CN115153577A - Monitoring method and system for heart rehabilitation exercise based on medical intelligent interactive equipment - Google Patents

Abstract

The invention discloses a monitoring method and a system for heart rehabilitation exercise based on medical intelligent interactive equipment, wherein the method comprises the following steps: s1, acquiring health data obtained by real-time monitoring in the exercise process of a user through wearable equipment; s2, setting a safety threshold according to the body state of the user, and comparing the safety threshold with the health data; s3, determining a next rehabilitation exercise scheme by combining big data analysis according to the comparison result; and S4, pushing the rehabilitation exercise scheme to a user and medical workers in an intelligent interaction mode. According to the invention, the rehabilitation exercise process of the user is monitored in real time, accurate health data information is acquired, the health data and the body state of the user are combined through big data analysis, the heart load caused by excessive movement of the user is avoided, a next rehabilitation exercise scheme is specifically formulated for the user when the movement does not reach the standard, sufficient movement intensity is effectively ensured, and the health requirements of the user under different states are further met.

Description

Monitoring method and system for cardiac rehabilitation exercise based on medical intelligent interactive equipment

technical field

The present invention relates to the technical field of cardiac rehabilitation exercise, in particular, to a monitoring method and system for cardiac rehabilitation exercise based on medical intelligent interactive equipment.

Background technique

Cardiac rehabilitation exercises can reduce the recurrence rate and reinfarction rate of postoperative patients, and help patients with heart disease to recover. "Cardiac Rehabilitation: Standard Treatment for Secondary Prevention of Cardiovascular Diseases" believes that there is a time window for cardiac rehabilitation/secondary prevention (CR/SP), and patients in different time windows need to perform different intensities of exercise, that is, different degrees of cardiac rehabilitation. train.

Effective cardiac rehabilitation training is beneficial to the recovery of patients after surgery. However, when training, you must strictly control the amount of activity, and you must do it step by step. Cardiac rehabilitation training can be divided into several stages, generally in the early stage of rehabilitation, the main thing is to do some aerobic exercise under the supervision of a doctor. Exercise must be avoided strenuous, avoid prolonged exercise, can be closely monitored, and gradually increase the amount of activity.

Then there is the middle period of recovery, that is, within half a year to a year, some aerobic physical exercise programs can be carried out, which can control the weight gain of the body, and is also conducive to the recovery of the disease. Usually also pay attention to strengthen nutrition, to ensure the intake of various nutrients.

Exercise prescriptions are prescribed by rehabilitation physicians, rehabilitation therapists or physical education teachers, social sports instructors, personal fitness trainers, etc., according to the age, gender, general medical examination, rehabilitation medical examination, exercise test, physical fitness/fitness of the patient or physical fitness person. Can test and other results, according to their age, gender, health status, physical fitness, as well as the functional status of cardiovascular and sports organs, combined with subjective and objective conditions, formulate the appropriate exercise content and exercise intensity for patients or fitness practitioners in the form of prescriptions , exercise time and frequency, and point out the precautions in exercise, so as to achieve the purpose of scientific and planned rehabilitation or preventive fitness.

In order to minimize the risk of cardiac rehabilitation exercise and maximize the efficiency, patients should exercise strictly in accordance with the exercise prescription. Therefore, in the process of daily cardiac rehabilitation exercise, the use of medical intelligent interactive equipment for real-time monitoring has become the current development direction.

For example, Patent No. CN110782991B discloses a real-time evaluation method for assisting the rehabilitation exercise of heart disease patients, and the method determines the stable threshold range of the patient's exercise center rate and blood pressure. The patient selects the type of exercise to start exercising; detects the patient's heart rate and blood pressure, determines whether the patient's heart rate and blood pressure are lower than the upper threshold limit, calculates the exercise fit score, and makes targeted recommendations to the patient according to the exercise fit score. However, this method has certain defects. For example, it uses foot sensors and inertial sensors to detect the patient's gait characteristics to judge the patient's movement state. However, the patient can perform various types of movement, which leads to the application of sensor detection. In addition, this method only judges whether the patient's movement meets the standard, and there are certain limitations in functionality. Today, with the increasing degree of intelligence, it cannot meet the needs of more humanized and comprehensive monitoring, and needs to be further enriched and improved. .

For the problems in the related technologies, no effective solutions have been proposed so far.

SUMMARY OF THE INVENTION

In view of the problems in the related art, the present invention proposes a cardiac rehabilitation exercise monitoring method and system based on a medical intelligent interactive device, so as to overcome the above-mentioned technical problems existing in the related art.

For this reason, the concrete technical scheme that the present invention adopts is as follows:

According to one aspect of the present invention, a monitoring method for cardiac rehabilitation exercise based on a medical intelligent interactive device is provided, the method comprising the following steps:

S1. Obtain the health data obtained by real-time monitoring during the user's exercise process through the wearable device;

S2. Set a safety threshold according to the user's physical state, and compare it with the health data;

S3. According to the comparison results, combined with big data analysis, determine the next rehabilitation exercise plan;

S4. Push the rehabilitation exercise program to the user and the medical worker in an intelligent interactive manner.

Further, the health data includes heart sound, ECG signal, heart rate, blood pressure, respiratory rate and blood oxygen;

The physical state of the user includes gender, age, height, weight and disease condition.

Further, according to the comparison results, combined with big data analysis to determine the next step of the rehabilitation exercise plan, including the following steps:

S31. If there is data in the health data that is greater than the safety threshold, remind the user to give an early warning through intelligent interaction, and provide corresponding diagnosis and nursing suggestions according to the data type;

S32. If the health data is not greater than the safety threshold, analyze and calculate the user's current exercise intensity, and analyze the user's current exercise degree through big data, and formulate a next-step rehabilitation exercise plan for the user.

Further, the analysis calculates the user's current exercise intensity, and analyzes the user's current exercise degree through big data, and formulates the user's next rehabilitation exercise plan, including the following steps:

S321, utilize the method of wavelet packet decomposition frequency band energy entropy to analyze the heart sound and electrocardiogram signal, and use the frequency band energy entropy as the current exercise intensity of the user;

S322, performing matching on the big data platform according to the physical state of the user, screening out the same type of crowd, and querying the exercise intensity and item type of the crowd's rehabilitation exercise process as a reference standard set;

S323, analyzing and comparing the current exercise intensity of the user with the reference standard set, to determine whether the current exercise intensity of the user meets the standard;

S324. If the current exercise intensity of the user is not lower than the reference standard set, remind the user to exercise in moderation and praise and encourage them through intelligent interaction;

S325. If the current exercise intensity of the user is lower than the reference standard set, the user is encouraged to increase the amount of exercise through intelligent interaction, and the user is asked whether intelligent recommendation is required to change the current exercise mode.

Further, the method of utilizing the wavelet packet to decompose the frequency band energy entropy analyzes the heart sound and the ECG signal, and uses the frequency band energy entropy as the current exercise intensity of the user, including the following steps:

S3211, performing wavelet packet decomposition on the heart sound and the ECG signal;

S3212, analyze the ratio of the energy of the low frequency component to the total energy of the signal, and judge the health of the heart;

S3213. Calculate by using the energy entropy calculation formula, and use the calculation result as an index of exercise intensity.

Further, the expression of the energy entropy calculation formula is:

In the formula, R represents the value of energy entropy;

E(i) represents the normalized energy of a certain frequency band, and satisfies E(i)=|g _i (k)| ² ;

E represents the sum of the band energy of a certain layer, and satisfies E=|g _n (k)| ² ;

g _i (k) represents the high-pass filter corresponding to the wavelet function of the i-th frequency band;

g _n (k) represents the high-pass filter corresponding to the wavelet function of a certain layer of frequency bands;

n represents the frequency band number;

i represents the ith frequency band sequence number.

Further, the intelligent recommendation adopts a genetics-based support vector machine algorithm, including the following steps:

S3251. Determine the chromosome of the genetic algorithm, including the support vector machine parameters and the characteristic value of the user's disease condition;

S3252. Determine the genetic operator and the fitness function;

S3253, using the roulette wheel and weighted depth-first search method to generate the initial population of the genetic algorithm, and using the adaptive and heuristic initialization method to ensure the uniformity of the population distribution;

S3254: Optimizing the crossover probability and mutation probability to realize self-adaptive adjustment of different evolutionary bands, and to realize global search while retaining useful genetic information.

Further, the genetic operator includes a selection operator, a crossover operator and a mutation operator;

The expression of the fitness function is:

f(x)=f ₁ (x)-η·f ₂ (x)

In the formula, f ₁ (x) represents the classification accuracy of disease conditions;

f ₂ (x) represents the number of selected eigenvalues;

η represents the adjustment weight parameter.

Further, the intelligent interaction method includes artificial voice chat and application interface push, and Su Sohu artificial voice chat has an intelligent question and answer function.

According to another aspect of the present invention, a monitoring system for cardiac rehabilitation exercise based on medical intelligent interactive equipment, the system comprises the following modules:

The wearable device monitoring module is used to monitor and obtain the user's health status in real time;

Intelligent interactive module, used to provide artificial voice chat function, provide users with voice broadcast and intelligent question and answer;

The medical database module is used to reserve professional medical care knowledge;

The intelligent analysis and processing module is used to identify and calculate the user's health data and exercise intensity, and provide users with intelligent recommendations and suggestions;

Big data platform, used to provide user sharing and medical big data and update it in real time;

The mobile terminal is used as an intelligent terminal to provide a mounting and application platform for each module.

The beneficial effects of the invention are as follows: by monitoring the user's rehabilitation exercise process in real time, accurate health data information is obtained, and then the user's health data and physical state are combined through big data analysis, so as to avoid excessive exercise caused by the user's heart load, and when the exercise does not meet the standard Users can formulate the next rehabilitation exercise program in a targeted manner to effectively ensure sufficient exercise intensity, thereby meeting the health needs of users in different states, and significantly improving the level of cardiac rehabilitation exercise; , provides humanized early warning reminders and professional medical care suggestions, and then encourages users, effectively drives users to exercise, and improves the effect and stickiness of users' exercise.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings required in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some of the present invention. In the embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

1 is a flowchart of a monitoring method for cardiac rehabilitation exercise based on a medical intelligent interactive device according to an embodiment of the present invention;

2 is a system block diagram of a monitoring system for cardiac rehabilitation exercise based on a medical intelligent interactive device according to an embodiment of the present invention.

In the picture:

1. Wearable device monitoring module; 2. Intelligent interactive module; 3. Medical database module; 4. Intelligent analysis and processing module; 5. Big data platform; 6. Mobile terminal.

Detailed ways

According to an embodiment of the present invention, a monitoring method for cardiac rehabilitation exercise based on a medical intelligent interactive device is provided.

The present invention will now be further described with reference to the accompanying drawings and specific embodiments. As shown in FIG. 1 , according to an embodiment of the present invention, a method for monitoring cardiac rehabilitation exercise based on a medical intelligent interactive device, the method includes the following steps:

S1. Obtain the health data obtained by real-time monitoring during the user's exercise process through the wearable device;

S2. Set a safety threshold according to the user's physical state, and compare it with the health data;

Wherein, the health data includes heart sound, ECG signal, heart rate, blood pressure, respiratory rate and blood oxygen;

Heart sounds and ECG signals, as the two most important physiological signals in the human body, contain a lot of physiological and pathological information about the human heart and blood vessels. The two can not only reflect the cardiac contractility and cardiac reserve, but also have the characteristics of high specificity, repeatability and objective quantification, non-invasiveness and sensitivity. Therefore, the detection and analysis of heart sounds and ECG signals is an essential method to understand whether the human heart is healthy or not. The characteristics of the above two physiological signals can be applied to various sports venues, so as to evaluate the physical fitness of the human body and judge the intensity of exercise.

Heart rate, blood pressure, and blood oxygen are important vital signs. The normal ranges of the three are as follows:

1. Heart rate: refers to the frequency of the heart beating, the normal range is 60-100 beats/min. If the heart rate is too fast, it is called tachycardia, and if the heart rate is too slow, it is called bradycardia.

2. Blood pressure: refers to the pressure of arterial blood, divided into high pressure and low pressure, the high pressure is 90-140mmHg, and the low pressure is 60-90mmHg. If the high pressure>140mmHg, the low pressure>90mmHg is called high blood pressure. High pressure <90mmHg, low pressure <60mmHg is called hypotension;

3. Blood oxygen saturation: refers to the ratio of hemoglobin oxidation in arterial blood, that is, the concentration of oxygen in arterial blood, which is usually above 95% and cannot be lower than 90%. If the blood oxygen saturation is too low, it indicates that the oxygen in the body is insufficient, and there may be pulmonary edema caused by heart disease, respiratory system diseases, such as chronic obstructive pulmonary disease, pulmonary infection, respiratory failure, etc.

The physical state of the user includes gender, age, height, weight and disease condition.

Users of different genders, ages, heights and weights have different degrees of acceptance of exercise. Therefore, it is necessary to classify them and provide different degrees of monitoring for different types of people.

S3. According to the comparison results, combined with big data analysis, determine the next rehabilitation exercise plan, including the following steps:

S31. If there is data in the health data that is greater than the safety threshold, remind the user to give an early warning through intelligent interaction, and provide corresponding diagnosis and nursing suggestions according to the data type;

For example, when high blood pressure and blood oxygen are detected, the information can be conveyed to the user in the form of voice broadcast, and relevant medical advice can be provided to help the user diagnose and treat in a timely manner.

S32, if the health data is not greater than the safety threshold, then analyze and calculate the user's current exercise intensity, and analyze the user's current exercise degree through big data, and formulate the user's next rehabilitation exercise plan, including the following steps:

S321, utilize the method of wavelet packet decomposition frequency band energy entropy to analyze the heart sound and electrocardiogram signal, and use the frequency band energy entropy as the current exercise intensity of the user, comprising the following steps:

S3211, performing wavelet packet decomposition on the heart sound and the ECG signal;

The ECG signal is a nonlinear, non-stationary weak signal with a frequency range of 0.05-100Hz, and 90% of the ECG signal energy is concentrated within 35Hz. The normal ECG signal includes P wave, QRS complex, T wave, U wave J junction, etc. By obtaining the frequency distribution of each component wave of the ECG signal, the ECG signal can be analyzed qualitatively and quantitatively.

There are various murmurs in heart sounds and ECG signals. Murmurs exist in normal people and patients with heart disease. The frequency of benign murmurs is similar to that of normal heart sounds, and belongs to the middle and low frequencies. The murmurs in patients with heart disease generally have a higher frequency. Therefore, by decomposing the signal by wavelet packet and analyzing the ratio of the energy of its low-frequency components to the total energy of the signal, the health of the heart can be judged, and the health of the heart is directly related to the intensity of exercise.

The essence of wavelet packet decomposition is to further decompose the high-frequency detail signals that are not decomposed after wavelet analysis. Compared with the disadvantage of poor resolution in the high-frequency part of the wavelet packet decomposition, the wavelet packet decomposition achieves the purpose of improving the time-frequency resolution, so the wavelet packet has a wider application value. Since the wavelet packet is orthogonally decomposed, each frequency band is decomposed without overlapping, and the bandwidth of the output frequency band is halved, so the sampling rate is halved but the information is well preserved.

S3212, analyze the ratio of the energy of the low frequency component to the total energy of the signal, and judge the health of the heart;

S3213. Calculate by using the energy entropy calculation formula, and use the calculation result as an index of exercise intensity.

Wherein, the expression of the energy entropy calculation formula is:

In the formula, R represents the value of energy entropy;

E(i) represents the normalized energy of a certain frequency band, and satisfies E(i)=|g _i (k)| ² ;

E represents the sum of the band energy of a certain layer, and satisfies E=|g _n (k)| ² ;

g _i (k) represents the high-pass filter corresponding to the wavelet function of the i-th frequency band;

g _n (k) represents the high-pass filter corresponding to the wavelet function of a certain layer of frequency bands;

n represents the frequency band number;

i represents the ith frequency band sequence number.

S322, performing matching on the big data platform according to the physical state of the user, screening out the same type of crowd, and querying the exercise intensity and item type of the crowd's rehabilitation exercise process as a reference standard set;

In the process of big data matching and screening, the physical status of users is matched, that is, comparison groups with similar age, gender, weight, height and disease conditions are screened out. The selection conditions can fluctuate within a certain range, and patients of the same type have more Higher and more effective contrast can play a good reference effect. In the reference standard set, the average value of the selected user's exercise intensity is calculated, and the corresponding reference standard is obtained, which can further strengthen the comparison effect. Within the range of the safety threshold, if the exercise intensity exceeds the reference standard, it can indicate that the exercise intensity meets the standard and can play an effective role. treatment effect.

S323, analyzing and comparing the current exercise intensity of the user with the reference standard set, to determine whether the current exercise intensity of the user meets the standard;

S324. If the current exercise intensity of the user is not lower than the reference standard set, remind the user to exercise in moderation and praise and encourage them through intelligent interaction;

S325. If the current exercise intensity of the user is lower than the reference standard set, the user is encouraged to increase the amount of exercise through intelligent interaction, and the user is asked whether intelligent recommendation is required to change the current exercise mode.

Wherein, the intelligent recommendation adopts the support vector machine algorithm based on genetics, including the following steps:

S3251. Determine the chromosome of the genetic algorithm, including the support vector machine parameters and the characteristic value of the user's disease condition;

S3252. Determine the genetic operator and the fitness function;

Wherein, the genetic operator includes a selection operator, a crossover operator and a mutation operator;

The fitness function is the only information searched by the genetic algorithm, which is used to evaluate the adaptability of each code string to the problem. The principles to be followed include: select as few feature subsets as possible; it should be universal; it is beneficial to improve the classification accuracy . Genetic operators mainly include selection operators, crossover operators and mutation operators. The selection operator copies chromosomes with high fitness values from the parent to the offspring, and at the same time eliminates individuals with low fitness values, generally using roulette. This method can effectively avoid the algorithm from falling into the local optimal solution; the crossover operator is to randomly select a pair of individuals in the population, and exchange part of the chromosome number strings to form a new individual. Randomly select 4 number strings for crossover between them; the mutation operator changes the genetic gene with a small probability, that is, the mutation probability, that is, the value of the number string in the chromosome is reversed, thereby improving the diversity of the population and preventing the search stagnation.

The expression of the fitness function is:

f(x)=f ₁ (x)-η·f ₂ (x)

In the formula, f ₁ (x) represents the classification accuracy of disease conditions;

f ₂ (x) represents the number of selected eigenvalues;

η represents the adjustment weight parameter.

S3253, using the roulette wheel and weighted depth-first search method to generate the initial population of the genetic algorithm, and using the adaptive and heuristic initialization method to ensure the uniformity of the population distribution;

S3254: Optimizing the crossover probability and mutation probability to realize self-adaptive adjustment of different evolutionary bands, and to realize global search while retaining useful genetic information.

S4. Push the rehabilitation exercise program to the user and the medical worker in an intelligent interactive manner.

That is, through the intelligent recommendation algorithm, according to the user's current disease situation and other data, the most effective exercise method or exercise type in the situation is matched in the big data, and by recommending the user, it can help him get more effective in the effective time. The excellent exercise effect can avoid the fatigue and boredom of the user caused by a single exercise mode for a long time.

Wherein, the intelligent interaction method includes artificial voice chat and application interface push, and Su Sohu artificial voice chat has the function of intelligent question and answer.

According to another embodiment of the present invention, as shown in FIG. 2, a monitoring system for cardiac rehabilitation exercise based on medical intelligent interactive equipment is also provided, and the system includes the following modules:

Wearable device monitoring module 1, used for real-time monitoring and acquisition of the user's health status;

Intelligent interactive module 2, used to provide artificial voice chatting function, to provide users with voice broadcast and intelligent question and answer;

Medical database module 3, used to reserve professional medical care knowledge;

The intelligent analysis and processing module 4 is used to identify and calculate the user's health data and exercise intensity, and provide intelligent recommendations and suggestions to the user;

Big data platform 5, used to provide user sharing and medical big data and update it in real time;

The mobile terminal 6 is used as an intelligent terminal to provide a mounting and application platform for each module.

To sum up, with the help of the above technical solutions of the present invention, accurate health data information can be obtained by monitoring the user's rehabilitation exercise process in real time, and then combined with the user's health data and physical state through big data analysis, so as to avoid excessive exercise caused by the user's heart load, And when the exercise does not meet the standard, the next step of rehabilitation exercise plan is formulated for the user to effectively ensure sufficient exercise intensity, thereby meeting the user's health needs in different states, and significantly improving the level of cardiac rehabilitation exercise; in addition, by adding an intelligent interactive system , which can provide humanized early warning reminders and professional medical care suggestions in the process of real-time monitoring, and then encourage users, effectively drive users to exercise, and improve the effect and stickiness of users' exercise.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of the present invention. within the scope of protection.

Claims (10)

1. The monitoring method for the heart rehabilitation exercise based on the medical intelligent interactive equipment is characterized by comprising the following steps:

s1, acquiring health data obtained by real-time monitoring in the exercise process of a user through wearable equipment;

s2, setting a safety threshold according to the body state of the user, and comparing the safety threshold with the health data;

s3, determining a next rehabilitation exercise scheme by combining big data analysis according to the comparison result;

and S4, pushing the rehabilitation exercise scheme to the user and the medical workers in an intelligent interaction mode.

2. The method for monitoring cardiac rehabilitation exercise based on medical intelligent interactive equipment as claimed in claim 1, wherein the health data includes heart sounds, electrocardiosignals, heart rate, blood pressure, respiratory rate and blood oxygen;

the physical state of the user comprises sex, age, height, weight and disease condition.

3. The monitoring method for cardiac rehabilitation exercise based on medical intelligent interactive equipment, as claimed in claim 2, wherein the step of determining the next rehabilitation exercise scheme according to the comparison result and by combining big data analysis, comprises the following steps:

s31, if the health data contains data larger than a safety threshold, reminding a user of early warning in an intelligent interaction mode, and providing corresponding diagnosis and care suggestions according to data types;

and S32, if the health data are not greater than the safety threshold, analyzing and calculating the current movement intensity of the user, analyzing the current movement degree of the user through big data, and formulating the next rehabilitation exercise scheme of the user.

4. The monitoring method for the cardiac rehabilitation exercise based on the medical intelligent interactive device, as claimed in claim 3, wherein the analyzing and calculating the current exercise intensity of the user, and analyzing the current exercise degree of the user through big data to formulate the next rehabilitation exercise scheme of the user, comprises the following steps:

s321, analyzing the heart sound and the electrocardio signal by utilizing a method of decomposing a frequency band energy entropy by utilizing a wavelet packet, and taking the frequency band energy entropy as the current motion intensity of the user;

s322, matching is carried out on a big data platform according to the physical state of the user, people of the same type are screened out, and the exercise intensity and the item type of the rehabilitation exercise process of the people are inquired and used as a reference standard set;

s323, analyzing and comparing the current exercise intensity of the user with the reference standard set, and judging whether the current exercise intensity of the user reaches the standard;

s324, if the current exercise intensity of the user is not lower than the reference standard set, reminding the user of appropriate exercise and agreeing to encouragement in an intelligent interaction mode;

and S325, if the current exercise intensity of the user is lower than the reference standard set, encouraging the user to increase the exercise amount in an intelligent interaction mode, inquiring whether intelligent recommendation is needed or not, and changing the current exercise mode.

5. The monitoring method of the medical intelligent interactive device-based heart rehabilitation exercise, as claimed in claim 4, wherein the method of decomposing the frequency band energy entropy by using wavelet packets analyzes the heart sounds and the electrocardio signals, and uses the frequency band energy entropy as the current exercise intensity of the user, comprising the following steps:

s3211, performing wavelet packet decomposition on the heart sound and the electrocardiosignal;

s3212, analyzing the ratio of the energy of the low-frequency component to the total energy of the signal, and judging the health degree of the heart;

and S3213, calculating by using an energy entropy calculation formula, and taking the calculation result as an index of the exercise intensity.

6. The monitoring method for cardiac rehabilitation exercise based on medical intelligent interactive equipment as claimed in claim 5, wherein the expression of the energy entropy calculation formula is as follows:

wherein R represents a value of energy entropy;

e (i) represents normalized energy of a certain frequency band, and satisfies E (i) = | g _i (k)| ² ；

E represents the sum of the energy of a certain layer of frequency bands and satisfies the condition that E = | g _n (k)| ² ；

g _i (k) A high-pass filter corresponding to a wavelet function representing the ith frequency band;

g _n (k) A high-pass filter corresponding to a wavelet function representing a certain layer of frequency band;

n represents a band number;

i represents the ith band number.

7. The method for monitoring cardiac rehabilitation exercise based on medical intelligent interactive equipment, as claimed in claim 6, wherein the intelligent recommendation employs a support vector machine algorithm based on genetics, comprising the steps of:

s3251, determining chromosomes of a genetic algorithm, including support vector machine parameters and characteristic values of user disease conditions;

s3252, determining a genetic operator and a fitness function;

s3253, generating an initial population of a genetic algorithm by using a roulette method and a weighted depth-first search method, and ensuring the uniformity of population distribution by using a self-adaptive heuristic initialization method;

s3254, optimizing the cross probability and the variation probability, realizing self-adaptive adjustment of different evolutionary band books, and realizing global search while reserving useful genetic information.

8. The method for monitoring cardiac rehabilitation exercise based on medical intelligent interactive equipment, as recited in claim 7, wherein the genetic operators include a selection operator, a crossover operator and a mutation operator;

the fitness function has the expression:

f(x)＝f ₁ (x)-η·f ₂ (x)

in the formula, f ₁ (x) Indicating the accuracy of classification of the disease condition;

f ₂ (x) Representing the number of selected feature values;

η represents the tuning weight parameter.

9. The monitoring method for cardiac rehabilitation exercise based on medical intelligent interactive equipment as claimed in claim 8, wherein the intelligent interactive mode comprises artificial voice chat and application interface push, and the artificial voice chat of Sovix has an intelligent question and answer function.

10. The monitoring system for cardiac rehabilitation exercise based on medical intelligent interactive equipment is used for realizing the monitoring method for cardiac rehabilitation exercise based on medical intelligent interactive equipment as claimed in any one of claims 1 to 9, and is characterized by comprising the following modules:

the wearable device monitoring module is used for monitoring and acquiring the health state of the user in real time;

the intelligent interaction module is used for providing an artificial voice chat function, providing voice broadcast for users and carrying out intelligent question answering;

the medical database module is used for storing professional medical care knowledge;

the intelligent analysis processing module is used for identifying and calculating the health data and the exercise intensity of the user and providing intelligent recommendation and suggestion for the user;

the big data platform is used for providing user sharing and medical big data and updating the user sharing and medical big data in real time;

and the mobile terminal is used as an intelligent terminal to provide a carrying and application platform for each module.

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