An upper limb rehabilitation system based on biological signals

Abstract

The invention discloses an upper limb rehabilitation system based on biological signals. An EEG cap is attached to the surface of the brain, and the EEG signal is sensed through the EEG cap. After the EEG signal is amplified, filtered, and denoised, the CSP feature The extraction algorithm and the classifier with adaptive LDA classification algorithm perform feature extraction and classification, and convert them into driving instructions for controlling the driving motor equipment and pneumatic muscle auxiliary equipment, and the central processing unit of the system combines the driving instructions and the motion state collected by the motion state collector After the data is fused and sorted, the number is fed back to the drive motor equipment, pneumatic tendon auxiliary equipment and mobile terminal, so as to drive the mechanical arm to perform rehabilitation exercises; the staff can remotely monitor the patient's exercise and rehabilitation status through the mobile terminal, and the patient can also input it through voice , The output device invokes the rehabilitation game in the display platform to perform auxiliary rehabilitation training. This improves the effectiveness of patient training while ensuring safety and stability during training.

Description

technical field [0001] The invention belongs to the technical field of computer intelligent control, and more specifically relates to an upper limb rehabilitation system based on biological signals. Background technique [0002] In modern society, upper limb movement disorders caused by stroke, hemiplegia and other brain diseases have brought many difficulties to the lives of many middle-aged and elderly people. They need scientific rehabilitation training to help them restore limb motor function. In this context, the upper limb rehabilitation system based on biosignals has attracted people's attention as an effective device for upper limb rehabilitation training. [0003] There are about 100 billion nerve cells in the human brain, of which there are 14 billion cells in the cerebral cortex, and each nerve cell has 10,000 neural connections at the same time, forming an extremely complex and huge network of nerve cells. Nerve cells are mainly composed of three parts: dendrite...

AI Technical Summary

Problems solved by technology

[0007] 2), the EEG signal is non-linear

[0008] Since the human brain is a nonlinear system with complex structure and functions, the EEG signals generated by it also have nonlinear characteristics, which is very inconvenient for us to analyze and process. The traditional signal processing methods based on linear systems are basically no longer applicable. for processing EEG signals

[0009] 3) The magnitude of the EEG signal is very weak

[0016] However, the traditional rehabilitation system often cannot complete the rehabilitation training independently by the patient, and in the process of rehabilitation exercise, the exercise status cannot be visually displayed and remotely monitored, and the feedback of the patient's movement cannot be fed back to the rehabilitation system in real time to realize the fine-tuning of the rehabilitation system Secondly, the operation process of most of the existing rehabilitation exercise equipment is often boring, which is easy to make the patient feel restless, and is susceptible to power frequency interference from other electronic components on the rehabilitation system, making the collection of EEG signals inaccurate and difficult to perform. effective rehabilitation

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