Fabric-based pneumatic actuators (FPAs) are extensively employed in the design of lightweight and compliant soft wearable assistive gloves. However, conventional FPAs typically exhibit limited output force, thereby restricting the applications of such gloves. This paper presents the development of a novel honeycomb pneumatic actuator (HPA) constructed using flexible thermoplastic polyurethane (TPU) coating through hot pressing or ultrasonic welding techniques. Compared to the previously utilized double-layer fabric-based pneumatic actuators (DLF-PAs), the HPAs yields a remarkable 862% increase in end output force. It can produce a tip force of 13.57 N at a pressure of 150 kPa. The integration of HPAs onto a soft pneumatic glove enables the facilitation of various activities of daily living. A series of trials involving nine patients were conducted to assess the effectiveness of the soft glove. The experimental results indicate that when assisted by the glove, the patients’ finger metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints achieved angles of 87.67 ± 19.27° and 64.2 ± 30.66°, respectively. Additionally, the average fingertip force reached 10.16 ± 4.24 N, the average grip force reached 26.04 ± 15.08 N, and the completion rate of daily functions for the patients increased from 39% to 76%. These outcomes demonstrate that the soft glove effectively aids in finger movements and significantly enhances the patients’ daily functioning.

Rehabilitation robots can aid patients in performing hand exercises in their own home. However, existing rehabilitation equipment is bulky and difficult to wear and carry, and therapists are unable to remotely assess a patient's finger muscle spasticity. This article describes a lightweight exoskeleton robot that facilitates hand rehabilitation exercises and enables muscle spasticity assessment at home. A hand exoskeleton with one degree of freedom assists patients in flexion and extension movements of their fingers. Its motor has a reduction ratio of 19:1, allowing passive back-driving. The exoskeleton's link lengths are determined by an optimization algorithm. The proposed device has a total weight of 0.356 kg and the torque of the dynamic structure to the metacarpophalangeal joint is 1.832 N $\cdot$ m, reflecting its lightweight and portable nature. To aid remote assessments of patients' muscle spasticity, the exoskeleton is controlled using a finger tension feedback algorithm. This enables the patient's rehabilitation process to be managed remotely. Experiments involving ten patients and three therapists are conducted to evaluate the robot's feasibility. The results demonstrate that the robot can flex and extend the fingers with a mean angle error of 1.16 $^\circ$ and a mean contact force error of 0.25 N. Moreover, the robot achieves 75% accuracy in assisting therapists with remote assessment of the patient's finger muscle spasticity.

The tendon–sheath system (TSS) has emerged as a promising solution for force transmission in robot applications, owing to the lightweight, low inertia, and good flexibility. However, the performance of tension control is affected by the configuration-dependent nonlinearities, and achieving precise control of the distal force of TSS in time-varying configurations remains a challenge. To address this issue, this article proposes a comprehensive method combining feedforward and feedback control to enhance the control accuracy of TSS by estimating the bending angle in real time, even when the system configuration is unknown. A bending angle estimation approach based on a friction model is proposed, and an improved friction model is developed to simulate the nonlinear friction more accurately. Furthermore, a double-TSS transmission structure is designed to facilitate the integration of control and configuration feedback in mechanical systems. The experimental results confirm the effectiveness of the proposed method in enhancing force-tracking performance in unknown configurations.

Magnetorheological actuator (MRA) exhibits complex nonlinear behaviors, including rate-dependence and creep, which pose challenges for accurate modeling. Existing differential-equation-based hysteresis models fail to accurately capture the complex characteristics of the MRA due to their single-dimensional approach. Therefore, this article proposes a novel multidimensional differential-equation-based hysteresis model to address this limitation. By adopting the multidimensional model structure, the proposed model significantly enhances performance. To effectively capture both rate-dependence and creep phenomena, a rate-dependent operator is introduced, and fractional calculus is employed to describe the creep compactly. Unlike existing models, the rate-dependence in the proposed operator originates from the differential equation’s structure rather than from adding dynamic factors or cascading functions. Furthermore, considering the numerical solution convergence of the differential-equation-based models, the parameter constraints are analyzed and incorporated into the parameter identification algorithm, which is neglected in the existing studies. The model’s effectiveness is validated through the theoretical analyses, the simulations, and the experimental verifications, demonstrating its capability to accurately capture both rate-dependence and creep phenomena in the MRA.

The application of robotics in the field of home rehabilitation training has revolutionized the way patients receive treatment. However, current rehabilitation robotic systems often lack diverse training methods and sufficient feedback channels, resulting in challenges for patients to sustain long-term engagement in rehabilitation training. This paper introduces a novel hand rehabilitation robot system that leverages virtual reality technology, multi-channel feedback technology, and mirror therapy to facilitate autonomous hand rehabilitation treatment for patients in a home setting. The system comprises a soft glove, a computer equipped with virtual interaction scenes, Leapmotion sensors, and a soft glove control box. To evaluate the usability and patient acceptance of the system, a clinical trial involving five patients was conducted. The trial results demonstrated noteworthy improvements in finger grip strength, with an increase from 8.74 ± 14.2 N to 17.82 ± 13.63 N when utilizing the soft glove. Furthermore, patients' upper extremity function assessment scale (ARAT) scores exhibited improvement from 12.4 ± 23.44 to 24.44 ± 25.89, and their functional ability for daily living (ADL) showed improvement from 43.8 ± 47.22 to 50.6 ± 43.24. These measurements indicated significant enhancements compared to the baseline, signifying that the proposed system did not compromise finger functionality. Additionally, the results of a user acceptance questionnaire, consisting of seven surveys administered to the patients, demonstrated a positive score of 4.18 ± 0.61 and a negative score of 0.95 ± 0.57. These outcomes reflect a high level of acceptance among patients, affirming the system's safety and effectiveness in providing a comfortable and reliable platform for rehabilitation training.