Heart rate calculation method based on physical and psychological health analysis and monitoring sensing device

Abstract

The invention provides a heart rate calculation method based on physical and psychological health analysis and a monitoring sensing device, and belongs to the technical field of medical equipment.The heart rate calculation method specifically comprises the steps that credible monitoring times in different stable monitoring times are determined by means of temperature change data, and the temperature change data in the different credible monitoring times are used for calculating the heart rate according to the credible monitoring times; the credible monitoring times are divided into different similar monitoring parameter groups, and when it is determined that the similar monitoring parameter groups with the health analysis results not meeting the requirements exist according to the analysis results of the heart rate data of the different credible monitoring times in the different similar monitoring parameter groups, the credible monitoring times are determined to be the credible monitoring times. According to the method, the distribution data and the temperature change data of the credible monitoring times in the different similar monitoring parameter groups are obtained, and the health analysis result of the monitoring analysis target is determined in combination with the health analysis results of the different similar monitoring parameter groups, so that the accuracy of the health analysis result is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical devices, and particularly relates to a heart rate calculation method and a monitoring sensing device based on physical and mental health analysis. Background Art

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

[0003] In the existing technical solutions, when calculating and analyzing heart rate, it is often easily affected by external factors. For example, strenuous exercise, environmental temperature, light, etc. during the measurement process will all have a certain impact on the accuracy of the results of heart rate calculation and analysis. Therefore, how to reduce the interference factors of the results of heart rate calculation and analysis has become an urgent technical problem to be solved.

[0004] In view of the above technical problems, specifically, the present application provides a heart rate calculation method and a monitoring sensing device based on physical and mental health analysis. Summary of the Invention

[0005] To achieve the object of the present invention, the present invention adopts the following technical solutions:

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

[0007] S1 Obtain the heart rate monitoring data in different sleep monitoring times of the monitoring and analysis target within a preset time period, and use the fluctuation data of the heart rate monitoring data to determine the stable monitoring times in the sleep monitoring times;

[0008] S2 Determine the temperature change data of the heart rate monitoring positions in different stable monitoring times, and use the temperature change data to determine the reliable monitoring times in different stable monitoring times;

[0009] S3 Use the temperature change data in different reliable monitoring times to divide the reliable monitoring times into different similar monitoring parameter groups. When it is determined that there is a similar monitoring parameter group with a health analysis result not meeting the requirements based on the analysis results of the heart rate data of different reliable monitoring times in different similar monitoring parameter groups, proceed to the next step;

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

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

[0012] By using the temperature change data to determine the reliable monitoring times among different stable monitoring times, it realizes the screening of the reliable monitoring times in the stable monitoring times from the temperature change situation among different stable monitoring times, avoids the influence of the drastic temperature change on the accuracy of the heart rate monitoring data, and also lays a foundation for further ensuring the accuracy of the health analysis result.

[0013] Based on the distribution data of the reliable monitoring times, the temperature change data, and the health analysis results within different similar monitoring parameter groups, the health analysis result of the monitoring analysis target is determined. By comprehensively considering the reliable monitoring times corresponding to different types of health analysis results and the temperature change situation, it realizes the analysis of the accuracy of different types of health analysis results, and further realizes ensuring the accuracy of the health analysis result of the monitoring analysis target from multiple data dimensions.

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

[0015] A further technical solution is that the fluctuation data of the heart rate monitoring data is determined by the fluctuation amount of the heart rate monitoring data between different moments.

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

[0017] Based on the fluctuation data of the heart rate monitoring data, determine the fluctuation amount of the heart rate monitoring data between different adjacent moments in the sleep monitoring times;

[0018] Based on the fluctuation amount of the heart rate monitoring data, determine the monitoring data fluctuation moments in different sleep stages;

[0019] According to the number of monitoring data fluctuation moments in different sleep stages, determine the stable monitoring times in the sleep monitoring times.

[0020] A further technical solution is that the monitoring data fluctuation moment is the moment when the fluctuation amount of the heart rate monitoring data with the adjacent moment does not meet the requirements.

[0021] A further technical solution is that the method for determining the health analysis result of the monitoring analysis target is as follows:

[0022] Based on the distribution data of the number of reliable detections within different groups of similar monitoring parameters, determine the proportion of the number of reliable detections within different groups of similar monitoring parameters. Based on the temperature change data of the number of reliable detections within the group of similar monitoring parameters, determine the proportion of the number of temperature change moments within the group of reliable monitoring parameters;

[0023] Based on the product of the proportion of the number of temperature change moments and the proportion of the number of reliable detections, determine the group weight coefficient for different groups of similar monitoring parameters;

[0024] Based on the health analysis results of different groups of similar monitoring parameters, determine the sum of the group weight coefficients corresponding to different types of health analysis results, and use the sum of the group weight coefficients corresponding to different types of health analysis results to determine the health analysis result of the monitoring analysis target.

[0025] A further technical solution lies in using the sum of the group weight coefficients corresponding to different types of health analysis results to determine the health analysis result of the monitoring analysis target, which specifically includes:

[0026] When the sum of the group weight coefficients for abnormal health analysis results is greater than the preset coefficient threshold, then determine that the health analysis result of the monitoring analysis target is abnormal;

[0027] When the sum of the group weight coefficients for normal health analysis results is not greater than the preset coefficient threshold, then determine that the health analysis result of the monitoring analysis target is normal.

[0028] In a second aspect, the present invention provides a heart rate monitoring sensing device, which adopts the above-mentioned heart rate calculation method based on physical and mental health analysis, and specifically includes:

[0029] A monitoring times screening module, a group data analysis module, and an analysis result output module;

[0030] Among them, the monitoring times screening module is responsible for determining the number of reliable detections in the sleep monitoring times;

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

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

[0033] Other features and advantages will be described in the subsequent specification. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification and the drawings.

[0034] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following provides preferred embodiments in conjunction with the accompanying drawings and describes them in detail as follows. Description of the Drawings

[0035] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.

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

[0037] Figure 2 is a flowchart of a method for determining the stable monitoring times in the sleep monitoring times;

[0038] Figure 3 is a flowchart of a method for determining the reliable monitoring times in the stable monitoring times;

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

[0040] Figure 5 is a framework diagram of a heart rate monitoring sensing device. Detailed Embodiments

[0041] To enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0042] In this application, since environmental temperature, installation method, etc. will all affect the accuracy of the heart rate calculation result, it is necessary to comprehensively consider multiple factors to determine the monitoring times with a relatively high degree of credibility, and determine the health analysis target of the monitoring analysis target based on the monitoring times with a relatively high degree of credibility.

[0043] The stable monitoring times are the sleep monitoring times when the deviation amount of the heart rate monitoring data between adjacent moments is within the preset deviation range.

[0044] The reliable monitoring times are the stable monitoring times when the deviation amount between the temperature data at different moments and the average value of the temperature data of the stable monitoring times is within the preset deviation range.

[0045] The reliable monitoring times with the deviation amount of the temperature data at different moments less than the preset deviation amount threshold are divided into the same similar monitoring parameter group.

[0046] Based on the analysis results of heart rate data with different reliable monitoring times, determine the moments when the heart rate variability with different reliable monitoring times is not within the preset interval. According to the proportion of the number of moments when the heart rate variability is not within the preset interval, determine the health analysis results for different reliable monitoring times. When the proportion of the number of moments when the heart rate variability is not within the preset interval is greater than 0.6, it is determined that the health analysis result is abnormal.

[0047] According to the health analysis result with the largest number of reliable monitoring times having consistent health analysis results in the similar monitoring parameter group, it is used as the health analysis result of the similar monitoring parameter group.

[0048] According to the proportion of the number of reliable monitoring times within different similar monitoring parameter groups, determine the weight coefficients of different similar monitoring parameter groups, and use the health analysis result with the largest sum of weight coefficients as the health analysis result of the monitoring analysis target.

[0049] Embodiment 1

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

[0051] S1 Obtain the heart rate monitoring data in different sleep monitoring times of the monitoring analysis target within a preset time period, and use the fluctuation data of the heart rate monitoring data to determine the stable monitoring times in the sleep monitoring times;

[0052] S2 Determine the temperature change data of the heart rate monitoring positions in different stable monitoring times, and use the temperature change data to determine the reliable monitoring times in different stable monitoring times;

[0053] S3 Use the temperature change data in different reliable monitoring times to divide the reliable monitoring times into different similar monitoring parameter groups. When it is determined that there is a similar monitoring parameter group with a health analysis result not meeting the requirements based on the analysis results of the heart rate data of different reliable monitoring times in different similar monitoring parameter groups, proceed to the next step;

[0054] S4 Obtain the distribution data and temperature change data of the reliable monitoring times within different similar monitoring parameter groups, and determine the health analysis result of the monitoring analysis target in combination with the health analysis results of different similar monitoring parameter groups.

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

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

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

[0058] Based on the fluctuation data of the heart rate monitoring data, determine the fluctuation amount of the heart rate monitoring data between different adjacent moments in the sleep monitoring times;

[0059] Based on the fluctuation amount of the heart rate monitoring data, determine the monitoring data fluctuation moments in different sleep stages;

[0060] According to the number of monitoring data fluctuation moments in different sleep stages, determine the number of stable monitoring times in the sleep monitoring times.

[0061] It should be noted that the monitoring data fluctuation moment is the moment when the fluctuation amount of the heart rate monitoring data with the adjacent moment does not meet the requirements.

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

[0063] It can be understood that when the number of monitoring data fluctuation moments in different sleep stages is greater than the preset number threshold, it is determined that the sleep monitoring times do not belong to the stable monitoring times.

[0064] Specifically, the temperature change data of the heart rate monitoring position is determined according to the monitoring data of the skin conductance monitoring device at the heart rate monitoring position.

[0065] Furthermore, the temperature change data of the heart rate monitoring position includes the distribution data of the monitoring moments in different temperature ranges in the stable monitoring times.

[0066] Specifically, as Figure 3 shown, the method for determining the number of reliable monitoring times in the stable monitoring times is as follows:

[0067] Based on the temperature change data of the heart rate monitoring device, determine the distribution data of the monitoring moments in different temperature ranges in the stable monitoring times;

[0068] According to the distribution data of the monitoring moments in different temperature ranges, determine the proportion of the number of monitoring moments in different temperature ranges in different sleep stages, and use the proportion to determine the high proportion range in the temperature range;

[0069] Based on the high proportion ranges in different sleep stages, determine the temperature distribution dispersion coefficients of different sleep stages, and determine the number of reliable monitoring times in the stable monitoring times according to the temperature distribution dispersion coefficients of different sleep stages.

[0070] It should be noted that the high proportion interval is a temperature interval in which the proportion of the number of monitoring moments is greater than the preset proportion.

[0071] It can be understood that the coefficient of variation of the temperature distribution in the sleep stage is determined according to the maximum value of the endpoint deviation of the high proportion interval. Specifically, the preset coefficient of variation corresponding to the maximum value is used as the coefficient of variation of the temperature distribution in the sleep stage.

[0072] Furthermore, when there is a sleep stage in which the coefficient of variation of the temperature distribution does not meet the requirements, it is determined that the stable monitoring times do not belong to the credible monitoring times.

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

[0074] Based on the temperature change data of the heart rate monitoring device, determine the distribution data of the monitoring moments in different temperature intervals in the stable monitoring times;

[0075] According to the distribution data of the monitoring moments in different temperature intervals, determine the proportion of the number of monitoring moments in different temperature intervals;

[0076] Determine the credible monitoring times in the stable monitoring times according to the proportion of the number of monitoring moments in different temperature intervals.

[0077] Furthermore, when there is no temperature interval in which the proportion of the number of monitoring moments is greater than the preset proportion threshold, it is determined that the stable monitoring times do not belong to the credible monitoring times.

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

[0079] S11 Based on the temperature change data of the heart rate monitoring device, determine the distribution data of the monitoring moments in different temperature intervals in the stable monitoring times. According to the distribution data of the monitoring moments in different temperature intervals, determine the proportion of the number of monitoring moments in different temperature intervals in different sleep stages;

[0080] S12 According to the proportion of the number of monitoring moments in different temperature intervals in different sleep stages, determine the distribution aggregation coefficient of different temperature intervals, and use the distribution aggregation coefficient of different temperature intervals to determine the discrete evaluation quantity in different sleep stages;

[0081] S13 Based on the discrete evaluation quantity of different sleep stages, determine the comprehensive discrete evaluation quantity of the stable monitoring times, and determine the credible monitoring times in the stable monitoring times according to the comprehensive discrete evaluation quantity.

[0082] Further, the discrete evaluation quantity in the sleep stage is determined according to the product of the proportion of the number of temperature intervals with a distribution aggregation coefficient greater than a preset aggregation coefficient and the average value of the distribution aggregation coefficients.

[0083] Specifically, the comprehensive discrete evaluation quantity of the stable monitoring times is determined according to the product of the discrete evaluation quantities of different sleep stages.

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

[0085] S111 uses the temperature change data of the heart rate monitoring device to determine the distribution data of the monitoring moments of different temperature intervals in the stable monitoring times. When the deviation amount of the average value of the temperature data at different moments of different sleep stages does not meet the requirements, it is determined that the stable monitoring times do not belong to the credible monitoring times. When the deviation amount of the average value of the temperature data at different moments of different sleep stages meets the requirements, it proceeds to step S112;

[0086] S112 When there is no temperature interval with a proportion of the number of monitoring moments greater than a preset proportion threshold, it is determined that the stable monitoring times do not belong to the credible monitoring times. When there is a temperature interval with a proportion of the number of monitoring moments greater than a preset proportion threshold, it proceeds to step S113;

[0087] S113 According to the distribution data of the monitoring moments of different temperature intervals, determine the proportion of the number of monitoring moments of different temperature intervals in different sleep stages. When there is no temperature interval with a proportion of the number of monitoring moments greater than a preset proportion threshold in any sleep stage, it is determined that the stable monitoring times do not belong to the credible monitoring times. When there are temperature intervals with a proportion of the number of monitoring moments greater than a preset proportion threshold in different sleep stages, it proceeds to step S12.

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

[0089] S121 takes the temperature interval with a proportion of the number of monitoring moments greater than a preset proportion threshold as the main interval. When the main intervals of different sleep stages are the same, it proceeds to step S122. When the main intervals of different sleep stages are different, it is determined that the stable monitoring times do not belong to the credible monitoring times;

[0090] S122 determines the distribution aggregation coefficients of different temperature intervals according to the proportion of the number of monitoring moments of different temperature intervals in different sleep stages. When the distribution aggregation coefficients of the main intervals in different sleep stages are all greater than the aggregation coefficient limit value, it is determined that the stable monitoring times belong to the credible monitoring times. When there is a sleep stage where the distribution aggregation coefficient of the main interval is not greater than the aggregation coefficient limit value, it proceeds to step S123;

[0091] In different sleep stages, when the distribution aggregation coefficients in different temperature ranges are used to determine that there are multiple temperature ranges with distribution aggregation coefficients greater than the preset aggregation coefficient threshold in different sleep stages, it is determined that the stable monitoring times do not belong to the credible monitoring times. When there are no multiple temperature ranges with distribution aggregation coefficients greater than the preset aggregation coefficient threshold in any one sleep stage, step S124 is entered;

[0092] S124 uses the distribution aggregation coefficients of different temperature ranges to determine the discrete evaluation quantities in different sleep stages. When the discrete evaluation quantity in any one sleep stage does not meet the requirements, it is determined that the stable monitoring times do not belong to the credible monitoring times. When the discrete evaluation quantities in different sleep stages all meet the requirements, step S13 is entered.

[0093] Furthermore, the credible monitoring times are divided into different similar monitoring parameter groups, specifically including:

[0094] Using the temperature change data in different credible monitoring times, determine the deviation amounts of the temperature data at different times for different credible monitoring times;

[0095] Using the average value of the deviation amounts of the temperature data at different times, divide the credible monitoring times into different similar monitoring parameter groups.

[0096] It can be understood that the credible monitoring times with the average value of the deviation amounts of the temperature data at different times within the preset deviation amount range are divided into the same similar monitoring parameter group.

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

[0098] Using the analysis results of the heart rate data in different credible monitoring times, determine the number of times when the heart rate variability is not within the preset range in different sleep stages;

[0099] According to the number of times when the heart rate variability is not within the preset range, determine the heart rate abnormality probability of different sleep stages;

[0100] Through the heart rate abnormality probability of different sleep stages and the preset weight coefficients of different sleep stages, determine the comprehensive abnormality coefficient of the credible monitoring times, and determine the analysis result of the heart rate data of the credible monitoring times according to the comprehensive abnormality coefficient.

[0101] It should be noted that the heart rate abnormality probability is determined by the preset abnormality probability corresponding to the number of times when the heart rate variability is not within the preset range.

[0102] It can be understood that when the comprehensive anomaly coefficient is greater than the preset anomaly coefficient threshold, it is determined that the analysis result of the heart rate data of the credible monitoring times is abnormal; when the comprehensive anomaly coefficient is not greater than the preset anomaly coefficient threshold, it is determined that the analysis result of the heart rate data of the credible monitoring times is normal.

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

[0104] Based on the analysis results of the heart rate data of different credible monitoring times in the similar monitoring parameter group, determine the credible monitoring times with abnormal analysis results within the similar monitoring parameter group, and use them as the abnormal monitoring processes;

[0105] According to the average value of the comprehensive anomaly coefficients of different abnormal monitoring processes within the similar monitoring parameter group, determine the average anomaly coefficient;

[0106] Based on the proportion of the number of abnormal monitoring processes within the similar monitoring parameter group and the average anomaly coefficient, determine the anomaly coefficient value of the similar monitoring parameter group, and use the anomaly coefficient value to determine the health analysis result of the similar monitoring parameter group.

[0107] Furthermore, when the anomaly coefficient value is greater than the preset threshold, it is determined that the health analysis result of the similar monitoring parameter group is abnormal.

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

[0109] Based on the analysis results of the heart rate data of different credible monitoring times in the similar monitoring parameter group, when it is determined that there are no credible monitoring times with abnormal analysis results within the similar monitoring parameter group, it is determined that the health analysis result of the similar monitoring parameter group is normal;

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

[0111] Use the credible monitoring times with abnormal analysis results within the similar monitoring parameter group as the abnormal monitoring processes. When the proportion of the number of abnormal monitoring processes within the similar monitoring parameter group is greater than the preset proportion of the number of processes, it is determined that the health analysis result of the similar monitoring parameter group is abnormal;

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

[0113] When the proportion of the number of abnormal monitoring processes in the similar monitoring parameter group is less than the proportion limit value and the number of abnormal monitoring processes is less than the preset number of processes, it is determined that there is no abnormality in the health analysis result of the similar monitoring parameter group;

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

[0115] According to the average value of the comprehensive abnormal coefficients of different abnormal monitoring processes in the similar monitoring parameter group, determine the average abnormal coefficient. When the average abnormal coefficient does not meet the requirements, it is determined that there is an abnormality in the health analysis result of the similar monitoring parameter group;

[0116] When the average abnormal coefficient meets the requirements:

[0117] Obtain the average abnormal coefficient of different reliable monitoring times in the similar monitoring parameter group. When the average abnormal coefficient of different reliable monitoring times does not meet the requirements, it is determined that there is an abnormality in the health analysis result of the similar monitoring parameter group;

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

[0119] Based on the proportion of the number of abnormal monitoring processes and the average abnormal coefficient in the similar monitoring parameter group, determine the abnormal coefficient value of the similar monitoring parameter group, and use the abnormal coefficient value to determine the health analysis result of the similar monitoring parameter group.

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

[0121] Specifically, when there is no similar monitoring parameter group whose health analysis result does not meet the requirements, it is determined that the health analysis result of the monitoring analysis target is normal.

[0122] It should be noted that the method for determining the health analysis result of the monitoring analysis target is:

[0123] Based on the distribution data of the reliable monitoring times in different similar monitoring parameter groups, determine the proportion of the number of reliable monitoring times in different similar monitoring parameter groups. Based on the temperature change data of the reliable monitoring times in the similar monitoring parameter group, determine the proportion of the number of temperature change moments in the reliable monitoring parameter group;

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

[0125] Based on the health analysis results of different similar monitoring parameter groups, determine the sum of the group weight coefficients corresponding to different types of health analysis results, and use the sum of the group weight coefficients corresponding to different types of health analysis results to determine the health analysis result of the monitoring analysis target.

[0126] Furthermore, using the sum of the group weight coefficients corresponding to different types of health analysis results to determine the health analysis result of the monitoring analysis target specifically includes:

[0127] When the sum of the group weight coefficients with abnormal health analysis results is greater than the preset coefficient threshold, determine that the health analysis result of the monitoring analysis target is abnormal;

[0128] When the sum of the group weight coefficients with normal health analysis results is not greater than the preset coefficient threshold, determine that the health analysis result of the monitoring analysis target is normal.

[0129] Optionally, the method for determining the health analysis result of the monitoring analysis target is:

[0130] Divide the similar monitoring parameter groups into abnormal groups and normal groups according to the type of health analysis results. When the number of abnormal groups is less than the preset group number, determine that the health analysis result of the monitoring analysis target is normal;

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

[0132] Determine the sum of reliable monitoring times with the sum of the number of reliable monitoring times within different abnormal groups. When the sum of reliable monitoring times is greater than the preset times threshold, determine that the health analysis result of the monitoring analysis target is abnormal;

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

[0134] When the proportion of the sum of reliable monitoring times within the abnormal group is greater than the preset proportion threshold of times, determine that the health analysis result of the monitoring analysis target is abnormal;

[0135] When the proportion of the sum of reliable monitoring times within the abnormal group is not greater than the preset proportion threshold of times:

[0136] Based on the distribution data of the number of reliable detections within different groups of similar monitoring parameters, determine the proportion of the number of reliable detections within different groups of similar monitoring parameters. Based on the temperature change data of the number of reliable detections within the group of similar monitoring parameters, determine the proportion of the number of temperature change moments within the group of reliable monitoring parameters;

[0137] Based on the product of the proportion of the number of temperature change moments and the proportion of the number of reliable detections, determine the group weight coefficient of different groups of similar monitoring parameters. When the proportion of the number of abnormal groups in the group of similar monitoring parameters with a group weight coefficient greater than the preset group coefficient threshold is greater than the preset group number proportion, then determine that the health analysis result of the monitoring analysis target is abnormal;

[0138] When the proportion of the number of abnormal groups in the group of similar monitoring parameters with a group weight coefficient greater than the preset group coefficient threshold is not greater than the preset group number proportion:

[0139] Through the health analysis results of different groups of similar monitoring parameters, determine the sum of the group weight coefficients corresponding to different types of health analysis results, and use the sum of the group weight coefficients corresponding to different types of health analysis results to determine the health analysis result of the monitoring analysis target.

[0140] Embodiment 2

[0141] In a second aspect, as Figure 5 shown, the present invention provides a heart rate monitoring sensing device, adopting the above-mentioned heart rate calculation method based on physical and mental health analysis, specifically including:

[0142] A monitoring frequency screening module, a group data analysis module, and an analysis result output module;

[0143] Wherein the monitoring frequency screening module is responsible for determining the number of reliable detections in the sleep monitoring frequency;

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

[0145] The analysis result output module is responsible for determining the health analysis result of the monitoring analysis target according to the health analysis results of different groups of similar monitoring parameters.

[0146] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of devices, equipment, and non-volatile computer storage media, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the corresponding parts of the method embodiments for relevant details.

[0147] The specific embodiments of this specification are described above. 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 in the embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0148] The above description is only for one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, various modifications and changes can be made to one or more embodiments of this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A heart rate calculation method based on physical and mental health analysis, characterized in that: Specifically include: Obtaining heart rate monitoring data of the monitoring and analysis target during different sleep monitoring times within a preset period, and determining a stable monitoring number of the sleep monitoring times using fluctuation data of the heart rate monitoring data; Determine temperature variation data of a heart rate monitoring position in different stable monitoring times, and determine credible monitoring times in different stable monitoring times using the temperature variation data; Using the temperature change data in different credible monitoring times, the credible monitoring times are divided into different similar monitoring parameter groups, and when it is determined that there is a similar monitoring parameter group whose health analysis result does not meet the requirements based on the analysis results of the heart rate data of different credible monitoring times in different similar monitoring parameter groups, the next step is entered; The distribution data of the credible monitoring times and the temperature change data in different similar monitoring parameter groups are obtained, and the health analysis results of the monitoring and analysis targets are determined in combination with the health analysis results of different similar monitoring parameter groups.

2. The heart rate calculation method based on physical and mental health analysis according to claim 1, characterized in that: The preset time period is determined according to the preset monitoring duration.

3. The heart rate calculation method based on physical and mental health analysis according to claim 1, characterized in that: The fluctuation data of the heart rate monitoring data includes determining the fluctuation amount of the heart rate monitoring data between different moments.

4. The heart rate calculation method based on physical and mental health analysis according to claim 1, characterized in that: The method for determining the number of stable monitoring times in the sleep monitoring times is: Based on the fluctuation data of the heart rate monitoring data, determining the fluctuation amount of the heart rate monitoring data between different adjacent moments in the sleep monitoring times; Determining the fluctuation moments of the monitoring data in different sleep stages based on the fluctuation amount of the heart rate monitoring data; The number of stable monitoring times in the sleep monitoring times is determined according to the number of monitoring data fluctuation moments in different sleep stages.

5. The heart rate calculation method based on physical and mental health analysis according to claim 4, characterized in that: The monitoring data fluctuation moment is a moment when the fluctuation amount of the heart rate monitoring data at adjacent moments does not meet the requirement.

6. The heart rate calculation method based on physical and mental health analysis according to claim 5, characterized in that: The sleep stages are as light sleep stage and deep sleep stage. A further technical solution is that when the number of fluctuation moments of the monitoring data in different sleep stages is greater than a preset number threshold, it is determined that the sleep monitoring times do not belong to stable monitoring times.

7. The heart rate calculation method based on physical and mental health analysis according to claim 1, characterized in that: The temperature change data of the heart rate monitoring position is determined based on the monitoring data of the skin conductance monitoring device at the heart rate monitoring position.

8. The heart rate calculation method based on physical and mental health analysis according to claim 1, characterized in that: The method for determining the health analysis result of the monitoring and analysis target is: Based on the distribution data of the credible monitoring times in different similar monitoring parameter groups, the frequency ratio of the credible monitoring times in different similar monitoring parameter groups is determined, and based on the temperature change data of the credible monitoring times in the similar monitoring parameter groups, the number ratio of the temperature change moments in the credible monitoring parameter groups is determined; Determining group weight coefficients of different similar monitoring parameter groups based on the product of the number ratio of the temperature change moments and the number ratio of the reliable monitoring times; The sum of group weight coefficients corresponding to different types of health analysis results is determined through health analysis results of different groups of similar monitoring parameters, and the health analysis result of the monitoring and analysis target is determined using the sum of group weight coefficients corresponding to different types of health analysis results.

9. The heart rate calculation method based on physical and mental health analysis according to claim 8, characterized in that: 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, specifically including: When the sum of the weight coefficients of the groups whose health analysis results are abnormal is greater than the preset coefficient threshold, it is determined that the health analysis result of the monitoring and analysis target is abnormal; When the sum of the weight coefficients of the groups whose health analysis results are normal is not greater than the preset coefficient threshold, it is determined that the health analysis result of the monitoring and analysis target is normal.

10. A heart rate monitoring sensor device, using a heart rate calculation method based on physical and mental health analysis according to any one of claims 1 to 9, characterized in that: Specifically include: Monitoring times screening module, group data analysis module, analysis result output module; The monitoring times screening module is responsible for determining the credible monitoring times among the sleep monitoring times; The group data analysis module is responsible for dividing the credible monitoring times 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 result of the monitoring and analysis target according to the health analysis results of different similar monitoring parameter groups.

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