CN110292378B - Depression remote rehabilitation system based on brain wave closed-loop monitoring - Google Patents
Depression remote rehabilitation system based on brain wave closed-loop monitoring Download PDFInfo
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Abstract
The invention discloses a depression remote rehabilitation system based on brain wave closed-loop monitoring, which adaptively adjusts the music type, meditation time and movement intensity by analyzing the real-time feedback condition of electroencephalogram signals generated by depression patients in specific music, meditation or virtual reality-based movement, thereby achieving the purposes of self-evaluation and rehabilitation treatment of the depression patients. The invention adopts a virtual reality technology to provide a comfortable treatment environment for a patient, adopts a sensing capture technology to collect electroencephalogram and motion information, and adopts a big data analysis method to evaluate and adjust a treatment scheme for the patient. The system comprises a client and a server, wherein the client comprises a virtual scene module, a rehabilitation module, an electroencephalogram acquisition module, a motion acquisition module, a processor and a display, and the server comprises a cloud service module, a database and a data analysis module. The device is simple and light, is beneficial to rehabilitation treatment of patients at home and in community hospitals, and is also beneficial to application and popularization.
Description
Technical Field
The invention relates to the technical field of computer-aided medical rehabilitation, in particular to a depression remote rehabilitation system based on brain wave closed-loop monitoring.
Background
According to the statistics of the world health organization, depression exceeds cardiovascular and cerebrovascular diseases, and becomes the disease with the highest incidence in developed countries. Surveys have shown that about 9000 tens of thousands of depression patients in china, account for 6.4% of the total population. About 3.5 million depression patients worldwide. Depression seriously disturbs the lives and work of patients, placing a heavy burden on families and society, and about 15% of depression patients die from suicide. A joint study by the world health organization, world bank and harvard university has shown that depression has become the second leading disease burden in china.
Depression is caused by various causes, and its basic clinical features are manifested as "significant and persistent mood swings, thought retardation, and speech movement reduction disproportionately to the situation". The current common treatment methods for depression patients mainly include drug treatment and psychological treatment. The drug therapy is the main treatment measure for the depressive episode with more than moderate degree, and has the defect that some drugs have large adverse reactions; the psychotherapy has obvious effect on the depressive episode with obvious psychosocial factor effect, and is particularly helpful for the treatment of the cognitive behaviors of patients. In addition to the above, studies have shown that exercise is a powerful weapon against mild to moderate depression, and in addition meditation and music therapy, have a positive effect on mood regulation in patients with depression, but patients often have difficulty adhering to at home.
The invention provides a depression remote rehabilitation system based on brain wave closed-loop monitoring, which aims to enable patients with mild and moderate depression to be self-treated at home. Through virtual reality technology (VR), the brain wave information of the forehead of the patient in the treatment process is uploaded to a cloud server by using motion, meditation and music therapy, and the brain wave information is analyzed and generated to generate a stable evaluation index and is fed back to the patient for self-evaluation and self-adaptive adjustment of a rehabilitation scheme. The method has the advantages that the emotional state of the patient can be monitored in a closed loop mode, the exercise intensity, the meditation time and the music type can be adjusted in a self-adaptive mode through the VR technology, the patient can be treated safely, the system is convenient and fast, and the method is suitable for application and popularization.
Disclosure of Invention
In view of the above technical problems, the present invention aims to provide a depression remote rehabilitation system based on brain wave closed-loop monitoring, which is based on depression motor therapy, meditation therapy and music therapy, and uses a virtual reality technology to design listening music, meditation and movement tasks with motivation measures for patients, and performs real-time acquisition, analysis and processing on electroencephalogram signals, calculates rehabilitation training feedback parameters for depression patients, and performs adaptive adjustment and motivation on the movement intensity, music type and meditation state of the depression patients based on the parameters.
In order to realize the purpose, the invention is realized according to the following technical scheme:
a depression remote rehabilitation system based on brain wave closed-loop monitoring is characterized by comprising a client and a server, wherein the client and the server transmit information through the Internet, the client comprises a virtual scene module, a rehabilitation module, an electroencephalogram acquisition module, a motion acquisition module, a processor and a display, and the server comprises a cloud service module, a database and a data analysis module; when the patient uses the device, firstly, the electroencephalogram equipment provided by the electroencephalogram acquisition module is worn on the forehead, then, according to personal interests and hobbies, a training scene provided by the virtual scene module and a training therapy provided by the rehabilitation module are selected, if a sports rehabilitation mode is selected, a sports apparatus of the installed sports acquisition module is further required to be used for training, wherein the virtual scene module generates a virtual scene with a relieving effect on emotion to provide a good treatment environment, the rehabilitation module provides sports, music, meditation and combined rehabilitation therapy thereof, the motion acquisition module installs the gyroscope sensor module in the auxiliary sports equipment, then, the motion information of the patient is acquired through the motion of the patient, and the motion information is transmitted to the processor through the Bluetooth equipment and then is integrated into the virtual scene module and displayed; the brain wave acquisition module acquires brain waves of the forehead of a patient by using a head-wearing brain wave sensor, transmits original brain signals, attention signals and relaxation signals to a processor through Bluetooth equipment, and further integrates and displays the signals in a virtual scene module;
on one hand, the processor receives the electroencephalogram signals and the motion signals through Bluetooth and transmits the electroencephalogram signals and the motion signals to the cloud service module through the Internet; on the other hand, the settings of the virtual scene module and the rehabilitation module are adaptively adjusted through the received feedback parameters of the cloud service module, and meanwhile, the generated virtual three-dimensional scene is output through the display; the cloud service module stores the data into a database, and performs big data analysis through a data analysis module, wherein the database stores music, a virtual scene model, patient information, brain wave data and motion data; the data analysis module evaluates the depression grade of the patient through analysis, provides feedback parameters, transmits the feedback parameters to the processor through the cloud service module, and displays the feedback parameters through the display; the patient can know the rehabilitation effect of the patient by watching the virtual scene output by the display and the feedback parameters, and can evaluate the patient.
In the technical scheme, the exercise therapy in the rehabilitation module provides options of exercise modes, exercise intensity and exercise time parameters of rope skipping, bicycle riding and running; the music therapy provides various music libraries, music playing time and volume options; meditation laws provide a meditation time option.
In the technical scheme, the cloud service module constructs a big data platform of forehead original brain wave, delta, theta, alpha and beta wave signals of depression patients under different music, different movement modes and meditation modes and a big data platform of attention and relaxation feedback parameters of corresponding depression patients, and dynamically updates a basic database along with the continuous increase of patients, movement types and music types.
In the technical scheme, the processor analyzes the motion data of the patient, calculates the motion intensity, the relaxation degree, the attention change and the meditation state information, respectively uploads the delta, theta, alpha and beta wave signals and the feedback parameters to the cloud service module through the internet, receives the corrected parameter information fed back by the cloud service module, and adaptively adjusts the motion intensity, the motion time, the music type, the music playing time, the volume and the meditation time parameters in the rehabilitation module through the algorithm.
In the technical scheme, the data analysis module performs big data analysis on brain wave data of patients under different degrees, different movement modes and different music therapies according to delta, theta, alpha and beta waves of the patients and the variation trend of attention parameters, comprehensively analyzes and evaluates how many contrasts, spike waves and high amplitude theta/alpha waves appear in a fixed time interval by counting the brain wave data of different patients in different rehabilitation stages, and provides evaluation standards of depression patients, reasonable exercise therapy treatment suggestions and parameter feedback so as to provide accurate guidance and rehabilitation for the patients through treatment and case accumulation of more and more patients.
In the above technical solution, the data analysis module performs big data analysis by the following steps:
step S1: respectively extracting the average value, the variance, the curvature entropy and the spike wave occurrence frequency of four wave bands of delta, theta, alpha and beta in the forehead electroencephalogram signal in fixed time, and the overall relaxation degree and attention indexes;
step S2: obtaining value ranges of 18 characteristics under different degrees of illness states by adopting a rule learning method on electroencephalograms collected by a patient with severe depression, a patient with moderate depression, a patient with mild depression and a normal person;
step S3: the recovery effect of the user is judged by calculating the Euclidean distance between the extracted features on the electroencephalogram signals collected before and after the user uses and the values of the features under different disease conditions;
step S4: continuously integrating a large amount of electroencephalogram data of patients at the cloud end, dynamically adjusting and updating the value range of the selected characteristics through rule learning until a stable judgment standard and a parameter feedback index capable of representing rehabilitation effectiveness are formed;
step S5: analyzing the average value, variance, curvature entropy and spike wave occurrence frequency of four wave bands of delta, theta, alpha and beta in the forehead electroencephalogram signals in the fixed time, and the overall looseness and attention index change conditions of different patients in the rehabilitation process of different therapies;
step S6: and estimating different movement modes, music types and meditation states according to evaluation standards and parameter feedback indexes, and gradually forming stable evaluation indexes for different movement, music types and meditation for the effectiveness of depression rehabilitation through long-time case accumulation and big data analysis so as to be referred to in the rehabilitation process of patients.
Compared with the prior art, the invention has the following advantages:
the invention selects music, motion and meditation therapies which are beneficial to depression patients, and combines the incentive measures of virtual reality scenes to increase the treatment effect and improve the long-term adherence ability and treatment interest of the patients; the emotion state of the patient is monitored in real time by using brain wave feedback, and the brain wave feedback is uploaded to a cloud server to establish an electroencephalogram big data platform, so that a more reliable evaluation basis is provided for the patient through a large number of case comprehensive analysis effects, and a client is guided to flexibly adjust a treatment scheme; the invention is convenient and efficient, has simple and light equipment, is beneficial to use in families, communities and hospitals, and is convenient to apply and popularize.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is an overall block diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention.
In the description of the present invention, it is to be understood that the terms "radial," "axial," "upper," "lower," "top," "bottom," "inner," "outer," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the present invention and simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated in a particular manner, and thus are not to be construed as limiting the present invention. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The invention discloses a depression remote rehabilitation system based on brain wave closed-loop monitoring, which comprises a client and a server, wherein the client comprises a virtual scene module, a rehabilitation module, an electroencephalogram acquisition module, a motion acquisition module, a processor and a display, the server comprises a cloud service module, a database and a data analysis module, and the client and the server carry out information transmission through the internet.
The virtual scene module is mainly used for generating a virtual scene with a function of relieving emotion so as to provide a good treatment environment. The virtual scene module mainly comprises two types of virtual reality scenes: music scenes and sports scenes. The music scene comprises a music field and an automatic screening mechanism for realizing music according to a feedback result; the sports scene comprises different character models and different three-dimensional sports field displays, and a mechanism for automatically setting the sports time and intensity according to the feedback result is arranged. In this embodiment, the virtual scene module is described by taking a rope skipping sports scene as an example, the provided character models include an animation character model, an ancient fashion model, a modern fashion model, a game character model and the like, and the provided places include a playground, a seaside, a maze, a royal palace, a residential quarter, a villa and the like.
The rehabilitation module is mainly used for providing a plurality of rehabilitation therapies, including exercise therapy, music therapy and meditation therapy, and different combination therapies of exercise, music and meditation. The exercise therapy provides exercise modes such as rope skipping, bicycle riding, running and the like, the music therapy provides various music libraries, and the meditation provides meditation time options. The rehabilitation module in this embodiment takes rope skipping in the exercise therapy as an example for explanation. The patient may select a character model and a training field based on personal interests. The rope skipping speed is qualified after being regulated to 30-60 times per minute, and the time length of each movement is 10 minutes. The exercise intensity can be monitored in real time during exercise, and the patient is supervised and encouraged to complete the rehabilitation task; simultaneously carrying out real-time feedback of alpha wave and relaxation degree, monitoring whether rope skipping movement has positive effect on a patient, and if the brain wave of the patient is abnormal in the movement process, adjusting the movement intensity in time until the patient has better relaxation degree and alpha wave performance; in the music library in the embodiment, the music playing a role in relieving emotion is classified by testing the alpha wave and the relaxation degree performance of normal people on different music, a patient is encouraged to select the classified music according to interest in the training process, and in the rehabilitation process, if the brain wave of the patient is abnormal in the process of listening to a certain piece of music, the music type is adjusted in time until the patient has better relaxation degree and alpha wave performance; the meditation in this embodiment will be selected by the patient after the movement whether to perform it or not.
The brain wave acquisition module is mainly used for acquiring brain waves of the forehead of the patient by using the head-wearing brain wave sensor and transmitting signals to a virtual scene through Bluetooth equipment. The electroencephalogram acquisition module in the embodiment adopts an electroencephalogram TGAM module to acquire the original brain waves of the forehead, the original electroencephalogram signals of four wave bands of delta, theta, alpha and beta, and indexes such as concentration degree and relaxation degree can be directly acquired through the sensor, and the indexes are transmitted to the virtual scene module through Bluetooth equipment.
The motion acquisition module installs the gyroscope sensor module in the auxiliary motion equipment, then acquires its motion information through patient's motion, and transmits the motion information to the virtual scene through the bluetooth equipment. The motion acquisition module described in this embodiment adopts a BWT901CL inclinometer accelerometer gyro sensor, and after being connected with a battery, the motion acquisition module is installed on a rope skipping rope handle. The BWT901CL transmits the collected information of acceleration, angular velocity and angle to the virtual scene module through the bluetooth device.
The processor is mainly used for receiving the electroencephalogram signals and the motion information, analyzing the motion data of the patient, calculating the motion intensity, the relaxation degree, the attention change and the meditation state information, transmitting the wave signals of delta (1-3 Hz), theta (4-7 Hz), alpha (8-13 Hz) and beta (14-30 Hz) and the feedback parameters to the cloud service module through the Internet, receiving the parameter information fed back by the cloud service module, adaptively adjusting the motion intensity, the music type and the meditation time through an algorithm, and adaptively updating the treatment scheme through the algorithm.
The processor described in this embodiment is a computer, wherein the motion data analysis method is as follows:
step 1, obtaining accelerations a in vertical, forward and left and right directions of a patient during jumpingx、ay、azAnd obtaining a sinusoidal track of the beating of the patient.
Step 2, recording the length l of the previous vectorn-1And a direction of motion vi(i 1,2,3) by variation of vector length
l＝ln-ln-1 (1)
lnFor the current vector length, the direction a of the current acceleration is judgedn。
Step 3, acceleration direction a of the previous timen-1With the direction a of the current accelerationnAnd conversely, counting and accumulating for multiple times to obtain the number of movements.
And 4, setting a length change threshold h and a change frequency f to remove interference jitter and obtain the real movement times.
The display is mainly used for displaying virtual scenes and displaying interfaces. The display in this embodiment is a computer display screen.
The cloud service module is used for constructing a big data platform of forehead brain wave information of depression patients, a big data platform of music, motion and feedback parameters, and updating a basic database. In the cloud service module described in this embodiment, a big data platform of forehead brain wave information, motion information of a skipping rope during patient motion, and feedback parameters provided after analysis by the data module is constructed.
The data analysis module carries out big data analysis according to delta, theta, alpha and beta waves, the relaxation degree and the change trend of the attention parameters of a large number of patients, carries out comprehensive analysis and evaluation on the contrast, the spike wave and the high amplitude theta/alpha wave of the delta, the theta, the alpha and the beta waves appearing in fixed time intervals by counting the brain wave data of different patients in different rehabilitation stages, and provides more accurate evaluation standards of depression patients and more reasonable parameter feedback so as to provide guidance for the patients through treatment and case accumulation of more and more patients.
The data analysis module performs big data analysis by the following steps:
step S1: respectively extracting the average value, the variance, the curvature entropy and the spike wave occurrence frequency of four wave bands of delta, theta, alpha and beta in the forehead electroencephalogram signal in fixed time, and the overall relaxation degree and attention indexes;
step S2: obtaining value ranges of 18 characteristics under different degrees of illness states by adopting a rule learning method on electroencephalograms collected by a patient with severe depression, a patient with moderate depression, a patient with mild depression and a normal person;
step S3: the recovery effect of the user is judged by calculating the Euclidean distance between the features extracted from the electroencephalogram signals collected before and after the user uses the electroencephalogram signals and the feature values under different disease conditions.
The rehabilitation effect of the user is judged by comparing the Euclidean distance between the characteristic distance before and after the user uses and the characteristic critical value of two adjacent disease states.
Step S4: continuously integrating a large amount of electroencephalogram data of patients at the cloud end, dynamically adjusting and updating the value range of the selected characteristics through rule learning until a stable judgment standard and a parameter feedback index capable of representing rehabilitation effectiveness are formed;
step S5: analyzing the average value, variance, curvature entropy and spike occurrence frequency of four wave bands of delta, theta, alpha and beta in the forehead EEG signal in a fixed time, and the change conditions of the overall relaxation degree and attention index of different patients in the rehabilitation process of different therapies (such as single movement, fixed type music stimulation or meditation);
step S6: according to the evaluation standard and the parameter feedback index, different movement modes, music types and meditation states are estimated, and for the effectiveness of depression rehabilitation, stable evaluation indexes are gradually formed for different movement, music types and meditation through long-time case accumulation and big data analysis and are used for reference in the rehabilitation process of patients.
The database is used for storing various data such as music, virtual scene models, patient information, brain wave data, motion data and the like. The database described in this embodiment stores information such as a virtual scene model, patient information, brain wave data, and time, intensity, and number of rope skips.
The working flow of the invention is further described below by taking rope skipping in the exercise therapy as an example:
1) the patient uses a processor (personal computer) to open the depression remote rehabilitation system client software of the invention, and selects a virtual skipping rope scene, a virtual character and a rehabilitation therapy mode (such as a sports therapy, a sports + music therapy or a sports + meditation therapy) which are interested in or beneficial to the patient through the virtual reality module, the rehabilitation module and the rehabilitation effect evaluation results of big data on different sports and music;
2) after the selection is finished, the patient wears the equipment provided with the TGAM module on the forehead, and puts the skipping rope provided with the BWT901CL gyroscope module in the hand to prepare for training;
3) the initial treatment regimen is qualified at 30-60 times per minute with a duration of 10 minutes per exercise, and if music therapy is selected, music therapy is added for 10 minutes after exercise, and if meditation therapy is selected, meditation therapy is added for 10 minutes after exercise.
4) During treatment, the electroencephalogram equipment collects electroencephalogram signals in real time, the gyroscope collects motion signals in real time, and data are transmitted to the Personal Computer (PC) through Bluetooth; the personal PC provided with the depression remote rehabilitation system client software can process the acquired signals in real time and upload the data to the cloud service module through the Internet;
5) the cloud service module stores the data in a database, evaluates the depression grade of the patient through a data analysis module, calculates feedback parameters to feed back the training state and the treatment effect of the patient, adjusts the training state of the patient in time through a virtual reality module, and gives treatment suggestions if the training state is suspended or increased, if the music type is changed, and the like;
6) the personal PC is connected with the display to display and output the virtual scene, the parameters fed back by the cloud server, the locally calculated movement times and movement time and the treatment opinions fed back by the cloud server;
7) the patient can know the personal training condition by observing the feedback parameters and treatment opinions.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (4)
1. A depression remote rehabilitation system based on brain wave closed-loop monitoring is characterized by comprising a client and a server, wherein the client and the server transmit information through the Internet, the client comprises a virtual scene module, a rehabilitation module, an electroencephalogram acquisition module, a motion acquisition module, a processor and a display, and the server comprises a cloud service module, a database and a data analysis module; when the patient uses the device, firstly, the electroencephalogram equipment provided by the electroencephalogram acquisition module is worn on the forehead, then, according to personal interests and hobbies, a training scene provided by the virtual scene module and a training therapy provided by the rehabilitation module are selected, if a sports rehabilitation mode is selected, a sports apparatus of the installed sports acquisition module is further required to be used for training, wherein the virtual scene module generates a virtual scene with a relieving effect on emotion to provide a good treatment environment, the rehabilitation module provides sports, music, meditation and combined rehabilitation therapy thereof, the motion acquisition module installs the gyroscope sensor module in the auxiliary sports equipment, then, the motion information of the patient is acquired through the motion of the patient, and the motion information is transmitted to the processor through the Bluetooth equipment and then is integrated into the virtual scene module and displayed; the brain wave acquisition module acquires brain waves of the forehead of a patient by using a head-wearing brain wave sensor, transmits original brain signals, attention signals and relaxation signals to a processor through Bluetooth equipment, and further integrates and displays the signals in a virtual scene module;
on one hand, the processor receives the electroencephalogram signals and the motion signals through Bluetooth and transmits the electroencephalogram signals and the motion signals to the cloud service module through the Internet; on the other hand, the settings of the virtual scene module and the rehabilitation module are adaptively adjusted through the received feedback parameters of the cloud service module, and meanwhile, the generated virtual three-dimensional scene is output through the display; the cloud service module stores the data into a database, and performs big data analysis through a data analysis module, wherein the database stores music, a virtual scene model, patient information, brain wave data and motion data; the data analysis module evaluates the depression grade of the patient through analysis, provides feedback parameters, transmits the feedback parameters to the processor through the cloud service module, and displays the feedback parameters through the display; the patient can know the rehabilitation effect of the patient by watching the virtual scene and the feedback parameters output by the display and evaluate the patient; the data analysis module carries out big data analysis on brain wave data of patients with different degrees, different movement modes and different music therapies according to delta, theta, alpha and beta waves, the relaxation degree and the attention parameter change trend of the patients, carries out comprehensive analysis and evaluation on the number of delta, theta, alpha and beta waves appearing in fixed time intervals and whether spike waves, spike waves and high amplitude theta/alpha waves appear or not by counting the brain wave data of different patients in different rehabilitation stages, and provides evaluation standards of depression patients, reasonable exercise therapy treatment suggestions and parameter feedback so as to provide accurate guidance and rehabilitation for the patients through treatment and case accumulation of more and more patients; the data analysis module performs big data analysis by the following steps:
step S1: respectively extracting the average value, the variance, the curvature entropy and the spike wave occurrence frequency of four wave bands of delta, theta, alpha and beta in the forehead electroencephalogram signal in fixed time, and the overall relaxation degree and attention indexes;
step S2: obtaining value ranges of 18 characteristics under different degrees of illness states by adopting a rule learning method on electroencephalograms collected by a patient with severe depression, a patient with moderate depression, a patient with mild depression and a normal person;
step S3: the recovery effect of the user is judged by calculating the Euclidean distance between the extracted features on the electroencephalogram signals collected before and after the user uses and the values of the features under different disease conditions;
step S4: continuously integrating a large amount of electroencephalogram data of patients at the cloud end, dynamically adjusting and updating the value range of the selected characteristics through rule learning until a stable judgment standard and a parameter feedback index capable of representing rehabilitation effectiveness are formed;
step S5: analyzing the average value, variance, curvature entropy and spike wave occurrence frequency of four wave bands of delta, theta, alpha and beta in the forehead electroencephalogram signals in the fixed time, and the overall looseness and attention index change conditions of different patients in the rehabilitation process of different therapies;
step S6: and estimating different movement modes, music types and meditation states according to evaluation standards and parameter feedback indexes, and gradually forming stable evaluation indexes for different movement, music types and meditation for the effectiveness of depression rehabilitation through long-time case accumulation and big data analysis so as to be referred to in the rehabilitation process of patients.
2. The depression remote rehabilitation system according to claim 1, wherein the exercise therapy in the rehabilitation module provides the exercise mode of skipping rope, cycling, running, exercise intensity and exercise time parameter options; the music therapy provides various music libraries, music playing time and volume options; meditation laws provide a meditation time option.
3. The depression remote rehabilitation system according to claim 2, wherein the cloud service module constructs a big data platform of forehead original brain wave, delta, theta, alpha, beta wave signals and corresponding attention and relaxation feedback parameters of depression patients in different music, different exercise modes and meditation modes, and dynamically updates the basic database as the patients, exercise types and music types increase.
4. The depression remote rehabilitation system according to claim 3, wherein the processor analyzes the patient exercise data, calculates exercise intensity, relaxation and attention change, meditation state information, and uploads the delta, theta, alpha, beta wave signals and feedback parameters to the cloud service module through the internet, respectively, and receives the modified parameter information fed back by the cloud service module, and adaptively adjusts the exercise intensity, exercise time, music type, music play time, volume and meditation time parameters in the rehabilitation module through an algorithm.
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