Exoskeletons have great potential to enhance the daily activities of individuals needing robotic assistance. This article presents a design for an underactuated hip exoskeleton providing both active and passive walking assistance. The system integrates a driving reversal device (DRD) and a hip energy storage walking device (HESWD) to improve performance. The DRD allows a single motor to drive both hips during the movement phase, while the HESWD stores negative work generated by the human hip joint and uses it as positive work during the “blank” phase of underactuated walking. Specialized motor trajectory planning and optimization are used to meet the active assistance phase requirements. A radial basis function (RBF) neural network-based trajectory tracking control method adapts to changes in human gait. Treadmill experiments demonstrate that the tracking error is below 0.4146∘. The results show the effectiveness of the proposed control method and the exoskeleton’s ability to adapt to slight gait variations.

With the development of society, aging population and the number of stroke patients is increasing year by year. Rehabilitation exoskeleton can help patients to carry out rehabilitation training and improve their activities of daily living (ADL). First of all, a reconfigurable exoskeleton for upper limb rehabilitation is designed in this paper. The exoskeleton combines gravity compensation with left-right arm switching function through its reconfigurability. Secondly, the motion space and singular configuration of the exoskeleton are analyzed. By changing the working mode of the gravity compensation device, the control experiment of the motor is carried out. The influence of gravity compensation device on motor driving torque and energy consumption is analyzed. Finally, the results of experiment show that, in the best case, the gravity compensation device can reduce the energy consumption by 41.15% and the maximum motor current by 33.56% of the driving element.

Robotic exoskeleton assistance is an effective method for the treatment of patients with movement disorders. First, this article presents a variable stiffness knee exoskeleton robot, which can independently control stiffness and position and has a large stiffness range under low preload. Then, combined with the designed variable stiffness exoskeleton, a physical human–robot interaction (pHRI) control scheme based on joint torque estimation is proposed. Different from previous studies, this method uses the mechanical properties of the exoskeleton to achieve pHRI without complex control algorithms and force/torque sensors. Furthermore, the joint torque is estimated based on the surface electromyography signal and the Hill-based muscle model, and the exoskeleton can realize assist-as-needed function: when the subject trains with less effort, the exoskeleton maintains a high output physical impedance to provide high tracking accuracy; and when the subject trains with greater effort, the exoskeleton maintains a low output impedance to provide high physical compliance. Finally, we conducted experiments on three healthy subjects and two subjects with lower limb motor dysfunction to verify the effectiveness of the torque estimation method and the pHRI control scheme.

The applications of robotics to the rehabilitation training of neuromuscular impairments have received increasing attention due to their promising prospects. The effectiveness of robot-assisted training directly depends on the control strategy applied in the therapy program. This paper presents an upper extremity exoskeleton for the functional recovery training of disabled patients. A minimal-intervention-based admittance control strategy is developed to induce the active participation of patients and maximize the use of recovered motor functions during training. The proposed control strategy can transit among three control modes, including human-conduct mode, robot-assist mode, and motion-restricted mode, based on the real-time position tracking errors of the end-effector. The human-robot interaction in different working areas can be modulated according to the motion intention of patient. Graphical guidance developed in Unity-3-D environment is introduced to provide visual training instructions. Furthermore, to improve training performance, the controller parameters should be adjusted in accordance with the hemiplegia degree of patients. For the patients with severe paralysis, robotic assistance should be increased to guarantee the accomplishment of training. For the patients recovering parts of motor functions, robotic assistance should be reduced to enhance the training intensity of effected limb and improve therapeutic effectiveness. The feasibility and effectiveness of the proposed control scheme are validated via training experiments with two healthy subjects and six stroke patients with different degrees of hemiplegia.