CN113506145B - Postpartum exercise course evaluation system and method based on data analysis - Google Patents
Postpartum exercise course evaluation system and method based on data analysis Download PDFInfo
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Abstract
The invention discloses a postpartum exercise course evaluation system and method based on data analysis, and belongs to the technical field of postpartum recovery. The system comprises a star-level evaluation acquisition module, a user information acquisition module, an action analysis module, an effect prediction module and a pushing module; the output end of the star rating acquisition module is connected with the input end of the user information acquisition module; the output end of the user information acquisition module is connected with the input end of the action analysis module; the output end of the action analysis module is connected with the input ends of the effect prediction module and the pushing module; the effect prediction module is connected with the push module, and the effect prediction module is connected with the push module.
Description
Technical Field
The invention relates to the technical field of postpartum recovery, in particular to a postpartum exercise course evaluation system and method based on data analysis.
Background
During the female pregnancy period, the pelvic floor muscles at the lowest part of the pelvis are subjected to more continuous pressure due to the increase of the weight of the fetus and the weight of amniotic fluid, so that the pelvic floor muscles gradually become relaxed; during delivery, a fetus needs to be delivered through a delivery passage, pelvic floor muscles can be excessively stretched, injury is further aggravated, the pelvic floor muscles are arranged at the bottom of a pelvis, a pubis is forwards and a coccyx backwards, pelvic organs such as a bladder, a rectum, a uterus and a vagina are supported, contraction and relaxation of the pelvic floor muscles are also involved in controlling defecation and urination, and sexual life quality is affected.
Therefore, exercise at postpartum pelvic floor muscle is vital, the relevant course of having postpartum exercise all exists on present all kinds of APP, most APP all are taught with the form of video teaching, but do not can in time correct user's action condition, and in the exercise of carrying out pelvic floor muscle, the user is when doing the action, can use the belly to exert oneself because under the condition that self can't master the key ring, lead to pelvic floor muscle not to obtain due training, final effect is not ideal, thereby the low-star evaluation is given.
Moreover, in the current APP, the effect of the user who has finished all the courses cannot be predicted according to the courses that the user has finished, so that some more accurate courses cannot be recommended to the user in time for adjustment to ensure the optimal effect.
Disclosure of Invention
The invention aims to provide a postpartum exercise course evaluation system and method based on data analysis, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
a postpartum exercise course evaluation system based on data analysis comprises a star evaluation acquisition module, a user information acquisition module, an action analysis module, an effect prediction module and a pushing module;
the star rating acquisition module is used for acquiring star rating submitted by users who take part in postpartum exercise courses; the user information acquisition module is used for acquiring the basic information condition of a user participating in the postpartum exercise course; the motion analysis module is used for analyzing whether the motion of the user in the postpartum exercise course is standard or not; the effect prediction module is used for predicting the exercise effect of the user after the user participates in the postpartum exercise course; the pushing module is used for pushing related courses;
the output end of the star rating acquisition module is connected with the input end of the user information acquisition module; the output end of the user information acquisition module is connected with the input end of the action analysis module; the output end of the action analysis module is connected with the input ends of the effect prediction module and the pushing module; the output end of the effect prediction module is connected with the input end of the pushing module.
According to the technical scheme, the star rating acquisition module comprises a star rating acquisition unit and a recording unit;
the star rating acquisition unit is used for acquiring star rating information submitted by a user; the recording unit is used for recording the information of the low star rating in the star rating acquisition unit;
and the output end of the star evaluation acquisition unit is connected with the input end of the recording unit.
According to the technical scheme, the action analysis module comprises a video conversion unit and an action analysis unit;
the video conversion unit is used for converting videos of a user for course training into pictures; the action analysis unit is used for analyzing the action on the picture and judging the main force-exerting part of the user in the course of participating in the course;
the output end of the video conversion unit is connected with the input end of the action analysis unit; and the output end of the action analysis unit is connected with the input ends of the effect prediction module and the pushing module.
According to the technical scheme, the effect prediction module comprises a state establishing unit and an effect prediction unit;
the state establishing unit is used for establishing state information of different users after the users take part in the postpartum training courses; the effect prediction unit is used for predicting the effect after the course is finished according to the current state information;
the output end of the state establishing unit is connected with the input end of the effect predicting unit; and the output end of the effect prediction unit is connected with the input end of the push module.
According to the technical scheme, the pushing module comprises an instruction unit and a pushing unit;
the instruction unit is used for analyzing what courses should be pushed to the corresponding users; the pushing unit is used for pushing corresponding courses;
the output end of the instruction unit is connected with the output end of the push unit.
A postpartum exercise course evaluation method based on data analysis comprises the following steps:
s1, obtaining user evaluation of the postpartum exercise course, and obtaining basic information of the evaluation user;
s2, obtaining evaluation star grades, paying attention to a user with low star grade evaluation, and analyzing the action level and the physical quality of the user;
s3, establishing a course recommendation model according to the analysis data in the step S2;
and S4, acquiring the use data of the user for a period of time, predicting the postpartum exercise condition of the user, predicting the probability of obtaining low star rating according to the big data, and pushing related courses in time.
According to the technical scheme, in step S1, a user acquires postpartum exercise courses through a network platform, and can make an evaluation based on self conditions after learning, and meanwhile, the platform can acquire basic information and exercise processes of the user;
the basic information of the user is denoted as set a, where a = { x = { (x)1、x2、x3、……、xn}; wherein x is1、x2、x3、……、xnEach representing one of the users' basic information.
The basic information of the user comprises the user age, the delivery time, the delivery times, the weight, the muscle degree, the training process and the like; for example, the user's weight is too large, which may result in prolonged abdominal pressure elevation, which may have some effect on the pelvic floor muscles.
According to the above technical solution, in step S2, the method further includes:
obtaining evaluation star level, setting star level threshold value to be N1For lower than N1The evaluation of the star rating is defined as low star rating;
acquiring basic information of a user with low star-level evaluation, and analyzing the action level and the physical quality of the user;
acquiring a motion video of a user, performing frame extraction processing on the motion video, acquiring a picture after frame extraction, and establishing a picture set;
in the picture set, obtaining the abdomen action picture of the user, taking each breath of the user as a stage, and recording the abdomen action picture under each stage as a set B;
judging each breath of the user, namely judging by the expansion and contraction of the abdomen of the user, wherein a breathing stage is formed between the expansion and the contraction;
the muscle is voluntary muscle, which is controlled by nerve, when force is applied, the muscle can contract by the signal transmitted by nerve, and when the muscle contracts, the length is shortened, the volume is unchanged, and the muscle naturally bulges and becomes hard from the width to the outside;
acquiring the muscle bulging degree of the abdominal action picture under the set B, acquiring a bulging difference value which is marked as h, wherein the bulging difference value is the muscle bulging difference value between calmness and force application;
acquiring basic information of a user, and selecting basic information related to muscles, including age, body fat rate, muscle level and weight, which are respectively recorded as x1、x2、x3、x4；
The difference h of the abdominal muscle distension of the user during the exercise can be established1Comprises the following steps:
h1=x1k1+x2k2+x3k3+x4k4
wherein k is1、k2、k3、k4Are respectively basic information x1、x2、x3、x4The influence factor coefficient of (2);
then, in the postpartum training course, the abdominal muscle engagement power degree value P of the user at any action can be calculated as:
P=（h1-h）/h1
setting the abdomen exertion threshold under each action in the postpartum training course, and recording the threshold as PiIf P > P is presentiIf the action is not standard, the force-exerting part is wrong, and a record is generated;
setting the threshold value of the times as J, and if the threshold value is exceeded by PiWhen the total times of the user exceeds the threshold J, the user is judged to have poor course training effect due to nonstandard actions.
In the process, the pelvic floor muscles are mainly exercised in the postpartum training course, but the abdominal strength is too much due to nonstandard actions of part of users, so that the pelvic floor muscles cannot be exercised, and the exercise effect is poor finally, so that the evaluation of low star level is performed; therefore, the examination of the low star rating further indicates the error of the exercise action mode of the user, thereby further improving the exercise effect.
According to the above technical solution, in step S3, the method further includes:
acquiring an abdominal muscle engagement power degree value P of the user,
sequencing all the power degree values P according to a time sequence, and acquiring the variation trend of the power degree values P;
acquiring the maximum value, the minimum value and the action times of a power degree value P in a T time period;
according to the formula:
wherein, P1One of the maximum value and the minimum value of the force degree value P is the prior sequence in the time period T; p2The other one of the maximum value and the minimum value of the force degree value P is the later one in the T time period; namely, description of P1-P2A negative value exists; d is P1And P2The number of actions; d is a trend deviation amount;
setting the threshold value to DmaxIf there is a tendency deviation D lower than DmaxWhen the user action changes slowly or falls into an action-nonstandard error zone, recommending an action guidance course;
if there is a trend deviation D higher than DmaxI.e. to say that the user action criterion level gradually improves, at which point a new exercise action session is recommended.
In this step, the recommended course is mainly performed by the variation of the trend variation, which mainly means whether the user has a gradual decrease in the abdominal exertion during the exercise, because the user's own pelvic floor is used in the postpartum exerciseThe muscles are defective, so that it is easy to apply the abdominal muscles to exert force during the initial period of exercise, while during the exercise, the pelvic floor muscles are gradually trained, and the force-exerting effect is better and better, i.e. below DmaxThere are two cases, one is negative, that is, the abdomen exertion part is getting larger and larger, and the other is slow change of action, so an action coaching course is recommended at this time.
According to the above technical solution, in step S4, the method further includes:
acquiring all respiratory stages of a user in a course, and capturing an abdominal muscle participation power degree value P in each respiratory stage;
calculating the average value of all the abdomen muscle participation power degree values P in each course;
establishing the state of the user in each class according to the average value;
state in markov prediction, "state" is an important term. The term "state" refers to a result of an event occurring at a certain time (or period). In general, states may be partitioned differently as the objectives of the events under study and their predictions vary. For example, in the prediction of sales of a product, there are states such as "free", "normal", "behind the market", and the like; in the agricultural harvest prediction, the states of 'rich harvest', 'flat harvest', 'poor harvest' and the like exist; in the present application, the states are excellent, good, general, poor, etc.;
that is, each state corresponds to an interval of the average value, for example, between 20 and 30, and is marked as an excellent state;
the state transition probability of the user is:
K0(Ei→Ej)=K0（Eji Ei）=Kij
Wherein E isi→EjRespectively representing two states of the user, K0Representing the user in state EiTransition to State EjState transition probability of time;
then, when there are n states, the state transition probability matrix is established as follows:
wherein K is a state transition probability matrix;
if the user is currently in state Ei(ii) a Then at the next instant it may be represented by state EiSteering E1，E2，…Ei…EnAny one of the states;
thus, KijThe conditions are satisfied:
according to the Markov prediction method, the final prediction result is pi x K;
according to the probability of the corresponding state in the result, the state distribution of the user after the postpartum training course is carried out can be obtained, so that the training effect probability distribution is obtained, and the postpartum exercise condition of the user is obtained through prediction;
acquiring historical data of a training effect of a user performing low-star evaluation, calculating the probability of performing low-star evaluation under the effect, and predicting to obtain the probability of performing low-star evaluation;
and pushing related courses in time according to the predicted training effect. When the effect is in a low effect, pushing a detailed action decomposition class course; when the effect is in the medium effect, the current action class course is continuously pushed; when the effect is in high effect, the step action class course is pushed.
Compared with the prior art, the invention has the following beneficial effects:
1. the method can be used for carrying out careful analysis on the user with low-star evaluation, judging the specific reason of the unsatisfactory effect of the user after participating in the post-partum exercise course, recommending the corresponding course, further reducing the low-star evaluation, improving the experience of the user, solving the problem of the user actually, meeting the requirement of the user and realizing the due effect of a course platform;
2. the invention can judge the exertion index of the abdomen under single action according to the change of the abdomen and judge the standard degree of action when the pelvic floor muscle is exercised, thereby determining the reason why the user obtains unsatisfied effect, correcting the action error of the user in time and avoiding the user from obtaining the effect which the user wants after long-term exercise.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow chart of a system and method for evaluating postpartum exercise sessions based on data analysis according to the present invention;
FIG. 2 is a schematic diagram of the steps of a postpartum exercise course evaluation method based on data analysis according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution:
a postpartum exercise course evaluation system based on data analysis comprises a star evaluation acquisition module, a user information acquisition module, an action analysis module, an effect prediction module and a pushing module;
the star rating acquisition module is used for acquiring star rating submitted by users who take part in postpartum exercise courses; the user information acquisition module is used for acquiring the basic information condition of a user participating in the postpartum exercise course; the motion analysis module is used for analyzing whether the motion of the user in the postpartum exercise course is standard or not; the effect prediction module is used for predicting the exercise effect of the user after the user participates in the postpartum exercise course; the pushing module is used for pushing related courses;
the output end of the star rating acquisition module is connected with the input end of the user information acquisition module; the output end of the user information acquisition module is connected with the input end of the action analysis module; the output end of the action analysis module is connected with the input ends of the effect prediction module and the pushing module; the output end of the effect prediction module is connected with the input end of the pushing module.
The star rating acquisition module comprises a star rating acquisition unit and a recording unit;
the star rating acquisition unit is used for acquiring star rating information submitted by a user; the recording unit is used for recording the information of the low star rating in the star rating acquisition unit;
and the output end of the star evaluation acquisition unit is connected with the input end of the recording unit.
The motion analysis module comprises a video conversion unit and a motion analysis unit;
the video conversion unit is used for converting videos of a user for course training into pictures; the action analysis unit is used for analyzing the action on the picture and judging the main force-exerting part of the user in the course of participating in the course;
the output end of the video conversion unit is connected with the input end of the action analysis unit; and the output end of the action analysis unit is connected with the input ends of the effect prediction module and the pushing module.
The effect prediction module comprises a state establishing unit and an effect prediction unit;
the state establishing unit is used for establishing state information of different users after the users take part in the postpartum training courses; the effect prediction unit is used for predicting the effect after the course is finished according to the current state information;
the output end of the state establishing unit is connected with the input end of the effect predicting unit; and the output end of the effect prediction unit is connected with the input end of the push module.
The pushing module comprises an instruction unit and a pushing unit;
the instruction unit is used for analyzing what courses should be pushed to the corresponding users; the pushing unit is used for pushing corresponding courses;
the output end of the instruction unit is connected with the output end of the push unit.
A postpartum exercise course evaluation method based on data analysis comprises the following steps:
s1, obtaining user evaluation of the postpartum exercise course, and obtaining basic information of the evaluation user;
s2, obtaining evaluation star grades, paying attention to a user with low star grade evaluation, and analyzing the action level and the physical quality of the user;
s3, establishing a course recommendation model according to the analysis data in the step S2;
and S4, acquiring the use data of the user for a period of time, predicting the postpartum exercise condition of the user, predicting the probability of obtaining low star rating according to the big data, and pushing related courses in time.
In step S1, the user obtains the postpartum exercise course through the network platform, and can issue an evaluation based on his/her own situation after learning, and the platform can obtain the user' S basic information and exercise process;
the basic information of the user is denoted as set a, where a = { x = { (x)1、x2、x3、……、xn}; wherein x is1、x2、x3、……、xnEach representing one of the users' basic information.
In step S2, the method further includes:
obtaining evaluation star level, setting star level threshold value to be N1For lower than N1The evaluation of the star rating is defined as low star rating;
acquiring basic information of a user with low star-level evaluation, and analyzing the action level and the physical quality of the user;
acquiring a motion video of a user, performing frame extraction processing on the motion video, acquiring a picture after frame extraction, and establishing a picture set;
in the picture set, obtaining the abdomen action picture of the user, taking each breath of the user as a stage, and recording the abdomen action picture under each stage as a set B;
judging each breath of the user, namely judging by the expansion and contraction of the abdomen of the user, wherein a breathing stage is formed between the expansion and the contraction;
acquiring the muscle bulging degree of the abdominal action picture under the set B, acquiring a bulging difference value which is marked as h, wherein the bulging difference value is the muscle bulging difference value between calmness and force application;
acquiring basic information of a user, and selecting basic information related to muscles, including age, body fat rate, muscle level and weight, which are respectively recorded as x1、x2、x3、x4；
The difference h of the abdominal muscle distension of the user during the exercise can be established1Comprises the following steps:
h1=x1k1+x2k2+x3k3+x4k4
wherein k is1、k2、k3、k4Are respectively basic information x1、x2、x3、x4The influence factor coefficient of (2);
then, in the postpartum training course, the abdominal muscle engagement power degree value P of the user at any action can be calculated as:
P=（h1-h）/h1
setting the abdomen exertion threshold under each action in the postpartum training course, and recording the threshold as PiIf P > P is presentiIf the action is not standard, the force-exerting part is wrong, and a record is generated;
setting the threshold value of the times as J, and if the threshold value is exceeded by PiWhen the total times of the user exceeds the threshold J, the user is judged to have poor course training effect due to nonstandard actions.
In step S3, the method further includes:
acquiring an abdominal muscle engagement power degree value P of the user,
sequencing all the power degree values P according to a time sequence, and acquiring the variation trend of the power degree values P;
acquiring the maximum value, the minimum value and the action times of a power degree value P in a T time period;
according to the formula:
wherein, P1One of the maximum value and the minimum value of the force degree value P is the prior sequence in the time period T; p2The other one of the maximum value and the minimum value of the force degree value P is the later one in the T time period; namely, description of P1-P2A negative value exists; d is P1And P2The number of actions; d is a trend deviation amount;
setting the threshold value to DmaxIf there is a tendency deviation D lower than DmaxWhen the user action changes slowly or falls into an action-nonstandard error zone, recommending an action guidance course;
if there is a trend deviation D higher than DmaxI.e. to say that the user action criterion level gradually improves, at which point a new exercise action session is recommended.
In step S4, the method further includes:
acquiring all respiratory stages of a user in a course, and capturing an abdominal muscle participation power degree value P in each respiratory stage;
calculating the average value of all the abdomen muscle participation power degree values P in each course;
establishing the state of the user in each class according to the average value;
the state transition probability of the user is:
K0(Ei→Ej)=K0（Eji Ei）=Kij
Wherein E isi→EjRespectively representing two states of the user, K0Representing the user in state EiTransition to State EjState transition probability of time;
then, when there are n states, the state transition probability matrix is established as follows:
wherein K is a state transition probability matrix;
if the user is currently in state Ei(ii) a Then at the next instant it may be represented by state EiSteering E1，E2，…Ei…EnAny one of the states;
thus, KijThe conditions are satisfied:
according to the Markov prediction method, the final prediction result is pi x K;
according to the probability of the corresponding state in the result, the state distribution of the user after the postpartum training course is carried out can be obtained, so that the training effect probability distribution is obtained, and the postpartum exercise condition of the user is obtained through prediction;
acquiring historical data of a training effect of a user performing low-star evaluation, calculating the probability of performing low-star evaluation under the effect, and predicting to obtain the probability of performing low-star evaluation;
and pushing related courses in time according to the predicted training effect.
In this embodiment:
taking all breathing stages of a user in a course, and capturing the abdomen muscle participation power degree value P in each breathing stage;
calculating the average value of all the abdomen muscle participation power degree values P in each course;
establishing the state of the user in each class according to the average value;
setting the existing states of the user to be excellent, good and general three states respectively;
setting the state transition probability of a user as follows:
K0(Ei→Ej)=K0（Eji Ei）=Kij
Wherein E isi→EjRespectively representing two states of the user, K0Representing the user in state EiTransition to State EjState transition probability of time;
now set up E1Stands for Excellent, E2Good representation, E3Represents a general term;
the 20 groups of data for any user a are captured as follows:
in the above table, the slave state E can be known3The rotated-out state has 10 groups, namely (1, 2, 4, 6, 8, 9, 11, 12, 14, 16), wherein E3→E26 groups, namely (2 → 3,4 → 5,6 → 7,9 → 10,14 → 15,16 → 17);
then there is K32=K（E3→E2）=6/10=0.6；
The state transition probability matrix is obtained analogously as follows:
setting pi = [ pi ]1、π2、π3]Then, there is π × K:
π1=0.333π1+0.143π2+0.1π3
π2=0.333π1+0.6π3
π3=0.333π1+0.857π2+0.3π3
after solving, the pi can be obtained1、π2、π3；
Namely the probability distribution of the final three states, the effect probability distribution of the training courses after the user finishes delivery can be obtained through prediction based on the probability distribution, and related guidance courses can be pushed in time according to the probability distribution, so that the probability of reaching excellent states of the user is improved.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A postpartum exercise course evaluation method based on data analysis is characterized by comprising the following steps: the method comprises the following steps:
s1, obtaining user evaluation of the postpartum exercise course, and obtaining basic information of the evaluation user;
s2, obtaining evaluation star grades, paying attention to a user with low star grade evaluation, and analyzing the action level and the physical quality of the user;
s3, establishing a course recommendation model according to the analysis data in the step S2;
s4, obtaining usage data of the user for a period of time, predicting the postpartum exercise condition of the user, predicting the probability of obtaining low star rating according to big data, and pushing related courses in time;
in step S1, the user acquires the postpartum exercise course through the network platform, and issues an evaluation based on his/her own condition after learning, and the platform acquires the user' S basic information and exercise process;
the basic information of the user is denoted as set a, where a = { x = { (x)1、x2、x3、……、xn}; wherein x is1、x2、x3、……、xnOne of the basic information respectively representing the user;
in step S2, the method further includes:
obtaining evaluation star level, setting star level threshold value to be N1For lower than N1The evaluation of the star rating is defined as low star rating;
acquiring basic information of a user with low star-level evaluation, and analyzing the action level and the physical quality of the user;
acquiring a motion video of a user, performing frame extraction processing on the motion video, acquiring a picture after frame extraction, and establishing a picture set;
in the picture set, obtaining the abdomen action picture of the user, taking each breath of the user as a stage, and recording the abdomen action picture under each stage as a set B;
judging each breath of the user, namely judging by the expansion and contraction of the abdomen of the user, wherein a breathing stage is formed between the expansion and the contraction;
acquiring the muscle bulging degree of the abdominal action picture under the set B, acquiring a bulging difference value which is marked as h, wherein the bulging difference value is the muscle bulging difference value between calmness and force application;
acquiring basic information of a user, and selecting basic information related to muscles, including age, body fat rate, muscle level and weight, which are respectively recorded as x1、x2、x3、x4；
Establishing the abdominal muscle distension difference h of the user during the exercise1Comprises the following steps:
h1=x1k1+x2k2+x3k3+x4k4
wherein k is1、k2、k3、k4Are respectively basic information x1、x2、x3、x4The influence factor coefficient of (2);
calculating the abdomen muscle participation power degree value P of the user in any action in the postpartum training course as follows:
P=（h1-h）/h1
setting the abdomen exertion threshold under each action in the postpartum training course, and recording the threshold as PiIf P > P is presentiIf the action is not standard, the force-exerting part is wrong, and a record is generated;
setting the threshold value of the times as J, and if the threshold value is exceeded by PiWhen the total times of the user exceeds the threshold J, the user is judged to have poor course training effect due to nonstandard actions.
2. The method of claim 1, wherein the method comprises the steps of: in step S3, the method further includes:
acquiring an abdominal muscle engagement power degree value P of the user,
sequencing all the power degree values P according to a time sequence, and acquiring the variation trend of the power degree values P;
acquiring the maximum value, the minimum value and the action times of a power degree value P in a T time period;
according to the formula:
wherein, P1Is the maximum value of the force-exerting degree value POne of the minimum value and the minimum value is the sequence in the T time period; p2The other one of the maximum value and the minimum value of the force degree value P is the later one in the T time period; namely, description of P1-P2A negative value exists; d is P1And P2The number of actions; d is a trend deviation amount;
setting the threshold value to DmaxIf there is a tendency deviation D lower than DmaxWhen the user action changes slowly or falls into an action-nonstandard error zone, recommending an action guidance course;
if there is a trend deviation D higher than DmaxI.e. to say that the user action criterion level gradually improves, at which point a new exercise action session is recommended.
3. The method of claim 2, wherein the method comprises the steps of: in step S4, the method further includes:
acquiring all respiratory stages of a user in a course, and capturing an abdominal muscle participation power degree value P in each respiratory stage;
calculating the average value of all the abdomen muscle participation power degree values P in each course;
establishing the state of the user in each class according to the average value;
the state transition probability of the user is:
K0(Ei→Ej)=K0（Eji Ei）=Kij
Wherein E isi→EjRespectively representing two states of the user, K0Representing the user in state EiTransition to State EjState transition probability of time;
then, when there are n states, the state transition probability matrix is established as follows:
wherein K is a state transition probability matrix;
if the user is currently in state Ei(ii) a Then at the next instant it may be represented by state EiSteering E1，E2，…Ei…EnAny one of the states;
thus, KijThe conditions are satisfied:
according to the Markov prediction method, the final prediction result is pi x K;
according to the probability of the corresponding state in the result, the state distribution of the user after the postpartum training course is carried out can be obtained, so that the training effect probability distribution is obtained, and the postpartum exercise condition of the user is obtained through prediction;
acquiring historical data of a training effect of a user performing low-star evaluation, calculating the probability of performing low-star evaluation under the effect, and predicting to obtain the probability of performing low-star evaluation;
and pushing related courses in time according to the predicted training effect.
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| CN114582464B (en) * | 2022-05-07 | 2022-07-15 | 南京麦豆健康管理有限公司 | Course personalized recommendation system and method for postpartum exercise |
| CN115040843B (en) * | 2022-07-19 | 2023-11-17 | 惠州优朵科技有限公司 | Intelligent dynamic adjustment method based on pelvic floor muscle training |
| CN115363606A (en) * | 2022-08-26 | 2022-11-22 | 左点实业（湖北）有限公司 | Pelvic floor muscle and abdominal muscle coordination analysis method, device and equipment |
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