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.

翻译：尽管在运动疾病和障碍中仅有小部分肌肉受到影响，但医学治疗并未区分健康与不健康的肌肉。本文提出一种方法，用于计算步态周期中下半身的神经刺激，并判断是否存在肌肉群功能异常。为此，我们构建了基于智能体的人体肌肉模型。该智能体能够将神经刺激转换为肌肉产生的力，反之亦然。该模型可广泛应用于医学教育、科研及假肢开发等领域。随后，基于人体下半身生物力学模型设计了Boots算法，通过计算各肌肉群产生的力来实现人体运动的逆向动力学。利用基于智能体的人体肌肉模型与Boots算法，我们开发了一款用户友好型应用，可计算步态周期中各肌肉接收到的神经刺激数量。临床专家可利用该应用区分健康与不健康的肌肉。