Patient-Centered Robot-Aided Passive Neurorehabilitation Exercise Based on Safety-Motion Decision-Making Mechanism

Abstract

Safety is one of the crucial issues for robot-aided neurorehabilitation exercise. When it comes to the passive rehabilitation training for stroke patients, the existing control strategies are usually just based on position control to carry out the training, and the patient is out of the controller. However, to some extent, the patient should be taken as a “cooperator” of the training activity, and the movement speed and range of the training movement should be dynamically regulated according to the internal or external state of the subject, just as what the therapist does in clinical therapy. This research presents a novel motion control strategy for patient-centered robot-aided passive neurorehabilitation exercise from the point of the safety. The safety-motion decision-making mechanism is developed to online observe and assess the physical state of training impaired-limb and motion performances and regulate the training parameters (motion speed and training rage), ensuring the safety of the supplied rehabilitation exercise. Meanwhile, position-based impedance control is employed to realize the trajectory tracking motion with interactive compliance. Functional experiments and clinical experiments are investigated with a healthy adult and four recruited stroke patients, respectively. The two types of experimental results demonstrate that the suggested control strategy not only serves with safety-motion training but also presents rehabilitation efficacy.

A Novel Robot-Aided Upper Limb Rehabilitation Training System Based on Multimodal Feedback

“…In our previous research, a robot-aided upper limb rehabilitation system was constructed, which primarily included the whole arm manipulator (WAM), arm support device, self-developed three-dimensional (3D) force sensor, and controlling personal computer (PC) (Pan et al, 2017(Pan et al, , 2019. The WAM works in a large workspace with four rotational degrees of freedom, and the self-developed 3D force sensor is installed at the endpoint of the WAM to measure the interactive force for use in some of the designed control algorithms, as shown in Figure 1.…”

“…During operation, four driver motor angles can be measured to detect the position of every joint in real time, and the control torque can be set to provide joint control. For detailed information about the hardware and software characteristics of the constructed motion rehabilitation training system, please refer to Pan et al (2017Pan et al ( , 2019. The framework of the rehabilitation control system in this study, which incorporates multimodal feedback, is presented in Figure 2.…”

The Effect of Repetitive Arm Cycling Training Priming with Transcranial Direct Current Stimulation on Post-Stroke: Pilot Study

“…In some programs, the transformation between the functional outcomes after the training cannot be noticed [1][2][3][4]. Thus, to a significant degree, the success of neurorehabilitation depends on the amount and effectiveness of rehabilitative training [5][6][7]. Effective rehabilitation therapies are needed for stroke patients showing long-term deficits in upper-arm function by increasing force and reduce spasticity in muscle.…”

Trajectory planning of a novel lower limb rehabilitation robot for stroke patient passive training

“…20,21 Therefore, the generation of the desired trajectory for the robot passive training has been the research hotspot. [22][23][24] At present, most of researchers just discuss the normal gait of healthy subjects, and the relevant normal gait data are used as the reference database for trajectory planning. 25,26 Rosati et al 27 address a trajectory planning of a rehabilitation mechanism, which is defined in both the Cartesian space and joint space.…”