for smooth muscle activity. Within this method a continuum-mechanical model, whose parameters relate to the mechanical properties of the artery, is fit to clinical data by solving a minimization problem. Including smooth muscle activity in the model increases the number of parameters. This may lead to overparameterization, implying that several parameter combinations solve the minimization problem equally well and it is therefore not possible to determine which set of parameters represents the mechanical properties of the artery best. To prevent overparameterization the model is fit to clinical data measured at different levels of smooth muscle activity. Three conditions are considered for the human abdominal aorta: basal during rest; constricted, induced by lower-body negative pressure; and dilated, induced by physical exercise. By fitting the model to these three arterial conditions simultaneously a unique set of model parameters is identified and the model prediction agrees well with the clinical data.

在本文中，现有的动脉体内参数识别方法被扩展以考虑平滑肌活动。在该方法中，连续介质模型（其参数与动脉的机械特性相关）通过解决最小化问题来拟合临床数据。在模型中包含平滑肌活动会增加参数的数量。这可能会导致过度参数化，这意味着几个参数组合同样可以很好地解决最小化问题，因此不可能确定哪组参数最能代表动脉的机械特性。为了防止过度参数化，该模型适合在不同平滑肌活动水平下测量的临床数据。人类腹主动脉需要考虑三个条件：休息时的基础；由下半身负压引起的收缩；并由体育锻炼引起扩张。通过将模型同时拟合这三种动脉状况，确定了一组独特的模型参数，并且模型预测与临床数据非常吻合。