CN116580811A - User characteristic-based spine rehabilitation scheme screening method and system - Google Patents
User characteristic-based spine rehabilitation scheme screening method and system Download PDFInfo
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- 238000012216 screening Methods 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012549 training Methods 0.000 claims abstract description 224
- 210000003205 muscle Anatomy 0.000 claims abstract description 152
- 238000012360 testing method Methods 0.000 claims abstract description 111
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- 210000003141 lower extremity Anatomy 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 238000010606 normalization Methods 0.000 description 1
- 206010039722 scoliosis Diseases 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 210000001364 upper extremity Anatomy 0.000 description 1
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Abstract
The invention provides a spine rehabilitation scheme screening method and system based on user characteristics, which relate to the technical field of data processing, and the method comprises the following steps: the method comprises the steps of collecting multiple pieces of characteristic information, obtaining a user characteristic information set, testing multiple test indexes, obtaining a muscle strength data set, inputting the user characteristic information set into a spine rehabilitation scheme database to obtain multiple primary spine rehabilitation schemes, analyzing the multiple training indexes to obtain multiple importance parameters, respectively inputting the muscle strength data set and the multiple primary spine rehabilitation schemes into a training effect analysis model to obtain multiple training score sets, and taking the primary spine rehabilitation scheme corresponding to the largest primary score as a spine rehabilitation scheme screening result of a target user.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a spine rehabilitation scheme screening method and system based on user characteristics.
Background
Many of the current work needs to be done on a long-term table, which results in modern people suffering from spinal diseases in neck and waist more or less, and patients often choose professional rehabilitation doctors and therapists to conduct offline rehabilitation guidance and training when affecting the life of the patients, but professional rehabilitation guidance training needs professional staff to know, analyze and execute face to face, and needs to persist for a long time, and needs to spend more time, cost and energy of the patients.
In the prior art, a patient can independently finish related exercises according to actions in a spinal rehabilitation training scheme, but because the independent training scheme is not formulated for the patient, the training actions are unreasonable, so that the rehabilitation effect is poor, and even secondary injuries occur.
In the prior art, when a patient autonomously performs the rehabilitation training of the spine, the training scheme suitable for the patient cannot be accurately obtained, so that the technical problem of poor training effect is caused.
Disclosure of Invention
The application provides a user characteristic-based spine rehabilitation scheme screening method and system, which are used for solving the technical problem that in the prior art, when spine rehabilitation training is performed autonomously, a training scheme suitable for the user cannot be accurately obtained, so that the training effect is poor.
In view of the above problems, the application provides a method and a system for screening a spinal rehabilitation scheme based on user characteristics.
In a first aspect, the present application provides a method for screening a spinal rehabilitation regimen based on user characteristics, the method comprising: collecting multiple pieces of characteristic information of a target user to obtain a user characteristic information set, wherein the target user is a user to be screened by remembering a rehabilitation scheme; according to a plurality of test indexes, testing the spine muscle strength of the target user to obtain a muscle strength data set comprising a plurality of muscle strength test data; inputting the user characteristic information set into a spine rehabilitation scheme database for preliminary index screening to obtain a plurality of primarily selected spine rehabilitation schemes, wherein each primarily selected spine rehabilitation scheme comprises training parameters for rehabilitation training on a plurality of training indexes, and the plurality of training indexes correspond to a plurality of test indexes; according to the muscle strength data set, analyzing the importance of the training indexes to the target user to obtain a plurality of importance parameters; respectively inputting the muscle strength data set, the muscle strength data in the multiple primary spinal rehabilitation schemes and training parameters into multiple analysis units in a training effect analysis model according to the multiple training indexes to obtain multiple training score sets; and carrying out weighted calculation on the training score sets by adopting the importance parameters to obtain a plurality of primary selection scores, and taking a primary selection spine rehabilitation scheme corresponding to the maximum primary selection score as a spine rehabilitation scheme screening result of the target user.
In a second aspect, the present application provides a spinal rehabilitation regimen screening system based on user characteristics, the system comprising: the system comprises a set acquisition module, a recovery module and a recovery module, wherein the set acquisition module is used for acquiring multiple items of characteristic information of a target user to obtain a user characteristic information set, and the target user is a user to be subjected to remembering recovery scheme screening; the testing module is used for testing the spine muscle strength of the target user according to a plurality of testing indexes to obtain a muscle strength data set comprising a plurality of muscle strength testing data; the primary index screening module is used for inputting the user characteristic information set into a spine rehabilitation scheme database to perform primary index screening to obtain a plurality of primary spine rehabilitation schemes, wherein each primary spine rehabilitation scheme comprises training parameters for rehabilitation training on a plurality of training indexes, and the plurality of training indexes correspond to a plurality of test indexes; the analysis module is used for analyzing the importance of the training indexes to the target user according to the muscle strength data set to obtain a plurality of importance parameters; the first input module is used for respectively inputting the muscle strength data set, the muscle strength data in the multiple primary spine rehabilitation schemes and training parameters into multiple analysis units in a training effect analysis model according to the multiple training indexes to obtain multiple training score sets; and the weighted calculation module is used for carrying out weighted calculation on the training score sets by adopting the importance parameters to obtain a plurality of primary selection scores, and taking a primary selection spine rehabilitation scheme corresponding to the maximum primary selection score as a spine rehabilitation scheme screening result of the target user.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the application provides a spine rehabilitation scheme screening method and system based on user characteristics, relates to the technical field of data processing, solves the technical problem that a training scheme suitable for the user cannot be accurately obtained when the spine rehabilitation training is performed autonomously in the prior art, and the training effect is poor, and achieves the purposes of formulating a spine rehabilitation training scheme suitable for a patient according to the characteristics of the patient, guiding the patient to perform autonomous rehabilitation training, and improving the applicability of scheme recommendation and the training effect.
Drawings
FIG. 1 is a schematic flow chart of a method for screening a spinal rehabilitation regimen based on user characteristics;
FIG. 2 is a schematic flow chart of a user characteristic information set obtained in a user characteristic-based spine rehabilitation scheme screening method;
FIG. 3 is a schematic diagram of a flow chart of a set of muscle strength data in a screening method of a spinal rehabilitation regimen based on user characteristics;
FIG. 4 is a schematic flow chart of a plurality of primary spinal rehabilitation schemes in a spinal rehabilitation scheme screening method based on user characteristics;
FIG. 5 is a schematic flow chart of a plurality of importance parameters in a method for screening a spinal rehabilitation regimen based on user characteristics;
FIG. 6 is a schematic diagram of a plurality of training score current flows in a method for screening a spinal rehabilitation regimen based on user characteristics;
FIG. 7 is a schematic diagram of a process for screening multiple primary scores in a spinal rehabilitation regimen based on user characteristics;
fig. 8 is a schematic diagram of a system for screening a spinal rehabilitation regimen based on user characteristics.
Reference numerals illustrate: the device comprises a set acquisition module 1, a test module 2, a preliminary index screening module 3, an analysis module 4, a first input module 5 and a weight calculation module 6.
Detailed Description
The application provides a method and a system for screening a spinal rehabilitation scheme based on user characteristics, which are used for solving the technical problem that in the prior art, when the spinal rehabilitation training is performed autonomously, a training scheme suitable for the user cannot be accurately obtained, so that the training effect is poor.
Example 1
As shown in fig. 1, an embodiment of the present application provides a method for screening a spinal rehabilitation solution based on user characteristics, where the method includes:
step S100: collecting multiple pieces of characteristic information of a target user to obtain a user characteristic information set, wherein the target user is a user to be screened by remembering a rehabilitation scheme;
Specifically, the spine rehabilitation scheme screening method based on the user characteristics is applied to a spine rehabilitation scheme screening system based on the user characteristics, in order to ensure the adaptability of a spine rehabilitation scheme generated by a target user, a plurality of characteristics contained by the target user are firstly required to be collected, the target user is a user to be screened by the system for remembering the rehabilitation scheme, meanwhile, the plurality of characteristics of the target user can contain age information, sex information, weight information, height information, spine and other past medical history information of the target user, and the spine rehabilitation scheme suitable for the target user is generated for later realization as an important reference basis by integrating and summarizing the age information, the sex information, the weight information, the height information, the spine and other past medical history information of the target user.
Step S200: according to a plurality of test indexes, testing the spine muscle strength of the target user to obtain a muscle strength data set comprising a plurality of muscle strength test data;
specifically, since the current-stage spinal muscle strength of the target user needs to be tested so as to be capable of adaptively generating a rehabilitation scheme corresponding to the rehabilitation of the spinal column of the target user in the later stage, a plurality of test indexes are required to be set, wherein the plurality of test indexes comprise neck left muscle strength, neck right muscle strength, waist left muscle strength, waist right muscle strength, neck left side flexion, neck right side flexion, waist left side flexion, waist right side flexion, neck front side muscle strength, neck rear side muscle strength, neck front side flexion strength and neck rear side flexion strength, the spinal deformation muscle strength of the target user is further detected according to a plurality of test indexes, each test index corresponds to one muscle strength test data, and finally all muscle strength data are summarized and then recorded as a muscle strength data set, so that the spinal rehabilitation scheme suitable for the target user is generated.
Step S300: inputting the user characteristic information set into a spine rehabilitation scheme database for preliminary index screening to obtain a plurality of primarily selected spine rehabilitation schemes, wherein each primarily selected spine rehabilitation scheme comprises training parameters for rehabilitation training on a plurality of training indexes, and the plurality of training indexes correspond to a plurality of test indexes;
specifically, on the basis of the scheme owned by the spine rehabilitation scheme, the scheme acquisition is performed on the spine rehabilitation scheme, wherein the scheme acquisition comprises, but is not limited to, a stretching training scheme of the spine, a three-point support method scheme, a kneeling trunk full stretching motion scheme, an upper limb and lower limb cross flat lifting training scheme and the like, and training frequencies, training strengths and the like of different training contents, on the basis, a spine rehabilitation scheme database is constructed, further, the obtained user characteristic information set is input into the constructed spine rehabilitation scheme database for carrying out adaptive preliminary index screening, and training parameters for carrying out rehabilitation training on a plurality of training indexes exist in each primary spine rehabilitation scheme contained in the spine rehabilitation scheme database, namely, the training parameters are acquired when a target user is subjected to rehabilitation training according to the training indexes, and meanwhile, the plurality of training indexes and the plurality of test indexes are in corresponding relation, so that a spine rehabilitation scheme tamping foundation suitable for the target user is generated for subsequent implementation.
Step S400: according to the muscle strength data set, analyzing the importance of the training indexes to the target user to obtain a plurality of importance parameters;
specifically, on the basis of a muscle strength data set including a plurality of muscle strength test data obtained by testing the spine muscle strength of a target user, according to the problems of the spine of the target user, the importance of a plurality of training indexes to the target user is analyzed, meanwhile, the training indexes which are effective for spine rehabilitation of the target user in the plurality of training indexes are extracted, the more effective the spine rehabilitation of the target user is, the higher the importance of the training indexes to the target user is, further, all the training indexes are ordered according to the importance from high to low, and a plurality of parameters with the importance of the training indexes accounting for 60% or more are integrated and summarized, so that a plurality of importance parameters are acquired, and the effect of limiting the generation of a spine rehabilitation scheme suitable for the target user is realized.
Step S500: respectively inputting the muscle strength data set, the muscle strength data in the multiple primary spinal rehabilitation schemes and training parameters into multiple analysis units in a training effect analysis model according to the multiple training indexes to obtain multiple training score sets;
Specifically, a plurality of training indexes are taken as the basis, muscle strength data sets, muscle strength data and training parameters contained in the obtained plurality of primary selected spinal rehabilitation schemes are respectively input into a plurality of analysis units in a training effect analysis model, namely, firstly, on the basis of a BP neural network, analysis units meeting preset conditions are constructed and supervised for training, a test index and training parameters contained in corresponding training indexes are arbitrarily selected, training scores after rehabilitation training is carried out on a user are completed, a plurality of analysis units corresponding to other test indexes and a plurality of training indexes are constructed similarly, all analysis units form a training effect analysis model, the training effect analysis model is input into the constructed corresponding analysis units for analysis, finally, the muscle strength data sets and the muscle strength data and the training parameters in the plurality of primary selected spinal rehabilitation schemes are respectively sequentially input into different analysis units corresponding to the training effect analysis model, and different training scores output by the different analysis units are recorded as a plurality of training score sets for integration so as to be used as reference data when the spinal rehabilitation scheme suitable for a target user is generated for later period.
Step S600: and carrying out weighted calculation on the training score sets by adopting the importance parameters to obtain a plurality of primary selection scores, and taking a primary selection spine rehabilitation scheme corresponding to the maximum primary selection score as a spine rehabilitation scheme screening result of the target user.
Specifically, the weighting calculation is performed on the obtained multiple training score sets according to the screened multiple importance parameters, the weighting calculation is performed on the multiple training score sets after the weighting calculation is performed on the basis of a large amount of data summarization and accurate weight determination, the weight distribution is performed on the multiple training score sets according to the size of the multiple importance parameters, the weight distribution is higher when the importance parameters are larger, meanwhile, the weighting calculation is performed on the training scores contained in the multiple training score sets according to the proportioned multiple weight values respectively, multiple primary selection scores are obtained according to the weighting calculation result, all primary selection scores respectively correspond to a primary selection spinal rehabilitation scheme, further, all primary selection spinal rehabilitation schemes are ranked according to the primary selection scores from large to small, and accordingly the primary selection spinal rehabilitation scheme with the largest primary selection score is used as the screening result of the spinal rehabilitation scheme of a final target user, the accurate establishment of the spinal rehabilitation training scheme according to the characteristics of a patient is realized, and the recommended applicability and the training effect of the scheme are improved.
Further, as shown in fig. 2, step S100 of the present application further includes:
step S110: collecting age information, sex information, weight information, height information and past medical history information of the target user;
step S120: and combining the age information, the gender information, the weight information, the height information and the past medical history information to obtain the user characteristic information set.
Specifically, in order to enable the finally output spine rehabilitation scheme to be matched with the target user, the age information, the sex information, the weight information, the height information and the past medical history information of the target user are required to be acquired, the past medical history can comprise the current medical history, the genetic history, the past medical history, the chronic medical history and the like of the target user, further, the age information, the sex information, the weight information, the height information and the past medical history information of the target user are combined in characteristics, namely, the age information of the target user refers to differences of the characteristics of the spines at different age ranges, the sex information of the target user refers to the fact that the rehabilitation scheme of the spines is selected according to the difference of sexes, the weight information of the target user can be regarded as that the higher the weight is, the higher the height is, the probability of scoliosis is also increased in the same ratio, the past medical history of the target user refers to the past medical history which influences the spines, and further, the characteristic information of the target user is extracted, and the characteristic information set of the target user is provided with important basis for realizing the generation of the spine rehabilitation scheme suitable for the target user.
Further, as shown in fig. 3, step S200 of the present application further includes:
step S210: obtaining the plurality of test indexes, wherein the plurality of test indexes comprise neck left muscle strength, neck right muscle strength, waist left muscle strength, waist right muscle strength, neck left side flexion, neck right side flexion, waist left side flexion, waist right side flexion, neck front side muscle strength, neck rear side muscle strength, neck front side flexion strength and neck rear side flexion strength;
step S220: and detecting the spine deformation muscle strength of the target user according to the plurality of test indexes to obtain a plurality of muscle strength test data of the plurality of test indexes, wherein the muscle strength test data are used as the muscle strength data set.
Specifically, the spine deformation muscle strength of the target user is detected by a plurality of test indexes including a neck left muscle strength, a neck right muscle strength, a waist left muscle strength, a waist right muscle strength, a neck left side flexion, a neck right side flexion, a waist left side flexion, a waist right side flexion, a neck front side muscle strength, a neck rear side muscle strength, a neck front side flexion strength and a neck rear side flexion strength, and the spine deformation muscle strength of the target user is detected by converting voltage changes generated based on spine deformation into a stress magnitude, and the plurality of indexes of the target user can be detected by a spine muscle strength detection method in the prior art. Specifically accessible patient rotates backbone position such as neck, waist, makes pivoted pressure apply the sensor, and this sensor takes place deformation, and the pressure that the sensor received is relevant with atress size to make the impedance in the sensor change, make excitation voltage change simultaneously, output a analog signal of change, and then according to preset analog signal standard, obtain the size of this pressure, and then detect and obtain muscle strength data set, contain a plurality of test index in the muscle strength data set simultaneously, with this guarantee later stage more accurate to be applicable to target user's backbone rehabilitation scheme and generate.
Further, as shown in fig. 4, step S300 of the present application further includes:
step S310: acquiring a plurality of historical user characteristic information sets of a plurality of historical users based on users who carry out spine rehabilitation scheme formulation in past time, and dividing according to the plurality of characteristic information sets to acquire a plurality of historical characteristic information sets;
step S320: constructing a plurality of index element sets according to the plurality of history feature information sets;
step S330: acquiring a historical spinal rehabilitation scheme set according to the historical spinal rehabilitation schemes formulated by the plurality of historical users;
step S340: constructing a plurality of data elements according to the historical spinal rehabilitation scheme set;
step S350: constructing the spine rehabilitation scheme database based on the index relation of the index element sets and the data elements;
step S360: and inputting the user characteristic information set into the spine rehabilitation scheme database for indexing to obtain a plurality of corresponding historical spine rehabilitation schemes as the plurality of primary spine rehabilitation schemes.
Specifically, the characteristic information of the user who has performed the spinal rehabilitation scheme formulation before the current moment is extracted, and the previous spinal rehabilitation scheme formulation is generally formulated by a professional doctor. In this way, a plurality of historical user characteristic information sets contained in a plurality of historical users are obtained, meanwhile, the plurality of historical characteristic information sets are subjected to characteristic division according to different characteristics in the plurality of characteristic information, a plurality of historical characteristic information sets are obtained, and each historical characteristic information set comprises a plurality of historical data of one type of user characteristic information.
Further, a fixed index element is correspondingly constructed for a plurality of divided historical characteristic information sets, namely, each characteristic is indexed in one layer, and simultaneously, after corresponding historical spinal rehabilitation schemes are formulated for a plurality of historical users to be summarized, data elements are constructed for each historical spinal rehabilitation scheme, the plurality of index element sets are indexes of a plurality of data elements, multi-layer index query is conducted for the plurality of data elements through the plurality of index element sets, the construction of a spinal rehabilitation scheme database is completed on the basis, after multi-layer indexes in the database, the spinal rehabilitation scheme of users with the same user characteristic information set in the past time is obtained and is used as a primary spinal rehabilitation scheme, further, the user characteristic information set is input into the constructed spinal rehabilitation scheme database to be indexed, so that a plurality of primary spinal rehabilitation schemes are correspondingly obtained and recorded as a plurality of primary spinal rehabilitation schemes to be output, and the spinal rehabilitation scheme suitable for target users is generated based on the plurality of primary spinal rehabilitation schemes.
Further, as shown in fig. 5, step S400 of the present application further includes:
Step S410: acquiring spine muscle strength test data of a historical user with the user characteristic information set in the past time, and acquiring a plurality of historical muscle strength data sets;
step S420: calculating the average value of the plurality of test indexes in the plurality of historical muscle strength data sets to obtain the average value of the plurality of test indexes;
step S430: calculating the deviation degree of the muscle strength data of a plurality of test indexes in the muscle strength data set and the average value of the corresponding test indexes to obtain a plurality of deviation parameters;
step S440: the plurality of deviation parameters are taken as the plurality of importance parameters.
Specifically, the spine muscle strength of the target user is tested in the past time, so that a spine muscle strength test data set of the historical user with the user characteristic information set is obtained, further, the average value of muscle strength data of a plurality of test indexes contained in the obtained historical muscle strength data sets is calculated, further, the deviation degree of the muscle strength data of the plurality of test indexes in the muscle strength data set of the target user and the average value of the corresponding test indexes is calculated, namely, the deviation parameter of the muscle strength data of each test index of the target user and the average value of the test indexes is calculated according to the average value of the spine muscle strength test data with the same user characteristic information set, namely, the ratio of the difference value of the muscle strength data of the target user and the average value of the test indexes is obtained, the deviation parameters are then recorded as the importance parameters, the larger the deviation parameter is, the more the muscle strength data of the target user on the test indexes are not standard, the basis of the test indexes is larger the importance of the training of the test indexes on the target user is, and the recovery scheme suitable for the target user is completed.
Further, as shown in fig. 6, step S500 of the present application further includes:
step S510: acquiring a first sample muscle strength data set and a first sample training parameter set based on the first test index and the corresponding first training index;
step S520: obtaining training scores after rehabilitation training is carried out on a user with muscle strength data in the first muscle strength data set by adopting training parameters in the first training parameter set of the sample, and obtaining a first training score set of the sample;
step S530: the first sample training parameter set, the first sample muscle strength data set and the first sample training score set are used as construction data, and a first analysis unit meeting preset conditions is constructed and supervised and trained based on a BP neural network;
step S540: continuously constructing a plurality of analysis units corresponding to a plurality of other test indexes and a plurality of training indexes to obtain the training effect analysis model;
step S550: and respectively and sequentially inputting the muscle strength data set, the muscle strength data in the plurality of primary spine rehabilitation schemes and training parameters into the plurality of analysis units to obtain the plurality of training score sets.
Specifically, a test index mark is arbitrarily selected from a plurality of test indexes to serve as a first test index, and the plurality of test indexes and the plurality of training indexes are in one-to-one correspondence, so that a first sample muscle strength data set based on the first test index and a first sample training parameter set based on the first training index are extracted through the selected first test index and the corresponding first training index, further, training after rehabilitation training is carried out on a user with muscle strength data in the first sample muscle strength data set by adopting training parameters in the first sample training parameter set, the higher the scoring score is, the better the rehabilitation training effect is, and therefore the first sample training score set is obtained, building data is that a first sample muscle strength data set, a first sample muscle strength data set and a first sample training score set are adopted on the basis of a BP neural network, a first analysis unit conforming to preset conditions is built and supervised training is obtained, the building process of the first analysis unit can be that each group of training data is input into the first analysis unit, the first analysis unit is supervised and the first analysis unit is adjusted by the corresponding data. Each group of training data in the training data set comprises construction data, the supervision data set is supervision data corresponding to the training data set one by one, when the output result of the first analysis unit is consistent with the supervision data, the current group training is finished, all the training data in the training data set are trained, and the training of the first analysis unit is finished.
In order to ensure the accuracy of the first analysis unit, the test processing of the first analysis unit may be performed by the test data set, for example, the test accuracy may be set to 85%, and when the test accuracy of the test data set satisfies 85%, the first analysis unit is constructed.
Meanwhile, one test index corresponds to one analysis unit, input data of the analysis unit is muscle strength data corresponding to the test index and training parameters corresponding to the training index, the test index comprises a training scheme, training frequency, training strength and the like, so that a plurality of analysis units corresponding to other test indexes and a plurality of training indexes are constructed in the same way, a training effect analysis model is formed based on all the constructed analysis units, and finally muscle strength data and training parameters in a muscle strength data set and a plurality of primary spine rehabilitation schemes are respectively and sequentially input into the constructed plurality of analysis units to obtain training scores of the plurality of training indexes of each primary spine rehabilitation scheme as a training score set, and a plurality of training score sets are further obtained to ensure high efficiency when the spine rehabilitation scheme suitable for a target user is generated.
Further, as shown in fig. 7, step S600 of the present application further includes:
step S610: according to the magnitude of the importance parameters, weight distribution is carried out to obtain a plurality of weight values;
step S620: and respectively carrying out weighted calculation on the training scores in the training score sets by adopting the weight values to obtain the primary score values.
Specifically, weight distribution is performed on a plurality of training score sets according to the magnitude of a plurality of importance parameters, the weight distribution is higher as the importance parameters are larger, the weight distribution can be performed through a hierarchical analysis method, the importance of each evaluation index of a target user is compared in pairs according to the data of a plurality of indexes in a muscle strength data set, a judgment matrix is built by utilizing the existing pair comparison matrix scale table in the hierarchical analysis method, further, consistency test is performed on the judgment matrix according to a consistency proportion, and a calculation formula of the consistency proportion is as follows:
λmax is the maximum eigenvalue of the judgment matrix, and n is the number of evaluation indexes;
RI represents an average random uniformity index, which is known by looking up a table.
When the consistency ratio is smaller than 0.1, the consistency of the judgment matrix is considered acceptable, the next step is executed, otherwise, the judgment matrix is corrected, the judgment matrix weight is calculated through a normalization algorithm and is used as the evaluation index weight of the training branches corresponding to the training indexes in the training score sets, and then the weighted arithmetic average value corresponding to the training score sets is calculated according to the weighted arithmetic average operator formula according to the evaluation index weights. The specific calculation formula of the weighted arithmetic mean is as follows:
WA w ＝A a *u 1 +A b *u 2 +A c *u 3 +A d *u 4 +A e *u 5 +A f *u 6 +A g *
u 7 +A h *u 8 +A i *u 9 +A j *u 10 +A k *u 11 +A l *u 12 ；
WB w ＝B a *u 1 +B b *u 2 +B c *u 3 +B d *u 4 +B e *u 5 +B f *u 6 +B g *
u 7 +B h *u 8 +B i *u 9 +B j *u 10 +B k *u 11 +B l *u 12 ；
WC w ＝C a *u 1 +C b *u 2 +C c *u 3 +C d *u 4 +C e *u 5 +C f *u 6 +C g *u 7 +
C h *u 8 +C i *u 9 +C j *u 10 +C k *u 11 +C l *u 12 ；
WD w ＝D a *u 1 +D b *u 2 +D c *u 3 +D d *u 4 +D e *u 5 +D f *u 6 +D g *
u 7 +D h *u 8 +D i *u 9 +D j *u 10 +D k *u 11 +D l *u 12 ；
WE w ＝E a *u 1 +E b *u 2 +E c *u 3 +E d *u 4 +E e *u 5 +E f *u 6 +E g *
u 7 +E h *u 8 +E i *u 9 +E j *u 10 +E k *u 11 +E l *u 12 ；
Wherein A, B, C, D, E respectively represent a primary spinal rehabilitation regimen;
u 1 、u 2 、u 3 、u 4 、u 5 、u 6 、u 7 、u 8 、u 9 、u 10 、u 11 、u 12 and after the weight distribution of the importance parameters, the weight values of the training indexes and the test indexes are represented.
WAw, WBw, WCw, WDw, WEw represents a weighted arithmetic mean for each of the primary spinal rehabilitation protocols.
a, b, c, d, e, f, g, h, i, j, k, l represent training scores for training protocols of a plurality of training indicators corresponding to a plurality of test indicators within each of the initially selected spinal rehabilitation protocols.
And sequencing the weighted arithmetic average values from large to small, and sequentially outputting corresponding primary screening results to serve as a plurality of primary selection scores.
In one embodiment, the weight distribution may be directly performed according to the magnitudes of the multiple importance parameters, so as to obtain a weight distribution result. And specifically calculating the ratio of the importance parameters to the sum of the importance parameters to obtain a plurality of weight values as weight distribution results.
In summary, the method and the system for screening the vertebral column rehabilitation scheme based on the user characteristics provided by the embodiment of the application at least comprise the following technical effects: the technical problem that the training effect is poor due to the fact that the training scheme suitable for the self cannot be accurately obtained when the spine rehabilitation training is carried out autonomously in the prior art is solved, the spine rehabilitation training scheme suitable for the patient is formulated according to the characteristics of the patient, the patient is guided to carry out autonomous rehabilitation training, and the applicability of scheme recommendation and the training effect are improved.
Example two
Based on the same inventive concept as the spine rehabilitation regimen screening method based on the user characteristics in the foregoing embodiments, as shown in fig. 8, the present application provides a spine rehabilitation regimen screening system based on the user characteristics, the system comprising:
the collection acquisition module 1 is used for acquiring multiple items of characteristic information of a target user to obtain a user characteristic information collection, wherein the target user is a user to be screened for remembering a rehabilitation scheme;
the testing module 2 is used for testing the spine muscle strength of the target user according to a plurality of testing indexes to obtain a muscle strength data set comprising a plurality of muscle strength testing data;
the primary index screening module 3 is used for inputting the user characteristic information set into a spine rehabilitation scheme database to perform primary index screening to obtain a plurality of primary spine rehabilitation schemes, wherein each primary spine rehabilitation scheme comprises training parameters for rehabilitation training on a plurality of training indexes, and the plurality of training indexes correspond to a plurality of test indexes;
the analysis module 4 is used for analyzing the importance of the training indexes to the target user according to the muscle strength data set to obtain a plurality of importance parameters;
The first input module 5 is configured to input the muscle strength data set and the muscle strength data and training parameters in the plurality of primarily selected spinal rehabilitation schemes into a plurality of analysis units in a training effect analysis model according to the plurality of training indexes, so as to obtain a plurality of training score sets;
the weighted calculation module 6 is configured to perform weighted calculation on the training score sets by using the importance parameters to obtain a plurality of primary scores, and use a primary spine rehabilitation scheme corresponding to the largest primary score as a spine rehabilitation scheme screening result of the target user.
Further, the system further comprises:
the information acquisition module is used for acquiring age information, sex information, weight information, height information and past medical history information of the target user;
and the combination module is used for combining the age information, the sex information, the weight information, the height information and the past medical history information to obtain the user characteristic information set.
Further, the system further comprises:
the index acquisition module is used for acquiring the plurality of test indexes, wherein the plurality of test indexes comprise neck left side muscle strength, neck right side muscle strength, waist left side muscle strength, waist right side muscle strength, neck left side flexion, neck right side flexion, waist left side flexion, waist right side flexion, neck front side muscle strength, neck rear side muscle strength, neck front side flexion strength and neck rear side flexion strength;
The intensity detection module is used for detecting the spine deformation muscle strength of the target user according to the plurality of test indexes to obtain a plurality of muscle strength test data of the plurality of test indexes as the muscle strength data set.
Further, the system further comprises:
the division module is used for obtaining a plurality of historical user characteristic information sets of a plurality of historical users based on users who make spine rehabilitation scheme formulation in the past time, and dividing the historical user characteristic information sets according to the plurality of characteristic information sets;
the element set construction module is used for constructing a plurality of index element sets according to the plurality of historical characteristic information sets;
the scheme set obtaining module is used for obtaining a historical backbone rehabilitation scheme set according to the historical backbone rehabilitation schemes formulated by the plurality of historical users;
the data element construction module is used for constructing a plurality of data elements according to the historical spinal rehabilitation scheme set;
the database construction module is used for constructing the spine rehabilitation scheme database based on the index relation between the index element sets and the data elements;
The index module is used for inputting the user characteristic information set into the spine rehabilitation scheme database for indexing, and obtaining a plurality of corresponding historical spine rehabilitation schemes serving as the plurality of primary spine rehabilitation schemes.
Further, the system further comprises:
the intensity data set obtaining module is used for obtaining spine muscle strength test data of the historical user with the user characteristic information set in the past time and obtaining a plurality of historical muscle strength data sets;
the first calculation module is used for calculating the average value of the plurality of test indexes in the plurality of historical muscle strength data sets to obtain a plurality of average values of the test indexes;
the second calculation module is used for calculating the deviation degree of the muscle strength data of the multiple test indexes in the muscle strength data set and the corresponding test index mean value to obtain multiple deviation parameters;
and the parameter module is used for taking the deviation parameters as the importance parameters.
Further, the system further comprises:
the set obtaining module is used for obtaining a first sample muscle strength data set and a first sample training parameter set based on the first test index and the corresponding first training index;
The training module is used for obtaining training scores after rehabilitation training is carried out on the user with the muscle strength data in the first muscle strength data set by adopting the training parameters in the first training parameter set of the sample, and obtaining a first training score set of the sample;
the first construction module is used for constructing and supervising training based on a BP neural network by adopting the first sample training parameter set, the first sample muscle strength data set and the first sample training score set as construction data to obtain a first analysis unit meeting preset conditions;
the second construction module is used for continuously constructing a plurality of analysis units corresponding to a plurality of other test indexes and a plurality of training indexes to obtain the training effect analysis model;
the second input module is used for sequentially inputting the muscle strength data set, the muscle strength data in the multiple primary spine rehabilitation schemes and training parameters into the multiple analysis units respectively to obtain multiple training score sets.
Further, the system further comprises:
the weight distribution module is used for distributing weights according to the magnitude of the importance parameters to obtain a plurality of weight values;
And the third calculation module is used for respectively carrying out weighted calculation on the training scores in the training score sets by adopting the weight values to obtain the primary score values.
The present disclosure is directed to a system for screening a spinal rehabilitation solution based on user characteristics, and the disclosure of the present disclosure is relatively simple and relevant to the description of the method section, because the apparatus disclosed in the embodiments corresponds to the method disclosed in the embodiments.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method for screening a spinal rehabilitation regimen based on user characteristics, the method comprising:
collecting multiple pieces of characteristic information of a target user to obtain a user characteristic information set, wherein the target user is a user to be screened by remembering a rehabilitation scheme;
according to a plurality of test indexes, testing the spine muscle strength of the target user to obtain a muscle strength data set comprising a plurality of muscle strength test data;
inputting the user characteristic information set into a spine rehabilitation scheme database for preliminary index screening to obtain a plurality of primarily selected spine rehabilitation schemes, wherein each primarily selected spine rehabilitation scheme comprises training parameters for rehabilitation training on a plurality of training indexes, and the plurality of training indexes correspond to a plurality of test indexes;
according to the muscle strength data set, analyzing the importance of the training indexes to the target user to obtain a plurality of importance parameters;
respectively inputting the muscle strength data set, the muscle strength data in the multiple primary spinal rehabilitation schemes and training parameters into multiple analysis units in a training effect analysis model according to the multiple training indexes to obtain multiple training score sets;
And carrying out weighted calculation on the training score sets by adopting the importance parameters to obtain a plurality of primary selection scores, and taking a primary selection spine rehabilitation scheme corresponding to the maximum primary selection score as a spine rehabilitation scheme screening result of the target user.
2. The method of claim 1, wherein collecting a plurality of pieces of characteristic information of the target user to obtain a set of user characteristic information, comprises;
collecting age information, sex information, weight information, height information and past medical history information of the target user;
and combining the age information, the gender information, the weight information, the height information and the past medical history information to obtain the user characteristic information set.
3. The method of claim 1, wherein testing the spinal muscular intensity of the target user based on a plurality of test metrics comprises:
obtaining the plurality of test indexes, wherein the plurality of test indexes comprise neck left muscle strength, neck right muscle strength, waist left muscle strength, waist right muscle strength, neck left side flexion, neck right side flexion, waist left side flexion, waist right side flexion, neck front side muscle strength, neck rear side muscle strength, neck front side flexion strength and neck rear side flexion strength;
And detecting the spine deformation muscle strength of the target user according to the plurality of test indexes to obtain a plurality of muscle strength test data of the plurality of test indexes, wherein the muscle strength test data are used as the muscle strength data set.
4. The method of claim 1, wherein inputting the set of user characteristic information into a spinal rehabilitation program database for preliminary index screening to obtain a plurality of preliminary spinal rehabilitation programs, comprising:
acquiring a plurality of historical user characteristic information sets of a plurality of historical users based on users who carry out spine rehabilitation scheme formulation in past time, and dividing according to the plurality of characteristic information sets to acquire a plurality of historical characteristic information sets;
constructing a plurality of index element sets according to the plurality of history feature information sets;
acquiring a historical spinal rehabilitation scheme set according to the historical spinal rehabilitation schemes formulated by the plurality of historical users;
constructing a plurality of data elements according to the historical spinal rehabilitation scheme set;
constructing the spine rehabilitation scheme database based on the index relation of the index element sets and the data elements;
and inputting the user characteristic information set into the spine rehabilitation scheme database for indexing to obtain a plurality of corresponding historical spine rehabilitation schemes as the plurality of primary spine rehabilitation schemes.
5. The method of claim 1, wherein analyzing the importance of the plurality of training metrics to the target user based on the set of muscle strength data to obtain a plurality of importance parameters comprises:
acquiring spine muscle strength test data of a historical user with the user characteristic information set in the past time, and acquiring a plurality of historical muscle strength data sets;
calculating the average value of the plurality of test indexes in the plurality of historical muscle strength data sets to obtain the average value of the plurality of test indexes;
calculating the deviation degree of the muscle strength data of a plurality of test indexes in the muscle strength data set and the average value of the corresponding test indexes to obtain a plurality of deviation parameters;
the plurality of deviation parameters are taken as the plurality of importance parameters.
6. The method of claim 1, wherein inputting the set of muscle strength data and the muscle strength data and training parameters in the plurality of initial spinal rehabilitation programs into a plurality of analysis units in a training effect analysis model, respectively, according to the plurality of training metrics, comprises:
acquiring a first sample muscle strength data set and a first sample training parameter set based on the first test index and the corresponding first training index;
Obtaining training scores after rehabilitation training is carried out on a user with muscle strength data in the first muscle strength data set by adopting training parameters in the first training parameter set of the sample, and obtaining a first training score set of the sample;
the first sample training parameter set, the first sample muscle strength data set and the first sample training score set are used as construction data, and a first analysis unit meeting preset conditions is constructed and supervised and trained based on a BP neural network;
continuously constructing a plurality of analysis units corresponding to a plurality of other test indexes and a plurality of training indexes to obtain the training effect analysis model;
and respectively and sequentially inputting the muscle strength data set, the muscle strength data in the plurality of primary spine rehabilitation schemes and training parameters into the plurality of analysis units to obtain the plurality of training score sets.
7. The method of claim 1, wherein weighting the plurality of training score sets using the plurality of importance parameters to obtain a plurality of preliminary scores comprises:
according to the magnitude of the importance parameters, weight distribution is carried out to obtain a plurality of weight values;
And respectively carrying out weighted calculation on the training scores in the training score sets by adopting the weight values to obtain the primary score values.
8. A spinal rehabilitation program screening system based on user characteristics, the system comprising:
the system comprises a set acquisition module, a recovery module and a recovery module, wherein the set acquisition module is used for acquiring multiple items of characteristic information of a target user to obtain a user characteristic information set, and the target user is a user to be subjected to remembering recovery scheme screening;
the testing module is used for testing the spine muscle strength of the target user according to a plurality of testing indexes to obtain a muscle strength data set comprising a plurality of muscle strength testing data;
the primary index screening module is used for inputting the user characteristic information set into a spine rehabilitation scheme database to perform primary index screening to obtain a plurality of primary spine rehabilitation schemes, wherein each primary spine rehabilitation scheme comprises training parameters for rehabilitation training on a plurality of training indexes, and the plurality of training indexes correspond to a plurality of test indexes;
the analysis module is used for analyzing the importance of the training indexes to the target user according to the muscle strength data set to obtain a plurality of importance parameters;
The first input module is used for respectively inputting the muscle strength data set, the muscle strength data in the multiple primary spine rehabilitation schemes and training parameters into multiple analysis units in a training effect analysis model according to the multiple training indexes to obtain multiple training score sets;
and the weighted calculation module is used for carrying out weighted calculation on the training score sets by adopting the importance parameters to obtain a plurality of primary selection scores, and taking a primary selection spine rehabilitation scheme corresponding to the maximum primary selection score as a spine rehabilitation scheme screening result of the target user.
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