Rehabilitation robot control method based on joint stiffness and muscle fatigue

Abstract

The invention discloses a rehabilitation robot control method based on joint stiffness and muscle fatigue. According to the rehabilitation robot control method, the joint angle signals and the surfaceelectromyogram signals of a subject are collected, individual physiological parameters of the muscles are identified through a genetic algorithm in combination with a positive and negative dynamics principle, a personalized joint skeletal muscle model is established, and joint stiffness information in the moving process is calculated; the median frequency of the surface electromyogram signal in the moving process is calculated, and the fatigue information of the subject is acquired by utilizing the relative variation value; and self-adaptive adjustment is carried out on the parameters of an impedance model by adopting the joint stiffness information and the motion fatigue information, meanwhile, the rigidity and the damping parameters are restrained through a saturation function, and theself-adaptive impedance control method of the rehabilitation robot is realized. According to the rehabilitation robot control method, the rigidity information related to joints and the fatigue information related to the muscles are considered at the same time, the rigidity information and the fatigue information are introduced into the rehabilitation robot for control, the safety of joints and muscles in the rehabilitation training process can be considered, and safe and effective rehabilitation training is realized.

Description

technical field [0001] The invention belongs to the field of rehabilitation robot control based on surface electromyographic signals, in particular to a rehabilitation robot control method based on joint stiffness and muscle fatigue. Background technique [0002] The realization of active rehabilitation control of rehabilitation robots needs to consider the human-computer interaction between the robot and the patient. By analyzing the biosensor information generated by the patient during the rehabilitation training process, the patient's movement intention can be obtained, and the corresponding operating state of the robot can be controlled accordingly, which can provide active and safe rehabilitation training for the patient. Interaction force signals and physiological signals are typical human-computer interaction information. The acquisition of interactive force signals usually depends on the mechanical structure of the rehabilitation robot, which is not convenient and f...

AI Technical Summary

Problems solved by technology

The parameter adjustment in this control method only considers the adaptive problem of the rehabilitation robot, and does not consider the impact on safety, which makes this method a potential safety hazard; moreover, it needs to acquire EEG signals and surface EMG signals for real-time processing. The implementation of this process more difficult

[0005] From the above analysis, it can be seen that the current design of active rehabilitation controllers is mostly based on muscle information, ignoring the influence of joints in the rehabilitation process, and not taking into account the safety of both

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