Bersani Alex, Davico Giorgio, Viceconti Marco
Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna,Italy.
Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna,Italy.
J Appl Biomech. 2023 Aug 16;39(5):294-303. doi: 10.1123/jab.2023-0015. Print 2023 Oct 1.
This review paper provides an overview of the approaches to model neuromuscular control, focusing on methods to identify nonoptimal control strategies typical of populations with neuromuscular disorders or children. Where possible, the authors tightened the description of the methods to the mechanisms behind the underlying biomechanical and physiological rationale. They start by describing the first and most simplified approach, the reductionist approach, which splits the role of the nervous and musculoskeletal systems. Static optimization and dynamic optimization methods and electromyography-based approaches are summarized to highlight their limitations and understand (the need for) their developments over time. Then, the authors look at the more recent stochastic approach, introduced to explore the space of plausible neural solutions, thus implementing the uncontrolled manifold theory, according to which the central nervous system only controls specific motions and tasks to limit energy consumption while allowing for some degree of adaptability to perturbations. Finally, they explore the literature covering the explicit modeling of the coupling between the nervous system (acting as controller) and the musculoskeletal system (the actuator), which may be employed to overcome the split characterizing the reductionist approach.
这篇综述文章概述了神经肌肉控制建模方法,重点关注识别神经肌肉疾病患者群体或儿童典型的非最优控制策略的方法。在可能的情况下,作者将方法描述紧密关联到潜在生物力学和生理学原理背后的机制。他们首先描述第一种也是最简化的方法,即还原论方法,该方法将神经和肌肉骨骼系统的作用分开。总结了静态优化和动态优化方法以及基于肌电图的方法,以突出其局限性并理解它们随时间发展的(必要性)。然后,作者探讨了最近引入的随机方法,该方法用于探索合理神经解决方案的空间,从而实施非受控流形理论,根据该理论,中枢神经系统仅控制特定运动和任务以限制能量消耗,同时允许对扰动有一定程度的适应性。最后,他们探究了涵盖神经系统(作为控制器)和肌肉骨骼系统(执行器)之间耦合显式建模的文献,这可用于克服还原论方法所具有的分离特征。