论文标题
基于代理的建模和模拟人类步态分析仪的开发的人类肌肉
Agent-based Modeling and Simulation of Human Muscle For Development of Human Gait Analyzer Application
论文作者
论文摘要
尽管只有一小部分肌肉受到运动疾病和疾病的影响，但医疗疗法并不能区分健康和不健康的肌肉。在本文中，设计了一种方法，以计算步态周期中下半身的神经刺激，并检查任何肌肉是否没有正常作用。因此，提出了基于代理的人类肌肉模型。该药物能够将神经刺激转化为肌肉产生的力，反之亦然。它可用于许多研究，包括医学教育，研究和假体发展。然后，靴子算法是基于人类下半身的生物力学模型设计的，以通过计算每个肌肉群产生的力来进行人类运动的反向动力学。使用以代理驱动的人类肌肉和靴子算法模型，开发了一种用户友好的应用，可以计算步态周期中每种肌肉接受的神经刺激的数量。临床专家可以使用该应用来区分健康和不健康的肌肉。
Despite the fact that only a small portion of muscles are affected in motion disease and disorders, medical therapies do not distinguish between healthy and unhealthy muscles. In this paper, a method is devised in order to calculate the neural stimuli of the lower body during gait cycle and check if any group of muscles are not acting properly. For this reason, an agent-based model of human muscle is proposed. The agent is able to convert neural stimuli to force generated by the muscle and vice versa. It can be used in many researches including medical education and research and prosthesis development. Then, Boots algorithm is designed based on a biomechanical model of human lower body to do a reverse dynamics of human motion by computing the forces generated by each muscle group. Using the agent-driven model of human muscle and boots algorithm, a user-friendly application is developed which can calculate the number of neural stimuli received by each muscle during gait cycle. The application can be used by clinical experts to distinguish between healthy and unhealthy muscles.
