A Cable-Driven Three-DOF Wrist Rehabilitation Exoskeleton With Improved Performance

Muscle-inspired bi-planar cable routing: a novel framework for designing cable driven lower limb rehabilitation exoskeletons (C-LREX)

There is a growing interest in the research and development of Cable Driven Rehabilitation Devices (CDRDs) due to multiple inherent features attractive to clinical applications, including low inertia, lightweight, high payload-to-weight ratio, large workspace, and modular design. However, previous CDRDs have mainly focused on modifying motor impairment in the sagittal plane, despite the fact that neurological disorders, such as stroke, often involve postural control and gait impairment in multiple planes. To address this gap, this work introduces a novel framework for designing a cable-driven lower limb rehabilitation exoskeleton which can assist with bi-planar impaired posture and gait. The framework used a lower limb model to analyze different cable routings inspired by human muscle architecture and attachment schemes to identify optimal routing and associated parameters. The selected cable routings were safeguarded for non-interference with the human body while generating bi-directional joint moments. The subsequent optimal cable routing model was then implemented in simulations of tracking reference healthy trajectory with bi-planar impaired gait (both in the sagittal and frontal planes). The results showed that controlling joints independently via cables yielded better performance compared to dependent control. Routing long cables through intermediate hinges reduced the peak tensions in the cables, however, at a cost of induced additional joint forces. Overall, this study provides a systematic and quantitative in silico approach, featured with accessible graphical user interface (GUI), for designing subject-specific, safe, and effective lower limb cable-driven exoskeletons for rehabilitation with options for multi-planar personalized impairment-specific features.

A Novel Soft Glove Utilizing Honeycomb Pneumatic Actuators (HPAs) for Assisting Activities of Daily Living

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 (DLFPAs), 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.

QPSO-MPC based tracking algorithm for cable-driven continuum robots

Cable-driven continuum robots (CDCRs) can flexibly travel through narrow space for complex workspace tasks. However, it is challenging to design the trajectory tracking algorithm for CDCRs due to their nonlinear dynamic behaviors and cable hysteresis characteristics. In this contribution, a model predictive control (MPC) tracking algorithm based on quantum particle swarm optimization (QPSO) is designed for CDCRs to realize effective trajectory tracking under constraints. In order to make kinematic analysis of a CDCR, the forward and inverse mapping among actuation space, joint space and work space is analyzed by using the piecewise constant curvature method and the homogeneous coordinate transformation. To improve the performance of conventional MPC for complex tracking tasks, QPSO is adopted in the rolling optimization of MPC for its global optimization performance, robustness and fast convergence. Both simulation and operational experiment results demonstrate that the designed QPSO-MPC presents high control stability and trajectory tracking precision. Compared with MPC and particle swarm optimization (PSO) based MPC, the tracking error of QPSO-MPC is reduced by at least 43 and 24%, respectively.

Motion intensity modeling and trajectory control of upper limb rehabilitation exoskeleton robot based on multi-modal information

The motion intensity of patient is significant for the trajectory control of exoskeleton robot during rehabilitation, as it may have important influence on training effect and human–robot interaction. To design rehabilitation training task according to situation of patients, a novel control method of rehabilitation exoskeleton robot is designed based on motion intensity perception model. The motion signal of robot and the heart rate signal of patient are collected and fused into multi-modal information as the input layer vector of deep learning framework, which is used for the human–robot interaction model of control system. A 6-degree of freedom (DOF) upper limb rehabilitation exoskeleton robot is designed previously to implement the test. The parameters of the model are iteratively optimized by grouping the experimental data, and identification effect of the model is analyzed and compared. The average recognition accuracy of the proposed model can reach up to 99.0% in the training data set and 95.7% in the test data set, respectively. The experimental results show that the proposed motion intensity perception model based on deep neural network (DNN) and the trajectory control method can improve the performance of human–robot interaction, and it is possible to further improve the effect of rehabilitation training.