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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A soft robot hand with fingertip haptic feedback for teleoperation is proposed to perform complex tasks and ensure safe and friendly human-machine interaction. This robot hand can perform finger flexion/extension and abduction/adduction motions. A data glove is used to collect the hand joint angle data of the operator. Flexion sensors are embedded in the soft robot hand to monitor the bending angles of the actuators. Pressure sensors on the fingertips of the robot hand collect contact force data, and haptic feedback actuators located on the fingertips of the operator display the contact force to the operator. Characterization tests and teleoperation performance tests involving humanparticipants are performed to prove the feasibility of the soft robot hand. The soft robot hand prototype satisfies the output force requirements and can meet 96.86% of the design requirements of the joint angles. The soft robot hand can be teleoperated to perform nine commonly used motions in daily operational tasks.The success rates of fingertip force discrimination, grasp, and pinch ability experiment are 100%, 95.00%, and 98.33%, respectively. The results of the experiment suggest that the soft robot hand with fingertip haptic feedback can perform complex tasks in teleoperation.

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