CN116130053A - Rehabilitation training system - Google Patents

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CN116130053A
CN116130053A CN202310244755.5A CN202310244755A CN116130053A CN 116130053 A CN116130053 A CN 116130053A CN 202310244755 A CN202310244755 A CN 202310244755A CN 116130053 A CN116130053 A CN 116130053A
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张静莎
李增勇
张腾宇
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National Research Center for Rehabilitation Technical Aids
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Abstract

The invention discloses a cerebral apoplexy rehabilitation training system, which comprises a rehabilitation training task recommending module, a rehabilitation training module and a rehabilitation training task completion evaluating module, wherein: the rehabilitation training task recommending module is used for recommending an adaptive rehabilitation training task for the patient according to the rehabilitation evaluation result of the cerebral apoplexy patient; the rehabilitation training module is used for performing rehabilitation training on the cerebral apoplexy patient according to the rehabilitation training task recommended by the rehabilitation training task recommending module; the rehabilitation training task completion evaluation module is used for evaluating the rehabilitation training completion degree of the patient and feeding back to the rehabilitation training task recommendation module; the rehabilitation training task recommending module comprises a rehabilitation training task recommending module which fuses a sports function and a brain function.

Description

Rehabilitation training system
The application is a divisional application of the invention patent application with the application number of 202210812775.3, the application date of 2022, the month of 07 and the invention name of 'a cerebral apoplexy rehabilitation training system'.
Technical Field
The invention relates to the technical field of rehabilitation training, in particular to a rehabilitation training system.
Background
Cerebral apoplexy is the first cause of death and disability of residents in China, and has the characteristics of high morbidity, high disability rate, high mortality rate and high recurrence rate. Although it can be accepted for rehabilitation, about 60% -80% of stroke patients remain with significant motor dysfunction, bringing heavy care costs to the home and society. Therefore, the high-quality and high-efficiency rehabilitation training is the most important means for solving the current problem, and is a necessary choice for helping patients to recover independent life and return to society.
Research shows that cerebral apoplexy patients are mainly damaged in nerve loops, so that comprehensive consideration of information of brain functions is needed in the rehabilitation evaluation and rehabilitation training process, and doctors are assisted to provide self-adaptive rehabilitation training methods for patients. Therefore, rehabilitation regimen recommendation that fuses motor function and brain function is an important trend in future rehabilitation exercises.
In addition, the multi-physical stimulation is also an important means for cerebral apoplexy rehabilitation, and the brain, the central nervous system, the muscles and the like of a patient are stimulated in a non-invasive way through physical stimulation such as acousto-optic and electromagnetic stimulation, and the cortical activity can be improved after the stimulation for a period of time so as to promote the nerve loop and the exercise function reconstruction, so that the multi-physical collaborative stimulation is an important development direction of future nerve regulation.
Therefore, the invention provides a training system suitable for cerebral apoplexy rehabilitation, an optimal rehabilitation training task recommendation model based on limb movement function, brain function, fusion movement function and brain function is constructed, and an information detection and physical stimulation means are assisted, so that the rehabilitation training efficiency of the combination of brain-muscle-limb multisource information and multiple physical stimuli is improved, and the optimal rehabilitation training effect is achieved.
Disclosure of Invention
The invention is realized by adopting the following technical scheme:
the utility model provides a cerebral apoplexy rehabilitation training system, includes rehabilitation training task recommendation module, rehabilitation training module and rehabilitation training task completion evaluation module, wherein: the rehabilitation training task recommending module is used for recommending an adaptive rehabilitation training task for the patient according to the rehabilitation evaluation result of the cerebral apoplexy patient; the rehabilitation training module is used for performing rehabilitation training on the cerebral apoplexy patient according to the rehabilitation training task recommended by the rehabilitation training task recommending module; the rehabilitation training task completion evaluation module is used for evaluating the rehabilitation training completion degree of the patient and feeding back to the rehabilitation training task recommendation module.
The cerebral apoplexy rehabilitation training system comprises: the rehabilitation training task recommending module comprises a rehabilitation training task recommending module based on limb movement functions and a rehabilitation training task recommending module based on brain functions, wherein the rehabilitation training task recommending module based on limb movement functions is used for recommending rehabilitation training tasks suitable for patients according to the evaluation results of the movement function scales of the patients; the rehabilitation training task recommending module based on brain functions is used for recommending rehabilitation training tasks suitable for the patient according to the nuclear magnetic resonance brain image evaluation result of the patient.
The cerebral apoplexy rehabilitation training system comprises a rehabilitation training task recommendation module based on limb movement functions, wherein the rehabilitation training task recommendation module recommends rehabilitation training tasks suitable for patients according to the movement function scale evaluation results of the patients in the following mode: constructing a rehabilitation training task database based on a cerebral apoplexy patient movement function scale, and preprocessing scale information and basic information in the database to respectively obtain a scale characteristic vector L RK And basic information vector J K The method comprises the steps of carrying out a first treatment on the surface of the Inputting the preprocessed scale data and basic information feature vectors into a convolutional neural network model for training and model optimization, and finally obtaining rehabilitation training task recommendation based on limb movement functionAnd (3) model:
Y RK ＝[BW i ,TP i ,HR i ,TM i ,F i ]＝G 1CNN (L RK ，J K )
wherein Y is RK The method comprises the steps of providing recommended rehabilitation training tasks, wherein the recommended rehabilitation training tasks comprise rehabilitation training task types, rehabilitation training task difficulties, rehabilitation training task time and rehabilitation training frequency of different limb parts; i has a value of [1,8 ]]Respectively representing different parts of the limb: left foot, left lower limb, left upper limb, left hand, right foot, right lower limb, right upper limb, right hand; BW (BW) i Is a certain limb part; TP (Transmission protocol) i The rehabilitation training task type is a rehabilitation training task type of a certain part; HR (HR) i The difficulty of a rehabilitation training task for a certain part; TM (TM) i The rehabilitation training task time of a certain part; f (F) i The task frequency of rehabilitation training for a certain part; g 1CNN The method comprises the steps of training a convolutional neural network model; l (L) RK The feature vector is a pre-processed scale feature vector; j (J) K Is the basic information of the patient after pretreatment.
The cerebral apoplexy rehabilitation training system comprises a rehabilitation training task recommendation module based on brain functions, wherein the rehabilitation training task recommendation module recommends rehabilitation training tasks suitable for patients according to nuclear magnetic resonance brain image evaluation results of the patients in the following mode:
constructing a rehabilitation training task database based on nuclear magnetic resonance brain images of cerebral apoplexy patients, and preprocessing the nuclear magnetic resonance brain images and report data in the database;
extracting the preprocessed nuclear magnetic resonance brain image and the reported data characteristics by using a convolutional neural network model;
H NK ＝Feedforward(W CNN ，B CNN ；JJ CNN ,CH CNN ；HC,BG；)
wherein H is NK Is a feature vector extracted from a convolutional neural network model; feedforward is a feed forward neural network function; w (W) CNN Is a weight matrix of a convolutional neural network model, B CNN Is a bias parameter; JJ (joint junction) CNN Is a convolution layer of a convolution module, the convolution kernel is 2 x 2, and the convolution step length is 2 x 2; CH (CH) CNN Is pooling of convolution modulesA layer, the pooling layer is the largest pooling core of 3*3; HC. BG is the input nmr brain image data and data report respectively;
inputting the extracted nuclear magnetic resonance brain image and the reported data characteristics into another convolutional neural network model for training and model optimization, and finally obtaining a rehabilitation training task recommendation model of brain functions:
Y NK ＝[BW i ,TP i ,HR i ,TM i ,F i ]＝G 2CNN (L RK ，H NK ，J K )
wherein Y is NK The method comprises the steps of providing recommended rehabilitation training tasks, wherein the recommended rehabilitation training tasks comprise rehabilitation training task types, rehabilitation training task difficulties, rehabilitation training task time and rehabilitation training frequency of different limb parts; i has a value of [1,8 ]]Respectively representing different parts of the limb: left foot, left lower limb, left upper limb, left hand, right foot, right lower limb, right upper limb, right hand; BW (BW) i Is a certain limb part; TP (Transmission protocol) i The rehabilitation training task type is a rehabilitation training task type of a certain part; HR (HR) i The difficulty of a rehabilitation training task for a certain part; TM (TM) i The rehabilitation training task time of a certain part; f (F) i The task frequency of rehabilitation training for a certain part; g 2CNN The method comprises the steps of training a convolutional neural network model; h NK For nuclear magnetic resonance brain images and report data features; j (J) K Is the basic information of the patient after pretreatment.
The cerebral apoplexy rehabilitation training system comprises a cerebral apoplexy rehabilitation training task recommendation module, wherein the rehabilitation training task recommendation module further comprises a rehabilitation training task recommendation module which fuses a movement function and a brain function, and is used for comprehensively recommending rehabilitation training tasks suitable for patients according to the scale movement function data and nuclear magnetic resonance brain image data of the patients in the following manner:
and inputting the scale feature vector and the nuclear magnetic resonance data feature into a convolutional neural network model for training and model optimization, and finally obtaining a rehabilitation training task recommendation model fusing the motor function and the brain function.
Y NH ＝[BW i ,TP i ,HR i ,TM i ,F i ]＝G 3CNN (L RK ，H NK ，J K )
Wherein Y is NH The method comprises the steps of providing recommended rehabilitation training tasks, wherein the recommended rehabilitation training tasks comprise rehabilitation training task types, rehabilitation training task difficulties, rehabilitation training task time and rehabilitation training frequency of different limb parts; i has a value of [1,8 ]]Respectively representing different parts of the limb: left foot, left lower limb, left upper limb, left hand, right foot, right lower limb, right upper limb, right hand; BW (BW) i Is a certain limb part; TP (Transmission protocol) i The rehabilitation training task type is a rehabilitation training task type of a certain part; HR (HR) i The difficulty coefficient of the rehabilitation training task for a certain part; TM (TM) i Training time for a rehabilitation training task of a certain part; f (F) i Training frequency for rehabilitation training tasks of a certain part; g 3CNN The method comprises the steps of training a convolutional neural network model; l (L) RK For scale feature vectors, H NK For nuclear magnetic resonance brain images and report data features; j (J) K Is the basic information of the patient after pretreatment.
The cerebral apoplexy rehabilitation training system comprises a rehabilitation training task recommending module, wherein the rehabilitation training task recommending module further comprises an optimal rehabilitation training task recommending module, and the rehabilitation training task recommending module is used for recommending the rehabilitation training task Y based on the limb movement function of the patient RK Rehabilitation training task Y based on brain function of patient NK Rehabilitation training task Y integrating exercise function and brain function NH Performing weight fusion to obtain an optimal rehabilitation training task ZR XL ：
ZR XL ＝(aY RK +bY NK +cY NH )*G WC *m
ZR XL Is the optimal rehabilitation training task, A, B, C is a weight matrix, A= [ a ] 1 ,a 2 ,a 3 ,...a 8 ],B＝[b 1 ,b 2 ,b 3 ,...b 8 ],C＝[c 1 ,c 2 ,c 3 ,...c 8 ],a i 、b i 、c i The value ranges are all 0,1],i∈[1,8]，G WC And m is the correction coefficient for the completion of the last rehabilitation training task. G when the matching degree of the patient for carrying out the rehabilitation training task for the first time or the last rehabilitation training task is 100 percent WC The value is 1; when the previous rehabilitation training task of the patient is matchedG when the compounding degree is lower than 100 percent WC The value is less than 1.
The cerebral apoplexy rehabilitation training system is characterized in that the rehabilitation training task is selected according to the following rules:
when three rehabilitation training tasks Y RK 、Y NK 、Y NH When the rehabilitation training task types aiming at a certain limb part are inconsistent, a rehabilitation training task with a better rehabilitation training task type is selected;
when three rehabilitation training tasks Y RK 、Y NK 、Y NH When one rehabilitation training task type aiming at a certain limb part is inconsistent with the other two rehabilitation training tasks, selecting two rehabilitation training tasks with the same rehabilitation training task type;
when three rehabilitation training tasks Y RK 、Y NK 、Y NH When the types of rehabilitation training tasks aiming at a certain limb part are consistent, three rehabilitation training tasks with the same type of rehabilitation training tasks are selected.
The cerebral apoplexy rehabilitation training system comprises the following weight coefficients:
Figure BDA0004125557870000061
Figure BDA0004125557870000062
Figure BDA0004125557870000063
wherein ρ (Y) RK-BWi ，Y NK-BWi ) For rehabilitation training task Y RK And Y NK Similarity of ρ (Y) RK-BWi ，Y NH-BWi ) For rehabilitation training task Y RK And Y NH Similarity of ρ (Y) NK-BWi ，Y NH-BWi ) For rehabilitation training task Y NH And Y NK Similarity of (2);
Figure BDA0004125557870000064
Figure BDA0004125557870000071
Figure BDA0004125557870000072
wherein Y is RK-HRi 、Y NK-HRi 、Y NH-HRi Respectively three rehabilitation training tasks Y RK 、Y NK 、Y NH Difficulty coefficient, Y RK-TMi 、Y NK-TMi 、Y NH-TMi Respectively three rehabilitation training tasks Y RK 、Y NK 、Y NH Training time of Y RK-Fi 、Y NK-Fi 、Y NH-Fi Respectively three rehabilitation training tasks Y RK 、Y NK 、Y NH Is used for training the training frequency of the device.
The cerebral apoplexy rehabilitation training system comprises a rehabilitation training module, a cerebral apoplexy rehabilitation training module and a cerebral apoplexy rehabilitation training module, wherein the rehabilitation training module comprises a virtual reality module, an information acquisition module, an information analysis module and a multi-physical stimulation regulation and control module.
The cerebral apoplexy rehabilitation training system comprises an information analysis module, a control module and a control module, wherein the information analysis module is used for carrying out time domain feature and frequency domain feature analysis on an eye electric signal after interference noise is removed, and the smoothness time domain feature of the eye electric signal is defined as follows:
Figure BDA0004125557870000073
wherein Z is uv For smoothness of electro-oculogram signal, Z iu Is the electrooculogram signal of the U electrode (the electrode of the upper eyelid),
Figure BDA0004125557870000074
is the mean value of the u-electrode electro-oculogram signal, +.>
Figure BDA0004125557870000075
Electrooculogram signal for V electrode (electrode of lower eyelid,)>
Figure BDA0004125557870000076
Is the mean value of the V electrode electro-oculogram signal;
the average power frequency domain characteristics of the electro-oculogram signal are defined as follows:
Figure BDA0004125557870000077
wherein Z is PF Is the average power frequency of the ocular level, f is the frequency of the ocular signal, f 1 And f 2 Representing a frequency range of the electro-ocular signal;
for the time domain features and the frequency domain features, the information analysis module establishes concentration model indexes as follows:
concentration f=a×z uv +B×Z PF
Wherein Z is uv For smoothness of electro-oculogram signal, Z PF For the eye level average power frequency, A, B are their weights, respectively. Experiments prove that the concentration of a cerebral apoplexy patient has the greatest correlation with the ocular signal smoothness and the ocular level average power frequency, and the ocular signal smoothness occupies a larger proportion, so the value of the weight A, B is set as follows: 0.5<A<1,0<B<0.5，A+B＝1。
The cerebral apoplexy rehabilitation training system, wherein the rehabilitation training task completion evaluation module is used for determining the rehabilitation training task matching degree of the patient according to the times, the concentration time, the rehabilitation training task completion degree and the like of the multi-physical stimulation regulation and control in the rehabilitation training module,
matching degree of rehabilitation training tasks:
Figure BDA0004125557870000081
wherein Gc is the matching degree of the rehabilitation training task, and T is the completion of one rehabilitation training taskTime t c For the total time of physical stimulation in the process of one rehabilitation training task, kc is the completion degree of the rehabilitation training task and Z C For the time period that the concentration of the patient reaches the standard in one rehabilitation training task, C1, C2 and C3 are weight coefficients, and the values of the weight coefficients C1, C2 and C3 are set as follows: 0.1<C1<0.4,0.6<C2<＝1,0.1<C2<0.4, and c1+c2+c3=1.
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FIG. 1 is a schematic diagram of the stroke rehabilitation training system of the present invention;
fig. 2 is a flowchart of the stroke rehabilitation training system according to the present invention.
Detailed Description
The following describes embodiments of the present invention in detail with reference to fig. 1-2.
As shown in fig. 1, the cerebral apoplexy rehabilitation training system comprises a rehabilitation training task recommendation module, a rehabilitation training module and a rehabilitation training task completion evaluation module. Wherein: the rehabilitation training task recommending module is used for recommending an adaptive rehabilitation training task for the patient according to the rehabilitation evaluation result of the cerebral apoplexy patient; the rehabilitation training module is used for performing rehabilitation training on the cerebral apoplexy patient according to the rehabilitation training task recommended by the rehabilitation training task recommending module; the rehabilitation training task completion evaluation module is used for evaluating the rehabilitation training completion degree of the patient and feeding back to the rehabilitation training task recommendation module.
The rehabilitation training task recommendation module comprises a rehabilitation training task recommendation module based on limb movement functions, a rehabilitation training task recommendation module based on brain functions, a rehabilitation training task recommendation module integrating movement functions and brain functions, and an optimal rehabilitation training task recommendation module. The concrete explanation is as follows:
the rehabilitation training task recommending module based on limb movement function is used for recommending rehabilitation training tasks suitable for patients according to the movement function scale evaluation results of the patients in the following mode, and specifically comprises the following steps:
constructing a rehabilitation training task database based on a cerebral apoplexy patient movement function scale, and preprocessing scale information and basic information in the database:normalizing and preprocessing Fugl-Meyer rating scale, barthel index, brunstrom rating scale, berg balance scale and basic information (such as age, gender, academy, occupation, hypertension history and other personal information) to obtain scale feature vector L RK And basic information vector J K 。
And inputting the preprocessed scale data and the basic information feature vector into a convolutional neural network model for training and model optimization, and continuously optimizing parameters of the model to finally obtain a rehabilitation training task recommendation model based on limb movement function.
Y RK ＝[BW i ,TP i ,HR i ,TM i ,F i ]＝G 1CNN (L RK ，J K )
Wherein Y is RK The method comprises the steps of providing recommended rehabilitation training tasks, wherein the recommended rehabilitation training tasks comprise rehabilitation training task types, rehabilitation training task difficulties, rehabilitation training task time and rehabilitation training frequency of different limb parts; i has a value of [1,8 ]]Respectively representing different parts of the limb: left foot, left lower limb, left upper limb, left hand, right foot, right lower limb, right upper limb, right hand; BW (BW) i Is a certain limb part; TP (Transmission protocol) i The rehabilitation training task type is a rehabilitation training task type of a certain part; HR (HR) i The difficulty of a rehabilitation training task for a certain part; TM (TM) i The rehabilitation training task time of a certain part; f (F) i The task frequency of rehabilitation training for a certain part; g 1CNN The method comprises the steps of training a convolutional neural network model; l (L) RK The feature vector is a pre-processed scale feature vector; j (J) K Is the basic information of the patient after pretreatment.
The convolutional neural network model consists of 3 convolutional layers and 3 pooling layers, each convolutional layer is connected with one pooling layer, the convolutional kernel of each convolutional layer is 2 x 2, and the convolutional step length is 1*1; the pooling layer is the maximum pooling of 2 x 2.
Preferably, the patient scale data information is updated every 1 month, and the rehabilitation training task is recommended to the patient again through the rehabilitation training task recommendation module based on the limb movement function.
And constructing a rehabilitation training task recommendation module based on brain functions, and recommending rehabilitation training tasks suitable for the patient according to the nuclear magnetic resonance brain image evaluation result of the patient. The method comprises the following steps:
constructing a rehabilitation training task database based on nuclear magnetic resonance brain images of cerebral apoplexy patients, and preprocessing the nuclear magnetic resonance brain images and report data in the database: filtering and removing noise from the nuclear magnetic resonance brain image by using Gaussian filtering and median filtering; and vectorizing the nuclear magnetic resonance data report result words by using word2vec word vector technology.
Extracting the preprocessed nuclear magnetic resonance brain image and the reported data characteristics by using a convolutional neural network model;
H NK ＝Feedforward(W CNN ，B CNN ；JJ CNN ,CH CNN ；HC,BG；)
wherein H is NK Is a feature vector extracted from a convolutional neural network model; feedforward is a feed forward neural network function; w (W) CNN Is a weight matrix of a convolutional neural network model, B CNN Is a bias parameter; JJ (joint junction) CNN Is a convolution layer of a convolution module, the convolution kernel is 2 x 2, and the convolution step length is 2 x 2; CH (CH) CNN The pooling layer is a pooling layer of the convolution module, and the pooling layer is a maximum pooling core of 3*3; HC. BG is the input nmr brain image data and data report, respectively.
And inputting the extracted nuclear magnetic resonance brain image and the reported data characteristics into another convolutional neural network model for training and model optimization, and continuously optimizing parameters of the model to finally obtain a rehabilitation training task recommendation model of brain functions.
Y NK ＝[BW i ,TP i ,HR i ,TM i ,F i ]＝G 2CNN (L RK ，H NK ，J K )
The other convolutional neural network model consists of 3 convolutional layers and 3 pooling layers, each convolutional layer is connected with one pooling layer, the convolution kernel of the first convolutional layer is 2 x 2, and the convolution step length is 2*1; the convolution kernel of the second convolution layer is 4*2, the convolution step length is 3*1, the convolution kernel of the first convolution layer is 8*4, the convolution step length is 4*1, and the pooling layers are all 2 x 2 maximum pooling.
Wherein Y is NK The method comprises the steps of providing recommended rehabilitation training tasks, wherein the recommended rehabilitation training tasks comprise rehabilitation training task types, rehabilitation training task difficulties, rehabilitation training task time and rehabilitation training frequency of different limb parts; i has a value of [1,8 ]]Respectively representing different parts of the limb: left foot, left lower limb, left upper limb, left hand, right foot, right lower limb, right upper limb, right hand; BW (BW) i Is a certain limb part; TP (Transmission protocol) i The rehabilitation training task type is a rehabilitation training task type of a certain part; HR (HR) i The difficulty of a rehabilitation training task for a certain part; TM (TM) i The rehabilitation training task time of a certain part; f (F) i The task frequency of rehabilitation training for a certain part; g 2CNN The method comprises the steps of training a convolutional neural network model; h NK For nuclear magnetic resonance brain images and report data features; j (J) K Is the basic information of the patient after pretreatment.
Preferably, the nuclear magnetic resonance brain image and the report information of the patient are updated every 2 months, and the rehabilitation training task is recommended to the patient again through the rehabilitation training task recommendation module based on the brain function.
And constructing a rehabilitation training task recommendation module integrating the exercise function and the brain function, and comprehensively recommending rehabilitation training tasks suitable for the patient according to the preprocessed scale exercise function data and nuclear magnetic resonance brain image data of the patient. The method comprises the following steps:
inputting the obtained scale feature vector and nuclear magnetic resonance data feature into a convolutional neural network model for training and model optimization, and continuously optimizing parameters of the model to finally obtain a rehabilitation training task recommendation model combining the motor function and the brain function.
Y NH ＝[BW i ,TP i ,HR i ,TM i ,F i ]＝G 3CNN (L RK ，H NK ，J K )
The convolutional neural network model consists of 4 convolutional layers and 4 pooling layers, each convolutional layer is connected with one pooling layer, the convolution kernel of the first convolutional layer is 2 x 2, and the convolution step length is 2*1; the convolution kernel of the second convolution layer is 3*3, the convolution step length is 3*1, the convolution kernel of the third convolution layer is 6*4, the convolution step length is 4*1, the convolution kernel of the fourth convolution layer is 8*4, the convolution step length is 4*1, and the pooling layers are the maximum pooling of 3*3.
Wherein Y is NH The method comprises the steps of providing recommended rehabilitation training tasks, wherein the recommended rehabilitation training tasks comprise rehabilitation training task types, rehabilitation training task difficulties, rehabilitation training task time and rehabilitation training frequency of different limb parts; i has a value of [1,8 ]]Respectively representing different parts of the limb: left foot, left lower limb, left upper limb, left hand, right foot, right lower limb, right upper limb, right hand; BW (BW) i Is a certain limb part; TP (Transmission protocol) i The rehabilitation training task type (passive, boosting, active, anti-blocking) of a certain part; HR (HR) i The difficulty coefficient of the rehabilitation training task for a certain part; TM (TM) i Training time for a rehabilitation training task of a certain part; f (F) i Training frequency for rehabilitation training tasks of a certain part; g 3CNN The method comprises the steps of training a convolutional neural network model; l (L) RK For scale feature vectors, H NK For nuclear magnetic resonance brain images and report data features; j (J) K Is the basic information of the patient after pretreatment.
Constructing an optimal rehabilitation training task recommendation module, and performing rehabilitation training task Y based on limb movement function of patients RK Rehabilitation training task Y based on brain function of patient NK Rehabilitation training task Y integrating exercise function and brain function NH Performing weight fusion to obtain an optimal rehabilitation training task ZR XL 。
ZR XL ＝(aY RK +bY NK +cY NH )*G WC *m
ZR XL Is the optimal rehabilitation training task, A, B, C is a weight matrix, A= [ a ] 1 ,a 2 ,a 3 ,...a 8 ],B＝[b 1 ,b 2 ,b 3 ,...b 8 ],C＝[c 1 ,c 2 ,c 3 ,...c 8 ],a i 、b i 、c i The value ranges are all 0,1],i∈[1,8]，G WC And m is the correction coefficient for the completion of the last rehabilitation training task. G when the matching degree of the patient for carrying out the rehabilitation training task for the first time or the last rehabilitation training task is 100 percent WC The value is 1; g when the matching degree of the last rehabilitation training task of the patient is lower than 100 percent WC The value is less than 1.
Specifically, A, B, C is a weight matrix, analyzed as follows:
when three rehabilitation training tasks Y RK 、Y NK 、Y NH When the rehabilitation training task types aiming at a certain limb part are inconsistent, the rehabilitation training task with a better rehabilitation training task type is selected. For example: when three rehabilitation training tasks Y RK 、Y NK 、Y NH Is aimed at a certain limb part BW i The rehabilitation training tasks of (1) are respectively as follows: passive, auxiliary and active, then choose the "passive" rehabilitation training task, weight coefficient a i ＝1，b i ＝c i ＝0。
When three rehabilitation training tasks Y RK 、Y NK 、Y NH When one rehabilitation training task type aiming at a certain limb part is inconsistent with the other two rehabilitation training tasks, two rehabilitation training tasks with the same rehabilitation training task type are selected. For example: when three rehabilitation training tasks Y RK 、Y NK 、Y NH Is aimed at a certain limb part BW i The rehabilitation training tasks of (1) are respectively as follows: passive, boosting and boosting, then selecting the "boosting" rehabilitation training task and weighting coefficient a i ＝0，b i ＝c i ＝0.5。
When three rehabilitation training tasks Y RK 、Y NK 、Y NH When the types of rehabilitation training tasks aiming at a certain limb part are consistent, three rehabilitation training tasks with the same type of rehabilitation training tasks are selected. For example: when three rehabilitation training tasks Y RK 、Y NK 、Y NH Is aimed at a certain limb part BW i When the rehabilitation training tasks are all passive, the passive rehabilitation training tasks are selected, and three rehabilitation training tasks Y are calculated RK 、Y NK 、Y NH Is aimed at a certain limb part BW i Similarity of rehabilitation training tasks. The weight coefficients are calculated as follows:
Figure BDA0004125557870000141
Figure BDA0004125557870000142
Figure BDA0004125557870000143
wherein ρ (Y) RK-BWi ，Y NK-BWi ) For rehabilitation training task Y RK And Y NK Similarity of ρ (Y) RK-BWi ，Y NH-BWi ) For rehabilitation training task Y RK And Y NH Similarity of ρ (Y) NK-BWi ，Y NH-BWi ) For rehabilitation training task Y NH And Y NK Is a similarity of (3).
Figure BDA0004125557870000144
Figure BDA0004125557870000145
Figure BDA0004125557870000146
Wherein Y is RK-HRi 、Y NK-HRi 、Y NH-HRi Respectively three rehabilitation training tasks Y RK 、Y NK 、Y NH Difficulty coefficient, Y RK-TMi 、Y NK-TMi 、Y NH-TMi Respectively three rehabilitation training tasks Y RK 、Y NK 、Y NH Training time of Y RK-Fi 、Y NK-Fi 、Y NH-Fi Respectively three rehabilitation training tasks Y RK 、Y NK 、Y NH Is used for training the training frequency of the device.
Further, when all a's are calculated i 、b i 、c i And obtaining A, B, C as a weight matrix.
The rehabilitation training module comprises a virtual reality module, an information acquisition module, an information analysis module and a multi-physical stimulation regulation and control module (comprising a transcranial magnetic stimulation module, a peripheral electric stimulation module, an auditory stimulation module and the like).
The virtual reality module is used for displaying the rehabilitation training tasks recommended by the rehabilitation training task recommendation module on a display screen and providing the rehabilitation training tasks based on VR.
The information acquisition module comprises: near-infrared brain function equipment is used for collecting near-infrared brain blood oxygen signals of a patient in the rehabilitation training process; the surface myoelectric instrument is used for collecting myoelectric signals of a patient in the rehabilitation training process; the eye movement instrument is used for collecting eye electric signals of a patient in the rehabilitation training process.
The information analysis module is used for carrying out filtering pretreatment and feature extraction analysis on near infrared cerebral blood oxygen signals, electromyographic signals and eye movement signals acquired by the information acquisition module, and calculating and obtaining brain activation, cerebral muscle coherence, concentration and the like of a patient. Specifically:
the information analysis module analyzes and calculates brain activation degree by carrying out wavelet transformation and complex transformation on the near infrared brain blood oxygen signals, wherein the brain activation degree reflects the activity degree of brain functions.
Figure BDA0004125557870000151
Wherein, WO is brain activation, J represents the number of channels with functional connection in brain region on the healthy side, WA i A cerebral blood oxygen signal wavelet amplitude value indicating that a functional connecting channel exists in a brain region on the healthy side; h represents the number of channels where functional connections exist in the affected lateral brain region, WA k Brain blood oxygen signal wavelet amplitude of functional connecting channels exist in the affected side brain area.
The information analysis module calculates the coherence of the cerebral blood oxygen signal and the electromyographic signal according to a power spectrum calculation method, and the correlation reflects the correlation of the two signals on a frequency spectrum.
Figure BDA0004125557870000161
Wherein MP is the coherence of brain blood oxygen signal and electromyographic signal, and CP XJY (f) For the cross power spectrum of brain blood oxygen signal and electromyographic signal, CP XHY (f) Is the cross power spectrum of the blood oxygen signal of the affected brain and the electromyographic signal, CP XJ (f) For strengthening brain blood oxygen signal self-power spectrum, CP XH (f) For the self-power spectrum of the blood oxygen signal of the brain of the affected side, CP Y (f) The self-power spectrum of the electromyographic signal is XJ, the brain blood oxygen signal on the healthy side is XH, the brain blood oxygen signal on the affected side is Y, and the electromyographic signal is Y.
The information analysis module performs time domain feature and frequency domain feature analysis on the electro-oculogram signal after the interference noise is removed. The smoothness time domain features of the ocular signal are defined as follows:
Figure BDA0004125557870000162
wherein Z is uv For smoothness of electro-oculogram signal, Z iu Is the electrooculogram signal of the U electrode (the electrode of the upper eyelid),
Figure BDA0004125557870000163
mean value of u-electrode electrooculogram signal, Z iv Electrooculogram signal for V electrode (electrode of lower eyelid,)>
Figure BDA0004125557870000164
Is the mean value of the V electrode electro-oculogram signal.
The average power frequency domain characteristics of the electro-oculogram signal are defined as follows:
Figure BDA0004125557870000165
/>
wherein Z is PF Is the average power frequency of the ocular level, f is the frequency of the ocular signal, f 1 And f 2 Representing the frequency range of the electro-oculogram signal.
For the time domain features and the frequency domain features, the information analysis module establishes concentration model indexes as follows:
concentration f=a×z uv +B×Z PF
Wherein Z is uv For smoothness of electro-oculogram signal, Z PF For the eye level average power frequency, A, B are their weights, respectively. Experiments prove that the concentration of a cerebral apoplexy patient has the greatest correlation with the ocular signal smoothness and the ocular level average power frequency, and the ocular signal smoothness occupies a larger proportion, so the value of the weight A, B is set as follows: 0.5<A<1,0<B<0.5，A+B＝1。
The multi-physical-stimulation regulation and control module is used for regulating and controlling the modes of transcranial magnetic stimulation, peripheral electric stimulation, auditory stimulation and the like according to the data of brain stimulation activity, brain muscle coherence, concentration and the like of the patient analyzed by the information analysis module. The specific regulation scheme is as follows:
when the brain activation degree of the patient is lower than a set threshold value, performing brain nerve regulation by transcranial magnetic stimulation; when the brain muscle relativity of the patient is lower than a set threshold value, performing neuromuscular control of peripheral electrical stimulation; when the concentration of the patient is lower than the set threshold, the concentration regulation of the auditory stimulus is performed, for example: playing rhythmic music, etc.
Further, transcranial magnetic stimulation, peripheral electrical stimulation and auditory stimulation can be respectively performed by single stimulation according to the conditions of brain stimulation activity, brain muscle coherence, concentration and the like of a patient, or by two-by-two combined stimulation, or by simultaneous stimulation of the three.
Preferably, when the brain stimulation activity and the brain muscle coherence of the patient are respectively lower than the respective set threshold values, the transcranial magnetic stimulation and the peripheral electrical stimulation can be simultaneously performed; when the analysis finds that the brain excitation activity and the concentration are respectively lower than the respective set threshold values, transcranial magnetic stimulation and auditory stimulation can be simultaneously carried out; when the analysis finds that the brain muscle coherence and concentration are respectively lower than the set threshold values, peripheral electric stimulation and auditory stimulation can be simultaneously carried out; when the analysis finds that the brain excitation activity, the brain muscle coherence and the concentration are respectively lower than the threshold values set by the brain stimulation activity, the brain muscle coherence and the concentration, the transcranial magnetic stimulation, the peripheral electrical stimulation and the auditory stimulation can be simultaneously performed.
Preferably, parameters such as stimulation position, stimulation frequency, stimulation duration, stimulation amplitude and the like of specific transcranial magnetic stimulation can be regulated and controlled by data indexes such as brain lateral property, brain function connection, effect connection and the like obtained through calculation and analysis of brain blood oxygen signals, so that accurate regulation and control are performed. The specific precise regulation scheme is as follows: the brain stimulation activity, brain deviation, brain function connectivity, basic clinical information, stimulation position, stimulation frequency, stimulation duration, stimulation amplitude and other data indexes of the optimal transcranial magnetic stimulation of the patient are continuously collected to establish a transcranial magnetic stimulation parameter adjustment database, a self-adaptive transcranial magnetic stimulation parameter model based on deep learning is established, training tests are continuously carried out, neural network model parameters are optimized, a self-adaptive transcranial magnetic stimulation parameter model is formed, personalized self-adaptive transcranial magnetic stimulation parameters can be intelligently recommended, fine adjustment is carried out according to real-time rehabilitation training conditions of the patient, and the times of manual parameter adjustment are reduced.
Preferably, the brain blood oxygen signals and the electromyographic signals can be further calculated and analyzed to obtain the data indexes such as muscle strength, muscle fatigue and the like, and the parameters such as the stimulation frequency, the stimulation duration, the stimulation pulse and the like of the specific peripheral electrical stimulation can be regulated and controlled, so that the accurate regulation and control can be performed. The specific precise regulation scheme is as follows: the peripheral electric stimulation parameter adjustment database is built by continuously collecting data indexes such as the brain-muscle coherence, the muscle strength, the muscle fatigue degree, the optimal electric stimulation frequency, the stimulation duration, the stimulation pulse and the like of a patient, the self-adaptive peripheral electric stimulation parameter model based on deep learning is built, training tests are continuously carried out, the neural network model parameters are optimized, the self-adaptive peripheral electric stimulation parameter model is formed, personalized self-adaptive peripheral electric stimulation parameters can be intelligently recommended, fine adjustment is carried out according to the real-time rehabilitation training condition of the patient, and the frequency of manual parameter adjustment is reduced.
The rehabilitation training task completion evaluation module is used for determining the rehabilitation training task matching degree of the patient according to the times, concentration time length, the rehabilitation training task completion degree and the like of multi-physical stimulation regulation and control in the rehabilitation training module.
Matching degree of rehabilitation training tasks:
Figure BDA0004125557870000191
wherein Gc is the matching degree of the rehabilitation training task, T is the time for completing one rehabilitation training task, and T c For the total time of physical stimulation in the process of one rehabilitation training task, kc is the completion degree of the rehabilitation training task and Z C For the time of reaching the standard of the concentration of the patient in one rehabilitation training task, C1, C2 and C3 are weight coefficients. Experiments show that the proportion of the completion degree of the rehabilitation training tasks in the matching degree of the rehabilitation training tasks is relatively large, so that the values of the weight coefficients C1, C2 and C3 are set as follows: 0.1<C1<0.4,0.6<C2<＝1,0.1<C2<0.4, and c1+c2+c3=1.
Compared with the prior art, the invention has the beneficial effects that:
(1) The rehabilitation training task recommendation of the patient is comprehensively carried out through the limb movement function data index, the brain function data index and the three indexes of the fusion movement function and the brain function data index of the cerebral apoplexy patient, the adaptation degree of multi-source information such as limb, brain-limb coordination and the like of the patient to the rehabilitation training task is fully considered, and the accurate recommendation of the rehabilitation training task of the patient is realized.
(2) And the brain stimulation activity, the brain muscle coherence, the concentration and the like in the rehabilitation training process of the patient are combined, the multi-physical stimulation modes such as transcranial magnetic stimulation, peripheral electrical stimulation, auditory stimulation and the like are utilized for regulation and control, and the cooperative optimization and real-time feedback of brain limb and physical stimulation data are promoted.
(3) By utilizing the system, personalized self-adaptive rehabilitation training task recommendation can be provided for patients, and the maximum gain effect is exerted by assisting multiple physical stimulation rehabilitation training in the rehabilitation training task process, so that the rehabilitation training efficiency and effect are improved.
Finally, it should be noted that: the foregoing embodiments are merely illustrative embodiments of the present invention, and not restrictive, and the scope of the invention is not limited to the foregoing embodiments, but it should be understood by those skilled in the art that any modification, variation or substitution of some technical features described in the foregoing embodiments may be made without departing from the spirit and scope of the technical solutions of the embodiments of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (3)

1. The utility model provides a cerebral apoplexy rehabilitation training system, includes rehabilitation training task recommendation module, rehabilitation training module and rehabilitation training task completion evaluation module, its characterized in that: the rehabilitation training task recommending module is used for recommending an adaptive rehabilitation training task for the patient according to the rehabilitation evaluation result of the cerebral apoplexy patient; the rehabilitation training module is used for performing rehabilitation training on the cerebral apoplexy patient according to the rehabilitation training task recommended by the rehabilitation training task recommending module; the rehabilitation training task completion evaluation module is used for evaluating the rehabilitation training completion degree of the patient and feeding back to the rehabilitation training task recommendation module; the rehabilitation training task recommending module comprises a rehabilitation training task recommending module which fuses a sports function and a brain function.
2. The stroke rehabilitation training system according to claim 1, wherein the rehabilitation training task completion evaluation module is configured to determine a rehabilitation training task matching degree of the patient according to the number of times of multi-physical stimulation regulation, concentration time length, rehabilitation training task completion degree and the like in the rehabilitation training module.
3. The stroke rehabilitation training system according to claim 1, wherein: the rehabilitation training task recommending module recommends a rehabilitation training task suitable for a patient according to a nuclear magnetic resonance brain image evaluation result of the patient in the following mode:
constructing a rehabilitation training task database based on nuclear magnetic resonance brain images of cerebral apoplexy patients, and preprocessing the nuclear magnetic resonance brain images and report data in the database;
and extracting the preprocessed nuclear magnetic resonance brain image and the reported data characteristics by using a convolutional neural network model.
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