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ObjectiveTo develop a bilateral rehabilitation robot motion assistance strategy based on admittance control, so that rehabilitation physicians can assist patients in rehabilitation training through remote teaching. MethodsA bilateral remote rehabilitation platform with upper limb terminal traction was constructed. Based on the velocity admittance control, the interactive movement between the master robot and the rehabilitation physician was realized, and the position information transmission of the master-slave robot was realized through the communication framework built. The slave robot received the position coordinates of the main robot, and drove the patient to carry out rehabilitation exercises under the attitude admittance controller. ResultsThe robot could drive the patient to accurately track the trajectory of the doctor's teaching in real time, and improve the safety and compliance of the training and human-computer interaction. ConclusionBy introducing two admittance controllers, the trajectory of the physician's end can be accurately tracked when driving the patient's movement from the robotic arm, which effectively avoids the discomfort of the patient's arm in process of rehabilitation.
RESUMEN
Introduction: This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance control to assist people with reduced mobility and improve their locomotion. Clinical research remark that these devices working in constant interaction with the neuromuscular and skeletal human system improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For recognition of the lower-limb motion intention and discrimination of knee movements, sEMG from both lower-limb and trunk are used, which implies a new approach to control robotic assistive devices. Methods A control system that includes a stage for human-motion intention recognition (HMIR), based on techniques to classify motion classes related to knee joint were developed. For translation of the user's intention to a desired state for the robotic knee exoskeleton, the system also includes a finite state machine and admittance, velocity and trajectory controllers with a function that allows stopping the movement according to the users intention. Results The proposed HMIR showed an accuracy between 76% to 83% for lower-limb muscles, and 71% to 77% for trunk muscles to classify motor classes of lower-limb movements. Experimental results of the controller showed that the admittance controller proposed here offers knee support in 50% of the gait cycle and assists correctly the motion classes. Conclusion The robotic knee exoskeleton introduced here is an alternative method to empower knee movements using sEMG signals from lower-limb and trunk muscles.