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.

Robotically assisted rehabilitation therapy is effective in recovering motor function following impairment. It is essential to make sure patients be actively involved in the motor training process using robot-assisted rehabilitation to achieve better rehabilitation outcomes. This paper introduces a brain-controlled wrist rehabilitation method using a low-cost EEG sensor. Active rehabilitation training is realized using a threshold of the attention level measured by the low-cost EEG sensor as a brain-controlled switch for a flexible wrist exoskeleton assisting wrist flexion/extension and radial/ulnar deviation. We present a prototype implementation of this active training method and provide a preliminary evaluation. The feasibility of the attention-based control is proven with the overall actuation success rate of 95% and the subjective score of 7.5 out of 10 given by the participants to assess whether the attention-based control for the wrist exoskeleton feels natural. Although the general threshold performed slightly better in the system evaluation experiment regarding the success rates, the time used before the robot actuation and the subjective scores showed no significant difference on the performance using a general threshold and using customized threshold.

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Hand exoskeleton pose monitoring is of great importance in the rehabilitation training of stroke patients to ensure precise robotic trajectory control and provide a patient recovery assessment mechanism. In this paper, a low-cost pose sensor unit based on the principle of photoelectric reflection is proposed to measure the pose of a multi-segment continuous structure in a hand rehabilitation exoskeleton. The sensor unit consists of five photosensitive elements that measure the rotation angle of an arrangement of adjacent segments, each integrated with a sensing element, to estimate the actuator's motion. An accurate device with a user-friendly interface is then designed for calibration of the sensing elements. The experimental results indicate that the sensitivity exceeds 0.047 V/° for the sensing elements, and hysteresis and repeatability errors are less than 1.1% and 1.8%, respectively. A comparison between the proposed sensor output and the results benchmarked by a VICON motion capture system demonstrates that the sensor can measure the bending angle of the multi-segment structure with a mean error of 3.23 degrees.

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