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

Abstract

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.

Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review

“…A final sample of 30 studies were included in the qualitative review. Among the 30 studies, 11 [75][76][77][78][79][80][81][82][83][84][85] were involved in testing the BCI-hand robot system on chronic and subacute stroke patients ( [75,80] are RCTs) while the rest involved testing on healthy participants [86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103][104]. Table 2 shows a summary of the relevant data fields extracted from these studies.…”

“…The most common approach was to design and implement a hand orthosis controlled by MI which accounted for 9 out of the 19 studies and were measured based on classification accuracy during the calibration/training period and online testing. Li et al [88] and Stan et al [94] also aimed to trigger a hand orthosis but instead of MI, the triggers used by Li et al is based on an attention threshold while Stan et al used a vision-based P300 speller BCI. Bauer et al [97] compared MI against ME using a BCI-device while Ono et al [100] studied the implementation of an action observation strategy with a combined visual and kinaesthetic feedback or auditory feedback.…”

Design Criteria of Soft Exogloves for Hand Rehabilitation-Assistance Tasks

“…New trends are oriented to hybrid designs where they combine rigid and soft components to obtain more hand poses and more DOF [90] and provide active training that encourages user participation [91].…”

“…Authors in [21] use DCX22 motors, a control board TMS320F2808®, and Simulink®. Additionally, graphical user interfaces (GUI)® have been implemented as a communication channel for SEG systems [88,91]. A broad range of operation and control possibilities exists to select microcontrollers and interfaces relying on desired real-time response, accuracy, number of components involved in the operation and control strategies, and specific requirements of each SEG system.…”

Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

“…A final list with 30 studies was identified as eligible for qualitative review. Among the 30 studies, 11 [60][61][62][63][64][65][66][67][68][69][70] were involved in testing the BCI-hand robot system on chronic and subacute stroke patients ( [60,65] are RCTs) while the rest involved testing on healthy participants [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89].…”