Sleep monitoring method and system

Abstract

The application discloses a sleep monitoring method and system, the method comprising: collecting a processing air pressure signal segment of an air bag mattress and calculating a corresponding standard deviation; when the standard deviation is less than a preset first threshold, calculating a peak point of the processing air pressure signal segment, judging a bed state of a user based on the peak point to determine whether the signal segment is qualified; otherwise, according to air pressure data in the processing air pressure signal segment, counting body movement in a sleep process; performing feature extraction processing on the qualified air pressure signal segment, obtaining a reconstructed respiratory signal waveform and calculating a human respiratory rate; and generating a sleep report based on the body movement in the sleep process and the human respiratory rate. The application realizes the determination of on / off bed of a user in a sleep process, the counting of body movement times and the monitoring of a human respiratory rate through air pressure changes on an air bag mattress. The monitoring mode does not affect the comfort of the user in the sleep process, and meanwhile, accurate and comprehensive sleep monitoring analysis is provided for the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sleep monitoring, in particular to a sleep monitoring method and system. BACKGROUND

[0002] Sleep plays an important role in human body, and monitoring sleep quality is crucial for maintaining health. Sleep monitoring can analyze sleep conditions, help identify factors affecting sleep and provide corresponding treatment to reduce the risk of respiratory events. Long-term continuous sleep monitoring can help identify abnormal conditions early, effectively prevent diseases through simple conditioning, improve health status, and avoid the occurrence of serious diseases. With increasing attention to healthy sleep, the market for smart mattresses is also expanding.

[0003] However, existing sleep monitoring technologies have some limitations. For example, a Chinese patent with publication number CN116763099A discloses an intelligent sleep posture recognition mattress system based on cECG, which can accurately recognize sleep posture, but requires the placement of contact-type electrocardiogram electrodes on the mattress, which affects the comfort of sleep and the ease of use of the device. A Chinese patent with publication number CN117322837A discloses a sleep monitoring method and air chamber mattress, which analyzes the body movement frequency of the user through the air chamber pressure change of the air chamber mattress, and then monitors the sleep state of the user, but does not further obtain physiological parameter information such as the respiration rate of the user during sleep, thereby limiting the comprehensiveness of the monitoring effect. SUMMARY

[0004] The present application provides a sleep monitoring method and system to solve the technical problem that existing sleep monitoring technologies cannot provide comfortable and comprehensive sleep monitoring services for users.

[0005] The first aspect of the present application provides a sleep monitoring method, the method comprising:

[0006] Real-time acquisition of an initial air pressure signal of an air chamber mattress, preprocessing of the acquired initial air pressure signal to obtain a processed air pressure signal;

[0007] Caching of air pressure data of a plurality of processed air pressure signals to obtain a processed air pressure signal segment, calculation of a standard deviation corresponding to the air pressure data in the processed air pressure signal segment;

[0008] When the standard deviation is less than a preset first threshold, calculating a peak point corresponding to the air pressure data in the processed air pressure signal segment, judging the bed state of the user based on the peak point, and determining whether the cached processed air pressure signal segment is qualified according to the bed state; when the standard deviation is not less than the preset first threshold, statistically analyzing the body movement during sleep according to the air pressure data in the processed air pressure signal segment;

[0009] cache the air pressure data of the plurality of qualified processing air pressure signal segments to obtain a qualified air pressure signal segment; perform feature extraction processing on the qualified air pressure signal segment to obtain a reconstructed respiratory signal waveform, and calculate a human respiratory rate based on the reconstructed respiratory signal waveform;

[0010] generate a sleep report based on the counted body movement during sleep and the calculated human respiratory rate.

[0011] Specifically, the step of collecting the initial air pressure signal of the air bag mattress in real time and preprocessing the collected initial air pressure signal to obtain a processing air pressure signal includes:

[0012] collecting the initial air pressure signal of the air bag mattress in real time;

[0013] performing analog-to-digital conversion on the initial air pressure signal to obtain a digital initial air pressure signal;

[0014] obtaining a processing air pressure signal by performing noise reduction processing on the digital initial air pressure signal.

[0015] Specifically, the step of calculating the peak value point corresponding to the air pressure data in the processing air pressure signal segment and determining the bed state of the user based on the peak value point when the standard deviation is less than the preset first threshold, and determining whether the cached processing air pressure signal segment is qualified according to the bed state includes:

[0016] When the standard deviation is less than the preset first threshold, the peak value point corresponding to the air pressure data in the processing air pressure signal segment is calculated, and it is determined that the user is in a bed state or a bed leaving state based on the peak value point.

[0017] If the user is in a bed state, it is determined that the cached processing air pressure signal segment is qualified.

[0018] If the user is in a bed leaving state, it is determined that the cached processing air pressure signal segment is unqualified.

[0019] Specifically, the step of calculating the peak value point corresponding to the air pressure data in the processing air pressure signal segment and determining the bed state of the user based on the peak value point when the standard deviation is less than the preset first threshold includes:

[0020] When the standard deviation is less than the preset first threshold, performing Fourier transform on the air pressure data in the processing air pressure signal segment to obtain a frequency spectrum graph of the processing air pressure signal segment;

[0021] extracting the peak value point from the preset frequency range in the frequency spectrum graph of the processing air pressure signal segment to determine the target peak value point of the preset frequency range;

[0022] When the target peak point exceeds a preset second threshold, it is determined that the user is in a bed state, otherwise, it is determined that the user is in an off-bed state.

[0023] Specifically, when the standard deviation is not less than a preset first threshold, the step of counting body movement during sleep according to the air pressure data in the processed air pressure signal segment comprises:

[0024] When the standard deviation is not less than a preset first threshold, the absolute value of the difference between the initial air pressure data and the tail air pressure data in the processed air pressure signal segment is calculated.

[0025] If the absolute value is in a preset interval, it is determined as a body movement event.

[0026] Specifically, the step of performing feature extraction processing on the qualified air pressure signal segment to obtain a reconstructed respiratory signal waveform, and calculating the human respiratory rate based on the reconstructed respiratory signal waveform comprises:

[0027] Discrete wavelet transform is performed on the qualified air pressure signal segment to obtain qualified air pressure signal small segments of different frequency bands;

[0028] The qualified air pressure signal small segments of low frequency bands are selected for wavelet reconstruction to obtain a reconstructed respiratory signal waveform;

[0029] The human respiratory rate is calculated based on the reconstructed respiratory signal waveform.

[0030] Specifically, the step of calculating the human respiratory rate based on the reconstructed respiratory signal waveform comprises:

[0031] A plurality of maximum values are sequentially screened out in the reconstructed respiratory signal waveform with normal respiratory cycles to determine a plurality of wave peaks of the reconstructed respiratory signal waveform;

[0032] The time intervals between adjacent wave peaks are calculated, and a plurality of effective time intervals are determined from the time intervals between adjacent wave peaks based on a preset time value, so as to determine effective maximum values;

[0033] The human respiratory rate is calculated based on the number of effective maximum values.

[0034] The second aspect of the present application provides a sleep monitoring system, the system comprises:

[0035] An air pressure signal acquisition module is configured to acquire an initial air pressure signal of an air bag mattress in real time.

[0036] A signal preprocessing module is configured to preprocess the acquired initial air pressure signal to obtain a processed air pressure signal.

[0037] The sleep condition monitoring module is configured to cache air pressure data of a plurality of processed air pressure signals to obtain a processed air pressure signal segment, calculate a standard deviation corresponding to the air pressure data in the processed air pressure signal segment, when the standard deviation is less than a preset first threshold, calculate a peak point corresponding to the air pressure data in the processed air pressure signal segment, determine a bed state of a user based on the peak point, and determine whether the cached processed air pressure signal segment is qualified according to the bed state, and when the standard deviation is not less than the preset first threshold, count body movement during sleep according to the air pressure data in the processed air pressure signal segment.

[0038] The respiration rate monitoring module is configured to cache air pressure data of a plurality of qualified processed air pressure signal segments to obtain a qualified air pressure signal segment, perform feature extraction processing on the qualified air pressure signal segment to obtain a reconstructed respiration signal waveform, and calculate a human respiration rate based on the reconstructed respiration signal waveform.

[0039] The sleep report generation module is configured to generate a sleep report based on the counted body movement during sleep and the calculated human respiration rate.

[0040] The third aspect of the present application provides a computer device including a memory and a processor, the memory stores a computer program, and the processor implements the steps of the sleep monitoring method according to any one of the above aspects when executing the computer program.

[0041] The fourth aspect of the present application provides a computer readable storage medium having a computer program stored thereon, and the computer program implements the steps of the sleep monitoring method according to any one of the above aspects when executed by a processor.

[0042] As can be seen from the above technical solutions, the present application has the following advantages:

[0043] The present application provides a sleep monitoring method and system, wherein the method comprises: collecting an initial air pressure signal of an air bag mattress in real time, preprocessing the collected initial air pressure signal to obtain a processed air pressure signal; caching air pressure data of a plurality of processed air pressure signals to obtain a processed air pressure signal segment, calculating a standard deviation corresponding to the air pressure data in the processed air pressure signal segment; when the standard deviation is less than a preset first threshold, calculating a peak point corresponding to the air pressure data in the processed air pressure signal segment, determining a bed state of a user based on the peak point, and determining whether the cached processed air pressure signal segment is qualified according to the bed state; when the standard deviation is not less than the preset first threshold, counting body movement during sleep according to the air pressure data in the processed air pressure signal segment; caching air pressure data of a plurality of qualified processed air pressure signal segments to obtain a qualified air pressure signal segment; performing feature extraction processing on the qualified air pressure signal segment to obtain a reconstructed respiration signal waveform, and calculating a human respiration rate based on the reconstructed respiration signal waveform; and generating a sleep report based on the counted body movement during sleep and the calculated human respiration rate.

[0044] The present application realizes the in-bed / out-of-bed determination of a user during sleep, the counting of body movement times and the monitoring of human respiratory rate through the air pressure change on the air bag mattress, which does not affect the comfort of the user during sleep, and provides accurate and comprehensive sleep monitoring analysis for the user, thereby solving the technical problem that the existing sleep monitoring technology fails to provide comfortable and comprehensive sleep monitoring service for the user. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0046] Figure 1 A step flow chart of a sleep monitoring method provided by the embodiment of the present application;

[0047] Figure 2 A structural block diagram of a sleep monitoring system provided by the embodiment of the present application;

[0048] Figure 3 A specific working flow chart of a sleep condition monitoring module provided by the embodiment of the present application;

[0049] Figure 4 A specific working flow chart of a respiratory rate monitoring module provided by the embodiment of the present application;

[0050] Figure 5 Typical air pressure waveform diagrams of the in-bed and out-of-bed conditions provided by the embodiment of the present application;

[0051] Figure 6 Air pressure segment spectrum comparison diagrams of the in-bed and out-of-bed conditions provided by the embodiment of the present application;

[0052] Figure 7 Respiratory signal waveform diagrams before and after the processing of the respiratory rate monitoring module provided by the embodiment of the present application. DETAILED DESCRIPTION

[0053] The embodiment of the present application provides a sleep monitoring method and system, which is used to solve the technical problem that the existing sleep monitoring technology fails to provide comfortable and comprehensive sleep monitoring service for the user.

[0054] In order to make the application purposes, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the following described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0055] Please refer to Figure 1 The first aspect of the present application provides a sleep monitoring method, the method comprising:

[0056] Step 101, collecting the initial air pressure signal of the air bag mattress in real time, and preprocessing the collected initial air pressure signal to obtain a processed air pressure signal.

[0057] It should be noted that when the user moves on the air bag mattress, the air bag in the air bag mattress will be subjected to corresponding pressure, resulting in a change in the internal air pressure. By collecting the initial air pressure signal of the air bag mattress, the user's on / off bed determination, body movement frequency statistics and human respiratory rate monitoring during the sleep process are realized. This monitoring method will not affect the comfort of the user during the sleep process.

[0058] It can be understood that the air bag mattress can be a single bed or a double bed. If it is a double bed, the air bags on the left and right sides of the mattress are not connected, so as to avoid confusion of the collected air pressure data and thus avoid affecting the subsequent sleep monitoring effect.

[0059] In this step, the preprocessing process includes: performing analog-digital conversion on the initial air pressure signal by an analog-digital converter to obtain a digital initial air pressure signal; and performing noise reduction processing such as wave trapping and filtering on the digital initial air pressure signal by a digital filter to obtain a processed air pressure signal, thereby improving the signal-to-noise ratio of the signal to highlight the features of the required effective signal.

[0060] Step 102, buffering the air pressure data of a plurality of processed air pressure signals to obtain a processed air pressure signal segment, and calculating the standard deviation corresponding to the air pressure data in the processed air pressure signal segment.

[0061] It should be noted that this step uses a sliding window method for data buffering, and each time the cache window is updated by moving a half window size step, that is, after each movement, the window will include half of the old data and half of the new data in the previous window. In this way, the data utilization rate and the result accuracy can be improved.

[0062] For example, the time window can be set as 30 seconds, and the first time the pressure data of the processed air pressure signal is cached for 30 seconds. The next time the pressure data is cached, the first 15 seconds of the time window caches half of the processed air pressure signal obtained by the first caching, and the last 15 seconds caches the pressure data of the processed air pressure signal collected at the current time. The same process is repeated.

[0063] In this step, the standard deviation of the pressure data in the calculated processed air pressure signal segment is used as the execution judgment basis of step 103, which can quickly and sensitively capture the change of the user's action.

[0064] In step 103, when the standard deviation is less than the first preset threshold, the peak point corresponding to the pressure data in the processed air pressure signal segment is calculated, the bed state of the user is judged based on the peak point, and whether the cached processed air pressure signal segment is qualified is judged according to the bed state. When the standard deviation is not less than the first preset threshold, the body movement during sleep is counted according to the pressure data in the processed air pressure signal segment.

[0065] Please refer to Figure 3 When the standard deviation is less than the first preset threshold, it indicates that the user has not moved, and the quality of the processed air pressure signal segment needs to be judged. When the standard deviation is not less than the first preset threshold, it indicates that the user has moved, and it is necessary to further confirm whether the user has body movement to record the corresponding body movement.

[0066] It should be noted that the "action" mentioned above refers to the action of the user during normal activities, such as getting out of bed, turning over, lying down, and sitting up, etc. The "body movement" refers to the action of the user during sleep, such as turning over during sleep, etc.

[0067] Specifically, when the standard deviation is less than the first preset threshold, the peak point corresponding to the pressure data in the processed air pressure signal segment is calculated, the bed state of the user is judged based on the peak point, and whether the cached processed air pressure signal segment is qualified according to the bed state includes the following steps:

[0068] In step S10, when the standard deviation is less than the first preset threshold, the peak point corresponding to the pressure data in the processed air pressure signal segment is calculated, and the user is in the in-bed state or the out-of-bed state based on the peak point.

[0069] In step S10, first, the Fourier transform is performed on the air pressure data in the processing air pressure signal segment to obtain a frequency spectrum diagram of the processing air pressure signal segment; then, peak points are captured from a preset frequency band range in the frequency spectrum diagram of the processing air pressure signal segment to determine target peak points of the preset frequency band range; and finally, when the target peak points exceed a preset second threshold, it is determined that the user is in the in-bed state; otherwise, it is determined that the user is in the out-of-bed state.

[0070] It can be understood that the preset frequency band range can be a frequency band range of normal breathing, i.e., 0.1 Hz-0.9 Hz; if the target peak points of the preset frequency band range exceed the preset second threshold, it is determined that the signal segment has regular breathing activity, indicating that the user is in the in-bed state; otherwise, it is considered that the signal segment is irregular fluctuation, and it is determined to be the out-of-bed state. A typical comparison of air pressure fluctuation segments of the in-bed state and the out-of-bed state is shown in Figure 5 , and the corresponding comparison of frequency spectrum diagrams is shown in Figure 6 .

[0071] In step S11, if the user is in the in-bed state, the cached processing air pressure signal segment is determined to be qualified; if the user is in the out-of-bed state, the cached processing air pressure signal segment is determined to be unqualified.

[0072] It should be noted that the qualified processing air pressure signal segment is used for subsequent statistical analysis of the human respiratory rate, and the unqualified processing air pressure signal segment can be discarded.

[0073] Specifically, when the standard deviation is not less than the preset first threshold, the steps of counting the body movement during sleep according to the air pressure data in the processing air pressure signal segment include:

[0074] In step S20, when the standard deviation is not less than the preset first threshold, the absolute value of the difference between the initial air pressure data and the tail air pressure data in the processing air pressure signal segment is calculated.

[0075] It should be noted that when the standard deviation is not less than the preset first threshold, it indicates that the user has moved, and at this time, the air pressure values before and after the movement can be compared to determine whether the user has body movement.

[0076] In step S21, if the absolute value is within a preset interval, it is determined to be a body movement event.

[0077] It can be understood that the preset interval can be set according to the actual sleep situation of the user.

[0078] In step 104, the air pressure data of the plurality of qualified processing air pressure signal segments is cached to obtain a qualified air pressure signal segment; the qualified air pressure signal segment is subjected to feature extraction processing to obtain a reconstructed respiratory signal waveform, and the human respiratory rate is calculated based on the reconstructed respiratory signal waveform.

[0079] It can be understood that this step also adopts a sliding window manner to perform data caching, and the caching manner is the same as that in step 102. A time window of 30 seconds can be taken as a time window, which will not be repeated here.

[0080] Referring to FIG. 6, the method for calculating the human respiratory rate based on the reconstructed respiratory signal waveform includes the following steps. Figure 4 The step of performing feature extraction processing on the qualified air pressure signal segment to obtain a reconstructed respiratory signal waveform, and calculating the human respiratory rate based on the reconstructed respiratory signal waveform includes:

[0081] In step S20, the qualified air pressure signal segment is subjected to discrete wavelet transform to obtain qualified air pressure signal small segments of different frequency bands.

[0082] It can be understood that the discrete wavelet transform processing can decompose the qualified air pressure signal segment into high-frequency, medium-frequency and low-frequency qualified air pressure signal small segments.

[0083] In step S21, the qualified air pressure signal small segment of the low-frequency band is selected for wavelet reconstruction to obtain a reconstructed respiratory signal waveform.

[0084] It should be noted that in this step, the qualified air pressure signal small segment of the normal breathing frequency band (i.e., the low-frequency band) is selected for wavelet reconstruction to obtain a relatively smooth reconstructed respiratory signal waveform.

[0085] Referring to FIG. 6, the method for calculating the human respiratory rate based on the reconstructed respiratory signal waveform includes the following steps. Figure 7 , Figure 7 The waveform diagram before wavelet reconstruction and the waveform diagram after wavelet reconstruction are shown in FIG. 6. In the waveform diagram after wavelet reconstruction, the waveform is smoother than that in the waveform diagram before wavelet reconstruction. Figure 7

[0086] In step S22, the human respiratory rate is calculated based on the reconstructed respiratory signal waveform.

[0087] Specifically, a plurality of maximum values are sequentially selected in the reconstructed respiratory signal waveform in a normal breathing cycle to determine a plurality of wave peaks of the reconstructed respiratory signal waveform. The time interval between adjacent wave peaks is calculated, a plurality of effective time intervals are determined from the time interval between adjacent wave peaks based on a preset time value, to determine effective maximum values. The human respiratory rate is calculated based on the number of effective maximum values. The "human respiratory rate" refers to the number of breaths of a user in 1 minute.

[0088] ​For example, a normal breathing cycle can be taken as a screening time range for screening out the maximum values in the reconstructed breathing signal waveform, such as taking 2 seconds as a screening cycle, so as to screen out a plurality of maximum values and determine the wave peaks corresponding to the maximum values; the time interval t between adjacent wave peaks is calculated, and if the time interval t between certain adjacent wave peaks is less than a preset time value, it is indicated that the time interval t between the adjacent wave peaks is invalid, only the larger maximum value corresponding to the adjacent wave peaks is retained, and the smaller one is discarded, so as to determine a plurality of effective maximum values. Taking Figure 7 For example, if the reconstructed breathing signal waveform corresponds to 30 seconds of waveform data, the number of effective maximum values determined is multiplied by 2 to obtain the human respiratory rate; if the reconstructed breathing signal waveform corresponds to 20 seconds of waveform data, the number of effective maximum values determined is multiplied by 3 to obtain the human respiratory rate.

[0089] In step 105, a sleep report is generated based on the counted body movement during sleep and the calculated human respiratory rate.

[0090] The present application can count the sleep monitoring results such as human respiratory rate and body movement of a user during sleep every day, and generate a sleep report according to the monitored sleep monitoring results; wherein the sleep report can include sleep basic data, body movement analysis, human respiratory rate analysis, and sleep quality evaluation, etc., to provide comprehensive sleep monitoring analysis for the user.

[0091] The sleep basic data can include total sleep duration, sleep time and wake-up time; the body movement analysis can include the number of overall body movements, body movement frequency period and body movement type; the human respiratory rate analysis can include average respiratory rate, respiratory rate fluctuation and respiratory abnormality; and the sleep quality evaluation can include sleep continuity analysis, sleep depth analysis and respiratory quality analysis, etc. Then, the user can refer to the corresponding sleep report for sleep monitoring, adaptively adjust the sleep environment, improve the work and rest habits, and pay attention to respiratory health, etc., so as to further improve the sleep quality of the user.

[0092] Please refer to Figure 2 The second aspect of the present application further provides a sleep monitoring system, which comprises:

[0093] The air pressure signal acquisition module is configured to acquire the initial air pressure signal of the air bag mattress in real time.

[0094] The signal preprocessing module is configured to preprocess the acquired initial air pressure signal to obtain a processed air pressure signal.

[0095] The sleep condition monitoring module is configured to cache a plurality of air pressure data of the processed air pressure signals to obtain a processed air pressure signal segment, calculate a standard deviation corresponding to the air pressure data in the processed air pressure signal segment, and determine a bed state of the user based on a peak point of the processed air pressure signal segment when the standard deviation is less than a preset first threshold value.

[0096] The respiration rate monitoring module is configured to cache a plurality of air pressure data of the processed air pressure signals to obtain a processed air pressure signal segment, calculate a standard deviation corresponding to the air pressure data in the processed air pressure signal segment, and determine a bed state of the user based on a peak point of the processed air pressure signal segment when the standard deviation is less than a preset first threshold value.

[0097] The sleep report generation module is configured to generate a sleep report based on the statistical body movement during sleep and the calculated human respiration rate.

[0098] In the present application, the air pressure signal acquisition module is connected to the air bag in the air bag mattress through an air pipe; the air pressure signal acquisition module comprises a pressure sensor device, a capacitor coupling device and a signal conditioning circuit connected in sequence; the pressure sensor device is configured to convert the air pressure change of the air bag into an electrical signal; the capacitor coupling device is configured to transmit the electrical signal of the pressure sensor device to the signal conditioning circuit, realize non-contact signal transmission between the pressure sensor device and the signal conditioning circuit, and perform multiple amplification highlighting and noise suppression processing on the electrical signal sensed by the pressure sensor device, so as to capture the originally weak air pressure fluctuation change affected by physiological activity; the signal conditioning circuit is configured to convert the received electrical signal into an initial air pressure signal; wherein the initial air pressure signal is an analog signal.

[0099] The signal preprocessing module comprises an analog-to-digital converter and a digital filter; the analog-to-digital converter is configured to convert the initial air pressure signal into a digital initial air pressure signal; and the digital filter is configured to perform noise reduction processing on the digital initial air pressure signal to obtain a processed air pressure signal.

[0100] The third aspect of the present application further provides a computer device comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the sleep monitoring method as described above when executing the computer program.

[0101] The fourth aspect of the present application further provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the steps of the sleep monitoring method as described above.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0103] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic, and the division of the units is merely a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0104] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0105] In addition, each functional unit in the embodiments of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of software functional units.

[0106] When the integrated unit is implemented in the form of software functional units and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially or substantially, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various media that can store program codes.

[0107] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A sleep monitoring method, characterized in that: The method comprises: Collecting the initial air pressure signal of the airbag mattress in real time, and pre-processing the collected initial air pressure signal to obtain a processed air pressure signal; caching air pressure data of a plurality of processed air pressure signals to obtain processed air pressure signal segments, and calculating a standard deviation corresponding to the air pressure data in the processed air pressure signal segments; When the standard deviation is less than a preset first threshold, calculating the peak point corresponding to the air pressure data in the processed air pressure signal segment, determining the user's bed status based on the peak point, and determining whether the cached processed air pressure signal segment is qualified according to the bed status; when the standard deviation is not less than the preset first threshold, calculating the body movement during sleep based on the air pressure data in the processed air pressure signal segment; caching air pressure data of a plurality of qualified processed air pressure signal segments to obtain qualified air pressure signal segments; performing feature extraction processing on the qualified air pressure signal segments to obtain a reconstructed respiratory signal waveform, and calculating a human respiratory rate based on the reconstructed respiratory signal waveform; Generate a sleep report based on the statistical body movements during sleep and the calculated human breathing rate; When the standard deviation is less than a preset first threshold, calculating a peak point corresponding to the air pressure data in the processed air pressure signal segment, determining the bed status of the user based on the peak point, and determining whether the cached processed air pressure signal segment is qualified according to the bed status, includes: When the standard deviation is less than a preset first threshold, calculating a peak point corresponding to the air pressure data in the processed air pressure signal segment, and determining whether the user is in bed or out of bed based on the peak point; If the user is in bed, determining whether the cached processed air pressure signal segment is qualified; If the user is out of bed, the cached processed air pressure signal segment is determined to be unqualified; When the standard deviation is less than a preset first threshold, calculating a peak point corresponding to the air pressure data in the processed air pressure signal segment, and determining whether the user is in bed or out of bed based on the peak point, includes: When the standard deviation is less than a preset first threshold, performing Fourier transform on the air pressure data in the processed air pressure signal segment to obtain a frequency spectrum of the processed air pressure signal segment; Capturing peak points from a preset frequency band within the spectrum of the processed air pressure signal segment, and determining a target peak point within the preset frequency band; When the target peak point exceeds a preset second threshold, it is determined that the user is in bed; otherwise, it is determined that the user is out of bed; When the standard deviation is not less than a preset first threshold, the step of collecting statistics on body movements during sleep based on the air pressure data in the processed air pressure signal segment includes: When the standard deviation is not less than a preset first threshold, calculating the absolute value of the difference between the starting air pressure data and the ending air pressure data in the processed air pressure signal segment; If the absolute value is within the preset interval, it is determined to be a body movement event.

2. The sleep monitoring method according to claim 1, wherein: The steps of collecting the initial air pressure signal of the airbag mattress in real time and preprocessing the collected initial air pressure signal to obtain a processed air pressure signal include: Real-time collection of the initial air pressure signal of the airbag mattress; Performing analog-to-digital conversion on the initial air pressure signal to obtain a digital initial air pressure signal; A processed air pressure signal is obtained by performing noise reduction processing on the digital initial air pressure signal.

3. The sleep monitoring method according to claim 1, wherein: The step of performing feature extraction processing on the qualified air pressure signal segment to obtain a reconstructed breathing signal waveform, and calculating the human respiratory rate based on the reconstructed breathing signal waveform includes: Performing discrete wavelet transform on the qualified air pressure signal segments to decompose them into qualified air pressure signal segments of different frequency bands; Select a small segment of qualified air pressure signal in the low frequency band for wavelet reconstruction to obtain the reconstructed respiratory signal waveform; The human respiratory rate is calculated based on the reconstructed respiratory signal waveform.

4. The sleep monitoring method according to claim 3, characterized in that: The step of calculating the human respiratory rate based on the reconstructed respiratory signal waveform comprises: sequentially screening out a plurality of maximum values ​​in the reconstructed respiratory signal waveform in a normal respiratory cycle, and determining a plurality of peaks of the reconstructed respiratory signal waveform; Calculating the time intervals between adjacent peaks, determining a plurality of valid time intervals from the time intervals between adjacent peaks based on a preset time value, and thereby determining a valid maximum value; The human respiratory rate is calculated based on the number of effective maxima.

5. A sleep monitoring system, characterized in that: The system comprises: Air pressure signal acquisition module, used to collect the initial air pressure signal of the airbag mattress in real time; The signal preprocessing module is used to preprocess the collected initial air pressure signal to obtain a processed air pressure signal; a sleep status monitoring module configured to cache air pressure data of a plurality of processed air pressure signals to obtain processed air pressure signal segments, calculate the standard deviation corresponding to the air pressure data within the processed air pressure signal segments; when the standard deviation is less than a preset first threshold, calculate the peak point corresponding to the air pressure data within the processed air pressure signal segments, determine the user's bed status based on the peak point, and determine whether the cached processed air pressure signal segments are qualified based on the bed status; and when the standard deviation is not less than the preset first threshold, calculate body movement statistics during sleep based on the air pressure data within the processed air pressure signal segments; a respiratory rate monitoring module configured to cache air pressure data of a plurality of qualified processed air pressure signal segments to obtain qualified air pressure signal segments; perform feature extraction processing on the qualified air pressure signal segments to obtain a reconstructed respiratory signal waveform; and calculate the human respiratory rate based on the reconstructed respiratory signal waveform; A sleep report generation module is used to generate a sleep report based on the statistical body movement during sleep and the calculated human respiratory rate; When the standard deviation is less than a preset first threshold, calculating a peak point corresponding to the air pressure data in the processed air pressure signal segment, determining the bed status of the user based on the peak point, and determining whether the cached processed air pressure signal segment is qualified according to the bed status, includes: When the standard deviation is less than a preset first threshold, calculating a peak point corresponding to the air pressure data in the processed air pressure signal segment, and determining whether the user is in bed or out of bed based on the peak point; If the user is in bed, determining whether the cached processed air pressure signal segment is qualified; If the user is out of bed, the cached processed air pressure signal segment is determined to be unqualified; When the standard deviation is less than a preset first threshold, calculating a peak point corresponding to the air pressure data in the processed air pressure signal segment, and determining whether the user is in bed or out of bed based on the peak point, includes: When the standard deviation is less than a preset first threshold, performing Fourier transform on the air pressure data in the processed air pressure signal segment to obtain a frequency spectrum of the processed air pressure signal segment; Capturing peak points from a preset frequency band within the spectrum of the processed air pressure signal segment, and determining a target peak point within the preset frequency band; When the target peak point exceeds a preset second threshold, it is determined that the user is in bed; otherwise, it is determined that the user is out of bed; When the standard deviation is not less than a preset first threshold, the step of collecting statistics on body movements during sleep based on the air pressure data in the processed air pressure signal segment includes: When the standard deviation is not less than a preset first threshold, calculating the absolute value of the difference between the starting air pressure data and the ending air pressure data in the processed air pressure signal segment; If the absolute value is within the preset interval, it is determined to be a body movement event.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the sleep monitoring method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the sleep monitoring method according to any one of claims 1 to 4 are implemented.

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