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Estimation of Muscle Response Using Three-Dimensional Musculoskeletal Models Before Impact Situation: A Simulation Study

机译:碰撞前使用三维骨骼模型评估肌肉反应的模拟研究

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When car crash experiments are performed using cadavers or dummies, the active muscles' reaction on crash situations cannot be observed. The aim of this study is to estimate muscles' response of the major muscle groups using three-dimensional musculoskeletal model by dynamic simulations of low-speed sled-impact. The three-dimensional musculoskeletal models of eight subjects were developed, including 241 degrees of freedom and 86 muscles. The muscle parameters considering limb lengths and the force-generating properties of the muscles were redefined by optimization to fit for each subject. Kinematic data and external forces measured by motion tracking system and dynamometer were then input as boundary conditions. Through a least-squares optimization algorithm, active muscles' responses were calculated during inverse dynamic analysis tracking the motion of each subject. Electromyography for major muscles at elbow, knee, and ankle joints was measured to validate each model. For low-speed sled-impact crash, experiment and simulation with optimized and unoptimized muscle parameters were performed at 9.4 m/h and 10 m/h and muscle activities were compared among them. The muscle activities with optimized parameters were closer to experimental measurements than the results without optimization. In addition, the extensor muscle activities at knee, ankle, and elbow joint were found considerably at impact time, unlike previous studies using cadaver or dummies. This study demonstrated the need to optimize the muscle parameters to predict impact situation correctly in computational studies using musculoskeletal models. And to improve accuracy of analysis for car crash injury using humanlike dummies, muscle reflex function, major extensor muscles' response at elbow, knee, and ankle joints, should be considered.
机译:当使用尸体或假人进行汽车碰撞实验时,无法观察到主动肌肉在碰撞情况下的反应。这项研究的目的是通过低速滑橇撞击的动态模拟,使用三维肌肉骨骼模型来估计主要肌肉群的肌肉反应。开发了八个对象的三维肌肉骨骼模型,包括241个自由度和86个肌肉。通过优化重新定义考虑肢体长度和肌肉产生力特性的肌肉参数,以适合每个受试者。然后将通过运动跟踪系统和测力计测量的运动数据和外力作为边界条件。通过最小二乘优化算法,在逆动态分析过程中跟踪每个对象的运动,从而计算出活动肌肉的反应。测量肘,膝和踝关节主要肌肉的肌电图以验证每个模型。对于低速雪橇撞击,在9.4 m / h和10 m / h的条件下进行了优化和未优化肌肉参数的实验和模拟,并比较了其中的肌肉活动。具有优化参数的肌肉活动比未经优化的结果更接近于实验测量。此外,与以前使用尸体或假人进行的研究不同,在撞击时膝,踝和肘关节的伸肌活动明显。这项研究表明,在使用肌肉骨骼模型的计算研究中,需要优化肌肉参数以正确预测撞击情况。为了提高使用类人假人进行的车祸伤害分析的准确性,应考虑肌肉反射功能,肘,膝和踝关节的主要伸肌的反应。

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