Heartbeat-to-sound-wave physical and psychological health monitoring and analyzing method and sensing device

Abstract

The invention provides a heartbeat-to-sound-wave physical and psychological health monitoring analysis method and a sensing device, and belongs to the technical field of data security. The method specifically comprises the following steps: determining health analysis results of different reference monitoring times according to analysis results of heartbeat-to-sound wave data of specific types of sleep postures in different effective monitoring time periods and sleep postures at different moments, and determining health analysis results of different reference monitoring times according to the health analysis results of the different reference monitoring times. When it is determined that the health analysis result of the monitoring target is abnormal, the heartbeat-to-sound wave data of the monitoring target in the current sleep process are obtained in real time, and the health analysis result of the monitoring target is determined according to the change conditions of the heartbeat-to-sound wave data under different sleep postures, 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 method and a sensing device for monitoring and analyzing physical and mental health by converting heartbeats into sound waves. Background Art

[0002] Some indicators of the heartbeat are related to the physical and mental health of the user. For example, arrhythmia, sinus tachycardia, sinus bradycardia, premature beats, ventricular fibrillation, atrioventricular block, etc. are all related to the physical state. In the existing technical solutions, the heartbeat data is used for the analysis and processing of the physical state. For example, in the invention patent applications CN109077711A "Method, Device, Wearable Device and Readable Storage Medium for Obtaining Dynamic Heart Rate Data" and CN117084646A "A Method and System for Monitoring and Warning Training Injuries Based on Electronic Sensing", similar technical solutions are given.

[0003] During the sleep monitoring process, improper sleep postures can cause certain compression on the heart, which may lead to a certain degree of abnormality in the monitored heartbeat data. Therefore, how to effectively screen the monitored data based on the monitoring results of the sleep postures of the monitoring target and ensure the accuracy of the physical and mental health monitoring and analysis results has become an urgent technical problem to be solved.

[0004] In view of the above technical problems, specifically, the present application provides a method and a sensing device for monitoring and analyzing physical and mental health by converting heartbeats into sound waves. Summary of the Invention

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

[0006] In a first aspect, the present application provides a method for monitoring and analyzing physical and mental health by converting heartbeats into sound waves, specifically including:

[0007] Determine the sleep postures of the monitoring target at different times according to the measurement results of the pressure monitoring device of the sleep device, and determine the reference monitoring times in the historical monitoring times based on the sleep data of different sleep postures;

[0008] Determine the effective monitoring period based on the sleep postures at different times in different reference monitoring times. Determine the health analysis results of different reference monitoring times through the analysis results of the heartbeat-to-sound wave data of specific types of sleep postures in different effective monitoring periods and the sleep postures at different times;

[0009] When it is determined that the health analysis result of the monitoring target is abnormal by using the health analysis results of different reference monitoring times, obtain the heartbeat-to-sound wave data of the monitoring target during the current sleep process in real time, and determine the health analysis result of the monitoring target by using the change situation of the heartbeat-to-sound wave data in different sleep postures;

[0010] When the health analysis result of the monitoring target shows no abnormality, the effective monitoring period is determined based on the analysis result of the sleep posture, and the heartbeat-to-sound wave data is acquired and the health analysis result is determined during the effective monitoring period.

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

[0012] By using the variation of the heartbeat-to-sound wave data in different sleep postures to determine the health analysis result of the monitoring target, fully considering the consistency and stability of the heartbeat-to-sound wave data in different sleep postures under different monitoring times when the health analysis result of the monitoring target shows an abnormality, the screening of the monitoring target with an abnormal health analysis result is realized under the premise of multiple monitoring times and sleep postures, ensuring the accuracy and reliability of the screening process.

[0013] Determining the health analysis result based on the heartbeat-to-sound wave data during the effective monitoring period avoids the influence of other sleep postures on the heartbeat-to-sound wave data, ensuring the accuracy and reliability of the heartbeat-to-sound wave data, and laying a foundation for ensuring the accuracy of the health analysis result.

[0014] A further technical solution is that the pressure monitoring device is arranged at the lower part of the bed body of the sleep device.

[0015] A further technical solution is that the method for determining the sleep posture at a certain moment is as follows:

[0016] Based on the analysis result of the monitoring data of the pressure monitoring device at that moment, determine the sleep area of the monitoring target;

[0017] Determine the sleep posture at that moment according to the sleep area of the monitoring target.

[0018] A further technical solution is that determining the sleep posture at that moment according to the sleep area of the monitoring target specifically includes:

[0019] Determine the sleep posture at that moment according to the preset corresponding relationship between the sleep area and the sleep posture.

[0020] A further technical solution is that the method for determining the reference monitoring times in the historical monitoring times is as follows:

[0021] Based on the sleep postures at different moments in the historical monitoring times, determine the moments when a specific sleep posture is adopted, and take them as the specific sleep posture moments;

[0022] Determine the reference monitoring times in the historical monitoring times according to the proportion of the number of specific sleep posture moments.

[0023] A further technical solution is that the specific sleeping posture is lying down.

[0024] A further technical solution is that when the proportion of the number of times of the specific sleeping posture moment in the historical monitoring times is greater than the preset proportion of the number of moments, the historical monitoring times are determined as the reference monitoring times.

[0025] A further technical solution is that the sleeping postures include supine, prone, left lateral lying, and right lateral lying.

[0026] A further technical solution is that the method for determining the health analysis result of the monitoring target is as follows:

[0027] Based on the variation of the heartbeat-to-sound wave data in different sleeping postures, determine the variation amount of the heart rate variability in different sleeping postures;

[0028] Based on the variation amount of the heart rate variability in different sleeping postures, determine the health analysis result of the monitoring target.

[0029] A further technical solution is that the variation amount of the heart rate variability is determined according to the variation of the heartbeat-to-sound wave data in the monitoring times closest to the current sleep process of the monitoring target.

[0030] In a second aspect, the present invention provides a sensing device, which adopts the above method for monitoring and analyzing the physical and mental health of heartbeat-to-sound wave, and specifically includes:

[0031] A heartbeat data acquisition module, a sound wave conversion module, and a monitoring and analysis module;

[0032] Wherein the heartbeat data acquisition module is responsible for acquiring and processing the heartbeat data of the monitoring target;

[0033] The sound wave conversion module is responsible for converting the heartbeat data into sound wave data;

[0034] The monitoring and analysis module is responsible for determining the health analysis result of the monitoring target by using the sound wave data.

[0035] Other features and advantages will be described in the subsequent description. The objectives and other advantages of the present invention are realized and obtained by the structures specifically pointed out in the description and the drawings.

[0036] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given in conjunction with the accompanying drawings and are described in detail as follows. Brief Description of the Drawings

[0037] By referring to the drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.

[0038] Figure 1 is a flowchart of a method for monitoring, analyzing physical and mental health by converting heartbeat into sound waves;

[0039] Figure 2 is a flowchart of a method for determining the sleeping posture at a moment;

[0040] Figure 3 is a flowchart of a method for determining the reference monitoring times in the historical monitoring times;

[0041] Figure 4 is a flowchart of a method for determining the effective monitoring period;

[0042] Figure 5 is a flowchart for determining that there is an abnormality in the health analysis result of the monitoring target;

[0043] Figure 6 is a framework diagram of a sensing device. Detailed implementation manners

[0044] In order 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 with reference to 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 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 protection scope of this specification.

[0045] During the sleep monitoring process, by monitoring the sleeping posture of the monitoring target, an effective period for monitoring and analyzing heartbeat data is obtained, the influence of postures such as lying on the side on the heartbeat data is excluded, and the physical health analysis result is determined by using the heartbeat data in the effective period, so as to improve the accuracy of the health analysis result.

[0046] The reference monitoring times are the historical monitoring times in which the proportion of the number of moments of lying down is greater than 0.9.

[0047] The effective monitoring period is a unit period in which the proportion of the number of moments of lying down is greater than 0.95.

[0048] Convert the heartbeat at the lying moments in different unit periods into sound wave data, use the sound wave data to determine the health analysis results at different lying moments, and determine the health analysis result of the reference monitoring times with the health analysis result corresponding to the lying moment with the most consistent health analysis results. The health analysis results include normal and abnormal.

[0049] When the proportion of the number of reference monitoring times with abnormal health analysis results is greater than 0.7, it is determined that the health analysis result of the monitoring target is abnormal. When the deviation amounts from the adjacent historical monitoring times in different sleep postures are all within the preset deviation amount range, it is determined that the health analysis result of the monitoring target is abnormal.

[0050] Using the heartbeat-to-sound wave data as the input quantity, and based on the output quantity of a classification model constructed using one or more of SVM, Bayesian, and decision tree, determine the health analysis result of the monitoring target.

[0051] Embodiment 1

[0052] As Figure 1 shown, in the first aspect provided by the present application, the present application provides a method for monitoring and analyzing the physical and mental health of heartbeat-to-sound wave, specifically including:

[0053] According to the measurement results of the pressure monitoring device of the sleep device, determine the sleep postures of the monitoring target at different times, and determine the reference monitoring times in the historical monitoring times based on the sleep data of different sleep postures;

[0054] Furthermore, the pressure monitoring device is arranged at the lower part of the bed body of the sleep device.

[0055] Specifically, as Figure 2 shown, the method for determining the sleep posture at the time is:

[0056] Based on the analysis result of the monitoring data of the pressure monitoring device at the time, determine the sleep area of the monitoring target;

[0057] Determine the sleep posture at the time according to the sleep area of the monitoring target.

[0058] Optionally, determining the sleep posture at the time according to the sleep area of the monitoring target specifically includes:

[0059] Determine the sleep posture at the time according to the preset corresponding relationship between the sleep area and the sleep posture.

[0060] Specifically, as Figure 3 shown, the method for determining the reference monitoring times in the historical monitoring times is:

[0061] Based on the sleep postures at different times in the historical monitoring times, determine the times when a specific sleep posture is adopted, and use them as specific sleep posture times;

[0062] Determine the reference monitoring times in the historical monitoring times according to the proportion of the number of specific sleep posture times.

[0063] Further, the specific sleeping posture is lying down.

[0064] It can be understood that when the proportion of the number of specific sleeping posture moments in the historical monitoring times is greater than the preset proportion of moment numbers, the historical monitoring times are determined as the reference monitoring times.

[0065] Optionally, the method for determining the reference monitoring times in the historical monitoring times is as follows:

[0066] Based on the sleeping postures at different times in the historical monitoring times, determine the moments when a specific sleeping posture is adopted and use them as specific sleeping posture moments;

[0067] Based on the distribution data of the specific sleeping posture moments, determine the continuous time periods of the specific sleeping posture moments in the historical monitoring times and use them as specific posture time periods;

[0068] Determine the reference monitoring times in the historical monitoring times according to the cumulative duration of the specific posture time periods.

[0069] Further, when the cumulative duration of the specific posture time periods in the historical monitoring times is greater than the preset cumulative duration, the historical monitoring times are determined as the reference monitoring times.

[0070] Optionally, the method for determining the reference monitoring times in the historical monitoring times is as follows:

[0071] S11 Based on the sleeping postures at different times in the historical monitoring times, determine the moments when a specific sleeping posture is adopted and use them as specific sleeping posture moments, use the proportion of the number of the specific sleeping posture moments to determine the specific proportion, and combine the number of the specific sleeping posture moments to determine the basic credibility coefficient;

[0072] S12 Based on the distribution data of the specific sleeping posture moments, find the time periods in which the proportion of the number of specific sleeping posture moments is greater than the preset proportion and use them as specific time periods, and determine the distribution aggregation coefficient according to the proportion of the number of specific sleeping posture moments in different specific time periods;

[0073] S13 Determine the credibility coefficient of the historical monitoring times according to the product of the basic credibility coefficient and the distribution aggregation coefficient, and use the credibility coefficient to determine the reference monitoring times in the historical monitoring times.

[0074] Further, when the credibility coefficient of the historical monitoring times is greater than the preset credibility coefficient, the historical monitoring times are determined as the reference monitoring times.

[0075] Optionally, the above step S11 includes the following content:

[0076] S111 determines the moments when a specific sleeping position is adopted based on the sleeping positions at different moments in the historical monitoring times, and takes them as specific sleeping position moments. When the number of specific sleeping position moments is less than the preset number of moments, it is determined that the historical monitoring times do not belong to the reference monitoring times. When the number of specific sleeping position moments is not less than the preset number of moments, it proceeds to step S112;

[0077] S112 When the proportion of the number of specific sleeping position moments in the historical monitoring times is less than the preset proportion threshold, it is determined that the historical monitoring times do not belong to the reference monitoring times. When the proportion of the number of specific sleeping position moments is not less than the preset proportion threshold, it proceeds to step S113;

[0078] S113 determines the basic credibility coefficient based on the specific proportion of the number of specific sleeping position moments and the number of specific sleeping position moments. When the basic credibility coefficient is less than the preset credibility coefficient, it is determined that the historical monitoring times do not belong to the reference monitoring times. When the basic credibility coefficient is not less than the preset credibility coefficient, it proceeds to step S12.

[0079] Optionally, the following content is included in the above step S12:

[0080] S121 According to the distribution data of the specific sleeping position moments, when it is determined that the proportion of the number of moments without specific sleeping position moments in the historical monitoring times is greater than the preset number proportion in a certain period, it is determined that the historical monitoring times do not belong to the reference monitoring times. When there is a period in the historical monitoring times where the proportion of the number of moments with specific sleeping position moments is greater than the preset number proportion, it proceeds to step S122;

[0081] S122 takes the period where the proportion of the number of moments with specific sleeping position moments is greater than the preset number proportion as a specific period. When the cumulative duration of the specific period does not meet the requirements, it is determined that the historical monitoring times do not belong to the reference monitoring times. When the cumulative duration of the specific period meets the requirements, it proceeds to step S123;

[0082] S123 determines the distribution aggregation coefficient according to the proportion of the number of specific sleeping position moments in different specific periods. When the distribution aggregation coefficient is greater than the preset aggregation coefficient threshold, it is determined that the historical monitoring times belong to the reference monitoring times. When the distribution aggregation coefficient is not greater than the preset aggregation coefficient threshold, it proceeds to step S13.

[0083] Based on the sleeping positions at different moments in different reference monitoring times, the effective monitoring periods are determined, and the health analysis results of different reference monitoring times are determined through the analysis results of the heartbeat-to-sound wave data of specific types of sleeping positions in different effective monitoring periods and the sleeping positions at different moments;

[0084] It should be noted that, as Figure 4 shown, the method for determining the effective monitoring period is as follows:

[0085] Based on the analysis results of the sleep postures at different times in different unit periods among the reference monitoring times;

[0086] Based on the analysis results of the sleep postures at different times in different unit periods, determine the times when a specific sleep posture is adopted in different unit periods, and take them as specific sleep posture times;

[0087] Determine whether the unit period is an effective monitoring period based on the proportion of the number of specific sleep posture times in different unit periods.

[0088] Furthermore, when the proportion of the number of specific sleep posture times in different unit periods is greater than the preset proportion of the number of times, it is determined that the unit period is an effective monitoring period.

[0089] Optionally, the method for determining the effective monitoring period is as follows:

[0090] Based on the analysis results of the sleep postures at different times in different unit periods among the reference monitoring times, when it is determined that there is no time when a specific sleep posture is adopted in the unit period, it is determined that the unit period does not belong to the effective monitoring period;

[0091] When there is a time when a specific sleep posture is adopted in the unit period:

[0092] Take the times when a specific sleep posture is adopted in the unit period as specific sleep posture times. When the proportion of the number of specific sleep posture times in the unit period is greater than the preset proportion of the number of times, it is determined that the unit period belongs to the effective monitoring period;

[0093] When the proportion of the number of specific sleep posture times in the unit period is not greater than the preset proportion of the number of times:

[0094] Based on the distribution data of the specific sleep posture times in the unit period, determine the number of interval times between the specific sleep posture times in the unit period, and combine the proportion of the number of specific sleep posture times in the unit period to determine the effective coefficient of the monitoring data in the unit period;

[0095] When the effective coefficient of the monitoring data in the unit period does not meet the requirements, it is determined that the unit period does not belong to the effective monitoring period;

[0096] When the effective coefficient of the monitoring data in the unit period meets the requirements:

[0097] When the effective coefficient of the monitoring data in the unit time period is greater than the preset effective coefficient threshold, it is determined that the unit time period belongs to the effective monitoring time period;

[0098] When the effective coefficient of the monitoring data in the unit time period is not greater than the preset effective coefficient threshold:

[0099] Based on the historical monitoring times corresponding to the unit time period, determine the proportion of the number of specific posture moments in the adjacent time periods of the unit time period. Determine the specific posture quantity ratio of the adjacent time periods based on the average value of the proportion of the number of specific posture moments in different adjacent time periods, and determine the data credibility coefficient of the unit time period according to the average value of the specific posture quantity ratios in different adjacent time periods. When the data credibility coefficient of the unit time period is greater than the preset data credibility threshold, it is determined that the unit time period belongs to the effective monitoring time period;

[0100] When the data credibility coefficient of the unit time period is not greater than the preset data credibility threshold, determine the comprehensive effective coefficient based on the product of the effective coefficient and the data credibility coefficient of the monitoring data in the unit time period, and use the comprehensive effective coefficient to determine whether the unit time period is an effective monitoring time period.

[0101] It should be noted that when the comprehensive effective coefficient of the unit time period meets the requirements, it is determined that the unit time period is an effective monitoring time period.

[0102] Specifically, as Figure 4 shown, the method for determining the health analysis result of the reference monitoring times is:

[0103] Based on the analysis results of the heartbeat conversion wave data of specific types of sleep postures in different effective monitoring time periods in the reference monitoring times, determine the number of moments with abnormal health analysis results in different effective monitoring time periods;

[0104] Based on the proportion of the number of moments with abnormal health analysis results in different effective monitoring time periods, determine the result anomaly values in different effective monitoring time periods;

[0105] Use the proportion of the number of moments of specific types of sleep postures in different effective monitoring time periods to determine the weight coefficients of different effective monitoring time periods. Based on the result anomaly value and the weight coefficient, determine the comprehensive anomaly value of the reference monitoring times, and determine the health analysis result of the reference monitoring times according to the comprehensive anomaly value.

[0106] Furthermore, the health analysis result of the reference monitoring times includes abnormal and normal. When the comprehensive anomaly value of the reference monitoring times is greater than the preset anomaly threshold, it is determined that the health analysis result of the reference monitoring times is abnormal.

[0107] When it is determined that the health analysis result of the monitoring target is abnormal based on the health analysis results of different reference monitoring times, the heartbeat conversion sound wave data of the monitoring target during the current sleep process is obtained in real time, and the health analysis result of the monitoring target is determined by using the variation of the heartbeat conversion sound wave data in different sleep postures;

[0108] It can be understood that, as Figure 5 shown, determining that the health analysis result of the monitoring target is abnormal specifically includes:

[0109] Dividing the reference monitoring times into abnormal monitoring times and normal monitoring times based on the health analysis results of different reference monitoring times;

[0110] Determining the abnormal frequency ratio based on the frequency ratio of the abnormal monitoring times in the reference monitoring times;

[0111] Determining whether the health analysis result of the monitoring target is abnormal according to the abnormal frequency ratio.

[0112] Furthermore, when the abnormal frequency ratio is greater than the preset frequency ratio, it is determined that the health analysis result of the monitoring target is abnormal.

[0113] Optionally, determining that the health analysis result of the monitoring target is abnormal specifically includes:

[0114] Dividing the reference monitoring times into abnormal monitoring times and normal monitoring times based on the health analysis results of different reference monitoring times;

[0115] Determining the weight coefficients of different abnormal monitoring times based on the ratio of the number of moments of specific types of sleep postures in different abnormal monitoring times;

[0116] Determining whether the health analysis result of the monitoring target is abnormal according to the sum of the weight coefficients of different abnormal monitoring times.

[0117] Furthermore, when the sum of the weight coefficients of different abnormal monitoring times is greater than the preset weight coefficient threshold, it is determined that the health analysis result of the monitoring target is abnormal.

[0118] Specifically, when the abnormal monitoring coefficient is greater than the preset abnormal coefficient threshold, it is determined that the health analysis result of the monitoring target is abnormal.

[0119] It should be noted that the sleep postures include supine, prone, left lateral lying, and right lateral lying.

[0120] It can be understood that the method for determining the health analysis result of the monitoring target is:

[0121] Determine the change amount of heart rate variability under different sleep postures based on the change of heart rate to acoustic wave data under different sleep postures;

[0122] Based on the change amount of heart rate variability under different sleep postures, determine the health analysis result of the monitoring target.

[0123] Furthermore, the change amount of heart rate variability is determined according to the change of heart rate to acoustic wave data of the monitoring times closest to the current sleep process of the monitoring target.

[0124] Specifically, when the change amounts of heart rate variability under different sleep postures are all within the preset change amount range, it is determined that there is an abnormality in the health analysis result of the monitoring target.

[0125] Furthermore, when there is a sleep posture in which the change amount of heart rate variability is not within the preset change amount range, it is determined that there may be a suspected abnormality in the heart rate to acoustic wave data of the monitoring times closest to the current sleep process of the monitoring target, and it is temporarily impossible to determine that there is an abnormality in the health analysis result of the monitoring target.

[0126] When there is no abnormality in the health analysis result of the monitoring target, determine the effective monitoring period based on the analysis result of the sleep posture, and obtain the heart rate to acoustic wave data and determine the health analysis result during the effective monitoring period.

[0127] Specifically, the method for determining the health analysis result is as follows:

[0128] Obtain the heart rate to acoustic wave data and determine the health analysis result during the effective monitoring period

[0129] Taking the heart rate to acoustic wave data at different moments during the effective monitoring period as the input quantity, and taking the output quantity of the preset classification model as the health analysis result.

[0130] It should be noted that the preset classification model is constructed by using one or more of SVM, Bayesian, and decision tree.

[0131] Embodiment 2

[0132] Second aspect, as Figure 6 shown, the present invention provides a sensing device, adopting the above-mentioned method for monitoring and analyzing heart rate to acoustic wave physical and mental health, specifically including:

[0133] A heart rate data acquisition module, an acoustic wave conversion module, and a monitoring and analysis module;

[0134] Wherein the heart rate data acquisition module is responsible for acquiring and processing the heart rate data of the monitoring target;

[0135] The converted sonic wave module is responsible for converting the heartbeat data into sonic wave data;

[0136] The monitoring and analysis module is responsible for determining the health analysis result of the monitoring target by using the sonic wave data.

[0137] Optionally, determining that the health analysis result of the monitoring target is abnormal specifically includes:

[0138] Based on the health analysis results of different reference monitoring times, when it is determined that the reference monitoring times are less than the preset monitoring times threshold, it is determined that the health analysis result of the monitoring target is not abnormal;

[0139] When the reference monitoring times are not less than the predicted monitoring times threshold:

[0140] Based on the health analysis results of different reference monitoring times, when it is determined that there is no reference monitoring time with an abnormal health analysis result, it is determined that the health analysis result of the monitoring target is not abnormal;

[0141] When there is a reference monitoring time with an abnormal health analysis result:

[0142] Based on the health analysis results, the reference monitoring times are divided into abnormal monitoring times and normal monitoring times. When the proportion of the number of abnormal monitoring times in the reference monitoring times does not meet the requirements, it is determined that the health analysis result of the monitoring target is abnormal;

[0143] When the proportion of the number of abnormal monitoring times in the reference monitoring times meets the requirements:

[0144] Based on the proportion of the number of moments of specific types of sleep postures in different abnormal monitoring times, the weight coefficients of different abnormal monitoring times are determined. When the weight coefficients of different abnormal monitoring times are all less than the coefficient preset value, it is determined that the health analysis result of the monitoring target is not abnormal;

[0145] When there is an abnormal monitoring time with a weight coefficient not less than the coefficient preset value:

[0146] Obtain the abnormal monitoring times with a weight coefficient not less than the coefficient preset value. When the number of abnormal monitoring times with a weight coefficient not less than the coefficient preset value is greater than the preset times threshold, it is determined that the health analysis result of the monitoring target is abnormal;

[0147] When the number of abnormal monitoring times with a weight coefficient not less than the coefficient preset value is not greater than the preset times threshold:

[0148] Determine an anomaly monitoring coefficient based on the product of the proportion of the number of anomaly monitoring times and the proportion of the sum of the weight coefficients of the number of anomaly monitoring times, and use the anomaly monitoring coefficient to determine whether there is an anomaly in the health analysis result of the monitoring target.

[0149] 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 the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0150] 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 can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0151] 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 changes and modifications can be made to one or more embodiments of this specification. Any modification, equivalent replacement, improvement, 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 method for monitoring and analyzing physical and mental health using heartbeat sound waves, characterized in that: Specifically include: Determine the sleeping posture of the monitoring target at different times according to the measurement result of the pressure monitoring device of the sleeping device, and determine the reference monitoring number in the historical monitoring number based on the sleeping data of different sleeping postures; Determine the effective monitoring period based on different reference monitoring times and sleeping postures at different times, and determine the health analysis results of different reference monitoring times through the analysis results of the heartbeat and sound wave data of specific types of sleeping postures in different effective monitoring periods and the sleeping postures at different times; When it is determined that the health analysis result of the monitoring target is abnormal by using the health analysis results of different reference monitoring times, the heartbeat and sound wave data of the monitoring target during the current sleep process are obtained in real time, and the health analysis result of the monitoring target is determined by using the changes in the heartbeat and sound wave data under different sleeping postures; When there is no abnormality in the health analysis result of the monitoring target, the effective monitoring period is determined based on the analysis result of the sleeping posture, and the heartbeat sound wave data is acquired and the health analysis result is determined during the effective monitoring period.

2. The method for monitoring and analyzing physical and mental health using heartbeat sound waves as claimed in claim 1, characterized in that: The pressure monitoring device is arranged at the lower part of the bed body of the sleeping device.

3. The method for monitoring and analyzing physical and mental health using heartbeat sound waves as claimed in claim 1, characterized in that: The method for determining the sleeping posture at the time is: Determining the sleeping area of ​​the monitoring target based on the analysis result of the monitoring data of the pressure monitoring device at the time; The sleeping posture at the moment is determined according to the sleeping area of ​​the monitoring target.

4. The method for monitoring and analyzing physical and mental health using heartbeat sound waves as claimed in claim 3, characterized in that: Determining the sleeping posture at the moment according to the sleeping area of ​​the monitoring target specifically includes: The sleeping posture at the moment is determined according to a preset correspondence between the sleeping area and the sleeping posture.

5. The method for monitoring and analyzing physical and mental health using heartbeat sound waves as claimed in claim 1, characterized in that: The method for determining the reference monitoring times in the historical monitoring times is: Determine the time when a specific sleeping posture is adopted based on the sleeping postures at different times in the historical monitoring times, and use the time as the specific sleeping posture time; The reference monitoring number in the historical monitoring number is determined according to the proportion of the number of specific sleeping posture moments.

6. The method for monitoring and analyzing physical and mental health using heartbeat sound waves as claimed in claim 5, characterized in that: The specific sleeping posture is lying down.

7. The method for monitoring and analyzing physical and mental health using heartbeat sound waves as claimed in claim 5, characterized in that: When the proportion of the specific sleeping posture moments in the historical monitoring times is greater than the proportion of the preset moments, the historical monitoring times are determined to be the reference monitoring times.

8. The method for monitoring and analyzing physical and mental health using heartbeat sound waves as claimed in claim 1, characterized in that: The method for determining the health analysis result of the monitoring target is: Determine the amount of change in heart rate variability under different sleeping postures based on the changes in the heartbeat sound wave data under different sleeping postures; The health analysis result of the monitoring target is determined based on the variation of the heart rate variability under different sleeping postures.

9. The method for monitoring and analyzing physical and mental health using heartbeat sound waves as claimed in claim 8, characterized in that: The amount of change in the heart rate variability is determined based on changes in the heartbeat sound wave data of the most recent monitoring times of the monitored target during the current sleep process.

10. A sensing device, using a method for monitoring and analyzing physical and mental health using heartbeat sound waves as claimed in any one of claims 1 to 9, characterized in that: Specifically include: Heartbeat data acquisition module, sound wave conversion module, monitoring and analysis module; The heartbeat data acquisition module is responsible for acquiring and processing the heartbeat data of the monitored target; The sound wave conversion module is responsible for converting the heartbeat data into sound wave data; The monitoring and analysis module is responsible for determining the health analysis result of the monitoring target using the sound wave data.

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