CN215459883U

CN215459883U - Rehabilitation training system based on motor imagery

Info

Publication number: CN215459883U
Application number: CN202121619901.0U
Authority: CN
Inventors: 胡凌燕; 谢晶晶
Original assignee: Nanchang University
Current assignee: Nanchang University
Priority date: 2021-07-16
Filing date: 2021-07-16
Publication date: 2022-01-11
Anticipated expiration: 2031-07-16

Abstract

The utility model discloses a rehabilitation training system based on motor imagery, which comprises an electroencephalogram signal acquisition module, a PC (personal computer) end data receiving and processing module, a mechanical arm and PC communication module and a mechanical arm movement module, wherein the electroencephalogram signal acquisition module is used for acquiring electroencephalogram signals; the output end of the electroencephalogram signal acquisition module is connected with the input end of the PC-end data receiving and processing module, the output end of the PC-end data receiving and processing module is connected with the input end of the mechanical arm and the PC communication module, and the output end of the mechanical arm and the PC communication module is connected with the input end of the mechanical arm movement module. The limb rehabilitation training device can effectively provide limb rehabilitation training for patients with cerebral apoplexy disability, and solves the problem that the traditional rehabilitation training is passive training at present, and the patients are easy to fatigue, so that the training effect is poor. By utilizing the rehabilitation training system based on motor imagery, active rehabilitation training experience with better effect can be effectively brought to patients.

Description

Rehabilitation training system based on motor imagery

Technical Field

The utility model mainly relates to the technical field of brain-computer interfaces, in particular to a rehabilitation training system based on motor imagery.

Background

The traditional rehabilitation training method mostly adopts (1) physical factors to treat and utilize various physical energies, including electric energy, light energy, heat energy, mechanical energy and the like, to act on the body, so as to cause various reactions of the human body, thereby promoting, adjusting, maintaining or recovering various physiological functions, influencing pathological processes or restraining causes of diseases, and achieving the purpose of preventing and treating diseases. (2) Exercise therapy is centered on active and passive movement of joints and muscles, encouraging active participation by the patient. The emphasis is on progressive, easy to get. The treatment positions are from lying position, sitting position to standing position. Can help the patient to recover certain muscle strength, correct bad posture and prevent complications, but because of passive training, the subjective will of the patient is not strong, and the rehabilitation effect of the patient is unsatisfactory. Meanwhile, professional rehabilitation therapists still have huge gaps in China as a key part in the exercise therapy. Therefore, how to help the patient to actively control the rehabilitation device by utilizing self idea is very important to realize independent rehabilitation training.

However, the existing method is to install a voice control interface or a myoelectric control interface on the rehabilitation equipment. Considering that different patients have different self accents, the voice control is limited by the accent problem and is difficult to operate continuously for a long time; for the limb handicapped patient, the electromyographic signals cannot be effectively applied, and the two control interfaces are greatly limited in operation mode and difficult to effectively popularize.

SUMMERY OF THE UTILITY MODEL

The utility model aims to solve the problems and provides a rehabilitation training system based on motor imagery. The electroencephalogram signals are used for controlling external equipment of the rehabilitation mechanical arm, and the active rehabilitation training effect is achieved. The motor imagery based rehabilitation training can be used for almost all nerve injury patients, and the modern medical rehabilitation theory proves that the functional rehabilitation training can promote the nervous system repair of paralyzed patients, and the patients can obtain better rehabilitation effect compared with passive training when actively participating.

In order to achieve the purpose, the utility model adopts the following technical scheme:

the utility model discloses a rehabilitation training system based on motor imagery, which comprises an electroencephalogram signal acquisition module, a PC (personal computer) end data receiving and processing module, a mechanical arm and PC communication module and a mechanical arm movement module, wherein the electroencephalogram signal acquisition module is used for acquiring electroencephalogram signals; the output end of the electroencephalogram signal acquisition module is connected with the input end of the PC-end data receiving and processing module, the output end of the PC-end data receiving and processing module is connected with the input end of the mechanical arm and the PC communication module, and the output end of the mechanical arm and the PC communication module is connected with the input end of the mechanical arm movement module.

Furthermore, the electroencephalogram signal acquisition module is used for acquiring electroencephalograms of a user, the electroencephalograms acquired by the electroencephalogram signal acquisition module are transmitted into a PC (personal computer) by means of a bus after passing through an amplifier, the PC-side data receiving and processing module receives the electroencephalograms acquired by the electroencephalogram signal acquisition module by means of the bus, feature extraction and classification are carried out on the electroencephalograms, the motion direction imagined by the user is judged, the mechanical arm and PC communication module is used for transmitting the imagined motion direction obtained by decoding the electroencephalograms by the PC side to the mechanical arm motion module by means of a socket communication protocol, the mechanical arm motion module receives the electroencephalogram signal decoding result transmitted by the PC and controls the motion of the self motion direction of the mechanical arm according to the result, and the motion direction of the mechanical arm is consistent with the motion direction of the arm imagined by the user.

Furthermore, the electroencephalogram signal acquisition module acquires electroencephalogram signals through an electroencephalogram cap, 32 electrodes for acquiring signals are arranged on the electroencephalogram cap and are used for acquiring signals, and the acquired signals are recorded in the electroencephalogram signal acquisition module in real time.

The rehabilitation training system based on motor imagery collects electroencephalogram signals generated when a user performs motor imagery through an electroencephalogram signal collecting module and transmits the electroencephalogram signals to a PC, a PC-side data receiving and processing module extracts and analyzes characteristics of the electroencephalogram signals through a designed algorithm, the arm movement direction of the user imagery is decoded, the direction information is transmitted to a mechanical arm movement module, and a mechanical arm is driven to move according to the corresponding direction to drive limbs of the patient to perform actual rehabilitation movement. Through the parts, the rehabilitation training system is formed by utilizing the rehabilitation mechanical arm and the brain-computer interface technology, so that the user can realize the auxiliary actual movement from imagination movement to the mechanical arm, and complete and active movement rehabilitation training is completed.

The utility model has the beneficial effects that:

(1) the utility model can effectively improve the effect of the existing rehabilitation training, and the user can send out the command for controlling the equipment to move actively by imagination without actual movement. The rehabilitation training does not finish the rehabilitation training under the ground traction of the rehabilitation equipment in the traditional rehabilitation training mode. The mode improves the initiative of the user and simultaneously improves the interest of rehabilitation training;

(2) the active rehabilitation training system provided by the utility model has the advantages of low time delay and high sensitivity of communication between the mechanical arm and electroencephalogram signal processing, and the real-time performance and flexibility of user experience are greatly improved.

Drawings

FIG. 1 is a general architecture diagram of a rehabilitation training system based on motor imagery;

FIG. 2 is a schematic view of a visual inducing interface according to the present invention;

FIG. 3 is a diagram of the electrode distribution of the brain cap of the present invention;

fig. 4 is a human-computer interaction diagram of a rehabilitation training system based on motor imagery.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Example (b): see fig. 1-4.

As shown in fig. 1, a rehabilitation training system based on motor imagery includes an electroencephalogram signal acquisition module, a PC-side data receiving and processing module, a robot-PC communication module, and a robot motion module.

Any one of the arrow pictures in fig. 2, namely, a visual guidance interface, is displayed on the PC to guide the user to perform motor imagery, and imagine that the arm moves in the direction indicated by the arrow.

When the user executes corresponding motor imagery, the electroencephalogram signal acquisition module acquires electroencephalograms when the user performs the motor imagery through an electroencephalogram cap which is worn by the user and can acquire scalp electroencephalograms, and the electrode distribution on the electroencephalogram cap is shown in fig. 3; the acquired electroencephalogram signals are recorded and transmitted into the PC through the amplifier by means of the bus, and after the electroencephalogram signals acquired by the electroencephalogram signal acquisition module are received by the PC-end data receiving and processing module, the acquired electroencephalogram signals are preprocessed to remove noise. And then, obtaining time-frequency domain characteristics of the electroencephalogram signals by using a local mean decomposition method, then respectively obtaining wavelength characteristics and linear non-dynamic characteristics by wavelength optimal spatial filtering and multi-scale entropy algorithm processing, carrying out co-spatial projection on the signals according to the three extracted characteristics, increasing the difference of different types of signals, and finally classifying the signals by a support vector machine to obtain the motor imagery intention of the patient. The mechanical arm and the PC communication module transmit the motor imagery intention of the patient to the mechanical arm movement module by means of a socket communication protocol, and the mechanical arm movement module can control the mechanical arm according to the received signal after receiving the corresponding signal, so that the movement direction of the mechanical arm is the same as the movement direction imagined by the user, and the user arm is driven to perform correct rehabilitation movement.

Referring to fig. 1-4, the operation principle of the rehabilitation training system based on motor imagery according to the present invention is as follows:

(1) the user wears the electroencephalogram cap capable of collecting electroencephalogram signals, the PC displays a visual induction interface to induce the user to execute the motor imagery corresponding to the picture, and the electroencephalogram signal collection module collects the electroencephalogram signals corresponding to the motor imagery of the user and transmits the electroencephalogram signals to the PC.

(2) And the PC-end data receiving and processing module receives data of the user during motor imagery, extracts and classifies the characteristics of the data, and identifies the imaginary arm movement direction of the patient during motor imagery.

(3) And the mechanical arm and PC communication module transmits the decoded result representing the arm movement direction imagined by the user to the mechanical arm movement module by means of a socket communication protocol.

(4) The mechanical arm movement module receives the movement direction result, and makes actual movement in a corresponding direction to drive the upper limbs of the patient to move.

Through the working principle, the conversion from the imagination of arm movement of the user to the actual movement of the arm driven by the machine is realized, and the purpose of helping the user to actively recover is achieved.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the utility model as defined in the appended claims.

Claims

1. A rehabilitation training system based on motor imagery is characterized in that: the device comprises an electroencephalogram signal acquisition module, a PC (personal computer) end data receiving and processing module, a mechanical arm and PC communication module and a mechanical arm movement module; the output end of the electroencephalogram signal acquisition module is connected with the input end of the PC-end data receiving and processing module, the output end of the PC-end data receiving and processing module is connected with the input end of the mechanical arm and the PC communication module, and the output end of the mechanical arm and the PC communication module is connected with the input end of the mechanical arm movement module.

2. A motor imagery based rehabilitation training system as claimed in claim 1, wherein: the electroencephalogram signal acquisition module is used for acquiring electroencephalograms of a user, the electroencephalograms acquired by the electroencephalogram signal acquisition module are transmitted into a PC (personal computer) through an amplifier by means of a bus, the data receiving and processing module at the PC end receives the electroencephalograms acquired by the electroencephalogram signal acquisition module by means of the bus, then the characteristics of the electroencephalograms are extracted and classified, the motion direction imagined by the user is judged, the mechanical arm and PC communication module is used for transmitting the imagination motion direction obtained by decoding the electroencephalograms by the PC end to the mechanical arm motion module by means of a socket communication protocol, the mechanical arm motion module receives the electroencephalogram signal decoding result transmitted by the PC and controls the motion direction motion of the mechanical arm according to the result, and the motion direction of the mechanical arm is consistent with the arm motion direction imagined by the user during the motor imagery.

3. A motor imagery based rehabilitation training system as claimed in claim 1, wherein: the electroencephalogram signal acquisition module acquires electroencephalogram signals through an electroencephalogram cap, 32 electrodes for acquiring signals are arranged on the electroencephalogram cap in total and are used for acquiring the signals, and the acquired signals are recorded in the electroencephalogram signal acquisition module in real time.