KTH MoveAbility, Dept. Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden.
Faculty of Behavioural and Movement Sciences, VU Amsterdam, Amsterdam, The Netherlands.
PLoS Comput Biol. 2024 Sep 13;20(9):e1012411. doi: 10.1371/journal.pcbi.1012411. eCollection 2024 Sep.
The metabolic energy rate of individual muscles is impossible to measure without invasive procedures. Prior studies have produced models to predict metabolic rates based on experimental observations of isolated muscle contraction from various species. Such models can provide reliable predictions of metabolic rates in humans if muscle properties and control are accurately modeled. This study aimed to examine how muscle-tendon model individualization and metabolic energy models influenced estimation of muscle-tendon states and time-series metabolic rates, to evaluate the agreement with empirical data, and to provide predictions of the metabolic rate of muscle groups and gait phases across walking speeds. Three-dimensional musculoskeletal simulations with prescribed kinematics and dynamics were performed. An optimal control formulation was used to compute muscle-tendon states with four levels of individualization, ranging from a scaled generic model and muscle controls based on minimal activations, inclusion of calibrated muscle passive forces, personalization of Achilles and quadriceps tendon stiffnesses, to finally informing muscle controls with electromyography. We computed metabolic rates based on existing models. Simulations with calibrated passive forces and personalized tendon stiffness most accurately estimate muscle excitations and fiber lengths. Interestingly, the inclusion of electromyography did not improve our estimates. The whole-body average metabolic cost was better estimated with a subset of metabolic energy models. We estimated metabolic rate peaks near early stance, pre-swing, and initial swing at all walking speeds. Plantarflexors accounted for the highest cost among muscle groups at the preferred speed and were similar to the cost of hip adductors and abductors combined. Also, the swing phase accounted for slightly more than one-quarter of the total cost in a gait cycle, and its relative cost decreased with walking speed. Our prediction might inform the design of assistive devices and rehabilitation treatment. The code and experimental data are available online.
个体肌肉的代谢能量率无法通过非侵入性程序进行测量。先前的研究已经提出了基于各种物种的肌肉收缩实验观察来预测代谢率的模型。如果肌肉特性和控制得到准确建模，此类模型可以为人类提供可靠的代谢率预测。本研究旨在检验肌肉-肌腱模型个体化和代谢能量模型如何影响肌肉-肌腱状态和时间序列代谢率的估计，评估与经验数据的一致性，并提供跨步行速度的肌肉群和步态阶段的代谢率预测。进行了带有规定运动学和动力学的三维肌肉骨骼模拟。使用最优控制公式计算肌肉-肌腱状态，个体化程度有四个级别，从缩放的通用模型和基于最小激活的肌肉控制开始，包括校准的肌肉被动力，个性化的跟腱和股四头肌肌腱刚度，最终通过肌电图提供肌肉控制信息。我们根据现有模型计算代谢率。带有校准的被动力和个性化的肌腱刚度的模拟最能准确估计肌肉激发和纤维长度。有趣的是，肌电图的包含并没有改善我们的估计。使用一组代谢能量模型可以更好地估计全身平均代谢成本。我们在所有步行速度下估计了接近初始站立、预摆动和初始摆动的代谢率峰值。在首选速度下，跖屈肌比肌肉群的代谢成本更高，与髋关节内收肌和外展肌的成本相似。此外，摆动阶段占步态周期总成本的略多于四分之一，其相对成本随步行速度降低。我们的预测可能为辅助设备和康复治疗的设计提供信息。代码和实验数据可在网上获取。
