Identification of Motor Control Objectives in Human Locomotion via Multi-Objective Inverse Optimal Control

IF 1.9 4区 工程技术 Q3 ENGINEERING, MECHANICAL
Matilde Tomasi, Alessio Artoni
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引用次数: 0

Abstract

Abstract Predictive simulations of human motion are a precious resource for a deeper understanding of the motor control policies encoded by the central nervous system. They also have profound implications for the design and control of assistive and rehabilitation devices, for ergonomics, as well as for surgical planning. However, the potential of state-of-the-art predictive approaches is not fully realized yet, making it difficult to draw convincing conclusions about the actual optimality principles underlying human walking. In the present study, we propose a novel formulation of a bilevel, inverse optimal control strategy based on a full-body three-dimensional neuromusculoskeletal model. In the lower level, prediction of walking is formulated as a principled multi-objective optimal control problem based on a weighted Chebyshev metric, whereas the contributions of candidate control objectives are systematically and efficiently identified in the upper level. Our framework has proved to be effective in determining the contributions of the selected objectives and in reproducing salient features of human locomotion. Nonetheless, some deviations from the experimental kinematic and kinetic trajectories have emerged, suggesting directions for future research. The proposed framework can serve as an inverse optimal control platform for testing multiple optimality criteria, with the ultimate goal of learning the control objectives that best explain observed human motion.2
基于多目标逆最优控制的人体运动控制目标辨识
人体运动的预测模拟是深入理解由中枢神经系统编码的运动控制策略的宝贵资源。它们还对辅助和康复装置的设计和控制、人体工程学以及手术计划有着深远的影响。然而,最先进的预测方法的潜力尚未完全实现,因此很难得出关于人类行走的实际最优性原则的令人信服的结论。在本研究中,我们提出了一种基于全身三维神经肌肉骨骼模型的双层逆最优控制策略的新公式。在较低的层次上,行走预测被表述为一个基于加权切比雪夫度量的原则性多目标最优控制问题,而在较高的层次上,候选控制目标的贡献被系统有效地识别。我们的框架已被证明在确定选定目标的贡献和再现人类运动的显著特征方面是有效的。尽管如此,与实验的运动学和动力学轨迹出现了一些偏差,为未来的研究提供了方向。所提出的框架可以作为测试多个最优性标准的逆最优控制平台,其最终目标是学习最能解释观察到的人体运动的控制目标
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来源期刊
CiteScore
4.00
自引率
10.00%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The purpose of the Journal of Computational and Nonlinear Dynamics is to provide a medium for rapid dissemination of original research results in theoretical as well as applied computational and nonlinear dynamics. The journal serves as a forum for the exchange of new ideas and applications in computational, rigid and flexible multi-body system dynamics and all aspects (analytical, numerical, and experimental) of dynamics associated with nonlinear systems. The broad scope of the journal encompasses all computational and nonlinear problems occurring in aeronautical, biological, electrical, mechanical, physical, and structural systems.
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