CN114504322B - Training effect prediction method, system and storage medium for muscle strength of lower limb - Google Patents
Training effect prediction method, system and storage medium for muscle strength of lower limb Download PDFInfo
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Abstract
The invention relates to a training effect prediction method, a system and a storage medium for lower limb muscle strength, wherein the method comprises the following steps: acquiring and constructing a two-classification pre-judging model according to original lower limb muscle strength data before training and training lower limb muscle strength data after training of each subject; and predicting the lower limb muscle strength training effect of the person to be trained based on the original lower limb muscle strength data and the two classification pre-judging models before training of the person to be trained, so as to obtain the target prediction effect of the person to be trained. According to the invention, through analyzing the variation trend and individual difference of the constant velocity muscle strength of the lower limb muscles before and after training, a pre-judging model of the individual difference of the constant velocity muscle strength training effect is established, and an accurate prediction result is provided for the evaluation of the training effect of the lower limb muscle strength after training of different trainers.
Description
Technical Field
The invention relates to the technical field of physical training prediction, in particular to a method, a system and a storage medium for predicting training effect of lower limb muscle strength.
Background
Lower limb muscle strength is a determinant of lower limb muscle function and plays an important role in maintaining human health and improving athletic performance.
In the index for evaluating the muscle strength of the lower limb, isokinetic muscle strength is a mechanical manifestation of the muscle function of the lower limb, and the isokinetic muscle strength test is recognized as a "gold standard" for the muscle strength evaluation. In one aspect, isokinetic muscle strength can provide reliable results for assessing muscle strength; on the other hand, isokinetic muscle force predicts the risk of motor injury.
However, in the prior art, there is no prediction on the muscle strength training effect of the lower limb of the human body, how to build a pre-judging model of individual difference of the constant-speed muscle strength training effect by analyzing the reactivity of the subject to different training schemes and the influence factors of the training effect, and predict the training effect of the trainer according to the pre-judging model is a problem to be solved currently.
Disclosure of Invention
In order to solve the technical problems, the invention provides a training effect prediction method, a system and a storage medium for lower limb muscle strength.
The technical scheme of the training effect prediction method for the muscle strength of the lower limb is as follows:
acquiring and constructing a two-classification pre-judging model according to original lower limb muscle strength data before training and training lower limb muscle strength data after training of each subject;
and predicting the lower limb muscle strength training effect of the person to be trained based on the original lower limb muscle strength data of the person to be trained before training and the two classification pre-judging models, so as to obtain the target prediction effect of the person to be trained.
The training effect prediction method for the muscle strength of the lower limb has the following beneficial effects:
according to the method, the variation trend and the individual difference of the constant velocity muscle strength of the lower limb muscles before and after training are analyzed, the pre-judging model of the individual difference of the constant velocity muscle strength training effect is established, and an accurate prediction result is provided for the evaluation of the lower limb muscle strength training effect of different trainers after training.
Based on the scheme, the training effect prediction method for the muscle strength of the lower limb can be improved as follows.
Further, the raw lower limb muscle force data includes: original constant-speed pedaling and bending peak force and original constant-speed pedaling and stretching peak force;
the training lower limb muscle strength data comprises: training constant-speed pedal deflection peak force and training constant-speed pedal stretching peak force;
the target prediction effect includes: the predicted training effect value of the constant-speed pedaling peak force and the predicted training effect value of the constant-speed pedaling stretching peak force.
Further, the classification pre-judgment model includes: a first predictive formula and a second predictive formula;
obtaining a predicted training effect value of the constant-speed pedaling peak force by using the first prediction formula, wherein the first prediction formula is as follows:wherein y1 is the predicted training effect value of the constant-speed pedaling peak force, and P1 is the original constant-speed pedaling peak force of the person to be trained, logit (P1) = -1.783+2.910×10-4×p1;
obtaining a predicted training effect value of the constant-speed pedaling peak force by using the second prediction formula, wherein the second prediction formula is as follows:wherein y2 is the predicted training effect value of the constant-speed pedaling peak force, P 2 For the initial constant pedaling peak force of the person to be trained, logit (P 2 )＝-1.721+3.182×10 -4 ×P 2 。
Further, the method further comprises the following steps:
obtaining an actual training effect value of each preset subject according to original lower limb muscle strength data before training and training lower limb muscle strength data after training of each preset subject;
and verifying the classification prejudging model according to the actual training effect value and the predicted training effect value before training of each preset subject.
Further, the verifying the classification pre-judging model specifically includes:
and judging whether the actual training effect value and the predicted training effect value of each preset subject pass the chi-square test.
Further, the obtaining the target prediction effect of the person to be trained specifically includes:
and obtaining and judging the target prediction effect of the person to be trained according to the prediction training effect value of the constant-speed pedaling peak force and the prediction training effect value of the constant-speed pedaling stretching peak force of the person to be trained.
Further, the determining the target prediction effect of the person to be trained according to the predicted training effect value of the constant-velocity pedaling and bending peak force and the predicted training effect value of the constant-velocity pedaling and stretching peak force of the person to be trained specifically includes:
when the predicted training effect value of the constant-speed pedaling peak force is greater than a first preset threshold value, and the predicted training effect value of the constant-speed pedaling stretching peak force is greater than a second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained and the constant-speed pedaling and bending peak force of the person to be trained have training effects;
when the predicted training effect value of the constant-speed pedaling peak force is greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is not greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained has a training effect, and the constant-speed pedaling and bending peak force of the person to be trained has no training effect;
when the predicted training effect value of the constant-speed pedaling peak force is not greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained has no training effect, and the constant-speed pedaling and bending peak force of the person to be trained has training effect;
when the predicted training effect value of the constant-speed pedaling peak force is not greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is not greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling peak force of the person to be trained and the constant-speed pedaling peak force of the person to be trained have no training effect.
The technical scheme of the training effect prediction system for the muscle strength of the lower limb is as follows:
comprising the following steps: a construction module and a prediction module;
the construction module is used for: acquiring and constructing a two-classification pre-judging model according to original lower limb muscle strength data before training and training lower limb muscle strength data after training of each subject;
the prediction module is used for: and predicting the lower limb muscle strength training effect of the person to be trained based on the original lower limb muscle strength data of the person to be trained before training and the two classification pre-judging models, so as to obtain the target prediction effect of the person to be trained.
The training effect prediction system for the muscle strength of the lower limb has the following beneficial effects:
the system establishes the pre-judging model of the individual difference of the constant velocity muscle strength training effect by analyzing the variation trend and the individual difference of the constant velocity muscle strength of the lower limb before and after training, and provides accurate prediction results for the evaluation of the constant velocity muscle strength training effect of different trainers.
Based on the scheme, the training effect prediction system for the muscle strength of the lower limb can be improved as follows.
Further, the raw lower limb muscle force data includes: original constant-speed pedaling and bending peak force and original constant-speed pedaling and stretching peak force;
the training lower limb muscle strength data comprises: training constant-speed pedal deflection peak force and training constant-speed pedal stretching peak force;
the target prediction effect includes: the predicted training effect value of the constant-speed pedaling peak force and the predicted training effect value of the constant-speed pedaling stretching peak force.
The technical scheme of the storage medium is as follows:
the storage medium stores instructions that, when read by a computer, cause the computer to execute the steps of the training effect prediction method for lower limb muscle strength according to the present invention.
Drawings
FIG. 1 is a schematic flow chart of a method for predicting the training effect of the muscle strength of a lower limb according to an embodiment of the invention;
fig. 2 is a schematic structural diagram of a training effect prediction system for lower limb muscle strength according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the method for predicting the training effect of the muscle force of the lower limb according to the embodiment of the invention comprises the following steps:
s1, acquiring and constructing a two-class pre-judging model according to original lower limb muscle strength data before training and training lower limb muscle strength data after training of each subject.
The lower limb muscle strength data is acquired by using an ISOMED 2000 muscle strength tester, and can be acquired by other similar muscle strength testing equipment without limitation. Each subject needed to collect lower limb muscle strength data before and after training. The test process is carried out by the same tester by adopting the principle of testing by instruments of the same brand type.
The testing process comprises the following steps: 1) Starting the test, and calibrating the instrument before a tester; the subject performs a warming up activity: 200m jogging, 2 groups multiplied by 10 times of body weight deep squat exercises, 2 groups multiplied by 10 times of bow-and-arrow squat exercises, and resting for 2 minutes after warm-up is finished to start testing.
2) During testing, the test subject is fixed, the pelvis and the back are required to be clung to the backrest, the waist is fixed by using the equipment waistband, and the soles are clung to the lower limb pedaling component. The maximum extension position of the pedaling of the subject is 130 degrees of the included angle between the lower leg and the thigh, and the maximum buckling position is 90 degrees of the included angle between the trunk and the thigh and 90 degrees of the included angle between the lower leg and the thigh.
3) After fixing, the position of the backrest and the knee extension component is recorded.
4) The subject had 3 opportunities to try to exercise both legs to exert force before starting the test procedure, and started the isokinetic muscle strength test 3min after resting after exercise.
5) In the test process, the force is required to be applied to the legs of a subject, and 3 times of initial centrifugation and then centripetal isokinetic pedaling are continuously completed each time of the force application, wherein the speed is 10cm/s; the peak constant pedaling force (peak torque flexor, PTf) and peak constant pedaling force (peak torque extensor, PTe) are recorded.
The classification pre-judging model is a pre-judging model obtained by adopting a forward method Logistic regression analysis according to the lower limb muscle strength data before and after training of each subject, the fitting degree of the classification pre-judging model is checked by adopting a likelihood ratio, and the prediction capability of the regression model is evaluated by using a back-substitution check.
S2, predicting the lower limb muscle strength training effect of the person to be trained based on the original lower limb muscle strength data before training of the person to be trained and the two-classification pre-judging model, and obtaining the target prediction effect of the person to be trained.
Specifically, the original lower limb muscle strength data of the person to be trained before training is substituted into the two-classification pre-judging model, so that the target prediction effect of the person to be trained is obtained.
Preferably, the raw lower limb muscle strength data comprises: original constant-speed pedaling and bending peak force and original constant-speed pedaling and stretching peak force;
the training lower limb muscle strength data comprises: training constant-speed pedal deflection peak force and training constant-speed pedal stretching peak force;
the target prediction effect includes: the predicted training effect value of the constant-speed pedaling peak force and the predicted training effect value of the constant-speed pedaling stretching peak force.
Wherein, the lower limb muscle strength data that gathers under the different circumstances all includes: constant velocity pedal deflection peak force and constant velocity pedal extension peak force. In the present invention, the lower limb muscle force data includes: original lower limb muscle strength data, training lower limb muscle strength data and the like, and target prediction effects comprise: the predicted training effect value of the constant-speed pedaling peak force and the predicted training effect value of the constant-speed pedaling stretching peak force.
Preferably, the classification pre-judgment model comprises: a first predictive formula and a second predictive formula;
obtaining a predicted training effect value of the constant-speed pedaling peak force by using the first prediction formula, wherein the first prediction formula is as follows:wherein y is 1 A predicted training effect value P for the constant-speed pedaling peak force 1 To be the instituteThe initial constant velocity pedaling peak force, logit (P 1 )＝-1.783+2.910×10 -4 ×P 1 ；
Obtaining a predicted training effect value of the constant-speed pedaling peak force by using the second prediction formula, wherein the second prediction formula is as follows:wherein y is 2 Predictive training effect value, P, for the constant velocity pedaling peak force 2 For the initial constant pedaling peak force of the person to be trained, logit (P 2 )＝-1.721+3.182×10 -4 ×P 2 。
TABLE 1 Logistic regression analysis Table for improving lower limb muscle strength
Preferably, the method further comprises:
and obtaining the actual training effect value of each preset subject according to the original lower limb muscle strength data of each preset subject before training and the training lower limb muscle strength data after training.
The preset subjects may be the same as or different from the crowd in the establishment of the two-classification predictive model, and the specific number of the subjects may be more or less, and may be arranged according to actual situations, which is not limited herein.
And verifying the classification prejudging model according to the actual training effect value and the predicted training effect value before training of each preset subject.
The preset subjects and the population of the subjects may be the same or different, and the preset subjects and the population of the subjects are not limited.
Preferably, the verifying the two classification prejudgment models specifically includes:
and judging whether the actual training effect value and the predicted training effect value of each preset subject pass the chi-square test.
Wherein, the condition of passing the verification is: and (3) comparing whether the actual training effect value and the predicted training effect value of each preset subject are different or not (i.e. no difference when p is more than 0.05) by using chi-square test on the actual training effect value and the predicted training effect value of all preset subjects. After verification is passed, the two classification pre-judging model can be used as a prediction model of the lower limb muscle strength training effect of the person to be trained.
For example, as shown in the following table, the two-class predictive model is verified based on the actual training effect value and the predicted training effect value of the preset subject. As can be seen from the data in the table, there is no difference between the actual training effect value and the predicted training effect value for each preset subject. n represents the number of persons predicted to be ineffective after model judgment. Taking the constant-speed pedaling peak force as an example, the verification part calculates 23 data of the individuals in total, 11 individuals (47.83%) are predicted to be ineffective, 13 individuals (56.52%) in the true values are ineffective, and the p values after verification are all larger than 1, so that no difference exists between the actual training effect value and the predicted training effect value.
Table 2 verification results of two-classification pre-judgment model
When the verification fails (i.e., p < 0.05), more subjects are selected again for training, the two-class pre-judgment model is trained for the second time after limb muscle strength data of the subjects are obtained, the two-class pre-judgment model after training is obtained, and the verification process is repeated until the verification passes.
Or when the verification is not passed (i.e., p < 0.05), correcting the coefficients in the two-class pre-judging model, and predicting by using the corrected two-class pre-judging model.
Preferably, the obtaining the target prediction effect of the person to be trained specifically includes:
and obtaining and judging the target prediction effect of the person to be trained according to the prediction training effect value of the constant-speed pedaling peak force and the prediction training effect value of the constant-speed pedaling stretching peak force of the person to be trained.
Preferably, the determining the target prediction effect of the person to be trained according to the predicted training effect value of the constant-velocity pedaling and bending peak force and the predicted training effect value of the constant-velocity pedaling and stretching peak force of the person to be trained specifically includes:
when the predicted training effect value of the constant-speed pedaling peak force is greater than a first preset threshold value, and the predicted training effect value of the constant-speed pedaling stretching peak force is greater than a second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained and the constant-speed pedaling and bending peak force of the person to be trained have training effects;
when the predicted training effect value of the constant-speed pedaling peak force is greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is not greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained has a training effect, and the constant-speed pedaling and bending peak force of the person to be trained has no training effect;
when the predicted training effect value of the constant-speed pedaling peak force is not greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained has no training effect, and the constant-speed pedaling and bending peak force of the person to be trained has training effect;
when the predicted training effect value of the constant-speed pedaling peak force is not greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is not greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling peak force of the person to be trained and the constant-speed pedaling peak force of the person to be trained have no training effect.
The values of the first preset threshold and the second preset threshold default to 0.3, or any other value, which is not limited herein.
For example, when the first pre-stageWhen the threshold value and the second preset threshold value are set to be 0.3, obtaining a predicted training effect value y of the constant-speed pedaling and buckling peak force of the trainer according to a first prediction formula and a second prediction formula in the two-classification prediction model 1 And a predicted training effect value y of constant-speed pedaling peak force 2 ；
When y is 1 More than or equal to 0.3, the constant-speed pedaling peak force of the person to be trained has training effect, when y 1 And less than 0.3, the constant-speed pedaling and stretching peak force of the person to be trained has no training effect. The rest of the cases and so forth are not repeated here.
According to the method, the variation trend and the individual difference of the constant velocity muscle strength of the lower limb muscles before and after training are analyzed, the pre-judging model of the individual difference of the constant velocity muscle strength training effect is established, and an accurate prediction result is provided for the evaluation of the lower limb muscle strength training effect of different trainers after training.
As shown in fig. 2, a training effect prediction system 200 for muscle strength of a lower limb according to an embodiment of the present invention includes: a construction module 210 and a prediction module 220;
the construction module 210 is configured to: acquiring and constructing a two-classification pre-judging model according to original lower limb muscle strength data before training and training lower limb muscle strength data after training of each subject;
the prediction module 220 is configured to: and predicting the lower limb muscle strength training effect of the person to be trained based on the original lower limb muscle strength data of the person to be trained before training and the two classification pre-judging models, so as to obtain the target prediction effect of the person to be trained.
Preferably, the raw lower limb muscle strength data comprises: original constant-speed pedaling and bending peak force and original constant-speed pedaling and stretching peak force;
the training lower limb muscle strength data comprises: training constant-speed pedal deflection peak force and training constant-speed pedal stretching peak force;
the target prediction effect includes: the predicted training effect value of the constant-speed pedaling peak force and the predicted training effect value of the constant-speed pedaling stretching peak force.
Preferably, the classification pre-judgment model comprises: a first predictive formula and a second predictive formula;
obtaining a predicted training effect value of the constant-speed pedaling peak force by using the first prediction formula, wherein the first prediction formula is as follows:wherein y is 1 A predicted training effect value P for the constant-speed pedaling peak force 1 For the initial constant velocity pedaling peak force of the person to be trained, logit (P 1 )＝-1.783+2.910×10 -4 ×P 1 ；
Obtaining a predicted training effect value of the constant-speed pedaling peak force by using the second prediction formula, wherein the second prediction formula is as follows:wherein y is 2 Predictive training effect value, P, for the constant velocity pedaling peak force 2 For the initial constant pedaling peak force of the person to be trained, logit (P 2 )＝-1.721+3.182×10 -4 ×P 2 。
Preferably, the method further comprises: a verification module;
the verification module is used for: obtaining an actual training effect value of each preset subject according to original lower limb muscle strength data before training and training lower limb muscle strength data after training of each preset subject;
and verifying the classification prejudging model according to the actual training effect value and the predicted training effect value before training of each preset subject.
Preferably, the verification module is specifically configured to:
and judging whether the actual training effect value and the predicted training effect value of each preset subject pass the chi-square test.
Preferably, the prediction module 220 is specifically configured to:
and obtaining and judging the target prediction effect of the person to be trained according to the prediction training effect value of the constant-speed pedaling peak force and the prediction training effect value of the constant-speed pedaling stretching peak force of the person to be trained.
Preferably, the prediction module 220 is specifically further configured to:
the step of judging the target prediction effect of the person to be trained according to the prediction training effect value of the constant-speed pedaling peak force and the prediction training effect value of the constant-speed pedaling stretching peak force of the person to be trained specifically comprises the following steps:
when the predicted training effect value of the constant-speed pedaling peak force is greater than a first preset threshold value, and the predicted training effect value of the constant-speed pedaling stretching peak force is greater than a second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained and the constant-speed pedaling and bending peak force of the person to be trained have training effects;
when the predicted training effect value of the constant-speed pedaling peak force is greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is not greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained has a training effect, and the constant-speed pedaling and bending peak force of the person to be trained has no training effect;
when the predicted training effect value of the constant-speed pedaling peak force is not greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained has no training effect, and the constant-speed pedaling and bending peak force of the person to be trained has training effect;
when the predicted training effect value of the constant-speed pedaling peak force is not greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is not greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling peak force of the person to be trained and the constant-speed pedaling peak force of the person to be trained have no training effect.
According to the method, the variation trend and the individual difference of the constant velocity muscle strength of the lower limb muscles before and after training are analyzed, the pre-judging model of the individual difference of the constant velocity muscle strength training effect is established, and an accurate prediction result is provided for the evaluation of the lower limb muscle strength training effect of different trainers after training.
The above steps for implementing corresponding functions by using the parameters and the modules in the training effect prediction system 200 for lower limb muscle strength according to the present embodiment may refer to the parameters and the steps in the above embodiments of the training effect prediction method for lower limb muscle strength, which are not described herein.
The storage medium provided by the embodiment of the invention comprises: the storage medium stores instructions, and when the instructions are read by the computer, the computer executes the steps of the method for predicting the training effect of the muscle force of the lower limb, and specifically, reference may be made to each parameter and step in the embodiment of the method for predicting the training effect of the muscle force of the lower limb, which are not described herein.
Computer storage media such as: flash disk, mobile hard disk, etc.
The electronic device provided by the embodiment of the invention comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, and is characterized in that when the processor executes the computer program, the computer is enabled to execute the steps of the method for predicting the training effect of the muscle strength of the lower limb, and the parameters and the steps in the embodiment of the method for predicting the training effect of the muscle strength of the lower limb can be specifically referred to, and are not repeated herein.
Those skilled in the art will appreciate that the present invention may be implemented as a method, system, storage medium, and electronic device.
Thus, the invention may be embodied in the form of: either entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or entirely software, or a combination of hardware and software, referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media, which contain computer-readable program code. Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (5)
1. The method for predicting the training effect of the muscle force of the lower limb is characterized by comprising the following steps of:
acquiring and constructing a two-classification pre-judging model according to original lower limb muscle strength data before training and training lower limb muscle strength data after training of each subject;
predicting the lower limb muscle strength training effect of the person to be trained based on the original lower limb muscle strength data of the person to be trained before training and the two classification pre-judging models to obtain a target prediction effect of the person to be trained;
the raw lower limb muscle strength data includes: original constant-speed pedaling and bending peak force and original constant-speed pedaling and stretching peak force;
the training lower limb muscle strength data comprises: training constant-speed pedal deflection peak force and training constant-speed pedal stretching peak force;
the target prediction effect includes: a predicted training effect value of the constant-speed pedaling peak force and a predicted training effect value of the constant-speed pedaling stretching peak force;
the obtaining the target prediction effect of the person to be trained specifically includes:
obtaining and judging the target prediction effect of the person to be trained according to the prediction training effect value of the constant-speed pedaling peak force and the prediction training effect value of the constant-speed pedaling stretching peak force of the person to be trained;
the step of judging the target prediction effect of the person to be trained according to the prediction training effect value of the constant-speed pedaling peak force and the prediction training effect value of the constant-speed pedaling stretching peak force of the person to be trained specifically comprises the following steps:
when the predicted training effect value of the constant-speed pedaling peak force is greater than a first preset threshold value, and the predicted training effect value of the constant-speed pedaling stretching peak force is greater than a second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained and the constant-speed pedaling and bending peak force of the person to be trained have training effects;
when the predicted training effect value of the constant-speed pedaling peak force is greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is not greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained has a training effect, and the constant-speed pedaling and bending peak force of the person to be trained has no training effect;
when the predicted training effect value of the constant-speed pedaling peak force is not greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained has no training effect, and the constant-speed pedaling and bending peak force of the person to be trained has training effect;
when the predicted training effect value of the constant-speed pedaling peak force is not greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is not greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained and the constant-speed pedaling and bending peak force of the person to be trained have no training effect;
the classification prejudging model comprises the following steps: a first predictive formula and a second predictive formula;
obtaining a predicted training effect value of the constant-speed pedaling peak force by using the first prediction formula, wherein the first prediction formula is as follows:wherein->For the predicted training effect value of the constant-speed pedaling peak force, < >>For the initial constant-speed pedaling peak force of the person to be trained, < >>；
Obtaining a predicted training effect value of the constant-speed pedaling peak force by using the second prediction formula, wherein the second prediction formula is as follows:wherein->For the predicted training effect value of the constant-speed pedaling peak force, < >>For the initial constant-speed pedaling peak force of the person to be trained, < >>。
2. The method for predicting the training effect of the muscle force of the lower limb according to claim 1, further comprising:
obtaining an actual training effect value of each preset subject according to original lower limb muscle strength data before training and training lower limb muscle strength data after training of each preset subject;
and verifying the classification prejudging model according to the actual training effect value and the predicted training effect value before training of each preset subject.
3. The method for predicting the training effect of the muscle force of the lower limb according to claim 2, wherein the verifying the classification pre-judgment model specifically comprises:
and judging whether the actual training effect value and the predicted training effect value of each preset subject pass the chi-square test.
4. A training effect prediction system for lower limb muscle strength, comprising: a construction module and a prediction module;
the construction module is used for: acquiring and constructing a two-classification pre-judging model according to original lower limb muscle strength data before training and training lower limb muscle strength data after training of each subject;
the prediction module is used for: predicting the lower limb muscle strength training effect of the person to be trained based on the original lower limb muscle strength data of the person to be trained before training and the two classification pre-judging models to obtain a target prediction effect of the person to be trained;
the raw lower limb muscle strength data includes: original constant-speed pedaling and bending peak force and original constant-speed pedaling and stretching peak force;
the training lower limb muscle strength data comprises: training constant-speed pedal deflection peak force and training constant-speed pedal stretching peak force;
the target prediction effect includes: a predicted training effect value of the constant-speed pedaling peak force and a predicted training effect value of the constant-speed pedaling stretching peak force;
the prediction module is specifically configured to:
obtaining and judging the target prediction effect of the person to be trained according to the prediction training effect value of the constant-speed pedaling peak force and the prediction training effect value of the constant-speed pedaling stretching peak force of the person to be trained;
the prediction module is specifically further configured to:
when the predicted training effect value of the constant-speed pedaling peak force is greater than a first preset threshold value, and the predicted training effect value of the constant-speed pedaling stretching peak force is greater than a second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained and the constant-speed pedaling and bending peak force of the person to be trained have training effects;
when the predicted training effect value of the constant-speed pedaling peak force is greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is not greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained has a training effect, and the constant-speed pedaling and bending peak force of the person to be trained has no training effect;
when the predicted training effect value of the constant-speed pedaling peak force is not greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained has no training effect, and the constant-speed pedaling and bending peak force of the person to be trained has training effect;
when the predicted training effect value of the constant-speed pedaling peak force is not greater than the first preset threshold value, and when the predicted training effect value of the constant-speed pedaling peak force is not greater than the second preset threshold value, the target predicted effect of the person to be trained is as follows: the constant-speed pedaling and bending peak force of the person to be trained and the constant-speed pedaling and bending peak force of the person to be trained have no training effect;
the classification prejudging model comprises the following steps: a first predictive formula and a second predictive formula;
obtaining the first predictive formulaThe first prediction formula is as follows:wherein->For the predicted training effect value of the constant-speed pedaling peak force, < >>For the initial constant-speed pedaling peak force of the person to be trained, < >>；
Obtaining a predicted training effect value of the constant-speed pedaling peak force by using the second prediction formula, wherein the second prediction formula is as follows:wherein->For the predicted training effect value of the constant-speed pedaling peak force, < >>For the initial constant-speed pedaling peak force of the person to be trained, < >>。
5. A storage medium having stored therein instructions which, when read by a computer, cause the computer to perform the training effect prediction method of lower limb muscle force according to any one of claims 1 to 3.
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| CN112807001A (en) * | 2019-11-15 | 2021-05-18 | 上海中研久弋科技有限公司 | Multi-modal intent recognition and motion prediction method, system, terminal, and medium |
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