Frontiers in Neurorobotics (May 2019)

An Attention-Controlled Hand Exoskeleton for the Rehabilitation of Finger Extension and Flexion Using a Rigid-Soft Combined Mechanism

  • Min Li,
  • Min Li,
  • Bo He,
  • Ziting Liang,
  • Chen-Guang Zhao,
  • Jiazhou Chen,
  • Yueyan Zhuo,
  • Guanghua Xu,
  • Guanghua Xu,
  • Jun Xie,
  • Jun Xie,
  • Kaspar Althoefer

DOI
https://doi.org/10.3389/fnbot.2019.00034
Journal volume & issue
Vol. 13

Abstract

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Hand rehabilitation exoskeletons are in need of improving key features such as simplicity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control. This article presents a brain-controlled hand exoskeleton based on a multi-segment mechanism driven by a steel spring. Active rehabilitation training is realized using a threshold of the attention value measured by an electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. We present a prototype implementation of this rigid-soft combined multi-segment mechanism with active training and provide a preliminary evaluation. The experimental results showed that the proposed mechanism could generate enough range of motion with a single input by distributing an actuated linear motion into the rotational motions of finger joints during finger flexion/extension. The average attention value in the experiment of concentration with visual guidance was significantly higher than that in the experiment without visual guidance. The feasibility of the attention-based control with visual guidance was proven with an overall exoskeleton actuation success rate of 95.54% (14 human subjects). In the exoskeleton actuation experiment using the general threshold, it performed just as good as using the customized thresholds; therefore, a general threshold of the attention value can be set for a certain group of users in hand exoskeleton activation.

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