Heine Cláudio Bastos, Menegaldo Luciano Luporini
Biomedical Engineering Program - COPPE, Federal University of Rio de Janeiro, Av. Horacio Macedo 2030, Bloco H-338, 21941-914, Rio de Janeiro, Brazil.
Biomedical Engineering Program - COPPE, Federal University of Rio de Janeiro, Av. Horacio Macedo 2030, Bloco H-338, 21941-914, Rio de Janeiro, Brazil.
Med Eng Phys. 2018 Mar;53:66-74. doi: 10.1016/j.medengphy.2018.01.006. Epub 2018 Feb 1.
Muscle models can be used to estimate muscle forces in motor tasks. Muscle model parameters can be estimated by optimizing cost functions based on error between measured and model-estimated joint torques. This paper is a numerical simulation study addressing whether this approach can accurately identify the parameters of the quadriceps femoris. The simulated identification task is a single joint maximum voluntary knee concentric-eccentric extension in an isokinetic dynamometer, keeping the hip fixed at a neutral position. A curve considered as the nominal torque was obtained by simulating the quadriceps femoris model exerting a maximum knee extension torque using a set of known parameter values. Three parameters, with different expected sensitivities of force estimations by Hill-type muscle models, were studied: very sensitive, sensitive and not sensitive, corresponding to slack tendon length, maximum isometric force, and pennation angle, respectively. The initial values of the parameters were randomly changed, simulating an ignorance of nominal values. EMG generation and torque measurement error models were used to obtain realistic simulated data corrupted by noise. Simulated annealing was chosen as the optimization algorithm. Different sequences of parameter identification and cost functions were tested. The best nominal torque curve reconstruction was obtained by optimizing the parameters sequentially, starting from slack tendon length using the Euclidean norm cost function. However, the simultaneous estimation of all parameters resulted in the most accurate values for the parameters, although dispersion was relatively large. In conclusion, in the present simulation study using realistic synthetic torque and EMG data, the optimization approach based on torque error curve was able to closely approximate the parameter values of the model's quadriceps femoris muscle.
肌肉模型可用于估计运动任务中的肌肉力量。肌肉模型参数可通过基于测量的和模型估计的关节扭矩之间的误差来优化成本函数进行估计。本文是一项数值模拟研究，探讨这种方法能否准确识别股四头肌的参数。模拟识别任务是在等速测力计中进行单关节最大自主膝关节向心-离心伸展，同时将髋关节固定在中立位置。通过使用一组已知参数值模拟股四头肌模型施加最大膝关节伸展扭矩，获得一条被视为名义扭矩的曲线。研究了三个参数，Hill型肌肉模型对其力估计的预期敏感度不同：非常敏感、敏感和不敏感，分别对应于松弛肌腱长度、最大等长力和羽状角。参数的初始值被随机改变，模拟对名义值的未知。使用肌电图生成和扭矩测量误差模型来获得受噪声干扰的真实模拟数据。选择模拟退火作为优化算法。测试了不同的参数识别顺序和成本函数。通过依次从松弛肌腱长度开始，使用欧几里得范数成本函数优化参数，获得了最佳的名义扭矩曲线重建。然而，同时估计所有参数得到的参数值最准确，尽管离散度相对较大。总之，在本使用真实合成扭矩和肌电图数据的模拟研究中，基于扭矩误差曲线的优化方法能够非常接近模型股四头肌的参数值。
