Dynamic identification system of motion risk for patients with heart disease

Abstract

The present application provides a system for dynamic identification of exercise risk for patients with heart disease, which relates to the field of data processing technology, including: wearable devices; user terminals; servers, including: a benchmark data matching module, which receives the pre-exercise type and pre-exercise duration, analyzes the indicators in combination with the body detection information and the ambient temperature, and obtains the benchmark indicator interval; a benchmark data verification module, which sends the benchmark indicator interval to the medical node to obtain the approved indicator interval; an alarm terminal, including: a dynamic comparison module, which performs risk analysis on the detection value based on the approved indicator interval to obtain the risk level; an early warning device. This application can solve the technical problem that traditional exercise risk monitoring equipment often monitors and warns based on fixed threshold values, resulting in frequent misjudgments and missed judgments, resulting in poor accuracy and reliability of monitoring and early warning, and can achieve the goal of accurate monitoring and early warning, and effectively ensure the safety of patient exercise.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a dynamic motion risk identification system for heart disease patients. Background Art

[0002] With the enhancement of people's health awareness and the growth of demand for sports and fitness, sports risk monitoring equipment has become an important tool to ensure sports safety and prevent sports injuries. However, existing traditional sports risk monitoring equipment generally uses fixed threshold values ​​for monitoring and early warning, and determines whether the user's physiological state is normal through preset fixed threshold values. This method has certain limitations in practical applications.

[0003] At present, traditional sports risk monitoring equipment often performs monitoring and early warning based on fixed threshold values, and is unable to dynamically adjust the early warning threshold values ​​according to actual scenario requirements, resulting in frequent misjudgments and missed judgments, causing technical problems such as poor accuracy and reliability of monitoring and early warning. Summary of the invention

[0004] The purpose of this application is to provide a dynamic motion risk identification system for heart disease patients, in order to solve the technical problem that traditional motion risk monitoring equipment often performs monitoring and early warning based on fixed threshold values, and is unable to dynamically adjust the early warning threshold value in accordance with actual scenario requirements, resulting in frequent misjudgments and missed judgments, and causing poor accuracy and reliability of monitoring and early warning.

[0005] In view of the above problems, the present application provides a dynamic identification system for exercise risk of patients with heart disease, comprising: a wearable device, which is deployed at a key point of a target user, and is used to collect exercise status information of the target user, wherein the exercise status information of the target user includes a blood oxygen saturation detection value, a blood pressure detection value, a heart rate detection value, and a body temperature detection value, and the key point of the target user is a monitoring position recommended by a medical node; a user terminal, which is used for the target user to upload a pre-exercise type and a pre-exercise duration before exercise; a server, comprising: a benchmark data matching module, which is used to receive the pre-exercise type and the pre-exercise duration from the user terminal before exercise, and perform a large sample analysis of the blood oxygen saturation index, blood pressure index, heart rate index, and body temperature index in combination with the target user's body detection information and the ambient temperature, to obtain a benchmark blood oxygen saturation interval, a benchmark blood pressure interval, a benchmark Accurate heart rate interval and benchmark body temperature interval; a benchmark data verification module, which is used to receive the benchmark blood oxygen saturation interval, the benchmark blood pressure interval, the benchmark heart rate interval and the benchmark body temperature interval from the benchmark data matching module before exercise, and send them to the medical node to obtain data verification results, wherein the data verification results include the confirmed blood oxygen saturation interval, the confirmed blood pressure interval, the confirmed heart rate interval and the confirmed body temperature interval; the alarm end, including: a dynamic comparison module, which is used to perform risk analysis on the blood oxygen saturation detection value, the blood pressure detection value, the heart rate detection value and the body temperature detection value based on the confirmed blood oxygen saturation interval, the confirmed blood pressure interval, the confirmed heart rate interval and the confirmed body temperature interval to obtain a risk level; an early warning device, which is used to execute voice and vibration warnings when the risk level is greater than or equal to the risk level threshold.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] By conducting a large sample analysis of the blood oxygen saturation index, blood pressure index, heart rate index and body temperature index based on the pre-exercise type, pre-exercise duration, target user's physical detection information and ambient temperature before the target user exercises, the baseline blood oxygen saturation interval, baseline blood pressure interval, baseline heart rate interval and baseline body temperature interval are obtained; then the baseline interval is reviewed and adjusted through the medical node to obtain the confirmed blood oxygen saturation interval, confirmed blood pressure interval, confirmed heart rate interval and confirmed body temperature interval; finally, based on the confirmed blood oxygen saturation interval, confirmed blood pressure interval, confirmed heart rate interval and confirmed body temperature interval, the blood oxygen saturation detection value, blood pressure detection value, heart rate detection value and body temperature detection value obtained by monitoring the wearable device are risk analyzed to obtain the risk level; when the risk level is greater than or equal to the risk level threshold, voice and vibration warnings are executed through the warning device; the adaptability of the warning threshold value to the actual scene requirements can be improved, making the warning threshold value more accurate and reasonable, and at the same time, the precision and comprehensiveness of the exercise risk analysis can be improved, thereby achieving the technical goal of accurate monitoring and early warning, and achieving the technical effect of effectively ensuring the safety of patient exercise.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 This is a schematic diagram of the structure of a dynamic identification system for motion risk of heart disease patients in this application;

[0011] Figure 2 This is a flow chart of execution steps of a benchmark data matching module in a dynamic identification system for motion risk of heart disease patients in the present application.

[0012] Description of reference numerals:

[0013] User end 10 , server 20 , alarm end 30 , wearable device 11 , benchmark data matching module 21 , benchmark data verification module 22 , dynamic comparison module 31 , and early warning device 32 . DETAILED DESCRIPTION

[0014] This application provides a dynamic identification system for sports risk in patients with heart disease, which solves the problem that traditional sports risk monitoring equipment often monitors and warns based on fixed threshold values, and cannot dynamically adjust the warning threshold value in combination with actual scene requirements, resulting in frequent misjudgments and missed judgments, and the technical problem of poor accuracy and reliability of monitoring and warning. The adaptability of the warning threshold value to the actual scene requirements can be improved, making the warning threshold value more accurate and reasonable, and at the same time, the precision and comprehensiveness of sports risk analysis can be improved, thereby achieving the technical goal of accurate monitoring and warning, and achieving the technical effect of effectively ensuring the safety of patients' sports.

[0015] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0016] For examples, please see the attached Figure 1 The present application provides a dynamic identification system for motion risk of patients with heart disease, comprising:

[0017] A wearable device 11 is deployed at a key point of a target user and is used to collect motion status information of the target user, wherein the motion status information of the target user includes a blood oxygen saturation detection value, a blood pressure detection value, a heart rate detection value, and a body temperature detection value, and the key point of the target user is a monitoring position recommended by a medical node.

[0018] Specifically, the dynamic identification system for motion risk of heart disease patients includes a wearable device 11, which is a portable motion monitoring device that can be worn directly on the body or integrated into the user's clothes or accessories. These devices are usually lightweight and portable, and can monitor the user's physiological parameters in real time and provide the user with instant health information. The wearable device 11 is communicatively connected with the dynamic comparison module 31 of the alarm terminal 30.

[0019] The wearable device 11 is deployed at the key points of the target user to collect the target user's motion status information, wherein the target user's key points are the monitoring positions recommended by the medical node. For example, the device can be deployed at the key points of the user's body, such as the wrist, chest, ear or other suitable parts, according to the advice of medical experts, to ensure the accuracy of the data and the comfort of wearing. Among them, the target user's motion status information includes blood oxygen saturation detection value, blood pressure detection value, heart rate detection value, and body temperature detection value. The blood oxygen saturation detection is used to measure the oxygen content in the blood through an optical sensor to evaluate the user's oxygen supply; the blood pressure detection is used to monitor the blood pressure changes during exercise; the heart rate detection is used to monitor the user's heart rate through a biosensor to evaluate the heart activity; the body temperature detection is used to measure the user's body temperature using a temperature sensor to monitor the body temperature changes during exercise. By obtaining the blood oxygen saturation detection value, blood pressure detection value, heart rate detection value, and body temperature detection value through real-time monitoring, the user's health status can be monitored in real time during exercise, providing data support for exercise risk assessment.

[0020] The user terminal 10 is used for the target user to upload the pre-exercise type and pre-exercise duration before exercising.

[0021] Specifically, the dynamic identification system for sports risk of heart patients also includes a user terminal 10, wherein the function of the user terminal 10 is to allow the target user to upload a predetermined sports type and an estimated sports duration before exercising, wherein the user terminal 10 is in communication connection with a reference data matching module 21 of a server 20, and the pre-exercise type refers to a user-defined sports type, such as running, cycling, swimming, strength training, etc.; the pre-exercise duration refers to the approximate length of time the user plans to exercise. By obtaining the pre-exercise type and pre-exercise duration, a reference basis is provided for subsequent actual scene analysis of the user's exercise, and then setting an adapted warning threshold value.

[0022] The server 20 includes:

[0023] The benchmark data matching module 21 is used to receive the pre-exercise type and the pre-exercise duration from the user terminal 10 before exercise, and perform a large sample analysis of the blood oxygen saturation index, blood pressure index, heart rate index and body temperature index in combination with the target user's physical detection information and ambient temperature to obtain a benchmark blood oxygen saturation interval, a benchmark blood pressure interval, a benchmark heart rate interval and a benchmark body temperature interval.

[0024] Specifically, the dynamic identification system for sports risk of heart disease patients also includes a server 20, wherein the server 20 includes a reference data matching module 21 and a reference data verification module 22. The reference data matching module 21 is used to receive the pre-exercise type and the pre-exercise duration from the user terminal 10 before exercise, and then obtain the target user's body detection information and ambient temperature, wherein the ambient temperature can be obtained by sensor collection, and the target user's body detection information includes height, weight, gender, age, and health detection data, etc., which can be set according to actual needs.

[0025] Then, based on the pre-exercise type, the pre-exercise duration, the target user's physical detection information and the ambient temperature, a large sample analysis is performed on the blood oxygen saturation index, blood pressure index, heart rate index and body temperature index. Large sample analysis refers to the use of a large number of users' exercise data, physical detection information and environmental data to analyze physiological indicators such as blood oxygen saturation, blood pressure, heart rate and body temperature, so as to determine the benchmark intervals of these indicators. The benchmark interval refers to the normal indicator value range adapted to the current scene, and the benchmark blood oxygen saturation interval, benchmark blood pressure interval, benchmark heart rate interval and benchmark body temperature interval are obtained. By setting the scene conditions and performing large sample analysis with the scene conditions as constraints, more accurate statistical results can be obtained based on a large amount of data analysis, thereby improving the accuracy, rationality and reliability of the benchmark indicator interval setting.

[0026] The benchmark data verification module 22 is used to receive the benchmark blood oxygen saturation interval, the benchmark blood pressure interval, the benchmark heart rate interval and the benchmark body temperature interval from the benchmark data matching module 21 before exercise, and send them to the medical node to obtain data verification results, wherein the data verification results include the confirmed blood oxygen saturation interval, the confirmed blood pressure interval, the confirmed heart rate interval and the confirmed body temperature interval.

[0027] Specifically, the benchmark data verification module 22 is used to receive the benchmark blood oxygen saturation interval, the benchmark blood pressure interval, the benchmark heart rate interval and the benchmark body temperature interval from the benchmark data matching module 21 before exercise, and send the benchmark blood oxygen saturation interval, the benchmark blood pressure interval, the benchmark heart rate interval and the benchmark body temperature interval to the medical node for review and adjustment, wherein the benchmark indicator interval is a standard interval obtained based on a large amount of data analysis, but considering the special circumstances of each user, medical personnel are required to make personalized adjustments to the benchmark indicator interval according to the user's health status and medical history, and obtain the data verification results after review and adjustment, wherein the data verification results include the approved blood oxygen saturation interval, the approved blood pressure interval, the approved heart rate interval and the approved body temperature interval. By medical personnel reviewing and adjusting the benchmark indicator interval according to the user's health status and medical history, the accuracy and applicability of the approved indicator interval setting can be improved, thereby further improving the accuracy of exercise risk monitoring and early warning.

[0028] The alarm terminal 30 comprises:

[0029] The dynamic comparison module 31 is used to perform risk analysis on the blood oxygen saturation detection value, the blood pressure detection value, the heart rate detection value and the body temperature detection value based on the approved blood oxygen saturation interval, the approved blood pressure interval, the approved heart rate interval and the approved body temperature interval to obtain a risk level.

[0030] Specifically, the dynamic identification system for motion risk of heart disease patients also includes an alarm terminal 30, wherein the alarm terminal 30 includes a dynamic comparison module 31 and an early warning device 32, and the dynamic comparison module 31 is used to perform risk analysis on the blood oxygen saturation detection value, the blood pressure detection value, the heart rate detection value and the body temperature detection value based on the approved blood oxygen saturation interval, the approved blood pressure interval, the approved heart rate interval and the approved body temperature interval, that is, to compare the deviation between the approved interval and the actual indicator detection value, and perform risk analysis based on the deviation comparison result. For example, if the actual detection value is within the approved interval, the motion risk level of the indicator is 0. If the actual detection value is not within the approved interval, the risk level is set according to the degree of deviation between the actual detection value and the approved interval, wherein the larger the deviation value, the greater the risk level corresponding to the indicator; finally, the risk levels corresponding to multiple detection indicators are fused and analyzed, and the motion risk level of the end user is output.

[0031] The warning device 32 is used to perform voice and vibration warning when the risk level is greater than or equal to the risk level threshold.

[0032] Specifically, the warning device 32 is used to execute voice and vibration warnings when the risk level is greater than or equal to the risk level threshold, where the risk level threshold can be set based on medical research and clinical experience, as well as the user's own status; when the detected risk level is greater than or equal to the set risk level threshold, the warning device 32 is triggered, and the device issues a voice alarm through the built-in speaker to alert the user, and at the same time the device generates vibrations through the vibration motor to increase the user's perception.

[0033] By providing a dynamic identification system for sports risks for heart disease patients, it is possible to solve the problem that traditional sports risk monitoring equipment often performs monitoring and early warning based on fixed threshold values, and cannot dynamically adjust the early warning threshold values ​​in combination with actual scene requirements, resulting in frequent misjudgments and missed judgments, and the technical problem of poor accuracy and reliability of monitoring and early warning. Before the target user exercises, a large sample analysis of blood oxygen saturation indicators, blood pressure indicators, heart rate indicators, and body temperature indicators is performed based on the pre-exercise type, pre-exercise duration, target user physical detection information, and ambient temperature to obtain a baseline blood oxygen saturation range, a baseline blood pressure range, a baseline heart rate range, and a baseline body temperature range; then the baseline range is reviewed and adjusted through the medical node to obtain the approved blood oxygen saturation range, approved blood pressure range, approved heart rate range, and approved body temperature range; finally, based on the approved blood oxygen saturation range, approved blood pressure range, approved heart rate range, and approved body temperature range, the system can identify the target user's sports risks dynamically. A fixed temperature interval is set, and risk analysis is performed on the blood oxygen saturation detection values, blood pressure detection values, heart rate detection values ​​and body temperature detection values ​​monitored by the wearable device 11 to obtain a risk level; when the risk level is greater than or equal to the risk level threshold, a voice and vibration warning is executed through the warning device 32; the adaptability of the warning threshold value to the actual scene requirements can be improved, making the warning threshold value more accurate and reasonable, and at the same time, the precision and comprehensiveness of the exercise risk analysis can be improved, thereby achieving the technical goal of accurate monitoring and early warning, and achieving the technical effect of effectively ensuring the safety of patient exercise.

[0034] Further, as attached Figure 2 As shown, the benchmark data matching module 21 performs the following steps:

[0035] The target user's physical detection information includes basic user information and selected detection information, wherein the basic user information includes at least height, weight, gender and age, and the selected detection information is detection data configured by the medical node; with the basic user information, the selected detection information, the ambient temperature, the pre-exercise type and the pre-exercise duration as constraints, a positive sample retrieval is performed on the blood oxygen saturation index, the blood pressure index, the heart rate index and the body temperature index to obtain a retrieval data set, wherein any one of the retrieval data includes a blood oxygen saturation record value, a blood pressure record value, a heart rate record value and a body temperature record value; based on the blood oxygen saturation record value, the blood pressure record value, the heart rate record value and the body temperature record value, the retrieval data set is aggregated and analyzed to obtain a high-frequency retrieval data set; based on the high-frequency retrieval data set, the baseline blood oxygen saturation interval, the baseline blood pressure interval, the baseline heart rate interval and the baseline body temperature interval are constructed.

[0036] Specifically, the target user's physical detection information includes basic user information and selected detection information, wherein the basic user information includes at least height, weight, gender and age, and technical personnel in this field may also add additional basic information according to actual conditions, such as lifestyle, exercise experience, drug use status, etc.; the selected detection information is the detection data configured by the medical node, including cholesterol level, thyroid function, liver function, etc.

[0037] Then, the user basic information, the selected detection information, the ambient temperature, the pre-exercise type and the pre-exercise duration are used as retrieval constraints. For example, a custom deviation threshold is configured for each constraint indicator. When the sample data meets the deviation threshold corresponding to the constraint condition, it will be regarded as retrieval data, and positive sample retrieval is performed on the blood oxygen saturation indicator, the blood pressure indicator, the heart rate indicator and the body temperature indicator. Positive sample retrieval is a process of searching for samples similar to or related to the query sample in a data set to obtain a retrieval data set, wherein any retrieval data includes blood oxygen saturation record values, blood pressure record values, heart rate record values ​​and body temperature record values.

[0038] Then, based on the blood oxygen saturation record value, the blood pressure record value, the heart rate record value and the body temperature record value, the retrieval data set is aggregated and analyzed to obtain a high-frequency retrieval data set, wherein the high-frequency retrieval data set refers to frequently occurring retrieval data. Further, based on the high-frequency retrieval data set, the reference blood oxygen saturation interval, the reference blood pressure interval, the reference heart rate interval and the reference body temperature interval are constructed.

[0039] Further, based on the blood oxygen saturation record value, the blood pressure record value, the heart rate record value and the body temperature record value, the retrieval data set is aggregated and analyzed to obtain a high-frequency retrieval data set, including:

[0040] According to the retrieval data set, extract the first blood oxygen saturation record value, the first blood pressure record value, the first heart rate record value and the first body temperature record value of the first retrieval data; traverse the retrieval data set, count the number of blood oxygen saturation deviations from the first blood oxygen saturation record value that are less than or equal to the blood oxygen saturation deviation threshold, and set it as the blood oxygen saturation record frequency; when the blood oxygen saturation record frequency is greater than or equal to the record frequency threshold, traverse the retrieval data set, count the number of blood pressure deviations from the first blood pressure record value that are less than or equal to the blood pressure deviation threshold, and set it as the blood pressure record frequency; when the blood pressure record frequency is greater than or equal to the record frequency threshold, traverse the retrieval data set, count the number of heart rate deviations from the first heart rate record value that are less than or equal to the heart rate deviation threshold, and set it as the heart rate record frequency; when the heart rate record frequency is greater than or equal to the record frequency threshold, traverse the retrieval data set, count the number of body temperature deviations from the first body temperature record value that are less than or equal to the body temperature deviation threshold, and set it as the body temperature record frequency; when the body temperature record frequency is greater than or equal to the record frequency threshold, add the first retrieval data to the high-frequency retrieval data set.

[0041] Specifically, first, randomly extract the first retrieval data from the retrieval data set, wherein the first retrieval data is any retrieval data in the retrieval data set, and obtain the first blood oxygen saturation record value, the first blood pressure record value, the first heart rate record value, and the first body temperature record value of the first retrieval data. Then traverse the retrieval data set, and compare the numerical deviations of the first retrieval data and other retrieval data in the retrieval data set. First, count the number of blood oxygen saturation deviations from the first blood oxygen saturation record value that are less than or equal to the blood oxygen saturation deviation threshold, and set it as the blood oxygen saturation recording frequency, wherein the blood oxygen saturation deviation threshold is set based on the actual scenario and data accuracy requirements. When the blood oxygen saturation recording frequency is greater than or equal to the recording frequency threshold, the recording frequency threshold can be customized according to actual conditions, then the retrieval data set is traversed, and the number of blood pressure deviations from the first blood pressure recording value that are less than or equal to the blood pressure deviation threshold is counted, and the result is set as the blood pressure recording frequency; when the blood pressure recording frequency is greater than or equal to the recording frequency threshold, the retrieval data set is traversed, and the number of heart rate deviations from the first heart rate recording value that are less than or equal to the heart rate deviation threshold is counted, and the result is set as the heart rate recording frequency; when the heart rate recording frequency is greater than or equal to the recording frequency threshold, the retrieval data set is traversed, and the number of body temperature deviations from the first body temperature recording value that are less than or equal to the body temperature deviation threshold is counted, and the result is set as the body temperature recording frequency; when the body temperature recording frequency is greater than or equal to the recording frequency threshold, the first retrieval data is added to the high-frequency retrieval data set until all retrieval data in the retrieval data set are compared and analyzed, and the high-frequency retrieval data set is output.

[0042] Furthermore, the present application also includes the following steps:

[0043] When the blood oxygen saturation recording frequency is less than the recording frequency threshold, or when the blood pressure recording frequency is less than the recording frequency threshold, or when the heart rate recording frequency is less than the recording frequency threshold, or when the heart rate recording frequency is less than the recording frequency threshold, the first retrieval data is eliminated.

[0044] Specifically, when performing aggregate analysis on the retrieval data set, when the blood oxygen saturation recording frequency is less than the recording frequency threshold, or when the blood pressure recording frequency is less than the recording frequency threshold, or when the heart rate recording frequency is less than the recording frequency threshold, or when the heart rate recording frequency is less than the recording frequency threshold, the first retrieval data is eliminated.

[0045] Furthermore, the steps performed by the dynamic comparison module 31 include:

[0046] When the blood oxygen saturation detection value belongs to the approved blood oxygen saturation interval, the blood oxygen saturation similarity is equal to 1; when the blood oxygen saturation detection value does not belong to the approved blood oxygen saturation interval, calculate the ratio of twice the product of the blood oxygen saturation detection value and the nearest boundary of the approved blood oxygen saturation interval to the sum of the squares of the blood oxygen saturation detection value and the nearest boundary of the approved blood oxygen saturation interval, and set it as the blood oxygen saturation similarity; traverse the blood pressure detection value, the heart rate detection value and the body temperature detection value for comparison to obtain the blood pressure similarity, the heart rate similarity and the body temperature similarity; through the risk level prediction channel, fit the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity, and output the risk level.

[0047] Specifically, when the blood oxygen saturation detection value belongs to the approved blood oxygen saturation interval, the blood oxygen saturation similarity is equal to 1; when the blood oxygen saturation detection value does not belong to the approved blood oxygen saturation interval, the blood oxygen saturation detection value and the nearest boundary of the approved blood oxygen saturation interval are calculated as twice the product of the blood oxygen saturation detection value and the nearest boundary of the approved blood oxygen saturation interval, and the blood oxygen saturation similarity is set; for example, assuming that the approved blood oxygen saturation interval is 90% to 96%, when the blood oxygen saturation detection value is 92%, the blood oxygen saturation similarity is equal to 1; when the blood oxygen saturation detection value is 88%, the blood oxygen saturation similarity is 2 times 88% times 90% and the sum of squares of 88% and 90%. Then, using the same method, the blood pressure detection value, the heart rate detection value and the body temperature detection value are traversed for comparison to obtain the blood pressure similarity, heart rate similarity and body temperature similarity.

[0048] Further, through the risk level prediction channel, the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity are fitted and analyzed to output a risk level, wherein the risk level represents the degree of risk of the user's exercise.

[0049] Further, the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity are fitted through the risk level prediction channel, and the risk level is output, including:

[0050] When any one of the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity is less than 1: taking the target user's body detection information and the ambient temperature as static elements, and taking the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity as dynamic elements to perform sample collection, and obtain historical sample collection results; counting the proportion of abnormal samples in the historical sample collection results, and setting it as the state abnormality probability; based on the probability-risk level table, analyzing the state abnormality probability, and obtaining the risk level, wherein the probability-risk level table is a custom configuration of the medical node.

[0051] Specifically, when any one of the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity, and the body temperature similarity is less than 1, the target user's body detection information and the ambient temperature are used as static elements, and the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity, and the body temperature similarity are used as dynamic elements to perform sample collection to obtain historical sample collection results, wherein the historical sample collection results include the historical physical states of multiple users, including normal states and abnormal states. Then, the abnormal sample ratio of the historical sample collection results is counted and set as the state abnormality probability, wherein the larger the abnormal sample ratio, the higher the frequency of abnormal physical states under the same state.

[0052] Then, a probability-risk level table is constructed, wherein the probability-risk level table is a custom configuration of the medical node and can be set by medical professionals based on clinical experience and professional knowledge, as well as the user's own situation, wherein the probability of abnormal state is proportional to the risk level, that is, the greater the probability of abnormal state, the higher the corresponding risk level; then, based on the probability-risk level table, the probability of abnormal state is analyzed to obtain the risk level.

[0053] Furthermore, the present application also includes:

[0054] When the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity are all equal to 1: obtain the blood oxygen saturation detection value variance, blood pressure detection value variance, heart rate detection value variance and body temperature detection value variance of the past preset time zone; configure the blood oxygen saturation variance threshold, blood pressure variance threshold, heart rate variance threshold and body temperature variance threshold through the medical node; when the blood oxygen saturation detection value variance is less than or equal to the blood oxygen saturation variance threshold, and the blood pressure detection value variance is less than or equal to the blood pressure variance threshold, and the heart rate detection value variance is less than or equal to the heart rate variance threshold, and the body temperature detection value variance is less than or equal to the body temperature variance threshold, the risk level is equal to 0.

[0055] Specifically, when the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity are all equal to 1, the historical monitoring data is retrieved to obtain the blood oxygen saturation detection value variance, blood pressure detection value variance, heart rate detection value variance and body temperature detection value variance in the past preset time zone; the preset time zone refers to the historical time period adjacent to the current detection node, for example, the past 3 minutes; then, through the medical node, the blood oxygen saturation variance threshold, blood pressure variance threshold, heart rate variance threshold and body temperature variance threshold are configured.

[0056] Then, the blood oxygen saturation detection value variance, blood pressure detection value variance, heart rate detection value variance and body temperature detection value variance are judged according to the blood oxygen saturation variance threshold, blood pressure variance threshold, heart rate variance threshold and body temperature variance threshold. When the blood oxygen saturation detection value variance is less than or equal to the blood oxygen saturation variance threshold, and the blood pressure detection value variance is less than or equal to the blood pressure variance threshold, and the heart rate detection value variance is less than or equal to the heart rate variance threshold, and the body temperature detection value variance is less than or equal to the body temperature variance threshold, the risk level is equal to 0.

[0057] Furthermore, the present application also includes:

[0058] When the blood oxygen saturation detection value variance is greater than the blood oxygen saturation variance threshold, or / and the blood pressure detection value variance is greater than the blood pressure variance threshold, or / and the heart rate detection value variance is greater than the heart rate variance threshold, or / and the body temperature detection value variance is greater than the body temperature variance threshold, the target user's body detection information and the ambient temperature are used as static elements, and the first variance deviation between the blood oxygen saturation detection value variance and the blood oxygen saturation variance threshold, the second variance deviation between the blood pressure detection value variance and the blood pressure variance threshold, the third variance deviation between the heart rate detection value variance and the heart rate variance threshold, and the fourth variance deviation between the body temperature detection value variance and the body temperature variance threshold are used as dynamic elements to collect samples and analyze the risk level.

[0059] Specifically, when the blood oxygen saturation detection value variance is greater than the blood oxygen saturation variance threshold, or / and the blood pressure detection value variance is greater than the blood pressure variance threshold, or / and the heart rate detection value variance is greater than the heart rate variance threshold, or / and the body temperature detection value variance is greater than the body temperature variance threshold, the target user's body detection information and the ambient temperature are used as static elements, and the first variance deviation between the blood oxygen saturation detection value variance and the blood oxygen saturation variance threshold, the second variance deviation between the blood pressure detection value variance and the blood pressure variance threshold, the third variance deviation between the heart rate detection value variance and the heart rate variance threshold, and the fourth variance deviation between the body temperature detection value variance and the body temperature variance threshold are used as dynamic elements to collect samples and obtain a second historical sample collection result.

[0060] Then, based on the second historical sample collection result, the abnormal sample ratio is calculated to obtain the abnormal probability of the second state; the second probability-risk level table is configured through the medical node; the abnormal probability of the second state is further analyzed through the second probability-risk level table to output the risk level.

[0061] By determining the abnormal status of users based on the variance of detection indicators, the user status can be evaluated based on the volatility of detection data, further improving the precision, comprehensiveness and reliability of user motion risk analysis.

[0062] In summary, the present application provides a dynamic identification system for motion risk of heart disease patients, which has the following technical effects:

[0063] By conducting a large sample analysis of the blood oxygen saturation index, blood pressure index, heart rate index and body temperature index based on the pre-exercise type, pre-exercise duration, target user's physical detection information and ambient temperature before the target user exercises, the baseline blood oxygen saturation interval, baseline blood pressure interval, baseline heart rate interval and baseline body temperature interval are obtained; then the baseline interval is reviewed and adjusted through the medical node to obtain the confirmed blood oxygen saturation interval, confirmed blood pressure interval, confirmed heart rate interval and confirmed body temperature interval; finally, based on the confirmed blood oxygen saturation interval, confirmed blood pressure interval, confirmed heart rate interval and confirmed body temperature interval, the blood oxygen saturation detection value, blood pressure detection value, heart rate detection value and body temperature detection value obtained by monitoring the wearable device are risk analyzed to obtain the risk level; when the risk level is greater than or equal to the risk level threshold, voice and vibration warnings are executed through the warning device; the adaptability of the warning threshold value to the actual scene requirements can be improved, making the warning threshold value more accurate and reasonable, and at the same time, the precision and comprehensiveness of the exercise risk analysis can be improved, thereby achieving the technical goal of accurate monitoring and early warning, and achieving the technical effect of effectively ensuring the safety of patient exercise.

[0064] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0065] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A dynamic identification system for motion risk in patients with heart disease, characterized in that: include: A wearable device, wherein the wearable device is deployed at a key point of a target user and is used to collect motion status information of the target user, wherein the motion status information of the target user includes a blood oxygen saturation detection value, a blood pressure detection value, a heart rate detection value, and a body temperature detection value, and the key point of the target user is a monitoring location recommended by a medical node; The user end is used for the target user to upload the pre-exercise type and pre-exercise duration before exercising; Servers, including: A reference data matching module is used to receive the pre-exercise type and the pre-exercise duration from the user terminal before exercising, and perform a large sample analysis on the blood oxygen saturation index, blood pressure index, heart rate index and body temperature index in combination with the target user's body detection information and the ambient temperature to obtain a reference blood oxygen saturation interval, a reference blood pressure interval, a reference heart rate interval and a reference body temperature interval; A reference data verification module, used for receiving the reference blood oxygen saturation interval, the reference blood pressure interval, the reference heart rate interval and the reference body temperature interval from the reference data matching module before exercise, and sending them to the medical node to obtain a data verification result, wherein the data verification result includes a verified blood oxygen saturation interval, a verified blood pressure interval, a verified heart rate interval and a verified body temperature interval; Alarm terminal, including: a dynamic comparison module, configured to perform risk analysis on the blood oxygen saturation detection value, the blood pressure detection value, the heart rate detection value and the body temperature detection value based on the approved blood oxygen saturation interval, the approved blood pressure interval, the approved heart rate interval and the approved body temperature interval to obtain a risk level; An early warning device, used for executing voice and vibration early warning when the risk level is greater than or equal to the risk level threshold; The steps of executing the dynamic comparison module include: When the blood oxygen saturation detection value belongs to the approved blood oxygen saturation interval, the blood oxygen saturation similarity is equal to 1; When the blood oxygen saturation detection value does not belong to the approved blood oxygen saturation interval, the ratio of twice the product of the blood oxygen saturation detection value and the nearest boundary of the approved blood oxygen saturation interval to the sum of the squares of the blood oxygen saturation detection value and the nearest boundary of the approved blood oxygen saturation interval is calculated and set as the blood oxygen saturation similarity; Traversing the blood pressure detection value, the heart rate detection value and the body temperature detection value for comparison, and obtaining blood pressure similarity, heart rate similarity and body temperature similarity; The blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity are fitted through a risk level prediction channel, and the risk level is output.

2. The system for dynamic identification of motion risk for patients with heart disease as claimed in claim 1, characterized in that: The benchmark data matching module performs the following steps: The target user's physical detection information includes basic user information and selected detection information, wherein the basic user information includes at least height, weight, gender and age, and the selected detection information is detection data configured by the medical node; Taking the user basic information, the selected detection information, the ambient temperature, the pre-exercise type and the pre-exercise duration as constraints, a positive sample search is performed on the blood oxygen saturation index, the blood pressure index, the heart rate index and the body temperature index to obtain a search data set, wherein any one of the search data includes a blood oxygen saturation record value, a blood pressure record value, a heart rate record value and a body temperature record value; Based on the blood oxygen saturation record value, the blood pressure record value, the heart rate record value and the body temperature record value, performing aggregate analysis on the search data set to obtain a high-frequency search data set; The reference blood oxygen saturation interval, the reference blood pressure interval, the reference heart rate interval and the reference body temperature interval are constructed according to the high-frequency search data set.

3. The system for dynamic identification of motion risk for patients with heart disease as claimed in claim 2, characterized in that: The benchmark data matching module performs the following steps: based on the blood oxygen saturation record value, the blood pressure record value, the heart rate record value and the body temperature record value, aggregate analysis is performed on the search data set to obtain a high-frequency search data set, and the specific steps include: Extracting, according to the retrieved data set, a first blood oxygen saturation record value, a first blood pressure record value, a first heart rate record value, and a first body temperature record value of the first retrieved data; Traversing the retrieved data set, counting the number of blood oxygen saturation deviations from the first blood oxygen saturation record value that are less than or equal to a blood oxygen saturation deviation threshold, and setting the number as the blood oxygen saturation record frequency; When the blood oxygen saturation recording frequency is greater than or equal to the recording frequency threshold, traverse the search data set, count the number of blood pressure deviations from the first blood pressure recording value that are less than or equal to the blood pressure deviation threshold, and set the number as the blood pressure recording frequency; When the blood pressure recording frequency is greater than or equal to the recording frequency threshold, traverse the search data set, count the number of heart rate deviations from the first heart rate recording value that are less than or equal to the heart rate deviation threshold, and set the number as the heart rate recording frequency; When the heart rate recording frequency is greater than or equal to the recording frequency threshold, traverse the retrieval data set, count the number of body temperature deviations from the first body temperature recording value that are less than or equal to the body temperature deviation threshold, and set the number as the body temperature recording frequency; When the body temperature recording frequency is greater than or equal to the recording frequency threshold, the first retrieval data is added to the high-frequency retrieval data set.

4. The system for dynamic identification of motion risk for patients with heart disease as claimed in claim 3, characterized in that: The benchmark data matching module performs the steps of: performing aggregate analysis on the search data set based on the blood oxygen saturation record value, the blood pressure record value, the heart rate record value and the body temperature record value to obtain a high-frequency search data set, and the specific steps also include: When the blood oxygen saturation recording frequency is less than the recording frequency threshold, or when the blood pressure recording frequency is less than the recording frequency threshold, or when the heart rate recording frequency is less than the recording frequency threshold, or when the heart rate recording frequency is less than the recording frequency threshold, the first retrieval data is eliminated.

5. The system for dynamic identification of motion risk for patients with heart disease as claimed in claim 1, characterized in that: The dynamic comparison module performs the steps of: fitting the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity through the risk level prediction channel, and outputting the risk level, including: When any one of the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity is less than 1: The target user's body detection information and the ambient temperature are used as static elements, and the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity are used as dynamic elements to collect samples, and obtain historical sample collection results; Count the percentage of abnormal samples in the historical sample collection results, and set it as the state abnormality probability; Based on the probability-risk level table, the abnormal state probability is analyzed to obtain the risk level, wherein the probability-risk level table is a custom configuration of the medical node.

6. The system for dynamic identification of motion risk for patients with heart disease as claimed in claim 1, characterized in that: Also includes: When the blood oxygen saturation similarity, the blood pressure similarity, the heart rate similarity and the body temperature similarity are all equal to 1: Obtain the variance of blood oxygen saturation detection values, blood pressure detection value variance, heart rate detection value variance and body temperature detection value variance in the past preset time zone; Configure blood oxygen saturation variance threshold, blood pressure variance threshold, heart rate variance threshold, and body temperature variance threshold through the medical node; When the blood oxygen saturation detection value variance is less than or equal to the blood oxygen saturation variance threshold, and the blood pressure detection value variance is less than or equal to the blood pressure variance threshold, and the heart rate detection value variance is less than or equal to the heart rate variance threshold, and the body temperature detection value variance is less than or equal to the body temperature variance threshold, the risk level is equal to 0.

7. The system for dynamic identification of motion risk for patients with heart disease as claimed in claim 6, characterized in that: Also includes: When the blood oxygen saturation detection value variance is greater than the blood oxygen saturation variance threshold, or / and the blood pressure detection value variance is greater than the blood pressure variance threshold, or / and the heart rate detection value variance is greater than the heart rate variance threshold, or / and the body temperature detection value variance is greater than the body temperature variance threshold, the target user's body detection information and the ambient temperature are used as static elements, and the first variance deviation between the blood oxygen saturation detection value variance and the blood oxygen saturation variance threshold, the second variance deviation between the blood pressure detection value variance and the blood pressure variance threshold, the third variance deviation between the heart rate detection value variance and the heart rate variance threshold, and the fourth variance deviation between the body temperature detection value variance and the body temperature variance threshold are used as dynamic elements to collect samples and analyze the risk level.

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