Department of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47906.
Department of Biomedical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47906.
J Biomech Eng. 2022 Oct 1;144(10). doi: 10.1115/1.4054275.
Part II of this study evaluates the predictive ability of the skeletal muscle force model derived in Part I within the ankle joint complex. The model is founded in dimensional analysis and uses electromyography and the muscle force-length, force-velocity, and force-frequency curves as inputs. Seventeen subjects (eight males, nine females) performed five different exercises geared toward activating the primary muscles crossing the ankle joint. Motion capture, force plate, and electromyography data were collected during these exercises for use in the analysis. A constant, Km, was calculated for each muscle of each subject using four of the five exercises. The fifth exercise was then used to validate the results by treating the moments due to muscle forces as known and all other components in Euler's second law as unknown. While muscle forces cannot be directly validated in vivo, methods can be developed to test these values with reasonable confidence. This study compared moments about the ankle joint due to the calculated muscle forces to the sum of the moments due to all other sources and the kinematic terms in the second Newton-Euler equation of rigid body motion. Average percent errors for each subject ranged from 4.2% to 15.5% with a total average percent error across all subjects of 8.2%, while maximum percent errors for each subject ranged from 33.3% to 78.0% with an overall average maximum of 52.4%. Future work will examine sensitivity analyses to identify any potential simplifications to the model and solution process, as well as validate the model on a more complex joint system to ensure it still performs at a satisfactory level.
本研究第二部分评估了第一部分中推导的骨骼肌肉力量模型在踝关节复合体中的预测能力。该模型基于量纲分析,使用肌电图和肌肉力量-长度、力量-速度和力量-频率曲线作为输入。17 名受试者(8 名男性,9 名女性)进行了五项不同的练习,旨在激活穿过踝关节的主要肌肉。在这些练习中,采集运动捕捉、力板和肌电图数据,用于分析。使用五项练习中的四项,为每个受试者的每个肌肉计算一个常数 Km。然后,使用第五项练习通过将肌肉力产生的力矩视为已知,将 Euler 第二定律中的所有其他分量视为未知,来验证结果。虽然肌肉力量不能在体内直接验证,但可以开发方法以合理的置信度测试这些值。本研究将计算的肌肉力产生的踝关节力矩与所有其他来源的力矩之和以及刚体运动第二牛顿-欧拉方程中的运动学项进行了比较。每个受试者的平均百分比误差范围为 4.2%至 15.5%,所有受试者的总平均百分比误差为 8.2%,而每个受试者的最大百分比误差范围为 33.3%至 78.0%,总体平均值为 52.4%。未来的工作将检查敏感性分析,以确定模型和解决方案过程的任何潜在简化,以及在更复杂的关节系统上验证模型,以确保其仍能达到令人满意的水平。