Koelewijn Anne D, Ijspeert Auke J
Biorobotics Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Machine Learning and Data Analytics Lab, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Front Bioeng Biotechnol. 2020 Aug 28;8:866. doi: 10.3389/fbioe.2020.00866. eCollection 2020.
Humans control balance using different feedback loops involving the vestibular system, the visual system, and proprioception. In this article, we focus on proprioception and explore the contribution of reflexes based on force and length feedback to standing balance. In particular, we address the questions of how much proprioception alone could explain balance control, and whether one modality, force or length feedback, is more important than the other. A sagittal plane neuro-musculoskeletal model was developed with six degrees of freedom and nine muscles in each leg. A controller was designed using proprioceptive reflexes and a dead zone. No feedback control was applied inside the dead zone. Reflexes were active once the center of mass moved outside the dead zone. Controller parameters were found by solving an optimization problem, where effort was minimized while the neuro-musculoskeletal model should remain standing upright on a perturbed platform. The ground was perturbed with random square pulses in the sagittal plane with different amplitudes and durations. The optimization was solved for three controllers: using force and length feedback (base model), using only force feedback, and using only length feedback. Simulations were compared to human data from previous work, where an experiment with the same perturbation signal was performed. The optimized controller yielded a similar posture, since average joint angles were within 5 degrees of the experimental average joint angles. The joint angles of the base model, the length only model, and the force only model correlated weakly (ankle) to moderately with the experimental joint angles. The ankle moment correlated weakly to moderately with the experimental ankle moment, while the hip and knee moment were only weakly correlated, or not at all. The time series of the joint angles showed that the length feedback model was better able to explain the experimental joint angles than the force feedback model. Changes in time delay affected the correlation of the joint angles and joint moments. The objective of effort minimization yielded lower joint moments than in the experiment, suggesting that other objectives are also important in balance control, which cause an increase in effort and thus larger joint moments.
人类通过涉及前庭系统、视觉系统和本体感觉的不同反馈回路来控制平衡。在本文中,我们重点关注本体感觉,并探讨基于力和长度反馈的反射对站立平衡的贡献。特别是,我们探讨了仅靠本体感觉能在多大程度上解释平衡控制,以及力反馈或长度反馈这两种方式中,哪一种比另一种更重要。我们开发了一个矢状面神经肌肉骨骼模型,每条腿有六个自由度和九块肌肉。使用本体感觉反射和一个死区设计了一个控制器。在死区内不应用反馈控制。一旦质心移出死区,反射就会激活。通过解决一个优化问题来确定控制器参数,即在神经肌肉骨骼模型应在受扰平台上保持直立的同时,使努力最小化。地面在矢状面受到不同幅度和持续时间的随机方波脉冲扰动。针对三种控制器求解优化问题:使用力和长度反馈(基础模型)、仅使用力反馈以及仅使用长度反馈。将模拟结果与之前工作中的人体数据进行比较,之前的工作进行了相同扰动信号的实验。优化后的控制器产生了相似的姿势,因为平均关节角度在实验平均关节角度的5度范围内。基础模型、仅长度模型和仅力模型的关节角度与实验关节角度的相关性较弱(脚踝)到中等。脚踝力矩与实验脚踝力矩的相关性较弱到中等,而髋部和膝部力矩仅具有较弱的相关性,或根本没有相关性。关节角度的时间序列表明,长度反馈模型比力反馈模型更能解释实验关节角度。时间延迟的变化影响了关节角度和关节力矩的相关性。努力最小化的目标产生的关节力矩比实验中的低,这表明其他目标在平衡控制中也很重要,这些目标会导致努力增加,从而产生更大的关节力矩。