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The coordinated rehabilitation of the upper limb is important for the recovery of the daily living abilities of stroke patients. However, the guidance of the joint coordination model is generally lacking in the current robot-assisted rehabilitation. Modular robots with soft joints can assist patients to perform coordinated training with safety and compliance. In this study, a novel coordinated path planning and impedance control method is proposed for the modular exoskeleton elbow–wrist rehabilitation robot driven by pneumatic artificial muscles (PAMs). A convolutional neural network-long short-term memory (CNN-LSTM) model is established to describe the coordination relationship of the upper limb joints, so as to generate adaptive trajectories conformed to the coordination laws. Guided by the planned trajectory, an impedance adjustment strategy is proposed to realize active training within a virtual coordinated tunnel to achieve the robot-assisted upper limb coordinated training. The experimental results showed that the CNN-LSTM hybrid neural network can effectively quantify the coordinated relationship between the upper limb joints, and the impedance control method ensures that the robotic assistance path is always in the virtual coordination tunnel, which can improve the movement coordination of the patient and enhance the rehabilitation effectiveness. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of high-level control techniques for upper-limb robotic training. It aims to compare and discuss the potentials of these different control algorithms, and specify future research direction. Included studies mainly come from selected papers in four review articles. To make selected studies complete and comprehensive, especially some recently-developed upper-limb robotic devices, a search was further conducted in IEEE Xplore, Google Scholar, Scopus and Web of Science using keywords ('upper limb*' or 'upper body*') and ('rehabilitation*' or 'treatment*') and ('robot*' or 'device*' or 'exoskeleton*'). The search is limited to English-language articles published between January 2013 and December 2017. Valuable references in related publications were also screened. Comparative analysis shows that high-level interaction control strategies can be implemented in a range of methods, mainly including impedance/admittance based strategies, adaptive control techniques, and physiological signal control. Even though the potentials of existing interactive control strategies have been demonstrated, it is hard to identify the one leading to maximum encouragement from human users. However, it is reasonable to suggest that future studies should combine different control strategies to be application specific, and deliver appropriate robotic assistance based on physical disability levels of human users. [ABSTRACT FROM AUTHOR]

This paper proposes a new adaptive patient-cooperative control strategy for improving the effectiveness and safety of robot-assisted ankle rehabilitation. This control strategy has been developed and implemented on a compliant ankle rehabilitation robot (CARR). The CARR is actuated by four Festo Fluidic muscles located to the calf in parallel, has three rotational degrees of freedom. The control scheme consists of a position controller implemented in joint space and a high-level admittance controller in task space. The admittance controller adaptively modifies the predefined trajectory based on real-time ankle measurement, which enhances the training safety of the robot. Experiments were carried out using different modes to validate the proposed control strategy on the CARR. Three training modes include: 1) a passive mode using a joint-space position controller, 2) a patient–robot cooperative mode using a fixed-parameter admittance controller, and 3) a cooperative mode using a variable-parameter admittance controller. Results demonstrate satisfactory trajectory tracking accuracy, even when externally disturbed, with a maximum normalized root mean square deviation less than 5.4%. These experimental findings suggest the potential of this new patient-cooperative control strategy as a safe and engaging control solution for rehabilitation robots. [ABSTRACT FROM PUBLISHER]

Ankle rehabilitation robots can play an important role in improving outcomes of the rehabilitation treatment by assisting therapists and patients in number of ways. Consequently, few robot designs have been proposed by researchers which fall under either of the two categories, namely, wearable robots or platform-based robots. This paper presents a review of both kinds of ankle robots along with a brief analysis of their design, actuation and control approaches. While reviewing these designs it was observed that most of them are undesirably inspired by industrial robot designs. Taking note of the design concerns of current ankle robots, few improvements in the ankle robot designs have also been suggested. Conventional position control or force control approaches, being used in the existing ankle robots, have been reviewed. Apparently, opportunities of improvement also exist in the actuation as well as control of ankle robots. Subsequently, a discussion on most recent research in the development of novel actuators and advanced controllers based on appropriate physical and cognitive human-robot interaction has also been included in this review. [ABSTRACT FROM AUTHOR]

Abstract: Robot assisted gait training may help in producing rapid improvements in functional gait parameters. This paper presents new experimental results with an intrinsically compliant robotic gait training orthosis. The newly developed robotic orthosis has 6 degrees of freedom (DOFs). A trajectory tracking controller based on the boundary layer augmented sliding control (BASMC) law was implemented to guide the subject’s limbs on physiological gait trajectories. The compliance of the robotic orthosis sagittal plane hip and knee joints was also controlled, independently of the trajectory tracking control. The robotic orthosis and the control scheme were evaluated on three neurologically intact subjects walking on a treadmill. A maximum trajectory tracking error of 10° was recorded at the hip and knee sagittal plane joints. The results showed that subjects can walk in the robotic orthosis with comfort and the BASMC law was able to guide the subject’s limbs on reference physiological trajectories. [Copyright &y& Elsevier]

The novelty of this paper is the adjustable workspace and torque capacity of a compliant ankle rehabilitation robot (CARR). The robot has three rotational degrees of freedom (DOFs) redundantly actuated by four compliant actuators. It suffers from conflicting workspace and actuation torque due to the use of a parallel mechanism and compliant actuators. To address this issue, also considering physical constraints imposed by human users, the CARR is designed with reconfigurability to make a trade-off between workspace and torque capacity for meeting different training requirements. Theoretical analysis indicates that varying kinematic and dynamic performance of the robot can be achieved by reconfiguring the layout of the actuators. Experiments with/without load also demonstrate the validity of the reconfigurable robotic design for practical applications. [ABSTRACT FROM AUTHOR]