Robot-assisted rehabilitation therapy has been proven to effectively improve upper limb motor function and daily behavior of patients with motor dysfunction, and the demand has increased at every stage of the rehabilitation recovery. According to the motor relearning program theory, upper limb motor dysfunction can be restored by a certain amount of repetitive training. Robotics devices can be an approach to accelerate the rehabilitation process by maximizing the patients’ training intensity. This paper develops a new end-effector upper limb rehabilitation robot (EULRR) first and then presents a controller that is suitable for the assist-as-needed (AAN) training of the patients when performing the rehabilitation training. The AAN controller is a strategy that helps the patient’s arm to stay close to the given trajectory while allowing for spatial freedom. This controller enables the patient’s arm to have spatial freedom by constructing a virtual channel around the predetermined training trajectory. Patients could move their arm freely in the allowed virtual channel during rehabilitation training while the robot provides assistance when deviating from the virtual channel. The AAN controller is preliminarily tested with a healthy male subject in different conditions based on the EULRR. The experimental results demonstrate that the proposed AAN controller could provide assistance when moving out of the virtual channel and provide no assistance when moving along the trajectory within the virtual channel. In the close future, the controller is planned to be used in elderly volunteers and help to increase the intensity of the rehabilitation therapy by assisting the arm movement and by provoking active participation.

Clinical studies have demonstrated that robot-involved therapy can effectively improve the rehabilitation training effect of motor ability and daily behavior ability of subjects with an upper limb motor dysfunction. This paper presents an impedance-based assist-as-needed controller that can be used in robot-aided rehabilitation training for subjects with an upper extremity dysfunction. Then, the controller is implemented on an end-effector upper extremity rehabilitation robot which could assist subjects in performing training with a spatial trajectory. The proposed controller enables subjects’ arms to have motion freedom by building a fault-tolerant region around the rehabilitation trajectory. Subjects could move their upper limb without any assistance within the fault-tolerant region while the robot would provide assistance according to the subjects’ functional ability when deviating from the fault-tolerant region. Besides, we also put forward the stiffness field around the fault-tolerant region to increase the robot’s assistance when subjects’ hand is moving outside the fault-tolerant region. A series of columnar rigid walls would be constructed in the controller according to the subjects’ functional ability, and the stiffness of the wall increases as the motion performance deteriorates. Furthermore, the controller contains five adjustable parameters. The controller would show different performances by adjusting these parameters and satisfy the requirement of robot-aided rehabilitation training at different rehabilitation stages such as passive, assistant, active, and resistant training. Finally, the controller was tested with an elderly female participant with different controller parameters, and experimental results verified the correctness of the controller and its potential ability to satisfy the training requirements at different rehabilitation stages. In the close future, the proposed controller in this work is planned to be applied on more subjects and also patients who have upper limb motor dysfunctions to demonstrate performance of the controller with different parameters.

In response to the ever-increasing demand of community-based rehabilitation, a novel smart rehab walker iReGo is designed to facilitate the lower limb rehabilitation training based on motion intention recognition. The proposed walker provides a number of passive degrees of freedom (DoF) to the pelvis that are used to smooth the hip rotations in such a way that the natural gait is not significantly affected, meanwhile, three actuated DoF that are actively controlled to assist patients with mobility disabilities. The walker first identifies the user's motion intention from the interaction forces in both left and right sides of the pelvis and then uses the kinematic model to generate appropriate driving velocities to support the body weight and improve mobility. In this paper, workspace, dexterity and the force field of the walker are analyzed based on the system Jacobian. Simulation and experiments with healthy subjects are carried out to verify the effectiveness and tip-over stability. These results demonstrate that the walker has sufficient workspace for pelvic motions, satisfactory dexterity and near-linear force feedback within the prescribed workspace, and that the walker is easily controlled to ensure normal gait.

Assessment is critical during the procedure of stroke rehabilitation. However, traditional assessment methods are time-consuming, laborious, and dependent on the skillfulness of the therapist. Moreover, they cannot distinguish whether the improvement comes from the abnormal compensation or the improvement of upper extremity motor function. To make up for the shortcomings of the traditional methods, this study proposes a novel assessment system, which consisted of a rehabilitation robot and motion capture (MoCAP) system. A 9-degree-of-freedom (DOF) kinematic model is established, which consists of the shoulder girdle, shoulder, elbow, and wrist joints. And seven assessment indices are selected for this assessment system, including a range of motion (ROM), shoulder girdle compensation (SGC), trunk compensation (TC), aiming angle (AA), motion error (ME), motion length ratio (MLR), and useful force (UF). For AA, ME, and MLR, all describe the motor ability of the upper extremity, and a linear model was proposed to map these three indices into one index, called motor control ability (MCA). Then, this system can quantitatively evaluate human upper extremity motor function from joint space kinematics, Cartesian space kinematics, and dynamics. Three healthy participants were invited to verify the effectiveness of this system. The preliminary results show that all participants’ handedness performs a little better than the nonhandedness. And the performance of the participants and the change of all the upper limb joints can be directly watched from the trajectory of the hand and joint angles’ curve. Therefore, this assessment system can evaluate the human upper limb motor function well. Future studies are planned to recruit elderly volunteers or stroke patients to further verify the effectiveness of this system.