阅读说明：本技术 一种基于脑电信号监测和训练肌肉张力的系统及其方法 (System and method for monitoring and training muscle tension based on electroencephalogram signals ) 是由 王栋 田东东 杨织萍 于 2020-04-24 设计创作，主要内容包括：本发明涉及一种基于脑电信号监测和训练肌肉张力的系统,包括脑电信号采集机构、辅助训练机构和传输机构。脑电信号采集机构包括脑电信息采集模块、数模转换模块和脑电信号处理模块,脑电信息采集模块连接有脑电信号采集接口,脑电信息采集模块、数模转换模块和脑电信号处理模块依次连接；辅助训练机构包括肌电信号采集模块、训练驱动模块和肌电信号处理模块,肌电信号采集模块连接有肌电信号采集接口,训练驱动模块连接有训练驱动接口,肌电信号处理模块连接在肌电信号采集模块与肌电信号采集模块之间；脑电信号处理器和肌电信号处理模块通过传输机构连接并传输信号。本发明通过脑电信息和肌电信息之间的传输对比,确定需要对肌肉做的刺激训练。(The invention relates to a system for monitoring and training muscle tension based on electroencephalogram signals, which comprises an electroencephalogram signal acquisition mechanism, an auxiliary training mechanism and a transmission mechanism. The electroencephalogram signal acquisition mechanism comprises an electroencephalogram information acquisition module, a digital-to-analog conversion module and an electroencephalogram signal processing module, the electroencephalogram information acquisition module is connected with an electroencephalogram signal acquisition interface, and the electroencephalogram information acquisition module, the digital-to-analog conversion module and the electroencephalogram signal processing module are sequentially connected; the auxiliary training mechanism comprises an electromyographic signal acquisition module, a training driving module and an electromyographic signal processing module, the electromyographic signal acquisition module is connected with an electromyographic signal acquisition interface, the training driving module is connected with a training driving interface, and the electromyographic signal processing module is connected between the electromyographic signal acquisition module and the electromyographic signal acquisition module; the electroencephalogram signal processor and the electromyogram signal processing module are connected through the transmission mechanism and transmit signals. According to the invention, the stimulation training of muscles is determined through the transmission and comparison between the electroencephalogram information and the myoelectricity information.)

1. A system for monitoring and training muscle tension based on electroencephalogram signals, comprising:

the electroencephalogram signal acquisition mechanism comprises an electroencephalogram information acquisition module, a digital-to-analog conversion module and an electroencephalogram signal processing module, the electroencephalogram information acquisition module is connected with an electroencephalogram signal acquisition interface, the signal input end of the digital-to-analog conversion module is connected with the signal output end of the electroencephalogram information acquisition module, the signal input end of the electroencephalogram signal processing module is connected with the signal output end of the digital-to-analog conversion module, the digital-to-analog conversion module converts analog signals acquired by the electroencephalogram information acquisition module into digital signals, and the digital-to-analog conversion module transmits the processed data to the electroencephalogram signal processing module;

the auxiliary training mechanism comprises an electromyographic signal acquisition module, a training driving module and an electromyographic signal processing module, wherein the electromyographic signal acquisition module is connected with an electromyographic signal acquisition interface, the training driving module is connected with a training driving interface, and the electromyographic signal processing module is connected between the electromyographic signal acquisition module and the electromyographic signal acquisition module;

the electroencephalogram signal processor and the electromyogram signal processing module are connected through the transmission mechanism and transmit signals.

2. The system for monitoring and training muscle tone based on electroencephalogram signals of claim 1, wherein: the electroencephalogram signal acquisition interface adopts a dry electrode which can be attached to the scalp.

3. The system for monitoring and training muscle tone based on electroencephalogram signals of claim 1, wherein: the electroencephalogram signal acquisition mechanism further comprises a signal filtering module, the signal input end of the signal filtering module is connected to the output end of the electroencephalogram information acquisition module, and the signal output end of the signal filtering module is connected to the signal input end of the digital-to-analog conversion module.

4. The system for monitoring and training muscle tone based on electroencephalogram signals of claim 3, wherein: the digital-to-analog conversion module is an AD data conversion module, and the signal filtering module is specifically a band-pass filter.

5. The system for monitoring and training muscle tone based on electroencephalogram signals of claim 3 or 4, wherein: the electroencephalogram signal acquisition mechanism further comprises a signal wave limiting module, the signal input end of the signal wave limiting module is connected to the output end of the signal filtering module, and the signal output end of the signal wave limiting module is connected to the signal input end of the digital-to-analog conversion module.

6. The system for monitoring and training muscle tone based on electroencephalogram signals of claim 5, wherein: the signal wave limiting module is a 40-60 Hz wave limiter.

7. The system for monitoring and training muscle tone based on electroencephalogram signals of claim 1, wherein: the electroencephalogram information acquisition module is connected with an operational amplifier.

8. The system for monitoring and training muscle tone based on electroencephalogram signals of claim 1, wherein: the myoelectric signal acquisition interface adopts an electrode plate for signal acquisition, which is attached to muscles, and the training driving interface adopts an electrode plate for muscle stimulation, which is attached to muscles.

9. A method for monitoring and training muscle tension based on electroencephalogram signals is characterized by comprising the following steps:

the electroencephalogram information acquisition module acquires an electroencephalogram analog signal through an electroencephalogram information acquisition interface;

the digital-to-analog conversion module converts the electroencephalogram analog signal into a digital signal;

transmitting the digital signal to an electroencephalogram signal processing module, wherein the electroencephalogram signal processing module gives a specific part to be trained;

the electromyographic signal processing module receives the trained part information sent by the electroencephalographic signal processing module through a wireless transmission mechanism and sends an acquired signal to the electromyographic signal acquisition module;

the electromyographic signal acquisition module acquires electromyographic information through an electromyographic signal acquisition interface and transmits the electromyographic information to the electromyographic signal processing module;

the electromyographic signal processing module analyzes signals to give the strength and time of muscle training, and the electromyographic signal processing module and the electroencephalographic signal processing module carry out data transmission comparison through a transmission mechanism to give specific training signals needing rehabilitation training;

the myoelectric signal processing module transmits the training signal to the training driving module, and the training driving module performs stimulation training on muscles through a training driving interface.

10. The method for monitoring and training muscle tension based on electroencephalogram signals of claim 9, wherein: further comprising: and removing the interference of the noise high-frequency signal from the electroencephalogram analog signal through a signal filtering module and a signal wave limiting module, and transmitting the electroencephalogram analog signal without the interference of the noise high-frequency signal to the digital-to-analog conversion module.

Technical Field

The application relates to the technical field of medical treatment, in particular to a system and a method for monitoring and training muscle tension based on electroencephalogram signals.

Background

In daily life, all the postures and actions of a person are regulated by muscle tension. Briefly, in a resting or resting state, the degree of tightness maintained by a muscle, i.e. muscle tension (muscle tone), is as follows: in battle or sitting posture, the human body is stable and can not topple over. When the central nervous system of the brain is injured by external force or non-external force, the ability to autonomously regulate the normal tension of muscles is lost, and a physiological response of hypertonia (stiffness of the limbs) or hypotony (flaccidity of the limbs) is generated, so that the person loses the ability to autonomously control specific actions of the body. Brain damage is irreversible damage, so a new brain regulatory region replacing the damaged region of the brain needs to be found to solve the problem of muscle tension regulation.

The patient of muscle atrophy and muscle relaxation accounts for a certain proportion in the society, especially permanent inpatient, because lack the exercise, the muscle appears serious atrophy condition and even can't be independently trained, need rely on external auxiliary instrument to go through the rehabilitation training.

Disclosure of Invention

The application provides a system and a method for monitoring and training muscle tension based on electroencephalogram signals, which aim to solve the problem that rehabilitation training is difficult to adjust the muscle tension of a user due to brain injury.

The technical scheme adopted by the application is as follows:

the invention provides a system for monitoring and training muscle tension based on electroencephalogram signals, which comprises:

the electroencephalogram signal acquisition mechanism comprises an electroencephalogram information acquisition module, a digital-to-analog conversion module and an electroencephalogram signal processing module, the electroencephalogram information acquisition module is connected with an electroencephalogram signal acquisition interface, the signal input end of the digital-to-analog conversion module is connected with the signal output end of the electroencephalogram information acquisition module, the signal input end of the electroencephalogram signal processing module is connected with the signal output end of the digital-to-analog conversion module, the digital-to-analog conversion module converts analog signals acquired by the electroencephalogram information acquisition module into digital signals, and the digital-to-analog conversion module transmits the processed data to the electroencephalogram signal processing module;

the auxiliary training mechanism comprises an electromyographic signal acquisition module, a training driving module and an electromyographic signal processing module, wherein the electromyographic signal acquisition module is connected with an electromyographic signal acquisition interface, the training driving module is connected with a training driving interface, and the electromyographic signal processing module is connected between the electromyographic signal acquisition module and the electromyographic signal acquisition module;

the electroencephalogram signal processor and the electromyogram signal processing module are connected through the transmission mechanism and transmit signals.

Furthermore, the electroencephalogram signal acquisition interface adopts a dry electrode which can be attached to the scalp.

Furthermore, the electroencephalogram signal acquisition mechanism further comprises a signal filtering module, the signal input end of the signal filtering module is connected to the output end of the electroencephalogram information acquisition module, and the signal output end of the signal filtering module is connected to the signal input end of the digital-to-analog conversion module.

Furthermore, the digital-to-analog conversion module is an AD data conversion module, and the signal filtering module is specifically a band-pass filter.

Furthermore, the electroencephalogram signal acquisition mechanism further comprises a signal wave limiting module, the signal input end of the signal wave limiting module is connected to the output end of the signal filtering module, and the signal output end of the signal wave limiting module is connected to the signal input end of the digital-to-analog conversion module.

Furthermore, the signal wave limiting module is a 40-60 Hz wave limiter.

Furthermore, the electroencephalogram information acquisition module is connected with an operational amplifier.

Furthermore, the myoelectric signal acquisition interface adopts an electrode plate for signal acquisition, which is attached to muscles, and the training driving interface adopts an electrode plate for muscle stimulation, which is attached to muscles.

Further, the method for monitoring and training muscle tension based on the electroencephalogram signals comprises the following steps:

the electroencephalogram information acquisition module acquires an electroencephalogram analog signal through an electroencephalogram information acquisition interface;

the digital-to-analog conversion module converts the electroencephalogram analog signal into a digital signal;

transmitting the digital signal to an electroencephalogram signal processing module, wherein the electroencephalogram signal processing module gives a specific part to be trained;

the electromyographic signal processing module receives the trained part information sent by the electroencephalographic signal processing module through a wireless transmission mechanism and sends an acquired signal to the electromyographic signal acquisition module;

the electromyographic signal acquisition module acquires electromyographic information through an electromyographic signal acquisition interface and transmits the electromyographic information to the electromyographic signal processing module;

the electromyographic signal processing module analyzes signals to give the strength and time of muscle training, and the electromyographic signal processing module and the electroencephalographic signal processing module carry out data transmission comparison through a transmission mechanism to give specific training signals needing rehabilitation training;

the myoelectric signal processing module transmits the training signal to the training driving module, and the training driving module performs stimulation training on muscles through a training driving interface.

Further, still include: and removing the interference of the noise high-frequency signal from the electroencephalogram analog signal through a signal filtering module and a signal wave limiting module, and transmitting the electroencephalogram analog signal without the interference of the noise high-frequency signal to the digital-to-analog conversion module.

The technical scheme of the application has the following beneficial effects:

the invention relates to a system and a method for monitoring and training muscle tension based on electroencephalogram signals.A electroencephalogram information acquisition module acquires electroencephalogram signals of a human brain through an electroencephalogram signal acquisition interface, a digital-to-analog conversion module converts analog signals acquired by the electroencephalogram information acquisition module into digital signals, and the digital-to-analog conversion module transmits processed data to an electroencephalogram signal processing module;

the myoelectric signal processing module is used for comparing the myoelectric information with the information which is required to be trained and is given by the electroencephalogram signal processing module to form an optimized training signal, transmitting the training signal to the training driving module, and performing stimulation training on the muscle through the training driving interface of the training driving module.

The method comprises the steps that through transmission and comparison between electroencephalogram information and electromyogram information between an electroencephalogram signal acquisition mechanism and an auxiliary training mechanism, stimulation training to be performed on muscles is determined, and when specific muscle tension is too high and Electromyogram (ENG) with too large signal waveform amplitude is received, muscle relaxation is regulated; on the contrary, when the specific muscle tension is too low, the electromyogram with too small signal waveform amplitude is received, so as to stimulate the muscle to contract, promote the disordered muscle tissue, achieve the benefit of timely stimulation and relaxation, and maintain the muscle and the normal tightness.

The auxiliary training system is used for exercising the muscles at the part, the myoelectric signal acquisition work is carried out at any time during the training, the training and the myoelectric signal acquisition are carried out simultaneously, the closed-loop effect of the training is realized, and the muscle tension training can be carried out more effectively and safely.

Drawings

In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic diagram of a system for monitoring and training muscle tone based on electroencephalogram signals according to an embodiment of the present invention;

illustration of the drawings:

wherein, 1-EEG signal acquisition mechanism: 11-an electroencephalogram information acquisition module; 12-a signal filtering module; 13-a signal limiting module; 14-a digital-to-analog conversion module; 15-an electroencephalogram signal processing module;

2-auxiliary training mechanism: 21-electromyographic signal acquisition module; 22. training a driving module; 23-electromyographic signal processing module.

Detailed Description

Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following examples do not represent all embodiments consistent with the present application. But merely as exemplifications of systems and methods consistent with certain aspects of the application, as recited in the claims.

Referring to fig. 1, it is an intention of a system for monitoring and training muscle tone based on electroencephalogram signals.

The application provides a system based on electroencephalogram signal monitoring and muscle tension training, which comprises an electroencephalogram signal acquisition mechanism and an auxiliary training mechanism.

Specifically, the method comprises the following steps:

the electroencephalogram signal acquisition mechanism comprises an electroencephalogram information acquisition module 11, a digital-to-analog conversion module 14 and an electroencephalogram signal processing module 15, the electroencephalogram information acquisition module 11 is connected with an electroencephalogram signal acquisition interface, the signal input end of the digital-to-analog conversion module 14 is connected with the signal output end of the electroencephalogram information acquisition module 11, the signal input end of the electroencephalogram signal processing module 15 is connected with the signal output end of the digital-to-analog conversion module 14, the digital-to-analog conversion module 14 converts analog signals acquired by the electroencephalogram information acquisition module 11 into digital signals, and the digital-to-analog conversion module 14 transmits the processed data to the electroencephalogram;

the auxiliary training mechanism comprises an electromyographic signal acquisition module 21, a training driving module 22 and an electromyographic signal processing module 23, the electromyographic signal acquisition module 21 is connected with an electromyographic signal acquisition interface, the training driving module 22 is connected with a training driving interface, and the electromyographic signal processing module 23 is connected between the electromyographic signal acquisition module 21 and the electromyographic signal acquisition module 21;

the transmission mechanism, the electroencephalogram signal processor and the electromyogram signal processing module 23 are connected through the transmission mechanism and transmit signals, and the transmission mechanism is wireless transmission or wired transmission. Specifically, the transmission mechanism of this embodiment is wireless transmission, specifically bluetooth, and certainly may also be other modes that can perform signal transmission, such as WiFi.

The electroencephalogram signal acquisition interface can directly adopt a dry electrode which can be attached to the scalp, and can also be a head-mounted electroencephalogram acquisition instrument, and the like, and the details are not repeated.

The electroencephalogram signal acquisition mechanism further comprises a signal filtering module 12, the signal input end of the signal filtering module 12 is connected to the output end of the electroencephalogram information acquisition module 11, and the signal output end of the signal filtering module 12 is connected to the signal input end of the digital-to-analog conversion module 14.

The electroencephalogram signal acquisition mechanism further comprises a signal wave limiting module 13, the signal input end of the signal wave limiting module 13 is connected to the output end of the signal filtering module 12, and the signal output end of the signal wave limiting module 13 is connected to the signal input end of the digital-to-analog conversion module 14.

The digital-to-analog conversion module 14 of this embodiment is an AD data processing conversion module, the signal filtering module 12 is specifically a band-pass filter, the signal wave-limiting module 13 is a 40-60 Hz wave limiter, and the wave limiter is specifically 50 Hz. The function of the band-pass filter and the wave limiter is to remove the interference of noise high-frequency signals.

The front end of the electroencephalogram information acquisition module 11 is connected with an operational amplifier so as to amplify the acquired signals.

The myoelectric signal acquisition interface adopts three contact pin type electrode plates for signal acquisition, which are attached to muscles, the training driving interface adopts two contact pin type electrode plates for muscle stimulation, which are attached to the muscles.

The electroencephalogram information acquisition module 11, the signal filtering module 12, the signal wave limiting module 13, the digital-to-analog conversion module 14, the electroencephalogram signal processing module 15, the electromyogram signal acquisition module 21, the training driving module 22 and the electromyogram signal processing module 23 are all connected with a power supply, the whole system is powered by two rechargeable polymer lithium batteries, the battery power supply voltage is DC3.7V,1000mAh can provide 8-hour working time and 24-hour standby time for each device. Wherein, the power of brain electrical information collection module 11, signal filtering module 12, signal wave-limiting module 13, digital-to-analog conversion module 14, brain electrical signal processing module 15 is rechargeable battery, and rechargeable battery is connected with brain electrical signal processing module 15, and the 15 output voltage types of brain electrical signal processing module include: +1.8V, -1.8V, +3.3V, -3.3V, +1.2V and GND; the electromyographic signal acquisition module 21, the training drive module 22 and the electromyographic signal processing module 23 are long-endurance batteries, the long-endurance batteries are connected with the electromyographic signal processing module 23, and the type of the output voltage of the electromyographic signal processing module 23 is as follows: +1.8V, -1.8V, +3.3V, -3.3V, +5V, -5V and GND.

The EEG information acquisition module 11 is an eight-way-guide EEG signal acquisition device, the EEG signal processing module 15 is an EEG main processor MCU, the working voltage of the EEG main processor MCU is 3.3V, the EEG main processor MCU is provided with a display screen, and the working voltage of AD data processing is 3.3V. The operational amplifier used for signal acquisition of the electroencephalogram information acquisition module 11 is powered by a bipolar power supply, namely +1.8V and-1.8V. The collecting time of the electromyographic signal collecting module 21 may be set on a display screen of the electromyographic signal processing module 23.

The training driver module 22 has a neuromuscular electrical stimulation circuit. The training driver module 22 employs neuromuscular electrical stimulation (NMES), which uses low frequency current for stimulation, primarily to stimulate denervating, spasmodic and smooth muscle, disuse muscle atrophy. The principle of the electric stimulation is that low-frequency current action is selected, and the physical characteristics are as follows:

1. waveform output

The waveform in the trainer has two modes, namely an asymmetric bidirectional square wave and a symmetric bidirectional square wave, and the two modes have the advantages that: a. the stimulation of the electrochemical action under the electrode to the skin can be avoided; b. the current intensity is quickly increased to a preset value, so that the adaptation phenomenon of nerve fibers can be avoided.

2. Wave width modulation output

Research shows that the wave width of 0.3MS is the most comfortable, the wave width is less than 1.0MS, stronger current intensity is needed to cause muscle contraction, high-intensity current can excite the fiber nerve to cause pain afferent, when the wave width is more than 1.0MS, the current can excite the pain nerve while causing the muscle contraction, and when the wave width is equal, the current intensity is slightly increased to generate obvious muscle contraction.

According to the characteristics of the wave width, the adjustable wave width range is adopted in the trainer: 0.2-1.0 ms, and can meet the requirements of different muscle tensions

3. Function of adjustable frequency output

In the aspect of frequency output, the single contraction of the muscle is caused by low-frequency pulse current (1-5 HZ), the muscle is not easy to cause muscle fatigue and discomfort, the incomplete tonic contraction of the muscle can be caused by the pulse current of 10-20 Hz, the complete tonic contraction can be caused by the frequency of 40-60 Hz, and the strength of the tonic contraction can be 4 times stronger than that of the single contraction, so that the high-frequency electric stimulation can be used for exercising normal muscle, but the muscle fatigue is easy to cause. The training of different frequency bands is selected according to different myoelectric signal characteristics so as to achieve the purpose of muscle tension training.

The method for monitoring and training muscle tension based on electroencephalogram signals comprises the following steps:

attaching a dry electrode of the head-wearing electroencephalogram acquisition instrument to the head of a user;

sticking the contact pin electrode plate for signal acquisition and the contact pin electrode plate for muscle stimulation on the muscle to be stimulated by a user;

the method comprises the following steps: the brain electrical information acquisition module 11 acquires brain analog electrical signals through a head-mounted brain electrical acquisition instrument;

step two: removing the interference of noise high-frequency signals from the electroencephalogram analog signals through a band-pass filter and a wave limiter;

step three: the data processed by the filter and the wave limiter are converted into digital signals by an AD data conversion module;

step four: the AD data conversion module transmits the processed digital signal to the electroencephalogram signal processing module 15; because the EEG signal processing module 15 is internally integrated with a high-precision and reliable EEG signal algorithm program, the transmitted brain wave signals are analyzed, and the specific part to be trained is displayed through a screen;

step five: the electroencephalogram signal processing module 15 transmits the information of the trained part to the electromyogram signal processing module 23 in a Bluetooth transmission mode, and the electromyogram signal processing module 23 sends a signal capable of acquiring electromyogram information to the electromyogram signal acquisition module 21;

step six: after the electromyographic signal acquisition module 21 receives the acquisition signal, the electromyographic signal acquisition module 21 acquires the electromyographic signal through the signal acquisition needle electrode plate, determines the current muscle state, and transmits the muscle information to the electromyographic signal processing module 23;

step seven: the electromyographic signal processing module 23 analyzes the signal to give the strength and time of muscle training, meanwhile, the electromyographic signal processing module 23 transmits and compares the training strength and the training time and the collected electromyographic information with the information of the part to be trained given by the electroencephalogram signal processing module 15 through Bluetooth, gives a specific training signal required to be rehabilitated, and forms an optimized training signal;

step eight: the electromyographic signal processing module 23 transmits the training signal to the training driving module 22, and the training driving module 22 performs stimulation training on the muscle through the contact pin type electrode plate for muscle stimulation.

The electromyographic signal acquisition module 21 acquires electromyographic information in real time, the electroencephalographic information acquisition module 11 acquires electroencephalographic information in real time, and meanwhile, the electroencephalographic signal processing module 15 and the electromyographic signal processing module 23 perform real-time processing and comparison on data to achieve the closed-loop effect of training so as to obtain the best training signal and perform muscle tension training more effectively and safely to ensure that muscles at the training part stretch normally.

The beneficial effects of this embodiment:

the brain electrical signal acquisition mechanism and the auxiliary training mechanism replace a damaged brain region to become a brain regulation region, namely a transmission path of brain regulation muscle tension: the brain → muscular tension, instead of brain-computer interface → muscular tension, uses the brain-computer interface to take over the brain use area, and detects the change of Electromyogram (EMG) of human muscle tissue to the change of electroencephalogram (EEG) at any time, so as to regulate the muscular tension in real time. When the specific muscle tension is too high, the brain-computer interface receives an Electromyogram (ENG) with too large signal waveform amplitude, and the muscle relaxation is regulated; on the contrary, when the specific muscle tension is too low, the brain-computer interface will receive the electromyogram with too small signal waveform amplitude, and stimulate the muscle to contract, so as to promote the disordered muscle tissue, achieve the benefit of timely stimulation and relaxation, and maintain the muscle and the normal tightness. The group applied in this embodiment is not only a user of the product, but also a user who has problems of dystonia in various diseases (stroke, alzheimer's disease, parkinson's disease, cerebral palsy, etc.) involving brain damage, even patients who have problems of dystonia due to unknown reasons.

The embodiment is mainly based on a brain-computer interface technology and a myoelectricity acquisition technology, and can be used for improving the conditions of muscular atrophy and muscular relaxation through auxiliary passive training for patients suffering from muscular atrophy and muscular relaxation.

By adopting the eight-lead EEG signal acquisition device, accurate training information of the brain to muscles needing to move is obtained through brain wave signal processing and complex calculation. The auxiliary training system is used for exercising muscles at the part, the myoelectric signal acquisition work is carried out while training is carried out, the training and the myoelectric signal acquisition are carried out simultaneously, the closed-loop effect of the training is realized, and the muscle tension training can be carried out more effectively and safely.

The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments extended according to the scheme of the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.