Exoskeleton rehabilitation equipment control method and system based on electroencephalogram signals

Through the control method based on EEG signal, patients' EEG signals are collected and analyzed in real time, and the movement parameters of exoskeleton rehabilitation equipment are dynamically adjusted, which solves the problem of lack of real-time feedback between the equipment and the patient's neurological state in the prior art, and achieves more efficient and personalized rehabilitation training.

CN119700494AActive Publication Date: 2025-03-28BEIJING TIANTAN HOSPITAL AFFILIATED TO CAPITAL MEDICAL UNIV
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Patent Information

Application Number
CN202510215475.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-26
Publication Date
2025-03-28
Estimated Expiration
2045-02-26

AI Technical Summary

Technical Problem

The lack of close real-time feedback and linkage between existing exoskeleton rehabilitation equipment and the patient's neurological status has limited the personalization, accuracy and real-time adaptability of rehabilitation.

Method used

Using an EEG signal-based control method, the patient's EEG signal acquisition device is used to collect the EEG signal in real time, and the short-time Fourier transform and intention classification model is used to extract signal characteristics and analyze the motion intention, and dynamically adjust the movement parameters of the leg stent.

Benefits of technology

It realizes close tracking and real-time analysis of changes in patients' neural activity, dynamically optimizes the exercise parameters of rehabilitation equipment, improves the accuracy and effectiveness of rehabilitation training, and significantly enhances the rehabilitation effect.

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Abstract

The invention discloses an exoskeleton rehabilitation equipment control method and system based on electroencephalogram signals, and relates to the technical field of artificial intelligence, and the method comprises the steps: inputting a plurality of signal features into an intention classification model for intention classification, receiving the plurality of signal features through an input layer, extracting the plurality of signal features through a feature extraction layer, a motion intention index calculation layer performs calculation on the basis of the characteristics required by calculation to obtain a motion intention index, and a decision layer determines the intention type of the patient on the basis of the motion intention index; generating a motion parameter adjustment instruction based on the patient intention type; and adjusting the motion speed and the motion amplitude of the leg support based on the motion parameter adjustment instruction. According to the technical scheme, the problems that in the prior art, close real-time feedback and linkage between exoskeleton rehabilitation equipment and the nerve state of a patient are lacked, and rehabilitation individuation, accuracy and real-time adaptability are all limited can be solved.
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