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通过仿生神经肌肉骨骼模型生成人类双足运动。

Generation of human bipedal locomotion by a bio-mimetic neuro-musculo-skeletal model.

作者信息

Ogihara N, Yamazaki N

机构信息

Department of Biomedical Engineering, Keio University, Yokohama, Japan.

出版信息

Biol Cybern. 2001 Jan;84(1):1-11. doi: 10.1007/PL00007977.

Abstract

To emulate the actual neuro-control mechanism of human bipedal locomotion, an anatomically and physiologically based neuro-musculo-skeletal model is developed. The human musculo-skeletal system is constructed as seven rigid links in a sagittal plane, with a total of nine principal muscles. The nervous system consists of an alpha motoneuron and proprioceptors such as a muscle spindle and a Golgi tendon organ for each muscle. At the motoneurons, feedback signals from the proprioceptors are integrated with the signal induced by foot-ground contact and input from the rhythm pattern generator; a muscle activation signal is produced accordingly. Weights of connection in the neural network are optimized using a genetic algorithm, thus maximizing walking distance and minimizing energy consumption. The generated walking pattern is in remarkably good agreement with that of actual human walking, indicating that the locomotory pattern could be generated automatically, according to the musculoskeletal structures and the connections of the peripheral nervous system, particularly due to the reciprocal innervation in the muscle spindles. Using the proposed model, the flow of sensory-motor information during locomotion is estimated and a possible neuro-control mechanism is discussed.

摘要

为了模拟人类双足运动的实际神经控制机制,开发了一种基于解剖学和生理学的神经肌肉骨骼模型。人体肌肉骨骼系统在矢状面内构建为七个刚性链接,共有九块主要肌肉。神经系统由一个α运动神经元和每个肌肉的本体感受器(如肌梭和高尔基腱器官)组成。在运动神经元处,来自本体感受器的反馈信号与脚底接触诱导的信号以及节律模式发生器的输入信号整合在一起;相应地产生肌肉激活信号。使用遗传算法优化神经网络中的连接权重,从而最大化步行距离并最小化能量消耗。生成的步行模式与实际人类步行模式非常吻合,这表明运动模式可以根据肌肉骨骼结构和周围神经系统的连接自动生成,特别是由于肌梭中的交互神经支配。使用所提出的模型,估计了运动过程中感觉运动信息的流动,并讨论了一种可能的神经控制机制。

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