Pregnant woman exercise prescription recommendation system and method

Abstract

The invention relates to the field of pregnant woman exercise prescription recommendation, in particular to a pregnant woman exercise prescription recommendation system and method. The invention discloses a pregnant woman exercise prescription recommendation system. A health information acquisition module acquires initial health information of a pregnant woman; the motion target acquisition module acquires a motion target of a pregnant woman; the exercise ability evaluation module obtains an exercise ability evaluation result of the pregnant woman; the exercise prescription recommendation generation module obtains a historical data set based on the initial health information, the exercise target and the exercise ability data of the pregnant woman to train an exercise prescription recommendation model, generates an initial recommended exercise prescription, and adjusts the initial recommended exercise prescription based on the use condition of the exercise equipment; and the exercise prescription optimization generation module analyzes the exercise effect achievement rate and optimizes and adjusts the exercise prescription based on the exercise effect achievement rate. Through multi-module cooperation, full-process closed-loop management from data acquisition to personalized exercise prescription generation and optimization is realized, and the accuracy of pregnant woman exercise prescription recommendation is improved.

Description

Technical Field

[0001] The present invention relates to the field of pregnant women's exercise prescription recommendation, and specifically relates to a pregnant women's exercise prescription recommendation system and method. Background Art

[0002] In the current wave of digitalization, people's demands for health management are moving towards diversification and refinement. As a special object of health management, the pregnant women group has an increasingly strong demand for prenatal health management. Pregnant women's exercise plays a crucial role in the prenatal health management system, and its importance has been confirmed by a large number of medical researches and practices. Scientific and reasonable pregnant women's exercise can not only effectively enhance the physical fitness of pregnant women, relieve common discomfort symptoms such as edema, low back pain during pregnancy, but also significantly improve the mental state of pregnant women, reduce adverse emotions such as anxiety and depression, create a good maternal environment for the healthy development of the fetus, and more importantly, provide strong support for the smooth progress of childbirth.

[0003] Pregnant women's exercise is by no means an ordinary physical activity, which is related to the dual health and safety of pregnant women and fetuses. During the exercise process, the precise control of exercise intensity is crucial. Excessive intensity may cause serious consequences such as uterine contractions and fetal hypoxia; incorrect exercise postures are also likely to lead to problems such as muscle strains and joint injuries. At the same time, the status of the fetus, such as fetal movement changes, needs to be monitored in real time during exercise. However, most pregnant women do not have professional exercise knowledge and fetal monitoring skills, which makes the importance of professional guidance more prominent. Although professional pregnant women's exercise instructors can provide certain help, due to time and space limitations, it is difficult to provide comprehensive and detailed guidance and monitoring for pregnant women at any time and anywhere.

[0004] Therefore, the recommendation of pregnant women's exercise prescriptions is of great significance for ensuring the health and safety of mothers and infants. Summary of the Invention

[0005] The present invention aims to provide a pregnant women's exercise prescription recommendation system and method. Based on data-driven and intelligent analysis, through the cooperation of multiple modules, it realizes the full-process closed-loop management from data collection to the generation and optimization of personalized exercise prescriptions, and improves the accuracy of pregnant women's exercise prescription recommendation.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A pregnant women's exercise prescription recommendation system, comprising:

[0008] A health information collection module, used for collecting the initial health information of pregnant women;

[0009] An exercise goal acquisition module, used for acquiring the exercise goals of pregnant women;

[0010] A motor ability assessment module, which is used to screen out unsuitable exercise items based on the initial health information of the pregnant woman and obtain the assessment result of the pregnant woman's motor ability;

[0011] A sports prescription recommendation generation module, which is used to obtain a historical data set based on the initial health information, exercise goals and exercise ability data of the pregnant woman, train a sports prescription recommendation model based on the historical data set, generate an initial recommended sports prescription based on the sports prescription recommendation model, and adjust the initial recommended sports prescription based on the usage of sports equipment;

[0012] A sports prescription optimization generation module, which is used to obtain the exercise data of the pregnant woman, analyze the exercise effect achievement rate based on the exercise data of the pregnant woman, and optimize and adjust the sports prescription when the exercise effect achievement rate is lower than the preset threshold.

[0013] The principle and advantages of this solution are as follows: In practical applications, the basic health information of the pregnant woman is obtained through the health information collection module, and the exercise goals of the pregnant woman are clarified through the exercise goal acquisition module, providing a basis for subsequent exercise ability assessment and sports prescription generation; based on the initial health information of the pregnant woman, the exercise ability assessment module screens out unsuitable exercise items and evaluates the physical fitness level of the pregnant woman to ensure that the recommended exercise items are both safe and suitable for the physical condition of the pregnant woman; by screening out a historical data set similar to the current pregnant woman's characteristics to improve data availability, using a machine learning model to train the historical data set to generate a sports prescription recommendation model, based on the initial health information, exercise goals and exercise ability data of the pregnant woman, generate an initial recommended sports prescription, and also consider the availability of sports equipment to dynamically adjust the initial recommended sports prescription to ensure the practical feasibility of the recommended plan; optimize the recommended plan again according to the achievement of the exercise effect, making the plan more personalized. This solution generates sports prescriptions based on a large amount of historical data and scientific algorithms, ensuring the scientificity and reliability of the recommended plan. At the same time, it makes personalized recommendations and dynamic adjustments according to the health information, exercise goals and exercise ability data, equipment usage, etc. of the pregnant woman, realizing resource optimization and efficiency improvement.

[0014] Preferably, as an improvement, the exercise ability assessment module further includes:

[0015] A test data acquisition sub-module, which is used to obtain the data of the six-minute walk test, RPE - Borg scale of perceived exertion and ADL - Activities of Daily Living Scale of the pregnant woman;

[0016] An exercise intensity standardization sub-module, which is used to obtain the standardized exercise intensity of each exercise of the pregnant woman based on the heart rate parameters of the pregnant woman;

[0017] An assessment sub-module, which is used to obtain the exercise ability of the pregnant woman based on a preset exercise ability evaluation form, and the exercise ability includes endurance, flexibility and muscle strength.

[0018] Technical effect: The physical conditions of pregnant women vary, and their tolerances to various sports also differ. By personalizing the exercise intensity, it is convenient to improve the availability and reference value of the historical data of pregnant women. Through the evaluation of exercise ability, it is convenient to accurately recommend exercise prescriptions.

[0019] Preferably, as an improvement, the exercise prescription recommendation generation module includes:

[0020] The historical dataset matching sub-module is used to match and obtain a target number of historical datasets through cluster analysis methods;

[0021] The exercise prescription recommendation model construction sub-module is used to train a multi-layer perceptron through historical datasets, taking the initial health information, exercise goals, and exercise ability data of pregnant women as inputs and the recommended exercise prescription as the output to obtain an exercise prescription recommendation model;

[0022] The exercise prescription adjustment sub-module is used to obtain the availability information of exercise equipment, judge whether there is a conflict between the initial recommended exercise prescription and the use of exercise equipment based on the availability information of exercise equipment. If it is judged to be yes, it dynamically adjusts the recommended exercise prescription based on the exercise item replacement strategy. If not, no adjustment is made.

[0023] Technical effect: Through the three core functions of historical data matching, intelligent modeling, and dynamic adjustment, personalized, intelligent, and dynamic exercise prescription recommendations are realized, improving the accuracy and scientificity of the recommendations, enhancing the flexibility and adaptability of the system, improving the user experience, and having strong social application value.

[0024] Preferably, as an improvement, the exercise item replacement strategy includes: obtaining the characteristic information of exercise equipment, based on the characteristic information of exercise equipment, obtaining all replaceable items of the exercise items that conflict with the use of the recommended exercise equipment, and obtaining the exercise equipment usage schedule of the replaceable items, and replacing the exercise items based on the exercise preferences of pregnant women and the exercise equipment usage schedule.

[0025] Technical effect: To a certain extent, the availability of equipment determines whether the current exercise prescription can be completed on time. By replacing exercise items, it is convenient to improve the availability of exercise prescriptions.

[0026] Preferably, as an improvement, the exercise prescription optimization generation module includes:

[0027] The exercise data acquisition sub-module is used to acquire the exercise process data and exercise result data of pregnant women;

[0028] The exercise process achievement rate analysis sub-module is used to analyze the exercise compliance and persistence of pregnant women based on the exercise process data of pregnant women, and judge whether the exercise process achievement rate is lower than the threshold based on the exercise compliance and persistence of pregnant women;

[0029] The exercise result achievement rate analysis sub-module is used to analyze the exercise target achievement rate of pregnant women based on exercise result data and determine whether the exercise target achievement rate is lower than a threshold value.

[0030] The optimization and adjustment sub-module is used to optimize and adjust the exercise prescription based on the optimization and adjustment strategy when the exercise process achievement rate or the exercise target achievement rate is lower than the threshold value.

[0031] Technical effect: Through a data-driven approach, the precise, dynamic, and intelligent adjustment of the exercise prescription is realized, the automatic adjustment is achieved, the need for human intervention is reduced, and the exercise compliance and target achievement efficiency of pregnant women are enhanced.

[0032] Preferably, as an improvement, the optimization and adjustment strategy includes: constructing a portrait of the exercise items of pregnant women including seasonal characteristics and adjusting the exercise plan based on the portrait of the exercise items.

[0033] Technical effect: Through the portrait of the exercise items of pregnant women, it is possible to accurately recommend exercise forms that are more acceptable to pregnant women, which is convenient for improving the participation and compliance of pregnant women.

[0034] Preferably, as an improvement, the exercise prescription includes the items, frequency, intensity, and time of exercise.

[0035] Technical effect: Through reasonable exercise items, frequency, intensity, and time, it is convenient to accurately control the exercise progress of pregnant women.

[0036] Preferably, as an improvement, it further includes an abnormal monitoring module for identifying abnormalities in the exercise process of pregnant women. When an exercise abnormality is identified, a recommended strategy for adjusting exercise equipment is generated, and the equipment speed, slope, resistance / power, and start / stop are adjusted in real time based on the recommended strategy for adjusting exercise equipment.

[0037] Technical effect: Facilitate ensuring the exercise safety of pregnant women.

[0038] Preferably, as an improvement, the exercise prescription recommendation generation module is also used for gestational age calculation. When the pregnancy stage changes, it triggers the data update of the health information collection module, the exercise target acquisition module, the exercise ability assessment module, the exercise prescription recommendation generation module, and the exercise prescription optimization generation module.

[0039] Technical effect: Pregnancy is a dynamic process. The physical condition and exercise ability of pregnant women will change with the increase of gestational age. Regularly triggering the data update of each module is convenient for ensuring the exercise effect and safety.

[0040] This application also includes a method for recommending an exercise prescription for pregnant women, which is applied to the recommendation of an exercise prescription for pregnant women. Description of the Drawings

[0041] Figure 1 It is a schematic structural diagram of a pregnant woman exercise prescription recommendation system. Specific implementation manners

[0042] The following is a further detailed description through specific implementation manners:

[0043] The embodiment is basically as shown in the appendix Figure 1 as follows:

[0044] A pregnant woman exercise prescription recommendation system includes:

[0045] A health information collection module, which is used to collect the initial health information of pregnant women. The initial health information includes age, height, weight, past medical history, family medical history, current gestational week, and pregnancy examination results. Age is an important factor reflecting the physical function of pregnant women. Elderly pregnant women (35 years old and above) have relatively weak physical functions and a higher risk of pregnancy complications, so they need to be more cautious during exercise; height and weight are used to calculate the body mass index (BMI) to evaluate whether the weight of pregnant women is within the normal range, providing a basis for calculating exercise intensity and calorie consumption; chronic diseases such as hypertension, heart disease, diabetes, thyroid diseases, etc. These diseases impose many restrictions on the exercise of pregnant women and affect the formulation of exercise prescriptions; obstetrics and gynecology related diseases such as habitual abortion, preterm birth history, uterine fibroids, etc. are closely related to the safety of pregnancy exercise, and the exercise method and intensity need to be selected according to the specific situation; the allergy history is convenient to understand whether pregnant women are allergic to certain exercise environments, equipment or drugs, and avoid contact during exercise to cause allergic reactions. Understanding whether there are genetic diseases, cardiovascular diseases, etc. in the family is convenient to evaluate the potential disease risk of pregnant women and has a reference role in the formulation of exercise prescriptions. If there is a family history of genetic heart disease, more attention should be paid to heart function monitoring during exercise. Pregnant women have different physical changes at different gestational weeks, and their exercise needs and taboos also vary. For example, strenuous exercise should be avoided in the early pregnancy, the exercise intensity can be appropriately increased in the second trimester, and the safety of exercise and its help for childbirth should be emphasized in the third trimester; pregnancy examinations such as ultrasound examinations are used to understand the size and position of the fetus. If the fetus is underdeveloped or there are problems such as abnormal fetal position, the exercise method and intensity need to be adjusted accordingly; pregnancy complications such as gestational hypertension, gestational diabetes, anemia, etc. Complications will affect the exercise ability and safety of pregnant women, and an exercise plan needs to be formulated according to the specific condition. Therefore, obtaining the initial health information of pregnant women is convenient to identify potential risks during the exercise process of pregnant women in advance and ensure exercise safety; at the same time, it is the basis for formulating personalized programs and setting reasonable exercise goals.

[0046] The exercise target acquisition module is used to acquire the exercise targets of pregnant women. In this embodiment, the exercise targets are acquired according to the diagnosis results issued by doctors. The exercise targets include, but are not limited to, enhancing cardiopulmonary function, relieving pregnancy discomfort, controlling weight, and regulating mental state. Different exercise targets correspond to different exercise items. For example, through regular aerobic exercise, cardiopulmonary function can be enhanced. The enhanced heart function enables the heart to pump blood more effectively, providing sufficient oxygen and nutrients for all organs of the body and the fetus. The improved lung function can increase the gas exchange efficiency and the body's oxygen uptake ability, making pregnant women less prone to fatigue in daily activities and having enough physical strength to complete the delivery process during childbirth. During pregnancy, hormonal changes can cause joints and ligaments to relax. Appropriate stretching exercises and yoga practices can further stretch muscles and ligaments, maintaining their elasticity and flexibility, helping to prevent muscle strains and relieve discomfort such as low back and leg pain caused by changes in body center of gravity and muscle tension. Good flexibility increases the mobility of pelvic joints, facilitating the smooth passage of the fetus through the birth canal during childbirth. Strengthening the muscles of the abdomen, pelvic floor, legs, etc. provides better support for the body during pregnancy, prevents and relieves physical discomfort, and improves the delivery efficiency. As the fetus grows, the abdominal muscles need to bear greater pressure. Strengthening the abdominal muscles can better support the enlarged uterus, reduce the burden on the lumbar muscles, and prevent and relieve low back pain. The pelvic floor muscles support pelvic organs such as the uterus and bladder. Strengthening the pelvic floor muscles can prevent urinary incontinence during pregnancy, better cooperate with uterine contractions during childbirth to help the fetus be delivered smoothly, and also reduce the risk of postpartum pelvic floor dysfunction diseases. The leg muscles are an important support for the body. Strengthening the leg muscles helps pregnant women maintain good body balance, reduce leg edema and fatigue, and make daily activities such as walking and going up and down stairs easier. If pregnant women consume too many calories and lack exercise during pregnancy, the excess energy will be converted into fat and accumulate, resulting in excessive weight gain. Appropriate exercise can increase energy consumption, making the intake and consumption reach a relative balance. Through aerobic exercise, the body's metabolic rate can be increased, promoting the oxidation and decomposition of fat, maintaining the weight within a reasonable range, and reducing the risks of macrosomia, dystocia, postpartum obesity, etc. Exercise can prompt the body to secrete neurotransmitters such as endorphins and dopamine, making pregnant women feel happy and relaxed, effectively relieving anxiety and depression, regulating mood, improving sleep. Participating in exercise can also enable pregnant women to divert their attention, reduce excessive worry about physical changes during pregnancy and childbirth, and enhance self-confidence and psychological adaptability.

[0047] The exercise ability assessment module is used to screen out unsuitable exercise items based on the initial health information of pregnant women and obtain the exercise ability data of pregnant women. The exercise ability assessment module further includes a test data acquisition sub-module, an exercise intensity standardization sub-module, and an assessment sub-module; the test data acquisition sub-module is used to obtain the data of the six-minute walk test, the RPE-Rating of Perceived Exertion scale, and the ADL-Activities of Daily Living assessment scale of pregnant women; the six-minute walk test (6-Minute Walk Test, 6MWT) is to let pregnant women adopt a walking exercise method to test the distance they can walk at the fastest speed they can bear within 6 minutes. This test has a systematic, comprehensive, and complete response during the exercise process, including the lungs, cardiovascular system, systemic circulation, peripheral circulation, blood, neuromuscular unit, and muscle metabolism. The RPE-Rating of Perceived Exertion scale measures the degree of exertion during exercise based on the subjective feelings of the exerciser about the exercise intensity. In this embodiment, the grading method of the degree of exertion adopts a version of 6-20 levels. 6 means completely relaxed without any sense of exertion; 7-8 is very, very relaxed; 9-10 is quite relaxed; 11-12 is relatively relaxed; 13-14 is a bit tired; 15-16 is tired; 17-18 is very tired; 19-20 means extremely tired to the extent that it is almost impossible to continue exercising. The ADL-Activities of Daily Living assessment scale is used to evaluate an individual's ability to perform various activities in daily life, reflecting the individual's self-care ability and degree of dependence. It includes two aspects. One is the basic activities of daily living, such as eating, dressing, washing, bathing, toileting, transferring (from bed to chair, etc.), walking, etc.; the other is the instrumental activities of daily living, such as cooking, shopping, cleaning, using transportation, taking medicine, handling finances, etc. The assessment results are presented in the form of a total score. The higher the score, the stronger the ability of daily living activities.

[0048] The exercise intensity standardization sub-module is used to obtain the standardized exercise intensity of each exercise of pregnant women based on the heart rate parameters of pregnant women; the standardized exercise intensity includes low intensity, medium intensity, and high intensity. The heart rate of low-intensity exercise is 50%-60% of the maximum heart rate, the heart rate of medium-intensity exercise is between 60%-75% of the maximum heart rate, and the heart rate of high-intensity exercise reaches 75%-90% of the maximum heart rate. The maximum heart rate = 207 - 0.7 × age. Since there are differences in the physical conditions among pregnant women, obtaining the standardized exercise intensity through the heart rate parameters of pregnant women can avoid some exercise equipment from unifying the exercise intensity, which is convenient for timely adjustment to improve the degree of personalization.

[0049] An evaluation sub-module is used to obtain the exercise ability of pregnant women based on a preset exercise ability evaluation form. The exercise ability includes endurance, flexibility, and muscle strength. The evaluation result of the exercise ability of pregnant women is obtained through the six-minute walk test, the RPE-Rating of Perceived Exertion scale, and the ADL-Activities of Daily Living scale. Based on the results of the above three evaluations, the exercise ability of pregnant women is evaluated as a whole. In this embodiment, the exercise ability of pregnant women is obtained by querying the preset exercise ability evaluation form.

[0050] An exercise prescription recommendation generation module is used to obtain a historical data set based on the initial health information, exercise goals, and exercise ability data of pregnant women, train an exercise prescription recommendation model based on the historical data set, generate an initial recommended exercise prescription based on the exercise prescription recommendation model, and adjust the initial recommended exercise prescription based on the usage of exercise equipment. The exercise prescription includes the items, frequencies, intensities, and durations of exercise. The exercise prescription recommendation generation module includes a historical data set matching sub-module, an exercise prescription recommendation model construction sub-module, and an exercise prescription adjustment sub-module.

[0051] The historical data set matching sub-module is used to match and obtain a target number of historical data sets through a clustering analysis method to provide a high-quality data basis for prescription recommendation. Specifically, multi-dimensional historical data related to the exercise of pregnant women is preprocessed, including but not limited to age, gestational week, BIM, health status, exercise items, exercise prescriptions, exercise effect feedback (such as weight change, physical fitness improvement, subjective feelings, etc.). Feature selection is performed according to the correlation between the initial health information and exercise goals and characteristics of pregnant women, and a cluster division result is generated through a clustering algorithm. By comparing the selected features with each cluster, the cluster with the closest distance is found, and a target number of historical data sets are randomly selected from this cluster as the matching result.

[0052] The exercise prescription recommendation model construction submodule is used to train a multi-layer perceptron through historical data sets, taking the initial health information, exercise goals, and exercise ability data of pregnant women as input, and taking the recommended exercise prescription as output to obtain an exercise prescription recommendation model. There is a complex nonlinear relationship between the initial health information, exercise goals, and exercise ability data of pregnant women and the recommended exercise prescription. The multi-layer perceptron has a strong nonlinear modeling capability and can learn these complex relationships through multiple hidden layers, so as to recommend exercise prescriptions more accurately. Specifically, the environment is prepared, including the deployment of the Python library numpy for numerical calculations, pandas for data processing, scikit-learn for data preprocessing and model evaluation, and tensorflow for building and training multi-layer perceptron models; the historical data set is organized into a format suitable for Python library processing, non-numeric features are encoded and converted into numerical data, the numerical initial health information and athletic ability data are standardized so that different features have the same scale, the processed data are divided into training and test sets, the training set is used to train the constructed multi-layer perceptron model, the trained model is saved for use, and in actual application, the initial health information, exercise goals and athletic ability data of new pregnant women are preprocessed and input into the saved model to obtain the initial recommended exercise prescription.

[0053] The exercise prescription adjustment submodule is used to obtain the availability information of the exercise equipment. In this embodiment, the exercise equipment also includes professional technicians and health monitoring equipment supporting the sports project. The exercise equipment includes but is not limited to upright ergometer, upright fitness bike, horizontal ergometer, horizontal fitness bike, treadmill, elliptical machine, and external counter-pulsation. The availability information of the equipment includes the available time period of the equipment, whether the equipment is damaged, and the obsolescence of the equipment. Based on the availability information of the sports equipment, it is judged whether the initial recommended exercise prescription conflicts with the use of the sports equipment. If it is judged to be yes, the recommended exercise prescription is dynamically adjusted based on the sports project replacement strategy. If not, no adjustment is made. The sports project replacement strategy includes: obtaining the characteristic information of the sports equipment, the characteristic information includes the equipment type, the equipment function, and the equipment intensity range; based on the characteristic information of the sports equipment, all replaceable items of the sports project that conflicts with the recommended sports equipment are obtained, and the replaceable items meet the sports intensity close to the original sports, and the sports goals are consistent with the original sports (such as strengthening physical strength and controlling weight); obtaining the sports equipment use schedule for replaceable items, replacing the sports items based on the sports preferences of pregnant women and the sports equipment use schedule, and the sports preferences of pregnant women include the preferred sports time period.

[0054] The exercise prescription optimization generation module is used to obtain the exercise data of pregnant women, analyze the exercise effect achievement rate based on the exercise data of pregnant women, and optimize and adjust the exercise prescription when the exercise effect achievement rate is lower than the preset threshold. The exercise prescription optimization generation module includes an exercise data acquisition sub-module, an exercise process achievement rate analysis sub-module, and an exercise result achievement rate analysis sub-module.

[0055] The exercise data acquisition sub-module is used to obtain the exercise process data and exercise result data of pregnant women. The exercise process data includes exercise parameters (such as exercise type, exercise intensity, exercise frequency, exercise duration, exercise rhythm), physiological indicators (such as heart rate, blood pressure, respiratory rate, body temperature), and behavior data (such as action standardization, activity trajectory, rest interval); the exercise result data includes changes in body indicators (such as weight change, BMI change, body fat percentage change, muscle mass change), improvement in health status (such as blood pressure control, blood glucose level, cardiopulmonary function), exercise goal-related data (such as weight loss goal, physical strength enhancement, pain relief), subjective feelings (such as fatigue, satisfaction, emotional state), and long-term trend data (such as historical comparison, achievement of phased goals). The above data is obtained through wearable devices (smart bracelets, smart watches, heart rate monitors, etc.), mobile applications (recording information such as exercise type, duration, and frequency through mobile phone apps), sensor devices (action capture sensors, pose recognition cameras, etc.), and medical devices (weighing scales, body fat analyzers, blood pressure monitors, blood glucose meters, etc.). The exercise process achievement rate analysis sub-module is used to analyze the exercise compliance and persistence of pregnant women based on the exercise process data of pregnant women, and judge whether the exercise process achievement rate is lower than the threshold based on the exercise compliance and persistence of pregnant women. The exercise result achievement rate analysis sub-module is used to analyze the exercise goal achievement rate of pregnant women based on the exercise result data and judge whether the exercise goal achievement rate is lower than the threshold. The optimization and adjustment sub-module is used to optimize and adjust the exercise prescription based on the optimization and adjustment strategy when the exercise process achievement rate or the exercise goal achievement rate is lower than the threshold. The optimization and adjustment strategy includes: constructing an exercise project portrait of pregnant women including seasonal characteristics, and adjusting the exercise plan based on the exercise project portrait. Including seasonal characteristics means that the same exercise project is suitable for indoor or outdoor exercise in different seasons.

[0056] It also includes an abnormal monitoring module for abnormal identification of the exercise process of pregnant women. In this embodiment, a series of threshold rules are set through a rule engine to judge whether an abnormality occurs, such as the heart rate exceeding the maximum safe heart rate range of pregnant women, the posture deviating from the standard angle, etc. When an exercise abnormality is identified, a recommended strategy for adjusting the exercise device is generated, and the device speed, slope, resistance / power, start and stop are adjusted in real time based on the exercise device adjustment strategy.

[0057] The exercise prescription recommendation generation module is also used for gestational age calculation. When the pregnancy stage changes, it triggers the data update of the health information collection module, the exercise goal acquisition module, the exercise ability assessment module, the exercise prescription recommendation generation module, and the exercise prescription optimization generation module.

[0058] This application also includes a method for recommending an exercise prescription for pregnant women, which is applied to the recommendation of an exercise prescription for pregnant women.

[0059] The above are only the embodiments of the present invention. Specific technical solutions and / or common knowledge such as characteristics known to the public are not described in detail here. It should be noted that for those skilled in the art, without departing from the technical solution of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicability of the patent. The protection scope required by this application should be subject to the content of its claims, and the specific implementation manners described in the specification can be used to interpret the content of the claims.

Claims

1. A system for recommending exercise prescriptions for pregnant women, characterized in that: include: Health information collection module, used to collect initial health information of pregnant women; A motion target acquisition module is used to acquire the motion target of the pregnant woman; The exercise capacity assessment module is used to screen out inappropriate exercise items based on the initial health information of pregnant women and obtain the exercise capacity assessment results of pregnant women; An exercise prescription recommendation generation module is used to obtain a historical data set based on the initial health information, exercise goals and exercise capacity data of the pregnant woman, train an exercise prescription recommendation model based on the historical data set, generate an initial recommended exercise prescription based on the exercise prescription recommendation model, and adjust the initial recommended exercise prescription based on the use of exercise equipment; The exercise prescription optimization generation module is used to obtain the exercise data of pregnant women, analyze the exercise effect achievement rate based on the exercise data of pregnant women, and optimize and adjust the exercise prescription when the exercise effect achievement rate is lower than a preset threshold.

2. The system for recommending exercise prescriptions for pregnant women according to claim 1, characterized in that: The sports ability assessment module also includes: The test data acquisition submodule is used to obtain the data of the six-minute walk test, RPE-rated scale of perceived exertion and ADL-activity of daily living assessment scale of pregnant women; An exercise intensity standardization submodule is used to obtain the standardized exercise intensity of various exercises of the pregnant woman based on the heart rate parameters of the pregnant woman; The evaluation submodule is used to obtain the exercise capacity of the pregnant woman based on a preset exercise capacity evaluation table, wherein the exercise capacity includes endurance, flexibility, and muscle strength.

3. The system for recommending exercise prescriptions for pregnant women according to claim 1, characterized in that: The exercise prescription recommendation generation module includes: The historical data set matching submodule is used to match and obtain the target number of historical data sets through cluster analysis method; The exercise prescription recommendation model building submodule is used to train a multi-layer perceptron through historical data sets, taking the initial health information, exercise goals and exercise capacity data of pregnant women as input, and taking the recommended exercise prescription as output to obtain an exercise prescription recommendation model; The exercise prescription adjustment submodule is used to obtain the availability information of the exercise equipment, and determine whether the initial recommended exercise prescription conflicts with the use of the exercise equipment based on the availability information of the exercise equipment. If so, the recommended exercise prescription is dynamically adjusted based on the exercise item replacement strategy; if not, no adjustment is made.

4. The system for recommending exercise prescriptions for pregnant women according to claim 3, characterized in that: The sports item replacement strategy includes: obtaining characteristic information of sports equipment, obtaining all replaceable items of sports items that conflict with the recommended sports equipment based on the characteristic information of the sports equipment, and obtaining a sports equipment usage plan for the replaceable items, and replacing the sports items based on the pregnant woman's exercise preferences and the sports equipment usage plan.

5. The system for recommending exercise prescriptions for pregnant women according to claim 1, characterized in that: The exercise prescription optimization generation module includes: The exercise data acquisition submodule is used to obtain the exercise process data and exercise result data of the pregnant woman; The exercise process achievement rate analysis submodule is used to analyze the exercise compliance and persistence of pregnant women based on their exercise process data, and to determine whether the exercise process achievement rate is lower than a threshold based on their exercise compliance and persistence; The exercise result achievement rate analysis submodule is used to analyze the exercise goal achievement rate of pregnant women based on the exercise result data and determine whether the exercise goal achievement rate is lower than a threshold; The optimization and adjustment submodule is used to optimize and adjust the exercise prescription based on the optimization and adjustment strategy when the exercise process achievement rate or the exercise goal achievement rate is lower than a threshold.

6. The system for recommending exercise prescriptions for pregnant women according to claim 5, characterized in that: The optimization and adjustment strategy includes: constructing a sports project portrait of pregnant women that includes seasonal characteristics, and adjusting the exercise plan based on the sports project portrait.

7. The system for recommending exercise prescriptions for pregnant women according to claim 1, characterized in that: The exercise prescription includes exercise items, frequency, intensity and time.

8. The system for recommending exercise prescriptions for pregnant women according to claim 1, characterized in that: It also includes an abnormal monitoring module for identifying abnormalities in the exercise process of pregnant women. When exercise abnormalities are identified, a recommended strategy for adjusting the exercise equipment is generated, and the equipment speed, slope, resistance / power, and start and stop are adjusted in real time based on the exercise equipment adjustment strategy.

9. The system for recommending exercise prescriptions for pregnant women according to claim 1, characterized in that: The exercise prescription recommendation generation module is also used for gestational week calculation. When the pregnancy stage changes, it triggers data updates in the health information collection module, exercise target acquisition module, exercise ability assessment module, exercise prescription recommendation generation module and exercise prescription optimization generation module.

10. A method for recommending exercise prescription for pregnant women, characterized by: Applied to a pregnant woman exercise prescription recommendation system as described in any one of claims 1-9.

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