Harper Christine M, Sylvester Adam D, Kramer Patricia Ann
Department of Anthropology, University of Washington, Seattle, Washington, United States of America.
Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, New Jersey, United States of America.
PLoS One. 2025 Mar 28;20(3):e0320516. doi: 10.1371/journal.pone.0320516. eCollection 2025.
Musculoskeletal modeling can be used to estimate forces during locomotion. These models, however, are dependent on underlying assumptions about the model inputs, such as muscle volumes and fiber lengths, to calculate muscle forces. Triceps surae (gastrocnemius medialis, gastrocnemius lateralis, soleus) muscle volume distributions vary among humans. Here we quantify how this muscle volume variation impacts maximum estimated lower limb muscle forces during the braking and propulsive phases of the stance phase of walking. Three triceps surae muscle volume distributions (AnyBody Modeling System standard cadaver [MS], average of 21 cadavers [C], average of 21 young, healthy adults [YHA]) were evaluated in a standard musculoskeletal model using the kinetic and kinematic data of 10 healthy individuals at three walking velocities. Maximum muscle forces were calculated using inverse dynamics and an algorithm to solve the muscle redundancy problem in the AnyBody Modeling System. Repeated measure ANOVAs were used to test for significant differences among the three muscle distribution configurations for each muscle/muscle group at each velocity. Triceps surae muscle volume distribution significantly affects gastrocnemius lateralis and soleus maximum muscle forces for both braking and propulsion at all three velocities (p < 0.001), with relatively larger muscle volumes typically producing relatively larger muscle forces. There was no significant difference in gastrocnemius medialis maximum force among configurations (p > 0.124) except at the self-selected spontaneous velocity during braking. Significant differences exist at some velocities for the hamstrings and gluteus maximus during braking (p < 0.046) and the other plantarflexors, dorsiflexors, evertors, hamstrings, quadriceps, sartorius, and gluteus maximus during propulsion (p < 0.042). Muscle volumes used in musculoskeletal models impact estimated muscle forces of both the muscles of interest and other muscles in the biomechanical chain. This is consistent with recent analyses demonstrating that input values can substantially impact results and suggests individualized muscle parameters may be needed depending on the research question.
肌肉骨骼建模可用于估计运动过程中的力。然而，这些模型依赖于关于模型输入的潜在假设，如肌肉体积和纤维长度，来计算肌肉力量。小腿三头肌（腓肠肌内侧头、腓肠肌外侧头、比目鱼肌）的肌肉体积分布在人群中各不相同。在此，我们量化了这种肌肉体积变化如何影响步行站立期制动和推进阶段下肢最大估计肌肉力量。使用10名健康个体在三种步行速度下的动力学和运动学数据，在一个标准肌肉骨骼模型中评估了三种小腿三头肌肌肉体积分布（AnyBody建模系统标准尸体[MS]、21具尸体的平均值[C]、21名年轻健康成年人的平均值[YHA]）。使用逆动力学和一种算法来解决AnyBody建模系统中的肌肉冗余问题，计算最大肌肉力量。重复测量方差分析用于检验每种速度下各肌肉/肌肉组的三种肌肉分布配置之间的显著差异。小腿三头肌肌肉体积分布在所有三种速度下对制动和推进时的腓肠肌外侧头和比目鱼肌最大肌肉力量均有显著影响（p < 0.001），肌肉体积相对较大通常会产生相对较大的肌肉力量。除了制动时的自选自发速度外，各配置之间腓肠肌内侧头最大力量没有显著差异（p > 0.124）。在制动期间，某些速度下腘绳肌和臀大肌存在显著差异（p < 0.046），在推进期间，其他跖屈肌、背屈肌、外翻肌、腘绳肌、股四头肌、缝匠肌和臀大肌存在显著差异（p < 0.042）。肌肉骨骼模型中使用的肌肉体积会影响感兴趣肌肉以及生物力学链中其他肌肉的估计肌肉力量。这与最近的分析一致，即输入值会对结果产生重大影响，并表明可能需要根据研究问题采用个性化的肌肉参数。
