In this research, we are about to present an agentbased model of human muscle which can be used in analysis of human movement. As the model is designed based on the physiological structure of the muscle, The simulation calculations would be natural, and also, It can be possible to analyze human movement using reverse engineering methods. The model is also a suitable choice to be used in modern prostheses, because the calculation of the model is less than other machine learning models such as artificial neural network algorithms and It makes our algorithm battery-friendly. We will also devise a method that can calculate the intensity of human muscle during gait cycle using a reverse engineering solution. The algorithm called Boots is different from some optimization methods, so It would be able to compute the activities of both agonist and antagonist muscles in a joint. As a consequence, By having an agent-based model of human muscle and Boots algorithm, We would be capable to develop software that can calculate the nervous stimulation of human's lower body muscle based on the angular displacement during gait cycle without using painful methods like electromyography. By developing the application as open-source software, We are hopeful to help researchers and physicians who are studying in medical and biomechanical fields.
翻译:在这个研究中,我们即将提出一个人类肌肉的代理模型,可用于分析人类运动。由于模型是根据肌肉生理结构设计的,模拟计算是自然的,并且可以使用反向工程方法分析人类运动。模型也是现代假肢中可以使用的合适选择,因为模型的计算比人工神经网络算法等其他机器学习模型要少一些,它使我们的算法电池更方便。我们还将设计一种方法,可以用反向工程解决方案来计算运动周期期间人类肌肉的强度。称为靴子的算法与某些优化方法不同,这样可以同时用反向工程方法分析人类运动和对立肌肉的活动。因此,模型也是用于现代假肢的合适选择,因为模型的计算比人工神经网络算法等其他机器学习模型要少一些,因此,我们可以开发出一种软件,用以计算在游戏周期中以角变形变形法为基础的人体肌肉神经刺激,而不必使用电传法等痛苦的方法。通过开发开源软件来应用“布特”算出“布特”的算法,我们希望能帮助研究人员和研究人员在生物系中进行研究。