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通过外周神经刺激实现的仿生感觉反馈可改善仿生手的灵活使用。

Biomimetic sensory feedback through peripheral nerve stimulation improves dexterous use of a bionic hand.

作者信息

George J A, Kluger D T, Davis T S, Wendelken S M, Okorokova E V, He Q, Duncan C C, Hutchinson D T, Thumser Z C, Beckler D T, Marasco P D, Bensmaia S J, Clark G A

机构信息

Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA.

Department of Neurosurgery, University of Utah, Salt Lake City, UT 84112, USA.

出版信息

Sci Robot. 2019 Jul 24;4(32). doi: 10.1126/scirobotics.aax2352.

Abstract

We describe use of a bidirectional neuromyoelectric prosthetic hand that conveys biomimetic sensory feedback. Electromyographic recordings from residual arm muscles were decoded to provide independent and proportional control of a six-DOF prosthetic hand and wrist-the DEKA LUKE arm. Activation of contact sensors on the prosthesis resulted in intraneural microstimulation of residual sensory nerve fibers through chronically implanted Utah Slanted Electrode Arrays, thereby evoking tactile percepts on the phantom hand. With sensory feedback enabled, the participant exhibited greater precision in grip force and was better able to handle fragile objects. With active exploration, the participant was also able to distinguish between small and large objects and between soft and hard ones. When the sensory feedback was biomimetic-designed to mimic natural sensory signals-the participant was able to identify the objects significantly faster than with the use of traditional encoding algorithms that depended on only the present stimulus intensity. Thus, artificial touch can be sculpted by patterning the sensory feedback, and biologically inspired patterns elicit more interpretable and useful percepts.

摘要

我们描述了一种能够传递仿生感觉反馈的双向神经肌电假手的应用。对残肢肌肉的肌电图记录进行解码,以实现对六自由度假手和手腕(DEKA LUKE手臂)的独立且成比例的控制。假体上接触传感器的激活通过长期植入的犹他倾斜电极阵列对残余感觉神经纤维进行神经内微刺激,从而在幻肢上唤起触觉感知。在启用感觉反馈的情况下,参与者在握力方面表现出更高的精度,并且能够更好地处理易碎物品。通过主动探索,参与者还能够区分小物体和大物体以及软物体和硬物体。当感觉反馈采用仿生设计以模仿自然感觉信号时,参与者能够比使用仅依赖当前刺激强度的传统编码算法更快地识别物体。因此,可以通过对感觉反馈进行模式化来塑造人工触觉,并且受生物启发的模式会引发更易于解释和有用的感知。

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