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Humans can integrate feedback of discrete events in their sensorimotor control of a robotic hand

机译:人类可以将离散事件的反馈整合到他们对机械手的感觉运动控制中

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摘要

Providing functionally effective sensory feedback to users of prosthetics is a largely unsolved challenge. Traditional solutions require high band-widths for providing feedback for the control of manipulation and yet have been largely unsuccessful. In this study, we have explored a strategy that relies on temporally discrete sensory feedback that is technically simple to provide. According to the Discrete Event-driven Sensory feedback Control (DESC) policy, motor tasks in humans are organized in phases delimited by means of sensory encoded discrete mechanical events. To explore the applicability of DESC for control, we designed a paradigm in which healthy humans operated an artificial robot hand to lift and replace an instrumented object, a task that can readily be learned and mastered under visual control. Assuming that the central nervous system of humans naturally organizes motor tasks based on a strategy akin to DESC, we delivered short-lasting vibrotactile feedback related to events that are known to forcefully affect progression of the grasp-lift-and-hold task. After training, we determined whether the artificial feedback had been integrated with the sensorimotor control by introducing short delays and we indeed observed that the participants significantly delayed subsequent phases of the task. This study thus gives support to the DESC policy hypothesis. Moreover, it demonstrates that humans can integrate temporally discrete sensory feedback while controlling an artificial hand and invites further studies in which inexpensive, noninvasive technology could be used in clever ways to provide physiologically appropriate sensory feedback in upper limb prosthetics with much lower band-width requirements than with traditional solutions.
机译:向假肢使用者提供功能上有效的感官反馈在很大程度上尚未解决。传统的解决方案需要高带宽来提供用于控制操作的反馈,但是在很大程度上并未成功。在这项研究中,我们探索了一种策略,该策略依赖于在技术上易于提供的时间离散的感觉反馈。根据离散事件驱动的感官反馈控制(DESC)策略,通过感官编码的离散机械事件以分隔的阶段组织人类的运动任务。为了探索DESC在控制方面的适用性,我们设计了一种范例,在该范例中,健康的人可以操纵人工机器人的手来抬起并更换已插入仪器的物体,该任务可以在视觉控制下轻松地学习和掌握。假设人类的中枢神经系统根据类似于DESC的策略自然地组织了运动任务,我们提供了与事件有关的短时触觉反馈,这些事件已知会强烈影响抓握和抓握任务的进程。训练后,我们通过引入短暂的延迟来确定人工反馈是否已与感觉运动控制相结合,我们确实观察到参与者大大延迟了该任务的后续阶段。因此,本研究为DESC政策假设提供了支持。此外,它证明了人类可以在控制人造手的同时整合时间离散的感觉反馈,并引发了进一步的研究,其中可以廉价地使用非侵入性技术巧妙地在带宽要求低得多的上肢假体中提供生理上合适的感觉反馈比传统解决方案要好。

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