Neural network control system of exoskeleton robot based on intention recognition

Abstract

The invention provides a neural network control system of an exoskeleton robot based on intention recognition. The system comprises: an intention classification module used for classifying intention of a wearer; an action and track planning module used for setting rehabilitation training actions of a patient, collecting lower limb electromyographic signals of a human body, collecting gait data when the human body walks, and planning a motion track in a joint space of the lower limb exoskeleton robot; and an exoskeleton state judgment module used for collecting state information of the exoskeleton and processing and analyzing the information to obtain current state information of the exoskeleton. According to the invention, the problem of system unknown items in calculation moment control based on model control is solved, and the real-time performance and robustness of control during conversion of different states are achieved. A wearer does not need to learn a complex exoskeleton operation method, the rehabilitation process is more suitable for the patient, man-machine interaction is enhanced, and the rehabilitation effect can be effectively improved.

Description

technical field [0001] The invention relates to the field of intention recognition and control of a lower limb exoskeleton robot for rehabilitation, in particular, to a neural network control system of an exoskeleton robot based on intention recognition. In particular, it relates to the neural network control system of the rehabilitation lower limb exoskeleton robot based on the wearer's intention recognition. Further, it relates to a wearer's intention recognition method based on a hidden Markov model and a Bayesian network, and a lower limb exoskeleton control method based on a radial basis neural network. Background technique [0002] At present, the number of various types of disabled people in China has exceeded 80 million, and the group whose living ability has decreased due to old age accounts for 14.3% of the national population. Among them, there are more than 3 million patients with motor dysfunction caused by Parkinson's, stroke, and craniocerebral injury every y...

AI Technical Summary

Problems solved by technology

However, it doesn’t mention how the brain’s excitement points specifically reflect people’s movement thoughts. At present, the method of collecting and identifying the wearer’s intentions through EEG signals is immature and complicated to operate.

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