A method for calculating heart rate and a monitoring sensor device based on physical and mental health analysis

Abstract

This invention provides a heart rate calculation method and monitoring sensor device based on physical and mental health analysis, belonging to the field of medical device technology. Specifically, it includes: determining reliable monitoring counts among different stable monitoring counts using temperature variation data; dividing these reliable monitoring counts into different similar monitoring parameter groups using temperature variation data from different reliable monitoring counts; determining, based on the analysis results of heart rate data from different reliable monitoring counts within different similar monitoring parameter groups, that there are similar monitoring parameter groups where health analysis results do not meet requirements; obtaining the distribution data of reliable monitoring counts and temperature variation data within different similar monitoring parameter groups; and combining the health analysis results from different similar monitoring parameter groups to determine the health analysis results of the monitoring and analysis target, thereby improving the accuracy of the health analysis results.

Description

Technical Field

[0001] This invention belongs to the field of medical device technology, and in particular relates to a heart rate calculation method and monitoring sensor device based on physical and mental health analysis. Background Technology

[0002] Heart rate can reflect a person's physical and mental health to a certain extent. For example, abnormal emotions such as tension, excessive exercise, or heart abnormalities will all be reflected in the heart rate measurement results. Existing technical solutions provide ways to determine a user's physical and mental health status using heart rate measurement results. For example, similar technical solutions are given in invention patent applications CN109077711A "Dynamic Heart Rate Data Acquisition Method, Device, Wearable Device and Readable Storage Medium" and CN117084646A "A Training Injury Monitoring and Early Warning Method and System Based on Electronic Sensing".

[0003] In existing technical solutions, the calculation and analysis of heart rate is often easily affected by external factors, such as strenuous exercise, ambient temperature during the measurement process, and light. These factors can all affect the accuracy of the heart rate calculation and analysis results. Therefore, how to reduce the interference factors in the heart rate calculation and analysis results has become an urgent technical problem to be solved.

[0004] To address the aforementioned technical issues, this application specifically provides a heart rate calculation method and monitoring sensor device based on physical and mental health analysis. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted:

[0006] A heart rate calculation method based on physical and mental health analysis, specifically including:

[0007] S1 acquires heart rate monitoring data from different sleep monitoring sessions within a preset time period for the target being monitored and analyzed, and uses the fluctuation data of the heart rate monitoring data to determine the stable monitoring sessions among the sleep monitoring sessions.

[0008] S2 determines the temperature variation data of the heart rate monitoring location in different stable monitoring counts, and uses the temperature variation data to determine the reliable monitoring counts in different stable monitoring counts;

[0009] S3 uses temperature variation data from different reliable monitoring counts to divide the reliable monitoring counts into different similar monitoring parameter groups. Based on the analysis results of heart rate data from different reliable monitoring counts in different similar monitoring parameter groups, when it is determined that there are similar monitoring parameter groups whose health analysis results do not meet the requirements, proceed to the next step.

[0010] S4 acquires the distribution data of reliable monitoring times and temperature variation data within different similar monitoring parameter groups, and determines the health analysis results of the monitoring and analysis target by combining the health analysis results of different similar monitoring parameter groups.

[0011] The beneficial effects of this invention are as follows:

[0012] By using temperature variation data to determine the reliable monitoring counts among different stable monitoring counts, the reliable monitoring counts among stable monitoring counts can be screened based on temperature variation from different stable monitoring counts. This avoids the impact of drastic temperature changes on the accuracy of heart rate monitoring data and lays the foundation for further ensuring the accuracy of health analysis results.

[0013] The health analysis results of the monitoring and analysis target are determined by using the distribution data of reliable monitoring times and temperature variation data within different similar monitoring parameter groups and health analysis results. By comprehensively considering the reliable monitoring times and temperature variation corresponding to different types of health analysis results, the accuracy of different types of health analysis results is analyzed, thereby ensuring the accuracy of the health analysis results of the monitoring and analysis target from multiple data dimensions.

[0014] A further technical solution is that the preset time period is determined according to a preset monitoring duration.

[0015] A further technical solution is that the fluctuation data of the heart rate monitoring data includes determining the amount of fluctuation in the heart rate monitoring data between different times.

[0016] A further technical solution is that the method for determining the stable monitoring count in the sleep monitoring count is as follows:

[0017] Based on the fluctuation data of heart rate monitoring data, determine the amount of fluctuation of heart rate monitoring data between different adjacent time points in the number of sleep monitoring sessions;

[0018] Based on the fluctuation of the heart rate monitoring data, the timing of the monitoring data fluctuation in different sleep stages is determined.

[0019] The number of stable monitoring sessions in the sleep monitoring count is determined based on the number of fluctuations in monitoring data during different sleep stages.

[0020] A further technical solution is that the time of fluctuation in the monitoring data is the time when the fluctuation of the heart rate monitoring data at adjacent times does not meet the requirements.

[0021] A further technical solution is that the method for determining the health analysis results of the monitored and analyzed target is as follows:

[0022] Based on the distribution data of reliable monitoring times in different similar monitoring parameter groups, the proportion of reliable monitoring times in different similar monitoring parameter groups is determined. Based on the temperature variation data of reliable monitoring times in the similar monitoring parameter groups, the proportion of temperature variation moments in the reliable monitoring parameter groups is determined.

[0023] Based on the product of the proportion of the number of temperature change moments and the proportion of the number of reliable monitoring times, the group weight coefficients of different similar monitoring parameter groups are determined.

[0024] By analyzing the health analysis results of different similar monitoring parameter groups, the sum of the group weight coefficients corresponding to different types of health analysis results is determined, and the health analysis result of the monitoring and analysis target is determined by using the sum of the group weight coefficients corresponding to different types of health analysis results.

[0025] A further technical solution involves determining the health analysis result of the monitoring and analysis target by summing the group weight coefficients corresponding to different types of health analysis results, specifically including:

[0026] When the sum of the weight coefficients of the groups whose health analysis results are abnormal is greater than a preset coefficient threshold, the health analysis result of the monitored and analyzed target is determined to be abnormal.

[0027] When the sum of the weight coefficients of the groups with normal health analysis results is not greater than the preset coefficient threshold, the health analysis result of the monitored and analyzed target is determined to be normal.

[0028] Secondly, the present invention provides a heart rate monitoring sensor device, employing the aforementioned heart rate calculation method based on physical and mental health analysis, specifically including:

[0029] Monitoring frequency filtering module, group data analysis module, analysis result output module;

[0030] The monitoring count screening module is responsible for determining the reliable monitoring counts in the sleep monitoring counts.

[0031] The group data analysis module is responsible for dividing the number of reliable monitoring events into different similar monitoring parameter groups and determining the health analysis results of different similar monitoring parameter groups;

[0032] The analysis result output module is responsible for determining the health analysis results of the monitoring and analysis target based on the health analysis results of different similar monitoring parameter groups.

[0033] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0034] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0035] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0036] Figure 1 This is a flowchart of a heart rate calculation method based on physical and mental health analysis;

[0037] Figure 2 This is a flowchart illustrating the method for determining the number of stable monitoring sessions in sleep monitoring.

[0038] Figure 3 This is a flowchart illustrating the method for determining the reliable monitoring count within the stable monitoring count;

[0039] Figure 4 This is a flowchart illustrating the method for determining the health analysis results of similar monitoring parameter groups;

[0040] Figure 5 This is a schematic diagram of a heart rate monitoring sensor. Detailed Implementation

[0041] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0042] In this application, since factors such as ambient temperature and installation method can affect the accuracy of heart rate calculation results, it is necessary to comprehensively consider multiple factors to determine the number of monitoring sessions with a high degree of reliability, and then determine the health analysis target of the monitoring and analysis target based on the number of monitoring sessions with a high degree of reliability.

[0043] The stable monitoring count is the number of sleep monitoring counts in which the deviation of the heart rate monitoring data between adjacent time points is within a preset deviation range.

[0044] The number of reliable monitoring times is the number of stable monitoring times in which the deviation between the temperature data at different times and the average value of the temperature data from the stable monitoring times is within a preset deviation range.

[0045] The number of reliable monitoring times in which the deviation of temperature data at different times is less than the preset deviation threshold are classified into the same similar monitoring parameter group.

[0046] By analyzing heart rate data from different reliable monitoring sessions, the times when the heart rate variability is outside the preset range are determined. The health analysis results for different reliable monitoring sessions are determined based on the proportion of times when the heart rate variability is outside the preset range. When the proportion of times when the heart rate variability is outside the preset range is greater than 0.6, the health analysis results are determined to be abnormal.

[0047] The health analysis result of the similar monitoring parameter group is the one with the most reliable monitoring frequency and consistent health analysis results.

[0048] Based on the proportion of credible monitoring times within different similar monitoring parameter groups, the weight coefficients of different similar monitoring parameter groups are determined, and the health analysis result with the largest sum of weight coefficients is taken as the health analysis result of the monitoring and analysis target.

[0049] Example 1

[0050] like Figure 1 As shown, this application provides a heart rate calculation method based on physical and mental health analysis, specifically including:

[0051] S1 acquires heart rate monitoring data from different sleep monitoring sessions within a preset time period for the target being monitored and analyzed, and uses the fluctuation data of the heart rate monitoring data to determine the stable monitoring sessions among the sleep monitoring sessions.

[0052] S2 determines the temperature variation data of the heart rate monitoring location in different stable monitoring counts, and uses the temperature variation data to determine the reliable monitoring counts in different stable monitoring counts;

[0053] S3 uses temperature variation data from different reliable monitoring counts to divide the reliable monitoring counts into different similar monitoring parameter groups. Based on the analysis results of heart rate data from different reliable monitoring counts in different similar monitoring parameter groups, when it is determined that there are similar monitoring parameter groups whose health analysis results do not meet the requirements, proceed to the next step.

[0054] S4 acquires the distribution data of reliable monitoring times and temperature variation data within different similar monitoring parameter groups, and determines the health analysis results of the monitoring and analysis target by combining the health analysis results of different similar monitoring parameter groups.

[0055] Furthermore, the preset time period is determined according to the preset monitoring duration.

[0056] Specifically, the fluctuation data of the heart rate monitoring data includes determining the amount of fluctuation in the heart rate monitoring data between different times.

[0057] Specifically, such as Figure 2 As shown, the method for determining the number of stable monitoring sessions in the sleep monitoring count is as follows:

[0058] Based on the fluctuation data of heart rate monitoring data, determine the amount of fluctuation of heart rate monitoring data between different adjacent time points in the number of sleep monitoring sessions;

[0059] Based on the fluctuation of the heart rate monitoring data, the timing of the monitoring data fluctuation in different sleep stages is determined.

[0060] The number of stable monitoring sessions in the sleep monitoring count is determined based on the number of fluctuations in monitoring data during different sleep stages.

[0061] It should be noted that the time of fluctuation in the monitoring data refers to the moment when the fluctuation of the heart rate monitoring data at the adjacent time does not meet the requirements.

[0062] Furthermore, the sleep stages are defined as light sleep and deep sleep.

[0063] It is understandable that when the number of monitoring data fluctuations in different sleep stages is greater than a preset threshold, the number of sleep monitoring sessions is determined to be a non-stable monitoring session.

[0064] Specifically, the temperature variation data at the heart rate monitoring location is determined based on the monitoring data from the skin conductance monitoring device at the heart rate monitoring location.

[0065] Furthermore, the temperature variation data at the heart rate monitoring location includes the distribution data of monitoring times in different temperature ranges during the stable monitoring counts.

[0066] Specifically, such as Figure 3 As shown, the method for determining the reliable monitoring count in the stable monitoring count is as follows:

[0067] Using temperature variation data from the heart rate monitoring device, the distribution data of monitoring times in different temperature ranges within the stable monitoring counts are determined;

[0068] Based on the distribution data of monitoring times in different temperature ranges, the proportion of monitoring times in different temperature ranges in different sleep stages is determined, and the high proportion range in the temperature range is determined using the proportion of the number of monitoring times.

[0069] Based on the high-proportion intervals in different sleep stages, the temperature distribution dispersion coefficients for different sleep stages are determined, and the reliable monitoring counts in the stable monitoring counts are determined based on the temperature distribution dispersion coefficients for different sleep stages.

[0070] It should be noted that the high-proportion range refers to the temperature range in which the proportion of monitored times is greater than the preset proportion.

[0071] It is understood that the temperature distribution dispersion coefficient during the sleep stage is determined based on the maximum value of the endpoint deviation of the high-proportion interval, and specifically, the preset dispersion coefficient corresponding to the maximum value is used as the temperature distribution dispersion coefficient during the sleep stage.

[0072] Furthermore, if there is a sleep stage where the temperature distribution dispersion coefficient does not meet the requirements, then the stable monitoring count is determined not to be a reliable monitoring count.

[0073] Optionally, the method for determining the reliable monitoring count in the stable monitoring count is as follows:

[0074] Using temperature variation data from the heart rate monitoring device, the distribution data of monitoring times in different temperature ranges within the stable monitoring counts are determined;

[0075] Based on the distribution data of monitoring times in different temperature ranges, determine the proportion of monitoring times in different temperature ranges.

[0076] The number of reliable monitoring times in the stable monitoring count is determined based on the proportion of monitoring times in different temperature ranges.

[0077] Furthermore, if there is no temperature range where the proportion of monitoring times is greater than a preset proportion threshold, then the stable monitoring count is determined not to be a reliable monitoring count.

[0078] In another possible embodiment, the method for determining the reliable monitoring count in the stable monitoring count is as follows:

[0079] S11 uses the temperature variation data of the heart rate monitoring device to determine the distribution data of monitoring time in different temperature ranges in the stable monitoring count, and determines the proportion of the number of monitoring time in different temperature ranges in different sleep stages based on the distribution data of monitoring time in different temperature ranges.

[0080] S12 determines the distribution clustering coefficient of different temperature ranges based on the proportion of monitoring times in different temperature ranges during different sleep stages, and uses the distribution clustering coefficient of different temperature ranges to determine the discrete assessment quantity in different sleep stages.

[0081] S13 determines a comprehensive discrete evaluation value for the number of stable monitoring sessions based on discrete evaluation values ​​for different sleep stages, and determines a reliable number of monitoring sessions from the number of stable monitoring sessions based on the comprehensive discrete evaluation value.

[0082] Furthermore, the discrete evaluation quantity in the sleep stage is determined by multiplying the proportion of temperature ranges with a distribution clustering coefficient greater than a preset clustering coefficient with the average value of the distribution clustering coefficient.

[0083] Specifically, the comprehensive discrete evaluation value of the stable monitoring count is determined based on the product of the discrete evaluation values ​​of different sleep stages.

[0084] Optionally, step S11 above includes the following:

[0085] S111 uses the temperature variation data of the heart rate monitoring device to determine the distribution data of monitoring times in different temperature ranges in the stable monitoring counts. When the deviation of the average value of temperature data at different times in different sleep stages does not meet the requirements, it is determined that the stable monitoring counts are not reliable monitoring counts. When the deviation of the average value of temperature data at different times in different sleep stages meets the requirements, proceed to step S112.

[0086] S112 When there is no temperature range where the proportion of monitoring times is greater than the preset proportion threshold, it is determined that the stable monitoring count is not a reliable monitoring count. When there is a temperature range where the proportion of monitoring times is greater than the preset proportion threshold, proceed to step S113.

[0087] S113 determines the proportion of monitoring times in different temperature ranges in different sleep stages based on the distribution data of monitoring times in different temperature ranges. If there is no temperature range in any sleep stage where the proportion of monitoring times is greater than the preset proportion threshold, then the stable monitoring count is determined to be a reliable monitoring count. If there are temperature ranges in different sleep stages where the proportion of monitoring times is greater than the preset proportion threshold, then proceed to step S12.

[0088] Optionally, step S12 above includes the following:

[0089] S121 takes the temperature range in which the proportion of the number of monitoring times is greater than the preset proportion threshold as the main range. When the main ranges of different sleep stages are consistent, the process proceeds to step S122. When the main ranges of different sleep stages are inconsistent, it is determined that the stable monitoring count does not belong to the reliable monitoring count.

[0090] S122 Based on the proportion of monitoring times in different temperature ranges during different sleep stages, determine the distribution clustering coefficient of different temperature ranges. When the distribution clustering coefficient of the main range in different sleep stages is greater than the clustering coefficient limit, it is determined that the stable monitoring count belongs to the reliable monitoring count. When there is a sleep stage where the distribution clustering coefficient of the main range is not greater than the clustering coefficient limit, proceed to step S123.

[0091] S123 If the distribution clustering coefficients of different temperature ranges in different sleep stages are used to determine that there are multiple temperature ranges with distribution clustering coefficients greater than the preset clustering coefficient threshold in different sleep stages, then the stable monitoring count is determined to be a reliable monitoring count. If there are no multiple temperature ranges with distribution clustering coefficients greater than the preset clustering coefficient threshold in any sleep stage, then proceed to step S124.

[0092] S124 uses the distribution clustering coefficient of different temperature ranges to determine the discrete evaluation quantity in different sleep stages. When the discrete evaluation quantity in any sleep stage does not meet the requirements, it is determined that the stable monitoring number is not a reliable monitoring number. When the discrete evaluation quantity in different sleep stages meets the requirements, proceed to step S13.

[0093] Furthermore, the number of reliable monitoring events is divided into different groups of similar monitoring parameters, specifically including:

[0094] Using temperature variation data from different reliable monitoring sessions, determine the deviation of temperature data at different times for different reliable monitoring sessions;

[0095] By using the average deviation of temperature data at different times, the number of reliable monitoring times is divided into different groups of similar monitoring parameters.

[0096] It is understandable that the number of reliable monitoring times when the average deviation of temperature data at different times falls within a preset deviation range is grouped into the same similar monitoring parameter group.

[0097] Specifically, such as Figure 4 As shown, the method for determining the health analysis results of the similar monitoring parameter group is as follows:

[0098] Based on the analysis results of heart rate data from different reliable monitoring sessions, the number of times the heart rate variability in different sleep stages is not within the preset range is determined.

[0099] The probability of abnormal heart rate during different sleep stages is determined based on the number of times the heart rate variability is outside the preset range.

[0100] By using the probability of abnormal heart rate during different sleep stages and the preset weighting coefficients for different sleep stages, a comprehensive abnormality coefficient for the number of reliable monitoring sessions is determined, and the analysis results of the heart rate data for the number of reliable monitoring sessions are determined based on the comprehensive abnormality coefficient.

[0101] It should be noted that the abnormal heart rate probability is determined by the preset abnormal probability corresponding to the number of times the heart rate variability is outside the preset range.

[0102] It is understood that when the comprehensive anomaly coefficient is greater than the preset anomaly coefficient threshold, the analysis result of the heart rate data of the reliable monitoring number is determined to be abnormal; when the comprehensive anomaly coefficient is not greater than the preset anomaly coefficient threshold, the analysis result of the heart rate data of the reliable monitoring number is determined to be non-abnormal.

[0103] Optionally, the method for determining the health analysis results of the similar monitoring parameter group is as follows:

[0104] Based on the analysis results of heart rate data from different reliable monitoring counts within similar monitoring parameter groups, the reliable monitoring counts in which the analysis results within the similar monitoring parameter groups are abnormal are determined and these counts are considered as abnormal monitoring processes.

[0105] The average value of the anomaly coefficient is determined based on the average value of the comprehensive anomaly coefficients of different anomaly monitoring processes within the same group of similar monitoring parameters;

[0106] Based on the proportion of abnormal monitoring processes and the average value of the abnormal coefficient within the similar monitoring parameter group, the abnormal coefficient value of the similar monitoring parameter group is determined, and the health analysis result of the similar monitoring parameter group is determined using the abnormal coefficient value.

[0107] Furthermore, when the abnormality coefficient value is greater than a preset threshold, the health analysis result of the similar monitoring parameter group is determined to be abnormal.

[0108] In another possible embodiment, the method for determining the health analysis results of the similar monitoring parameter group is as follows:

[0109] If, based on the analysis results of heart rate data from different reliable monitoring counts within a similar monitoring parameter group, it is determined that there are no reliable monitoring counts with abnormal analysis results within the similar monitoring parameter group, then it is determined that the health analysis results of the similar monitoring parameter group are not abnormal.

[0110] When there are credible monitoring times with abnormal analysis results within the group of similar monitoring parameters:

[0111] The number of credible monitoring times in which the analysis results of similar monitoring parameter groups show anomalies is taken as an anomaly monitoring process. When the proportion of the number of anomaly monitoring processes in the similar monitoring parameter group is greater than the proportion of the number of preset processes, it is determined that the health analysis results of the similar monitoring parameter group are abnormal.

[0112] When the proportion of abnormal monitoring processes within the similar monitoring parameter group is not greater than the preset proportion of process numbers:

[0113] When the proportion of abnormal monitoring processes within the similar monitoring parameter group is less than the proportion limit and the number of abnormal monitoring processes is less than the preset number of processes, it is determined that the health analysis results of the similar monitoring parameter group are not abnormal.

[0114] When the proportion of abnormal monitoring processes within the similar monitoring parameter group is not less than the proportion limit or the number of abnormal monitoring processes is not less than the preset number of processes:

[0115] The average value of the abnormality coefficient is determined based on the average value of the comprehensive abnormality coefficients of different abnormal monitoring processes within the similar monitoring parameter group. If the average value of the abnormality coefficient does not meet the requirements, it is determined that the health analysis results of the similar monitoring parameter group are abnormal.

[0116] When the average value of the anomaly coefficients meets the requirements:

[0117] Obtain the average anomaly coefficient of different credible monitoring times within a similar monitoring parameter group. If the average anomaly coefficient of different credible monitoring times does not meet the requirements, it is determined that the health analysis results of the similar monitoring parameter group are abnormal.

[0118] When the average anomaly coefficient of different reliable monitoring times meets the requirements:

[0119] Based on the proportion of abnormal monitoring processes and the average value of the abnormal coefficient within the similar monitoring parameter group, the abnormal coefficient value of the similar monitoring parameter group is determined, and the health analysis result of the similar monitoring parameter group is determined using the abnormal coefficient value.

[0120] Furthermore, when the abnormal coefficient value of the similar monitoring parameter group is greater than the preset abnormal coefficient threshold, the health analysis result of the similar monitoring parameter group is determined to be abnormal; when the abnormal coefficient value of the similar monitoring parameter group is not greater than the preset abnormal coefficient threshold, the health analysis result of the similar monitoring parameter group is determined to be normal.

[0121] Specifically, when there is no similar monitoring parameter group whose health analysis results do not meet the requirements, the health analysis result of the monitoring and analysis target is determined to be normal.

[0122] It should be noted that the method for determining the health analysis results of the monitored and analyzed targets is as follows:

[0123] Based on the distribution data of reliable monitoring times in different similar monitoring parameter groups, the proportion of reliable monitoring times in different similar monitoring parameter groups is determined. Based on the temperature variation data of reliable monitoring times in the similar monitoring parameter groups, the proportion of temperature variation moments in the reliable monitoring parameter groups is determined.

[0124] Based on the product of the proportion of the number of temperature change moments and the proportion of the number of reliable monitoring times, the group weight coefficients of different similar monitoring parameter groups are determined.

[0125] By analyzing the health analysis results of different similar monitoring parameter groups, the sum of the group weight coefficients corresponding to different types of health analysis results is determined, and the health analysis result of the monitoring and analysis target is determined by using the sum of the group weight coefficients corresponding to different types of health analysis results.

[0126] Furthermore, the health analysis results of the monitoring and analysis target are determined by summing the group weight coefficients corresponding to different types of health analysis results, specifically including:

[0127] When the sum of the weight coefficients of the groups whose health analysis results are abnormal is greater than a preset coefficient threshold, the health analysis result of the monitored and analyzed target is determined to be abnormal.

[0128] When the sum of the weight coefficients of the groups with normal health analysis results is not greater than the preset coefficient threshold, the health analysis result of the monitored and analyzed target is determined to be normal.

[0129] Optionally, the method for determining the health analysis results of the monitored and analyzed target is as follows:

[0130] Based on the type of health analysis results, the similar monitoring parameter groups are divided into abnormal groups and normal groups. When the number of abnormal groups is less than the number of preset groups, the health analysis result of the monitoring and analysis target is determined to be normal.

[0131] When the number of abnormal groups is not less than the preset number of groups:

[0132] The number of credible monitoring counts is determined by the sum of the number of credible monitoring counts within different abnormal groups. When the sum of the number of credible monitoring counts is greater than a preset threshold, the health analysis result of the monitoring and analysis target is determined to be abnormal.

[0133] When the sum of the number of reliable monitoring times is not greater than a preset threshold:

[0134] When the percentage of trusted monitoring counts within the abnormal group is greater than a preset percentage threshold, the health analysis result of the monitored target is determined to be abnormal.

[0135] When the percentage of trusted monitoring counts within the abnormal group is not greater than a preset percentage threshold:

[0136] Based on the distribution data of reliable monitoring times in different similar monitoring parameter groups, the proportion of reliable monitoring times in different similar monitoring parameter groups is determined. Based on the temperature variation data of reliable monitoring times in the similar monitoring parameter groups, the proportion of temperature variation moments in the reliable monitoring parameter groups is determined.

[0137] Based on the product of the proportion of the number of temperature change moments and the proportion of the number of reliable monitoring times, the group weight coefficient of different similar monitoring parameter groups is determined. When the proportion of the number of abnormal groups in the similar monitoring parameter groups whose group weight coefficient is greater than the preset group coefficient threshold is greater than the preset group proportion, the health analysis result of the monitoring and analysis target is determined to be abnormal.

[0138] When the proportion of abnormal groups in similar monitoring parameter groups whose group weight coefficient is greater than the preset group coefficient threshold is not greater than the preset group proportion:

[0139] By analyzing the health analysis results of different similar monitoring parameter groups, the sum of the group weight coefficients corresponding to different types of health analysis results is determined, and the health analysis result of the monitoring and analysis target is determined by using the sum of the group weight coefficients corresponding to different types of health analysis results.

[0140] Example 2

[0141] Secondly, such as Figure 5 As shown, the present invention provides a heart rate monitoring sensor device, which employs the aforementioned heart rate calculation method based on physical and mental health analysis, specifically including:

[0142] Monitoring frequency filtering module, group data analysis module, analysis result output module;

[0143] The monitoring count screening module is responsible for determining the reliable monitoring counts in the sleep monitoring counts.

[0144] The group data analysis module is responsible for dividing the number of reliable monitoring events into different similar monitoring parameter groups and determining the health analysis results of different similar monitoring parameter groups;

[0145] The analysis result output module is responsible for determining the health analysis results of the monitoring and analysis target based on the health analysis results of different similar monitoring parameter groups.

[0146] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0147] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0148] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A heart rate monitoring sensor, characterized in that, Specifically, it includes: Monitoring frequency filtering module, group data analysis module, analysis result output module; The monitoring count screening module is responsible for determining the reliable monitoring counts in the sleep monitoring counts. The group data analysis module is responsible for dividing the number of reliable monitoring events into different similar monitoring parameter groups and determining the health analysis results of different similar monitoring parameter groups; The analysis result output module is responsible for determining the health analysis results of the monitoring and analysis target based on the health analysis results of different similar monitoring parameter groups; A heart rate calculation method based on physical and mental health analysis is adopted, specifically including: The heart rate monitoring data of the target during different sleep monitoring sessions within a preset time period is obtained and the stable monitoring sessions are determined by using the fluctuation data of the heart rate monitoring data. Determine the temperature variation data of the heart rate monitoring location in different stable monitoring counts, and use the temperature variation data to determine the reliable monitoring counts in the different stable monitoring counts; Using temperature variation data from different reliable monitoring counts, the reliable monitoring counts are divided into different similar monitoring parameter groups. Based on the analysis results of heart rate data from different reliable monitoring counts within different similar monitoring parameter groups, when it is determined that there are similar monitoring parameter groups whose health analysis results do not meet the requirements, proceed to the next step. Obtain the distribution data of reliable monitoring times and temperature variation data within different similar monitoring parameter groups, and combine the health analysis results of different similar monitoring parameter groups to determine the health analysis results of the monitoring and analysis target; The method for determining the number of stable monitoring sessions in the sleep monitoring data is as follows: Based on the fluctuation data of heart rate monitoring data, determine the amount of fluctuation of heart rate monitoring data between different adjacent time points in the number of sleep monitoring sessions; Based on the fluctuation of the heart rate monitoring data, the timing of the monitoring data fluctuation in different sleep stages is determined. The number of stable monitoring times in the sleep monitoring count is determined based on the number of fluctuations in monitoring data during different sleep stages. The method for determining the health analysis results of the monitored and analyzed targets is as follows: Based on the distribution data of reliable monitoring times in different similar monitoring parameter groups, the proportion of reliable monitoring times in different similar monitoring parameter groups is determined. Based on the temperature variation data of reliable monitoring times in the similar monitoring parameter groups, the proportion of temperature variation moments in the reliable monitoring parameter groups is determined. Based on the product of the proportion of the number of temperature change moments and the proportion of the number of reliable monitoring times, the group weight coefficients of different similar monitoring parameter groups are determined. By analyzing the health analysis results of different similar monitoring parameter groups, the sum of the group weight coefficients corresponding to different types of health analysis results is determined, and the health analysis result of the monitoring and analysis target is determined by using the sum of the group weight coefficients corresponding to different types of health analysis results.

2. The heart rate monitoring sensor device as described in claim 1, characterized in that, The preset time period is determined according to the preset monitoring duration.

3. The heart rate monitoring sensor device as described in claim 1, characterized in that, The fluctuation data of the heart rate monitoring data includes the determination of the fluctuation amount of heart rate monitoring data between different times.

4. The heart rate monitoring sensor device as described in claim 1, characterized in that, The time of fluctuation in the monitoring data is the moment when the fluctuation of the heart rate monitoring data at the adjacent time does not meet the requirements.

5. The heart rate monitoring sensor device as described in claim 4, characterized in that, The sleep stages are defined as light sleep and deep sleep. If the number of monitoring data fluctuations in different sleep stages is greater than a preset threshold, then the number of sleep monitoring sessions is determined to be a non-stable monitoring session.

6. The heart rate monitoring sensor device as described in claim 1, characterized in that, The temperature variation data at the heart rate monitoring location is determined based on the monitoring data from the skin conductance monitoring device at the heart rate monitoring location.

7. The heart rate monitoring sensor device as described in claim 1, characterized in that, The health analysis results for the monitoring and analysis target are determined by summing the group weight coefficients corresponding to different types of health analysis results, specifically including: When the sum of the weight coefficients of the groups whose health analysis results are abnormal is greater than a preset coefficient threshold, the health analysis result of the monitored and analyzed target is determined to be abnormal. When the sum of the weight coefficients of the groups with normal health analysis results is not greater than the preset coefficient threshold, the health analysis result of the monitored and analyzed target is determined to be normal.

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