Method for identifying deep sleep stage in sleep stage, apparatus, and storage medium

Abstract

The present invention relates to the technical field of sleep staging, specifically provides a method for identifying a deep sleep stage in a sleep stage, an apparatus, and a storage medium, and aims to solve the problem that existing sleep staging methods cannot accurately identify the deep sleep stage. For this purpose, the method in the present invention comprises: performing data preprocessing on the basis of acquired user sleep feature data to obtain a feature data sequence; and on the basis of a currently stored average baseline value and the feature data sequence, identifying and obtaining a deep sleep stage in a sleep stage to be tested, wherein the initial value of the average baseline value is obtained in light of medical-level sleep feature data, and the feature data sequence is obtained on the basis of a combination result of body motion data and heart rate data. The deep sleep stage identified and obtained by means of the solution of the present invention can be closer to a sleep staging detection result of a medical-level monitoring device. Therefore, the solution of the present invention can effectively improve the accuracy of identification of the deep sleep stage in the sleep staging.

Description

Method, device and storage medium for identifying deep sleep period in sleep period

[0001] This application claims priority to Chinese patent application No. 202311284668.9 filed on October 7, 2023, entitled “A method, device and storage medium for identifying deep sleep periods during sleep periods”. The entire contents of the above Chinese patent application are incorporated into this application by reference. Technical Field

[0002] The present invention relates to the technical field of sleep staging, and specifically provides a method, device and storage medium for identifying a deep sleep stage in a sleep stage. Background Art

[0003] With the development of the economy and the improvement of people's living standards, people are paying more and more attention to their health status. Among them, sleep monitoring is a very important scenario in the field of health monitoring. In the current existing technology, common sleep monitoring devices include smart bracelets and smart watches, which can monitor daily sleep quality and effectively provide feedback on key information such as the physiological signals of the human body during sleep. Sleep staging is to divide the sleep process into different stages according to the different changes in the physiological signals of the human body during sleep. For example, the sleep period is divided into deep sleep, light sleep and rapid eye movement (REM). Accurate sleep staging results can help us understand the user's health and stress status, and also help professionals develop intervention measures to improve sleep.

[0004] In the existing technology, the light sleep and deep sleep stages in the sleep stage classification are usually determined by relying on the body movement and heart rate signals fed back by the sleep monitoring device, and the deep sleep stage is usually identified by judging the body movement signals based on the body movement threshold. This method has low accuracy in identifying the deep sleep stage.

[0005] Accordingly, the art needs a solution that can accurately identify the deep sleep period in the sleep period to solve the above problem.

[0006] Summary of the Invention

[0007] In order to overcome the above-mentioned defects, the present invention is proposed to provide a method, device and storage medium for identifying deep sleep periods in sleep periods, which solve or at least partially solve the technical problem of being unable to accurately identify deep sleep periods.

[0008] In a first aspect, the present invention provides a method for identifying a deep sleep period in a sleep period, comprising:

[0009] Obtaining user sleep characteristic data during the sleep period to be measured;

[0010] Performing data preprocessing based on the user's sleep characteristic data to obtain a characteristic data sequence;

[0011] The currently stored average baseline value is obtained, and the deep sleep period in the sleep period to be measured is identified based on the average baseline value and the characteristic data sequence.

[0012] Preferably, the user sleep characteristic data of the sleep period to be measured is specifically the user sleep characteristic data of a preset sleep duration; the user sleep characteristic data includes at least body movement data and heart rate data; and the data preprocessing based on the user sleep characteristic data to obtain a characteristic data sequence specifically includes:

[0013] According to the preset sleep duration and the preset number of segmented intervals, the body movement data and the heart rate data are divided to obtain body movement data and heart rate data of a plurality of segmented intervals, and the body movement data and heart rate data of each segmented interval correspond one to one;

[0014] Based on the body motion data and heart rate data of each segmented interval, the characteristic data sequence is obtained by data merging.

[0015] Preferably, the step of obtaining the characteristic data sequence by merging the body motion data and heart rate data of each segmented interval specifically includes:

[0016] Normalize the body motion data and heart rate data of each segmented interval to obtain the body motion value sequence and heart rate value sequence of each segmented interval;

[0017] Obtaining a feature value of each segmented interval based on the body motion value sequence and the heart rate value sequence;

[0018] The feature data sequence is formed based on the feature value of each segment interval.

[0019] In one technical solution of the above method, the currently stored average baseline value is an initial value of the average baseline value obtained in combination with medical-grade sleep characteristic data;

[0020] The initial value of the average baseline value obtained by combining the medical-grade sleep characteristic data specifically includes:

[0021] Acquire historical sleep characteristic data of a preset sleep duration, and perform data preprocessing based on the historical sleep characteristic data to obtain a characteristic data sequence;

[0022] Obtaining a reference value; the reference value is a proportion of deep sleep obtained by analyzing medical-grade sleep characteristic data based on the preset sleep duration;

[0023] An initial value of the average baseline value is determined according to a preset benchmark value, the characteristic data sequence and the reference value.

[0024] The determining of the initial value of the average baseline value according to the preset benchmark value, the characteristic data sequence and the reference value specifically includes:

[0025] Counting and obtaining the number of characteristic values ​​in the characteristic data sequence that are smaller than the preset reference value, and obtaining a current deep sleep period proportion value based on the number of characteristic values ​​and the number of preset segmented intervals;

[0026] The preset reference value is adjusted based on the current deep sleep period ratio value and the reference value until the difference between the current deep sleep period ratio value obtained based on the adjusted preset reference value and the reference value is less than a preset value, and an initial value of the average baseline value is determined based on the adjusted preset reference value.

[0027] Preferably, the above-mentioned identification and obtaining of the deep sleep period in the sleep period to be measured based on the average baseline value and the characteristic data sequence is specifically as follows: statistically obtaining the number of characteristic values ​​in the characteristic data sequence that are smaller than the average baseline value, and obtaining the current deep sleep period proportion value based on the number of characteristic values ​​and the preset number of segmented intervals; and identifying and obtaining the deep sleep period in the sleep period to be measured based on the current deep sleep period proportion value.

[0028] In one technical solution of the above method, the currently stored average baseline value is an updated average baseline value;

[0029] The method further includes the step of updating the currently stored average baseline value, specifically as follows:

[0030] If the current deep sleep period proportion value corresponding to the deep sleep period in the sleep period to be tested is identified to be outside the preset value range, the average baseline value is adjusted based on the current deep sleep period proportion value and the preset value range until the current deep sleep period proportion value obtained based on the adjusted average baseline value does not exceed the preset value range, and the currently stored average baseline value is updated with the average baseline value obtained based on the adjustment.

[0031] Preferably, the method further includes: a step of performing data continuity processing on the identified deep sleep period in the sleep period to be tested, specifically as follows: if the non-deep sleep period between two adjacent deep sleep periods in the sleep period to be tested is less than a preset time length, the non-deep sleep period is re-identified as a deep sleep period.

[0032] In a second aspect, the present invention provides a control device comprising a processor and a storage device, wherein the storage device is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the method described in any one of the technical solutions of the method for identifying deep sleep periods in the sleep period.

[0033] In a third aspect, the present invention provides a computer-readable storage medium storing a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute the method described in any one of the technical solutions of the method for identifying deep sleep periods in sleep periods.

[0034] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0035] In implementing the technical solution of the present invention, data preprocessing is first performed based on the acquired user sleep characteristic data to obtain a characteristic data sequence; the deep sleep period in the sleep period to be measured is identified based on the currently stored average baseline value and the characteristic data sequence; wherein the initial value of the average baseline value is obtained in combination with medical-grade sleep characteristic data, and the characteristic data sequence is obtained based on the combined result of body movement data and heart rate data. The deep sleep period identified by the solution of the present invention can be closer to the sleep staging detection result of medical-grade monitoring equipment, so the solution of the present invention can effectively improve the accuracy of identifying the deep sleep period in sleep staging. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The disclosure of the present invention will be more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Furthermore, similar numbers in the drawings represent similar components, wherein:

[0037] FIG1 is a flow chart of a method for obtaining an initial value of an average baseline value by combining medical-grade sleep characteristic data according to one embodiment of the present invention;

[0038] FIG2 is a schematic flow chart of specific implementation steps of step S13 shown in FIG1 ;

[0039] FIG3 is a flowchart showing the main steps of a method for identifying a deep sleep period in a sleep period according to an embodiment of the present invention;

[0040] FIG4 is a schematic flow chart of specific implementation steps of step S22 shown in FIG3 ;

[0041] FIG5 is a schematic block diagram of the main structure of a deep sleep period identification device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] Some embodiments of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0043] In the description of the present invention, "module" and "processor" may include hardware, software, or a combination of both. A module may include hardware circuitry, various suitable sensors, communication ports, and memory. It may also include software components, such as program code, or a combination of software and hardware. A processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor may be implemented in software, hardware, or a combination of both. Non-transitory computer-readable storage media include any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, and the like. The term "A and / or B" refers to all possible combinations of A and B, such as only A, only B, or both A and B. The terms "at least one of A or B" or "at least one of A and B" have similar meanings to "A and / or B" and may include only A, only B, or both A and B. The singular forms "one" and "the" may also include the plural forms.

[0044] In one application scenario of the present invention, the deep sleep period is identified by a retrospective sleep staging method, that is, the staging is performed after obtaining complete sleep monitoring data. The sleep staging identification based on historical data is more accurate, and the staging results can be used to display a sleep report to the user.

[0045] The method for identifying deep sleep within a sleep period provided by an embodiment of the present invention first requires obtaining an initial average baseline value based on medical-grade sleep characteristic data and storing the initial value in a sleep monitoring device. When a user first uses the sleep monitoring device, the average baseline value obtained will be the pre-stored initial value. For example, the sleep monitoring device can be a smart bracelet, smart watch, or sleep belt.

[0046] Referring to FIG. 1 , FIG. 1 is a main step flow chart of obtaining an initial value of an average baseline value by combining medical-grade sleep characteristic data provided by an embodiment of the present invention, as shown in FIG. 1 , mainly including the following steps S11 to S13 .

[0047] Step S11: Acquire historical sleep characteristic data of a preset sleep duration, and perform data preprocessing based on the historical sleep characteristic data to obtain a characteristic data sequence;

[0048] In this embodiment, the historical sleep characteristic data includes heart rate data and body movement data. The characteristic data sequence obtained by performing data preprocessing based on the heart rate data and body movement data can be specifically as follows:

[0049] First, according to the preset sleep duration and the preset number of segmented intervals, the body movement data and heart rate data are divided to obtain body movement data and heart rate data of multiple segmented intervals, and the body movement data and heart rate data of each segmented interval correspond one to one.

[0050] For example, the preset sleep time is 9 hours. Taking 200 lengths for each hour as an example, it is equivalent to obtaining body movement data and heart rate data with a data length of 1800. According to the preset number of segmented intervals of 100, 100 segmented intervals of body movement data and 100 segmented intervals of heart rate data can be obtained. The length of each segmented interval is 18. For example, each segmented interval can be represented as segmented interval 1, segmented interval 2, segmented interval 3,..., segmented interval 100.

[0051] Secondly, normalizing the body motion data and heart rate data of each segmented interval to obtain a body motion value sequence and a heart rate value sequence for each segmented interval; obtaining a feature value for each segmented interval based on the body motion value sequence and the heart rate value sequence, and forming the feature data sequence based on the feature value for each segmented interval;

[0052] It is understandable that, since the heart rate value during sleep usually ranges from 45 to 80, the heart rate values ​​that are equal to 0 or too low in the heart rate data of each segmented interval can be first eliminated, and then the heart rate data can be normalized.

[0053] For example, the body movement value sequence and heart rate value sequence of the segmented interval 1 after normalization can be expressed as [body movement 1, body movement 2, body movement 3,…, body movement 18] and [heart rate 1, heart rate 2, heart rate 3,…, heart rate 18], respectively. Similarly, the body movement value sequence and heart rate value sequence of the segmented interval 100 after normalization can be expressed as [body movement 1783, body movement 1784, body movement 1785,…, body movement 1800] and [heart rate 1783, heart rate 1784, heart rate 1785,…, heart rate 1800], respectively.

[0054] The representation of the characteristic value CombArray of each segment interval obtained based on the body motion value sequence NormBodyMotion(w1) and the heart rate value sequence NormHR(w1) is as follows:

[0055] CombArray(i)=sum(NormBodyMotion(w1)*NormHR(w1));

[0056] Where i=1:100, CombArray(i) represents the eigenvalue of the i-th segment interval, w1=1:18 corresponding to i=1, w1=19:37 corresponding to i=1,…, w1=1783:1800 corresponding to i=100.

[0057] For example, the characteristic value corresponding to segment interval 1 is CombArray(1)=body movement 1*heart rate 1+body movement 2*heart rate 2…body movement 17*heart rate 17+body movement 18*heart rate 18.

[0058] Correspondingly, the feature data sequence formed based on the feature value of each segmented interval can be expressed as [CombArray(1), CombArray(2), ..., CombArray(100)].

[0059] Furthermore, when the preset sleep duration and the preset number of segmented intervals cannot be divided evenly, the data length of each segmented interval can be determined by rounding down. For example, after dividing according to the data length of each segmented interval being 18, if the data length in the last segmented interval is less than 18, the data in the previous segmented interval of the last segmented interval will replace the current data of the last segmented interval.

[0060] Step S12: Obtaining a reference value; the reference value is a deep sleep period ratio obtained by analyzing medical-grade sleep characteristic data based on the preset sleep duration;

[0061] In this embodiment, medical-grade sleep characteristic data refers to sleep characteristic data obtained by collecting electrophysiological signals using medical-grade collection equipment such as polysomnography (PSG), which can be referred to as PSG data. Similarly, this sleep characteristic data includes body movement data and heart rate data.

[0062] The deep sleep percentage obtained by analyzing the medical-grade sleep characteristic data based on the preset sleep duration can be directly obtained using existing sleep staging methods based on PSG data, without limitation. For example, based on PSG data and existing sleep staging models, the deep sleep percentage of a sleep period can be directly obtained. If the obtained deep sleep percentage is 36%, the reference value is taken as 36%.

[0063] Step S13: determining an initial value of the average baseline value according to a preset benchmark value, the characteristic data sequence and the reference value.

[0064] In one embodiment, the specific implementation of this step is shown in FIG2 , including the following steps S131 and S132 .

[0065] Step S131: Counting and obtaining the number of characteristic values ​​in the characteristic data sequence that are smaller than the preset reference value, and obtaining the current deep sleep period proportion based on the number of characteristic values ​​and the number of preset segment intervals;

[0066] For example, the preset baseline value is 400. The number of eigenvalues ​​in the feature data sequence whose eigenvalue CombArray(i) is less than 400 is counted. The preset number of segmented intervals is the total number of eigenvalues ​​in the feature data sequence. Therefore, the current deep sleep percentage can be calculated based on the ratio of the number of all eigenvalues ​​less than the preset baseline value to the number of preset intervals. For example, if the number of eigenvalues ​​less than the preset baseline value of 400 is 20, and the preset number of intervals is 100, the current deep sleep percentage is 20%.

[0067] Step S132: Adjust the preset benchmark value based on the current deep sleep period ratio value and the reference value until the difference between the current deep sleep period ratio value obtained based on the adjusted preset benchmark value and the reference value is less than the preset value, and determine the initial value of the average baseline value based on the adjusted preset benchmark value.

[0068] For example, if the current deep sleep percentage is 20% and the reference value is 36%, the preset value indicates how close the current deep sleep percentage is to the reference value, with the minimum preset value being 0. If the current deep sleep percentage based on the adjusted preset reference value is adjusted from 20% to 36%, the adjusted preset reference value is used as the initial value of the average baseline value. For example, by adjusting the preset reference value from 400 to 480, the corresponding current deep sleep percentage is adjusted from 20% to 36%.

[0069] Furthermore, historical sleep characteristic data for multiple consecutive days can be obtained, and initial values ​​of multiple average baseline values ​​can be obtained based on the above steps S11 to S13. The final initial value to be stored can be determined based on the multiple initial values ​​obtained. For example, the three initial values ​​obtained based on data from three consecutive days are 480, 478, and 490 respectively. The final initial value to be stored can be obtained by calculating the mode or average.

[0070] 3, which is a flow chart illustrating the main steps of a method for identifying deep sleep periods according to an embodiment of the present invention. As shown in FIG3, the method for identifying deep sleep periods in the embodiment of the present invention mainly includes the following steps S21 to S23.

[0071] Step S21: Acquire the user's sleep characteristic data during the sleep period to be measured;

[0072] In this embodiment, the user sleep characteristic data of the sleep period to be measured is specifically the user sleep characteristic data of a preset sleep duration; the user sleep characteristic data at least includes body movement data and heart rate data.

[0073] Step S22: performing data preprocessing based on the user's sleep characteristic data to obtain a characteristic data sequence;

[0074] In a specific implementation, the specific implementation of this step is shown in FIG4 , and may include the following steps S221 and S222 .

[0075] Step S221: dividing the body movement data and heart rate data into a plurality of segmented intervals according to the preset sleep duration and the preset number of segmented intervals, wherein the body movement data and heart rate data of each segmented interval correspond one to one;

[0076] Step S222: Based on the body motion data and heart rate data of each segmented interval, the characteristic data sequence is obtained by data merging.

[0077] In this embodiment, the implementation of this step is as follows:

[0078] Normalize the body motion data and heart rate data of each segmented interval to obtain the body motion value sequence and heart rate value sequence of each segmented interval;

[0079] The characteristic value of each segment interval is obtained by multiplying the body movement value sequence and the heart rate value sequence;

[0080] The feature data sequence is formed based on the feature value of each segment interval.

[0081] Step S23: obtaining a currently stored average baseline value, and identifying and obtaining a deep sleep period in the sleep period to be measured based on the average baseline value and the characteristic data sequence.

[0082] It is understandable that when a user uses the deep sleep monitoring device for the first time, the currently stored average baseline value obtained is the initial value of the average baseline value determined according to the method shown in FIG1 .

[0083] Specifically in this embodiment, the number of characteristic values ​​in the characteristic data sequence that are smaller than the average baseline value is obtained by counting, and the current deep sleep period proportion value is obtained based on the number of characteristic values ​​and the preset number of segmented intervals; and the deep sleep period in the sleep period to be tested is identified based on the current deep sleep period proportion value.

[0084] In an application scenario of this embodiment, since according to objective laws, the reasonable range of the proportion of deep sleep period in the entire sleep period is 25% to 40%, after obtaining the deep sleep period proportion based on the above step S23, it also includes judging whether the obtained deep sleep period proportion is consistent with the above reasonable range. If it does not meet the above reasonable range, the average baseline value can be adaptively adjusted to regain a deep sleep period proportion that meets the above reasonable range. The specific adjustment method can refer to the description of the above step S132 and will not be repeated here.

[0085] Correspondingly, based on the above step S23, the step of updating the currently stored average baseline value is also included, which is specifically as follows:

[0086] If the current deep sleep period ratio corresponding to the deep sleep period in the sleep period to be measured is identified and obtained, it exceeds a preset value range, then the average baseline value is adjusted based on the current deep sleep period ratio and the preset value range until the current deep sleep period ratio obtained based on the adjusted average baseline value does not exceed the preset value range, and the currently stored average baseline value is updated with the average baseline value obtained based on the adjustment. For example, the preset value range is 25% to 40%. Accordingly, the currently stored average baseline value obtained in step S23 can be an updated average baseline value.

[0087] For example, taking the identified deep sleep period proportion of the sleep period to be tested as 60%, which exceeds the upper limit of the preset range value of 40%, the deep sleep period proportion can be optimized to less than or equal to 40% by adjusting the average baseline value, and the average baseline value corresponding to the optimized deep sleep period proportion is used as the new average baseline value to update the currently stored average baseline value.

[0088] Furthermore, after the above step S23, the step of performing data serialization processing on the identified deep sleep period in the sleep period to be measured may be further included, specifically as follows:

[0089] If the non-deep sleep period between two adjacent deep sleep periods in the sleep period to be tested is less than a preset time length, the non-deep sleep period is re-identified as a deep sleep period.

[0090] For example, the distribution of deep sleep periods identified in the sleep period to be measured is as follows: a 2-minute deep sleep period is followed by a 4-minute REM period, which is then followed by a 5-minute deep sleep period, with the preset duration being 10 minutes. Through data continuity processing, the 4-minute REM period between the two deep sleep periods can be re-identified and classified as a deep sleep period, resulting in a continuous 11-minute deep sleep period. This processing method can reduce fragmented deep sleep periods in the sleep period to be measured, achieving data continuity.

[0091] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0092] Furthermore, the present invention also provides a deep sleep period identification device.

[0093] Refer to Figure 5, which is a block diagram of the main structure of a deep sleep recognition device according to an embodiment of the present invention. As shown in Figure 5, the deep sleep recognition device in this embodiment of the present invention mainly includes a first acquisition module 301, a data processing module 302, a second acquisition module 303, and an identification module 304. In some embodiments, one or more of the first acquisition module 301, the data processing module 302, the second acquisition module 303, and the identification module 304 can be combined into a single module. Among them:

[0094] The first acquisition module 301 is used to obtain the user's sleep characteristic data during the sleep period to be measured;

[0095] The data processing module 302 is configured to perform data preprocessing based on the user sleep characteristic data acquired by the first acquisition module 301 to obtain a characteristic data sequence;

[0096] The second acquisition module 303 is used to obtain the currently stored average baseline value;

[0097] The identification module 304 is configured to identify and obtain the deep sleep period in the sleep period to be measured based on the average baseline value obtained by the second obtaining module 303 and the characteristic data sequence obtained by the data processing module 302 .

[0098] For the description of the specific implementation functions of each module in this embodiment, please refer to the description of steps S21 to S23 above.

[0099] The above-mentioned deep sleep identification device is used to execute the embodiment of the deep sleep identification method shown in Figure 3. The technical principles, technical problems solved, and technical effects produced by the two are similar. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process and related instructions of the deep sleep identification device can refer to the contents described in the embodiment of the deep sleep identification method, and will not be repeated here.

[0100] Those skilled in the art will appreciate that all or part of the processes in the method for implementing the above-mentioned embodiment of the present invention may also be accomplished by instructing the relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, it may implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal, and software distribution medium capable of carrying the computer program code. It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0101] Furthermore, the present invention also provides a control device. In one embodiment of the control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the method for identifying deep sleep periods in the sleep period of the above-mentioned method embodiment, and the processor can be configured to execute the program in the storage device, which includes but is not limited to a program for executing the method for identifying deep sleep periods in the sleep period of the above-mentioned method embodiment. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The control device can be a control device device formed by various electronic devices.

[0102] Furthermore, the present invention also provides a sleep monitoring device, including the above-mentioned control device. The sleep monitoring device can be a product such as a smart bracelet, a smart watch, and a sleep belt.

[0103] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the method of identifying deep sleep periods in sleep periods in the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned method of identifying deep sleep periods in sleep periods. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-transitory computer-readable storage medium.

[0104] Furthermore, it should be understood that since the configuration of each module is merely for the purpose of illustrating the functional units of the apparatus of the present invention, the physical devices corresponding to these modules may be the processor itself, or a portion of the software in the processor, a portion of the hardware, or a combination of software and hardware. Therefore, the number of modules in the figure is merely illustrative.

[0105] Those skilled in the art will appreciate that the various modules in the device can be adaptively split or merged. Such splitting or merging of specific modules does not cause the technical solution to deviate from the principles of the present invention. Therefore, the technical solutions after splitting or merging will fall within the scope of protection of the present invention.

[0106] The relevant user personal information that may be involved in the various embodiments of this application is strictly in accordance with the requirements of laws and regulations, follows the principles of legality, legitimacy and necessity, and is processed based on the reasonable purposes of the business scenarios to process the personal information that the user actively provides during the use of the product / service or is generated due to the use of the product / service, as well as the personal information obtained with the user's authorization.

[0107] The user personal information processed by this application will vary depending on the specific product / service scenario and should be based on the specific scenario in which the user uses the product / service. This may involve the user's sleep-related information. This application will treat the user's personal information and its processing with a high degree of diligence.

[0108] This application attaches great importance to the security of user personal information and has taken reasonable and feasible security protection measures that comply with industry standards to protect user information and prevent personal information from being accessed, disclosed, used, modified, damaged or lost without authorization.

[0109] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for identifying a deep sleep period during a sleep period, characterized in that: The method comprises: Obtaining the user's sleep characteristic data during the sleep period to be tested; Performing data preprocessing based on the user's sleep characteristic data to obtain a characteristic data sequence; The currently stored average baseline value is obtained, and the deep sleep period in the sleep period to be tested is identified based on the average baseline value and the characteristic data sequence.

2. The method according to claim 1, characterized in that: The user sleep characteristic data of the sleep period to be measured is specifically the user sleep characteristic data of the preset sleep duration; the user sleep characteristic data at least includes body movement data and heart rate data; The performing data preprocessing based on the user's sleep characteristic data to obtain a characteristic data sequence specifically includes: According to the preset sleep duration and the preset number of segmented intervals, the body movement data and the heart rate data are divided to obtain body movement data and heart rate data of multiple segmented intervals, and the body movement data and heart rate data of each segmented interval correspond to each other one by one; Based on the body motion data and heart rate data of each segmented interval, the characteristic data sequence is obtained by data merging.

3. The method according to claim 2, characterized in that: The step of obtaining the characteristic data sequence by merging the body motion data and the heart rate data of each segmented interval specifically includes: Normalizing the body motion data and heart rate data of each segmented interval to obtain a body motion value sequence and a heart rate value sequence of each segmented interval; Obtaining a characteristic value of each segmented interval based on the body movement value sequence and the heart rate value sequence; The feature data sequence is formed based on the feature value of each segment interval.

4. The method according to claim 1, characterized in that: The currently stored average baseline value is an initial value of the average baseline value obtained in combination with the medical-grade sleep characteristic data; The initial value of the average baseline value obtained by combining the medical-grade sleep characteristic data specifically includes: Acquire historical sleep characteristic data of a preset sleep duration, and perform data preprocessing based on the historical sleep characteristic data to obtain a characteristic data sequence; Obtain a reference value; the reference value is a deep sleep period ratio obtained by analyzing medical-grade sleep characteristic data based on the preset sleep duration; An initial value of the average baseline value is determined according to a preset benchmark value, the characteristic data sequence and the reference value.

5. The method according to claim 4, characterized in that The determining of the initial value of the average baseline value according to the preset benchmark value, the characteristic data sequence and the reference value specifically includes: Counting and obtaining the number of characteristic values ​​in the characteristic data sequence that are smaller than the preset reference value, and obtaining the current deep sleep period proportion value based on the number of characteristic values ​​and the number of preset segment intervals; The preset benchmark value is adjusted based on the current deep sleep period proportion value and the reference value until the difference between the current deep sleep period proportion value obtained based on the adjusted preset benchmark value and the reference value is less than the preset value, and the initial value of the average baseline value is determined according to the adjusted preset benchmark value.

6. The method according to claim 1, characterized in that The method of identifying and obtaining the deep sleep period in the sleep period to be tested based on the average baseline value and the characteristic data sequence is specifically as follows: statistically obtaining the number of characteristic values ​​in the characteristic data sequence that are smaller than the average baseline value, and obtaining the current deep sleep period proportion value based on the number of characteristic values ​​and the preset number of segmented intervals; and identifying and obtaining the deep sleep period in the sleep period to be tested based on the current deep sleep period proportion value.

7. The method according to claim 1, characterized in that The currently stored average baseline value is an updated average baseline value; The method further includes the step of updating the currently stored average baseline value, specifically as follows: If the current deep sleep period proportion value corresponding to the deep sleep period in the sleep period to be tested is identified to exceed the preset value range, the average baseline value is adjusted based on the current deep sleep period proportion value and the preset value range until the current deep sleep period proportion value obtained based on the adjusted average baseline value does not exceed the preset value range, and the currently stored average baseline value is updated with the average baseline value obtained after the adjustment.

8. The method according to claim 1, characterized in that The method further includes: a step of performing data continuity processing on the identified deep sleep period in the sleep period to be tested, which is specifically as follows: If the non-deep sleep period between two adjacent deep sleep periods in the sleep period to be tested is less than a preset time length, the non-deep sleep period is re-identified as a deep sleep period.

9. A control device, comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and executed by the processor to execute the method for identifying a deep sleep period among sleep periods according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the method for identifying a deep sleep period among sleep periods according to any one of claims 1 to 8.

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