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Control of Movement: Multidigit force control during unconstrained grasping in response to object perturbations

机译:运动控制:无限制抓握过程中的多位数字力控制可响应物体的干扰

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摘要

Because of the complex anatomy of the human hand, in the absence of external constraints, a large number of postures and force combinations can be used to attain a stable grasp. Motor synergies provide a viable strategy to solve this problem of motor redundancy. In this study, we exploited the technical advantages of an innovative sensorized object to study unconstrained hand grasping within the theoretical framework of motor synergies. Participants were required to grasp, lift, and hold the sensorized object. During the holding phase, we repetitively applied external disturbance forces and torques and recorded the spatiotemporal distribution of grip forces produced by each digit. We found that the time to reach the maximum grip force during each perturbation was roughly equal across fingers, consistent with a synchronous, synergistic stiffening across digits. We further evaluated this hypothesis by comparing the force distribution of human grasping vs. robotic grasping, where the control strategy was set by the experimenter. We controlled the global hand stiffness of the robotic hand and found that this control algorithm produced a force pattern qualitatively similar to human grasping performance. Our results suggest that the nervous system uses a default whole hand synergistic control to maintain a stable grasp regardless of the number of digits involved in the task, their position on the objects, and the type and frequency of external perturbations.>NEW & NOTEWORTHY We studied hand grasping using a sensorized object allowing unconstrained finger placement. During object perturbation, the time to reach the peak force was roughly equal across fingers, consistently with a synergistic stiffening across fingers. Force distribution of a robotic grasping hand, where the control algorithm is based on global hand stiffness, was qualitatively similar to human grasping. This suggests that the central nervous system uses a default whole hand synergistic control to maintain a stable grasp.
机译:由于人手的解剖结构复杂,在没有外部约束的情况下,可以使用大量的姿势和力组合来获得稳定的抓握。电机协同作用提供了解决该电机冗余问题的可行策略。在这项研究中,我们利用创新的感测对象的技术优势,在运动协同作用的理论框架内研究不受约束的手部抓握。要求参与者抓住,抬起并握住感应物体。在保持阶段,我们反复施加外部干扰力和扭矩,并记录每个手指产生的抓地力的时空分布。我们发现,在每个微扰期间达到最大抓握力的时间在手指上大致相等,这与手指间同步,协同的加劲一致。我们通过比较人工抓握和机器人抓握的力分布来评估该假设,在这种情况下,控制策略是由实验者设定的。我们控制了机械手的整体手的刚度,发现该控制算法产生的定性模式与人类的抓握性能在质量上相似。我们的结果表明,神经系统使用默认的全手协同控制来保持稳定的抓地力,而不管任务中涉及的手指的数目,它们在物体上的位置以及外部扰动的类型和频率如何。 在物体扰动期间,达到最大作用力的时间在两根手指之间大致相等,并且在两根手指之间协同增效。控制算法基于整体手的刚度的机器人抓握手的力分布在质量上与人类抓握相似。这表明中枢神经系统使用默认的全手协同控制来保持稳定的抓地力。

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