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Adaptive motor behaviors through dynamic interactions among the body, brain and environment

机译:通过身体,大脑和环境中的动态相互作用自适应电机行为

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In daily life, humans must compensate for the resultant force arising from interaction with the physical environment. It has been shown that humans can acquire a neural representation of the relation between motor command and movement, i.e. learn an internal model of the environment dynamics. For example, Shadmehr et al have analyzed various reaching movements under velocity-dependent force field (VF) where the hand receives the external load in proportion to the hand velocity. It is then shown that human compensates for the external load by the feedforward control based on the internal model. It is here called. On the other hand, in manipulation tasks, such as opening a door, grasping a cup etc., the dynamic interaction between the human arm and external environment determines the stability of motion. Therefore, it has much important to adjust the arm impedance corresponding to the environment dynamics, which is called . The talk will discuss adaptive motor behaviors to dynamic environments in human voluntary movements. The experimental results demonstrate that human adaptation to a varying dynamic environment during reaching movements is achieved by programming the internal model control and impedance control in a feedforward manner. In addition, the talk will refer to application of motor learning to physical rehabilitation.
机译:在日常生活中,人类必须弥补所产生的力量与物理环境的互动。已经表明,人类可以获得电动机命令和运动之间关系的神经表示,即学习环境动态的内部模型。例如,Shadmehr等人已经在速度相关的力场(VF)下分析了各种达到的运动,其中手接收到手速度成比例的外部负载。然后表明,人们通过基于内部模型的前馈控制来补偿外部负荷。它在这里被称为。另一方面,在操纵任务中,例如打开门,抓住杯子等,人臂和外部环境之间的动态相互作用决定了运动的稳定性。因此,调整与环境动态对应的臂阻抗有很大的重要性,该臂被调用。谈话将讨论人类自愿运动中动态环境的自适应电机行为。实验结果表明,通过以前馈方式编程内部模型控制和阻抗控制来实现在达到运动期间对变化动态环境的人体适应。此外,谈判将参考电机学习施加物理康复的应用。

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