Muscle models are commonly based on intrinsic properties pooled across a number of individuals, often from a different species, and rarely validated against directly measured muscle forces. Here we use a rich data set of rat medial gastrocnemius muscle forces recorded during in-situ and in-vivo isometric, isotonic, and cyclic contractions to test the accuracy of forces predicted using Hill-type muscle models. We identified force-length and force-velocity parameters for each individual, and used either these subject-specific intrinsic properties, or population-averaged properties within the models. The modeled forces for cyclic in-vivo and in-situ contractions matched with measured muscle-tendon forces with r² between 0.70 and 0.86, and root-mean square errors (RMSE) of 0.10 to 0.13 (values normalized to the maximum isometric force). The modeled forces were least accurate at the highest movement and cycle frequencies and did not show an improvement in r² when subject-specific intrinsic properties were used; however, there was a reduction in the RMSE with fewer predictions having higher errors. We additionally recorded and tested muscle models specific to proximal and distal regions of the muscle and compared them to measures and models from the whole muscle belly: there was no improvement in model performance when using data from specific anatomical regions. These results show that Hill-type muscle models can yield very good performance for cyclic contractions typical of locomotion, with small reductions in errors when subject-specific intrinsic properties are used.

肌肉模型通常基于多个个体（通常来自不同物种）的内在属性，很少针对直接测量的肌肉力量进行验证。在这里，我们使用在原位和体内等长、等渗和循环收缩期间记录的大鼠内侧腓肠肌力的丰富数据集，以测试使用 Hill 型肌肉模型预测的力的准确性。我们确定了每个人的力-长度和力-速度参数，并在模型中使用这些特定于主题的内在属性或人口平均属性。循环体内和原位收缩的建模力与测量的肌肉肌腱力相匹配，r²在 0.70 到 0.86 之间，均方根误差 (RMSE) 为 0.10 到 0.13（归一化为最大等距力的值）。在最高运动和循环频率下，建模力最不准确，并且没有显示r ^2的改进当使用特定主题的内在属性时；但是，RMSE 有所降低，预测越少，误差越大。我们还记录和测试了特定于肌肉近端和远端区域的肌肉模型，并将它们与整个肌肉腹部的测量值和模型进行了比较：当使用来自特定解剖区域的数据时，模型性能没有改善。这些结果表明，Hill 型肌肉模型可以为典型的运动循环收缩产生非常好的性能，当使用特定于受试者的内在特性时，误差会小幅减少。