CN116327113A

CN116327113A - Method and device for monitoring sleep and monitoring equipment

Info

Publication number: CN116327113A
Application number: CN202111546688.XA
Authority: CN
Inventors: 苑红伟; 许升; 赵永才; 李玉强; 虞朝丰; 崔鸿鹏; 吕守鹏; 刘超英
Original assignee: Qingdao Haier Smart Technology R&D Co Ltd; Haier Smart Home Co Ltd; Haier Shenzhen R&D Co Ltd
Current assignee: Qingdao Haier Smart Technology R&D Co Ltd; Haier Smart Home Co Ltd; Haier Shenzhen R&D Co Ltd
Priority date: 2021-12-16
Filing date: 2021-12-16
Publication date: 2023-06-27

Abstract

The application relates to the technical field of intelligent household appliances, and discloses a method for monitoring sleep, which comprises the following steps: acquiring a body movement signal of a user, and determining a sleep interval of the user according to a change trend of the body movement signal; according to the action intensity of the body movement signals of the user in the sleep interval, adjusting the threshold value of each action intensity for carrying out sleep stage; and backtracking the body movement signal of the user according to the adjusted action intensity threshold value, and correcting the sleep stage result. By processing the body movement signals in the sleep interval of the user, the action intensity threshold conforming to the sleep process of the current user is dynamically determined for sleep stage, so that when the body movement signals in the sleep of the user are traced back, the whole data can be observed and analyzed, and the sleep stage of each stage can be more accurately carried out. The application also discloses a device and monitoring equipment for monitoring sleep.

Description

Method and device for monitoring sleep and monitoring equipment

Technical Field

The application relates to the technical field of intelligent household appliances, and for example relates to a method and device for monitoring sleep and monitoring equipment.

Background

At present, along with the continuous development of intelligent household appliances, the sleeping environment of a user is more comfortable, and the sleeping quality also becomes an important evaluation index of people on the health condition of the user. In the related art, the sleep quality of healthy people is analyzed by detecting the sleep stage condition of a user in the sleep process, and corresponding sleep advice is given; alternatively, monitoring and corresponding instruction are performed for a user with sleep disorder.

In the related art, sleep staging is performed by detecting an activity micro-motion signal of a human body, i.e., a body movement signal, through a wearable device such as a bracelet, a sleep belt, etc. Typically, the user is judged to fall asleep, awake, and in sleep sessions, shallow and deep sleep. For example, a body movement signal of the user is acquired through the vibration sensor, so that whether the user is currently converted from the sleep period to the wake-up period is determined according to the magnitude relation between the body movement times and the sleep period threshold.

In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art:

the action amounts of different people in different sleep stages are different, so that the corresponding sleep stage is judged by comparing the body movement times with the fixed threshold value in real time, and the accuracy is low.

Disclosure of Invention

The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview, and is intended to neither identify key/critical elements nor delineate the scope of such embodiments, but is intended as a prelude to the more detailed description that follows.

The embodiment of the disclosure provides a method and a device for monitoring sleep and monitoring equipment, so as to improve the accuracy of identifying the sleep stage corresponding to a user according to a body movement signal.

In some embodiments, the method for monitoring sleep comprises: acquiring a body movement signal of a user, and determining a sleep interval of the user according to a change trend of the body movement signal; according to the action intensity of the body movement signals of the user in the sleep interval, adjusting the threshold value of each action intensity for carrying out sleep stage; and backtracking the body movement signal of the user according to the adjusted action intensity threshold value, and correcting the sleep stage result.

In some embodiments, the apparatus for monitoring sleep comprises a processor and a memory storing program instructions, the processor being configured to perform the above-described method for monitoring sleep when the program instructions are executed.

In some embodiments, the monitoring device comprises: body movement signal detection device and above-mentioned device that is used for monitoring sleep.

The method, the device and the monitoring equipment for monitoring sleep provided by the embodiment of the disclosure can realize the following technical effects:

by processing the body movement signals in the sleep interval of the user, the action intensity threshold conforming to the sleep process of the current user is dynamically determined for sleep stage, so that when the body movement signals in the sleep of the user are traced back, the whole data can be observed and analyzed, and the sleep stage of each stage can be more accurately carried out. Thus, compared with the sleep stage performed in real time, the occurrence of delay, misjudgment and other conditions can be reduced through backtracking data, and the accuracy of sleep stage identification is improved.

The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.

Drawings

One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which like reference numerals refer to similar elements, and in which:

FIG. 1 is a schematic view of an environment of use of a monitoring device provided in an embodiment of the present disclosure;

FIG. 2 is a flow chart of a method for monitoring sleep provided by an embodiment of the present disclosure;

FIG. 3 is monitoring data of a body movement signal during sleep of a user by a monitoring device in an embodiment of the present disclosure;

FIG. 4 is monitoring data of user movement signals acquired by a millimeter wave radar detection device in an embodiment of the present disclosure;

FIG. 5 is a schematic illustration of sleep stage results formed from the monitored data of FIG. 4;

FIG. 6 is a flow chart of another method for monitoring sleep provided by embodiments of the present disclosure;

FIG. 7 is a correspondence between heart rate signals and PSG sleep sessions;

fig. 8 is a schematic diagram of another apparatus for monitoring sleep provided by an embodiment of the present disclosure.

Detailed Description

So that the manner in which the features and techniques of the disclosed embodiments can be understood in more detail, a more particular description of the embodiments of the disclosure, briefly summarized below, may be had by reference to the appended drawings, which are not intended to be limiting of the embodiments of the disclosure. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may still be practiced without these details. In other instances, well-known structures and devices may be shown simplified in order to simplify the drawing.

The terms first, second and the like in the description and in the claims of the embodiments of the disclosure and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe embodiments of the present disclosure. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.

The term "plurality" means two or more, unless otherwise indicated.

In the embodiment of the present disclosure, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B.

The term "and/or" is an associative relationship that describes an object, meaning that there may be three relationships. For example, a and/or B, represent: a or B, or, A and B.

The term "corresponding" may refer to an association or binding relationship, and the correspondence between a and B refers to an association or binding relationship between a and B.

According to sleep structure staging theory, the human sleep process is generally divided into a rapid eye movement sleep period (rapid eye movement sleep, REM) and a non-rapid eye movement sleep period (non-rapid eye movement sleep, NREM). NREM is classified into a deep sleep stage and a shallow sleep stage, and is classified into one, two, three, four stages, etc. in medical treatment, the closer to four stages is the deep sleep stage, and the closer to one stage is the shallow sleep stage. After one person falls asleep, the person firstly enters first-stage sleep, then second-stage sleep, third-stage sleep and fourth-stage sleep occur, and then returns from deep to shallow sequentially. When the sleep is returned to the second stage, REM is usually entered, then another sleep period is entered again, from shallow to deep, from deep to shallow, and REM is used to make the sleep to and fro. The night sleep is about 4-5 sleep cycles, each cycle being about 90 minutes, progressively shorter from the first cycle back.

Fig. 1 is a schematic view of a usage environment of a monitoring device according to an embodiment of the present disclosure, and in conjunction with fig. 1, the usage scenario includes a monitoring device 100 and a home cloud platform 110 for communicating with the monitoring device.

The monitoring device 100 is used for monitoring sleeping conditions of a user, and may be an intelligent pillow, an intelligent bracelet, or other devices that contact the user, or may be other monitoring devices 100 that perform non-contact detection on the user. Typically, the monitoring device 100 may access a WiFi network in a home, and communicate with a control terminal such as a mobile phone, a cloud server, and the like. The user may also control the operation of the monitoring device 100 or view the monitoring data through the smartphone-side application.

The monitoring device 100 communicates with the home cloud platform 110 through a WiFi network, receives real-time status data of the monitoring device 100 for subscription by a big data platform and an application program service, and receives and issues running program instructions from other service servers, the big data platform, an application program end and an intelligent terminal.

In other implementation scenarios of the present solution, a terminal device may be further included, for communicating with the monitoring device 100 and/or the home cloud platform, where the terminal device refers to an intelligent device in a smart home application scenario, such as a smart phone, a wearable device, an intelligent mobile device, a virtual display device, etc., and may also be an intelligent home appliance, such as an intelligent refrigerator, an intelligent television, an intelligent washing machine, an intelligent air conditioner, an intelligent sound box, an intelligent lamp, an intelligent curtain, etc., or any combination thereof.

Here, the monitoring device 100 includes a body movement signal detection means. The body movement signal refers to a movement detection signal during sleep of the user. In the embodiment of the disclosure, the body movement signal detection device may be a millimeter wave radar device, and in other embodiments, the body movement signal detection device may also be a device capable of detecting and reflecting the movement of a human body, such as a gravitational accelerometer, a contact piezoelectric device, an infrared sensing device, and the like.

Fig. 2 is a schematic flow chart of a method for monitoring sleep, which is provided in an embodiment of the present disclosure and is applied to the above-mentioned monitoring device. The method for monitoring sleep can be executed by a processor of the monitoring device in the monitoring device, and can also be executed by a control end of the monitoring device, such as an operation panel, keys and the like; the method can also be executed in a server, such as a family cloud platform communicated with the monitoring equipment; it can also be executed at the terminal equipment, such as the control terminal of the intelligent household appliance and the APP of the intelligent mobile phone. In the embodiment of the present disclosure, a description will be given of a description of an embodiment with a processor of a monitoring device as an execution subject.

Step S201, a body movement signal of a user is obtained, and a sleep interval of the user is determined according to the change trend of the body movement signal.

Here, the body movement signal refers to an amount of movement of the user during the entire sleep, and generally includes a parameter value such as movement intensity and movement duration. The motion intensity refers to the variation amplitude of body motion, taking a millimeter wave radar device for acquiring a body motion signal as an example, the larger the motion amplitude is, the higher the motion intensity is, and the larger the signal intensity energy amplitude measured by the millimeter wave radar device is. Therefore, the action intensity can be judged through the change of different degrees of the energy amplitude of the data. The trend of the motion signal may include an increasing trend or a decreasing trend of the motion intensity of the motion signal.

The sleep interval refers to the length of time between the time when the user falls asleep and the time when he wakes up.

Optionally, determining the sleep interval of the user according to the change trend of the body movement signal includes:

determining sleeping time and waking time according to the change trend of the body movement signal;

and under the condition that the time length from the time of falling asleep to the time of waking is longer than the set sleep time length, determining the time length from the time of falling asleep to the time of waking as the sleep interval of the user.

Here, the set sleep time period is used to represent the set shortest sleep time period. The sleep time period may be set to a value of 3.5 hours to 4.5 hours, for example, 3.5 hours, 4 hours, or 4.5 hours. Thus, sleep stage can be carried out according to the actual body movement signal condition of the user in the sleep interval, and the accuracy of the recognition of the sleep stages of each stage is improved.

Alternatively, the obtaining of the body movement signal of the user may be performed in response to an operation instruction of the user, for example, the body movement signal of the user is obtained according to an operation instruction of the user to view the sleep quality; in some embodiments, it may also be performed in response to a sleep end instruction.

Step S202, according to the action intensity of the body movement signal of the user in the sleep interval, adjusting each action intensity threshold for carrying out sleep stage.

The amount of activity of the user varies from sleep stage to sleep stage. For example, the activity indexes of the user in different sleep stages are in a clear stage, a light sleep stage, a REM stage and a deep sleep stage from large to small. Here, by setting a plurality of action intensity thresholds for sleep stages, different sleep stages are distinguished according to the body movement signal of the user.

The action intensity thresholds used for sleep stage include a first action intensity threshold, a second action intensity threshold and a third action intensity threshold from low to high.

Further, the process of acquiring the reference value (i.e., the value before adjustment) of each action intensity threshold may be acquired through the user's historical sleep data.

Specifically, the reference value of each action intensity threshold value can be obtained as follows:

Obtaining a plurality of pieces of historical sleep monitoring data of monitoring equipment matched with current user information; each history sleep monitoring data comprises each sleep stage duration data of the user and body movement signal detection data under each sleep stage;

determining a body movement signal detection value of a user in the sleep stage according to the quotient of the body movement signal detection data and the sleep stage duration data;

an average value of body movement signal detection values in the same sleep stage is obtained as a reference value corresponding to an action intensity threshold value of the sleep stage, and stored in a database.

Thus, the sleep stage processing of the current user can be realized by calling the action intensity threshold value corresponding to the current user in the database.

In some embodiments, the reference value for each action intensity threshold may also be obtained from big data. For example, the average action intensity threshold value of the same region or the average action intensity threshold value of the users of the same age group is obtained as the reference value of the corresponding action intensity threshold value for the sleep stage processing of the current user.

Step S203, backtracking the body movement signal of the user according to the adjusted action intensity threshold value, and correcting the sleep stage result.

Therefore, by carrying out data processing on the body movement signals in the sleep interval of the user, the action intensity threshold conforming to the sleep process of the current user is dynamically determined and used for sleep stage, so that when the body movement signals in the sleep of the user are traced back, the whole data can be observed and analyzed, and the sleep stage of each stage can be more accurately carried out. Thus, compared with the sleep stage performed in real time, the occurrence of delay, misjudgment and other conditions can be reduced through backtracking data, and the accuracy of sleep stage identification is improved.

Further, the action duration threshold refers to a specific duration of sleep stage different from the user, including a first action duration threshold T1, a second action duration threshold T2, and a third action duration threshold T3 from short to long. The acquisition of each action duration threshold may be determined by an average of the action durations of the different sleep stages of the user.

For example, obtaining a plurality of pieces of historical sleep monitoring data of a monitoring device that match current user information; each historical sleep monitoring data includes a duration of action under each sleep stage of the user;

and determining an action duration threshold corresponding to the sleep stage according to the average value of the action durations of the same stage, and storing the action duration threshold in a database.

In some embodiments, the action duration thresholds may also be obtained from big data. For example, action duration thresholds of each stage in the user history sleep data of the same region, or action duration thresholds of each stage of the user of the same age group are acquired as corresponding action duration thresholds for sleep stage processing of the current user.

Optionally, the determining of the time to fall asleep includes:

obtaining a body movement signal after the action intensity of the body movement signal is reduced to be lower than the sleep threshold value;

determining the moment when the motion intensity of the body motion signal is reduced to be lower than the sleep-in threshold value as the sleep-in moment when the motion duration of the body motion signal is larger than the second motion duration threshold value and the average motion intensity in the motion duration is smaller than the first motion intensity threshold value;

wherein the second action duration threshold is less than the set duration.

Here, the first action intensity threshold is an action intensity threshold for performing sleep staging to infer whether to fall asleep based on the body movement characteristics of the user.

The sleep threshold is used for representing the condition that the action intensity is low after entering the sleep stage.

Generally, after sleep starts, the motion signal is stopped or reduced, and the number and intensity of body movements are reduced, so that data processing can be performed at the moment when the motion intensity of the body motion signal is reduced to be lower than the sleep threshold value, so as to determine whether the user falls asleep.

The average motion intensity over the motion duration is determined by the ratio of the sum of the motion intensities over the motion duration to the motion duration.

When the action duration of the body movement signal is greater than the second action duration threshold and the average action intensity in the action duration is less than the first action intensity threshold, the user is indicated to have more continuous activity, the activity amount in the action duration is smaller, and the condition of falling asleep stage is met.

Optionally, the determining of the awake time instant includes:

obtaining a body movement signal after the movement intensity of the body movement signal rises to a moment higher than the sleep threshold value;

determining the time when the motion intensity of the body motion signal rises to be higher than the sleep-in threshold as the waking time when the motion duration of the body motion signal is larger than the second motion duration threshold and smaller than the third motion duration threshold and the average motion intensity in the motion duration is larger than the third motion intensity threshold; or alternatively, the first and second heat exchangers may be,

determining the moment when the motion intensity of the body motion signal rises to be higher than the sleep-in threshold as the awake moment when the motion duration of the body motion signal is greater than the third motion duration threshold and the average motion intensity in the motion duration is greater than the second motion intensity threshold;

Wherein the second action duration threshold is less than the third action duration threshold; the second actuation strength threshold is less than the third actuation strength threshold.

Here, the third action intensity threshold value and the second action intensity threshold value are action intensity threshold values for performing sleep stage, and are used for estimating whether the user is awake or not according to the body movement characteristics of the user.

Generally, at the end of sleep, the number and intensity of the body movements of the user will increase, so that the data processing can be performed at the moment when the intensity of the movement of the body movement signal increases above the sleep threshold to determine whether the user is awake.

When the action duration of the user is greater than the second action duration threshold and less than the third action duration threshold, the activity behavior of the user has a continuous activity duration, and the average action intensity in the action duration is greater than the third action intensity threshold, which indicates that the activity amount in the action duration is greater and accords with the condition of the awake stage. When the action duration of the user is greater than the third action duration threshold, the activity behavior of the user has a more continuous activity duration, and in this case, the average action intensity of the user is greater than the second action intensity threshold, and the user has a certain activity amount, so that the user can meet the condition of the awake stage.

Fig. 3 is monitoring data of a body movement signal during sleep of a user. The frame is used for representing a sleeping interval of a user, one end of the frame is at a sleeping moment, and the other end of the frame is at a waking moment, so that the action intensity threshold can be adjusted through a user body movement signal in the sleeping interval. For specific adjustment, see the examples below.

According to the action intensity of the body movement signal of the user in the sleep interval, adjusting each action intensity threshold for carrying out sleep stage, comprising:

obtaining the average intensity of the user body movement signal in the sleep interval;

determining a corresponding adjusting factor according to the difference value between the average intensity and the set intensity threshold value;

and adjusting the action intensity thresholds according to the adjusting factors.

Because different people have different action amounts in different stages of sleep, backtracking data is needed, and the reference value of the action intensity threshold is adjusted to improve the accuracy of sleep stage identification.

The average intensity of the user body movement signal in the sleep interval can be obtained by the quotient of the sum of the action intensity data of the user in the sleep interval and the sleep interval duration. The average value is used for representing the actual activity behavior of the user in the sleeping process.

Here, the correspondence between the intensity difference value and the adjustment factor may be acquired through a data table. The data table is stored in a database, and after the current intensity difference value between the average intensity and the set intensity threshold value is obtained, the adjustment factor corresponding to the current intensity difference value can be obtained by inquiring the database.

Or the corresponding relation between the intensity difference value and the adjusting factor exists in the form of a control algorithm, wherein the intensity difference value is an input quantity, and the adjusting factor is an output quantity. Thus, after the current intensity difference value is obtained, the current intensity difference value is input into the control algorithm, and an adjusting factor corresponding to the current intensity difference value output by the control algorithm can be obtained, so that the adjustment of each action intensity threshold value is realized.

The control algorithm here may be a PID control algorithm, an LQR control algorithm, or other control algorithm with bias elimination.

Further, the adjustment factor and the intensity difference are in positive correlation. The absolute value range of the regulating factor is 5% -10%.

Then, the adjusted action intensity thresholds may be obtained as follows:

M'＝M×(1+m)

wherein M' is the adjusted action intensity threshold value, M is the reference value of the action intensity threshold value, and M is the adjusting factor.

Specifically, determining the corresponding adjustment factor according to the difference between the average intensity and the set intensity threshold value includes:

determining an adjustment factor for reducing each action intensity threshold when the difference between the average intensity and the set intensity threshold is less than 0;

when the difference between the average intensity and the set intensity threshold is greater than 0, an adjustment factor for increasing each action intensity threshold is determined.

Here, the adjustment direction of the action intensity threshold is determined according to the magnitude relation between the average intensity and the set intensity threshold; that is, the positive and negative values of the adjustment factors are determined based on the magnitude relation between the average intensity and the set intensity threshold.

Further, when the adjusting factor is used for reducing the threshold value of each action intensity, the value range of the adjusting factor is-10% -5%, and can be-10%, -8% or-5%; when the adjusting factors are used for improving the action intensity threshold values, the value range of the adjusting factors is 5% -10%, and can be 5%, 6%, 8% or 10%.

Therefore, the method and the device realize dynamic determination of each action intensity threshold for sleep stage according to the condition of body movement signal monitoring of the user in the sleep interval, so that different sleep stage judgment can be carried out according to individual differences, and the accuracy of identifying each sleep stage is improved.

In the embodiment of the disclosure, according to one or more parameters of the action intensity, the action duration and the action intensity change rate of the user body movement signal, the body movement signal monitoring data in the sleeping process of the user can be traced back to improve the accuracy of sleeping stage.

Here, the body movement signal is subjected to data processing by the operation intensity threshold value, the operation duration threshold value, and the operation intensity change rate threshold value.

The action intensity threshold comprises a first action intensity threshold M1, a second action intensity threshold M2 and a third action intensity threshold M3, wherein M1 is more than M2 and less than M3;

the action duration threshold comprises a first action duration threshold T1, a second action duration threshold T2 and a third action duration threshold T3, wherein T1 < T2 < T3;

the action intensity change rate threshold comprises a first action intensity change rate threshold K1, a second action intensity change rate threshold K2 and a third action intensity change rate threshold K3, wherein K1 is less than K2 and less than K3. The calculation mode of the action intensity change rate is obtained through the change amount of the action intensity in the action duration. And the action intensity change rate threshold value is obtained through the corresponding action intensity in a specific time period.

Specifically, table 1 shows a selection relationship between the action intensity change rate threshold and the sleep stage of the user in this embodiment, that is, the action intensity change rate threshold is determined by selecting a reference value according to the action intensity change amounts of the user in different sleep stages and the corresponding specific time periods.

Selection phase of specific duration	Selection of reference values for a specific duration
		Wakening	T3
Deep sleep	T1
		REM	T2
Shallow sleep	T1

TABLE 1

As shown in table 1, the corresponding action intensity change amount and different specific time periods can be determined according to different sleep stages, and used as the above action intensity change threshold value for the sleep stage processing of the current user.

Here, it is prescribed that the minimum T1 is selected from the deep sleep stage sleep data or the shallow sleep stage sleep data of the user in order to facilitate rapid judgment of REM stage when sleep staging is performed.

And then, according to the adjusted action intensity threshold, backtracking the body movement signal of the user, and correcting the sleep stage result, wherein the method comprises the following steps:

backtracking a body movement signal of a user according to the adjusted action intensity threshold value, and determining the starting moment of each sleep stage; the sleep stages include a shallow sleep stage, a deep sleep stage, and a rapid eye movement stage;

and reserving the starting time from the corresponding sleep stage to the next sleep stage to obtain a corrected sleep stage result.

Here, after judging each sleep stage, state retention is performed until the starting time of the next sleep stage is determined, and state switching is performed, so that a complete sleep stage result is formed.

Different sleep stages correspond to different judgment bases. For example, a user is considered to transition from a deep sleep phase or REM phase to a shallow sleep phase when the user's body movement signal appears to have a duration of movement T3 for a set duration of time T, and a higher intensity of movement within T (e.g., the average intensity of movement of the body movement signal within T3 is above a third intensity threshold), or the body movement signal appears to have a rate of change of movement intensity above a third intensity rate of movement. For another example, the user is considered to be transitioning from the light sleep phase or the deep sleep phase to the REM phase when the user's body movement signal appears to have a movement duration T2 (T2 is less than T3) within the set duration T and a medium movement intensity within T (e.g., the body movement signal has an average movement intensity within T2 that is higher than the second movement intensity threshold and less than the third movement intensity threshold), or the body movement signal appears to have a movement intensity rate of change that is greater than the second movement intensity rate of change and less than the third movement intensity rate of change. For another example, when the body movement signal of the user appears to have a shorter movement duration T1 (T1 is less than T2) within the set duration T and a lower movement intensity within T (e.g., the average movement intensity of the body movement signal within T1 is higher than the first movement intensity threshold and less than the second movement intensity threshold), or the body movement signal appears to have a movement intensity rate of change greater than the first movement intensity rate of change and less than the second movement intensity rate of change, the user is considered to be shifted from the light sleep stage or REM stage to the deep sleep stage.

Specifically, the determination of the starting moment of the shallow sleep phase comprises:

when the body movement signal of the user shows that the change rate of the movement intensity in the movement duration is smaller than K3 and larger than K2, determining the starting moment of the movement duration as the starting moment of the shallow sleep stage; or alternatively, the first and second heat exchangers may be,

when the next phase is the awake phase, when the user body movement signal shows that the movement duration is longer than T2 and smaller than T3, and when the average movement intensity in the movement duration is greater than the second movement intensity, the start time of the movement duration is determined as the start time of the shallow sleep phase.

The determination of the starting moment of the deep sleep phase comprises:

when the body movement signal of the user shows that the change rate of the action intensity in the action duration is smaller than K1 and the action intensity is smaller than M1, determining the starting moment of the action duration of the user as the starting moment of the deep sleep stage.

The determination of the starting moment of the REM phase comprises:

when the body movement signal of the user shows that the change rate of the movement intensity in the movement duration is smaller than K2 and larger than K1, and meanwhile, the movement intensity is smaller than M2 and larger than M1, the starting moment of the movement duration is determined to be the starting moment of the REM phase.

Fig. 4 is monitoring data of a user body movement signal acquired by the millimeter wave radar detection device during sleep of the user, wherein the abscissa is time and the ordinate is a body movement signal detection value.

Fig. 5 is a schematic diagram of sleep stage results formed from the monitored data of fig. 4. The upper part of fig. 5 is a real-time sleep stage result, the middle part is a retrospective sleep stage result, and the lower part is a medical-grade PSG (Polysomnography) sleep stage result.

Wherein the abscissa is time, and the ordinate is sleep stage result, and the sleep depth is represented from deep to shallow by the value from small to large, for example, 1=deep sleep stage, 2.5=shallow sleep stage, 4=rem stage, and 5=awake stage.

When the user judges deep sleep through the real-time sleep stage, although the motion state (action intensity) of the user is almost continuously fluctuated, the real-time stage algorithm cannot immediately judge that the user is deep sleep only through short-time data, and the user may have actions such as turning over and the like at the next moment, and the user is shallow sleep at the moment, so that larger delay and misjudgment exist when judging the sleep state.

By contrast, the retrospective sleep stage result has higher similarity with the PSG sleep stage result than the real-time sleep stage result, and is judged in deep sleep, shallow sleep, and stage between stages, especially at the stage moments (as outlined) between awake-shallow sleep stages. The backtracking sleep stage result is compared with the real-time sleep stage, so that the situations of delay, frequent switching in real time and the like do not exist, and the action intensity threshold value for the sleep stage is dynamically selected, so that the accuracy is higher.

Fig. 6 is a schematic flow chart of a method for monitoring sleep, which is provided in an embodiment of the present disclosure and is applied to the above-mentioned monitoring device. The method for monitoring sleep can be executed by a processor of the monitoring device in the monitoring device, and can also be executed by a control end of the monitoring device, such as an operation panel, keys and the like; the method can also be executed in a server, such as a family cloud platform communicated with the monitoring equipment; it can also be executed at the terminal equipment, such as the control terminal of the intelligent household appliance and the APP of the intelligent mobile phone. In the embodiment of the present disclosure, a description will be given of a description of an embodiment with a processor of a monitoring device as an execution subject.

Step S601, a body movement signal of a user is obtained, and a sleep interval of the user is determined according to the change trend of the body movement signal.

Step S602, according to the action intensity of the body movement signal of the user in the sleep interval, adjusting each action intensity threshold for carrying out sleep stage.

Step S603, backtracking the body movement signal of the user according to the adjusted action intensity threshold value, and correcting the sleep stage result.

Step S604, obtaining a heart rate signal of the user in a period corresponding to the shallow sleep stage and/or the rapid eye movement stage, and determining whether the current period is the rapid eye movement stage according to the change of the heart rate signal of the user.

Fig. 7 shows a correspondence between a heart rate signal and a PSG sleep session, and it can be seen that a change in the heart rate signal has a correspondence with a REM phase in the sleep session. The principle of its formation is that the autonomic nervous system of humans is divided into the sympathetic nervous system and the parasympathetic nervous system. Normally, when a person changes from a quiet state to an active state, the autonomic regulation nervous system can realize rapid rise of heart rate through the sympathetic nervous system, so that the person is in a 'spare combat' state at any time, and blood pressure, respiration and the like are also raised or accelerated; meanwhile, the parasympathetic nervous system of the human body plays roles of slowing down heart beat, lowering blood pressure, slowing down respiration and the like, and heart rate and the like can be reduced along with the change of the human body from dynamic to static. The sympathetic nervous system and the parasympathetic nervous system are synchronously coordinated to ensure that the functions of the human body are in a relative balance state. Therefore, when the user is in the shallow sleep stage or REM stage, the current period can be secondarily determined according to the heart rate signal, so that a more accurate monitoring result can be obtained.

Further, determining whether the current period is a REM phase according to the change of the heart rate signal of the user includes: when the heart rate signal of the user changes to be increased and then the trend is reduced in the current period, determining that the current period is a REM phase; or when the change rate of the heart rate signal of the user in the current period is firstly more than 0 and then is reduced to be less than 0, determining that the current period is a REM phase.

As shown in fig. 7, where the boxes show the corresponding positions of the REM phases, it can be seen that the trend of the heart rate corresponding to this phase is rising and then decreasing. Therefore, the relation between heart rate variation and REM stage can be utilized to further correct and trace back the result of sleep stage, and the accuracy of the sleep stage result is improved.

As shown in connection with fig. 8, an embodiment of the present disclosure provides an apparatus for monitoring sleep, including a processor (processor) 800 and a memory (memory) 801. Optionally, the apparatus may further comprise a communication interface (Communication Interface) 802 and a bus 803. The processor 800, the communication interface 802, and the memory 801 may communicate with each other via the bus 803. The communication interface 802 may be used for information transfer. The processor 800 may invoke logic instructions in the memory 801 to perform the method for monitoring sleep of the above-described embodiments.

Further, the logic instructions in the memory 801 described above may be implemented in the form of software functional units and sold or used as a separate product, and may be stored in a computer readable storage medium.

The memory 801 is a computer readable storage medium that may be used to store a software program, a computer executable program, and program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 800 executes functional applications and data processing by running program instructions/modules stored in the memory 801, i.e., implements the method for monitoring sleep in the above-described embodiments.

The memory 801 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the terminal device, etc. In addition, the memory 801 may include a high-speed random access memory, and may also include a nonvolatile memory.

The embodiment of the disclosure provides a monitoring device, which comprises a body movement signal detection device and the device for monitoring sleep.

Embodiments of the present disclosure provide a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for monitoring sleep.

The disclosed embodiments provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-described method for monitoring sleep.

The computer readable storage medium may be a transitory computer readable storage medium or a non-transitory computer readable storage medium.

Embodiments of the present disclosure may be embodied in a software product stored on a storage medium, including one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of a method according to embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium including: a plurality of media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or a transitory storage medium.

The above description and the drawings illustrate embodiments of the disclosure sufficiently to enable those skilled in the art to practice them. Other embodiments may involve structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in, or substituted for, those of others. Moreover, the terminology used in the present application is for the purpose of describing embodiments only and is not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a," "an," and "the" (the) are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, when used in this application, the terms "comprises," "comprising," and/or "includes," and variations thereof, mean that the stated features, integers, steps, operations, elements, and/or components are present, but that the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof is not precluded. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method or apparatus comprising such elements. In this context, each embodiment may be described with emphasis on the differences from the other embodiments, and the same similar parts between the various embodiments may be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method sections disclosed in the embodiments, the description of the method sections may be referred to for relevance.

Those of skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. The skilled artisan may use different methods for each particular application to achieve the described functionality, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

In the embodiments disclosed herein, the disclosed methods, articles of manufacture (including but not limited to devices, apparatuses, etc.) may be practiced in other ways. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units may be merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form. The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to implement the present embodiment. In addition, each functional unit in the embodiments of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Claims

1. A method for monitoring sleep, comprising:

acquiring a body movement signal of a user, and determining a sleep interval of the user according to a change trend of the body movement signal;

according to the action intensity of the body movement signals of the user in the sleep interval, adjusting the threshold value of each action intensity for carrying out sleep stage;

and backtracking the body movement signal of the user according to the adjusted action intensity threshold value, and correcting the sleep stage result.

2. The method of claim 1, wherein determining the sleep interval of the user based on the trend of the body movement signal comprises:

and under the condition that the time length from the falling sleep time to the waking time is longer than the set sleep time length, determining the time length from the falling sleep time to the waking time as the sleep interval of the user.

3. The method of claim 2, wherein the determination of the time to fall asleep comprises:

Wherein the second action duration threshold is less than a set duration.

4. The method of claim 2, wherein the determination of the awake time instant comprises:

determining the moment when the motion intensity of the body motion signal rises to be higher than the sleep threshold value as a waking moment when the motion duration of the body motion signal is higher than the second motion duration threshold value and is smaller than the third motion duration threshold value and the average motion intensity in the motion duration is higher than the third motion intensity threshold value; or alternatively, the first and second heat exchangers may be,

determining the moment when the motion intensity of the body motion signal rises to be higher than the sleep-in threshold as the wakefulness moment when the motion duration of the body motion signal is greater than the third motion duration threshold and the average motion intensity in the motion duration is greater than the second motion intensity threshold;

5. The method of claim 1, wherein adjusting each motion intensity threshold for performing sleep staging based on the motion intensity of the user's body movement signal within the sleep interval comprises:

and adjusting the action intensity thresholds according to the adjustment factors.

6. The method of claim 5, wherein determining the corresponding adjustment factor based on the difference between the average intensity and a set intensity threshold comprises:

determining an adjustment factor for reducing the action intensity threshold under the condition that the difference value between the average intensity and the set intensity threshold is smaller than 0;

and determining an adjustment factor for improving the action intensity threshold value when the difference value between the average intensity and the set intensity threshold value is larger than 0.

7. The method according to any one of claims 1 to 6, wherein the step of backtracking the body movement signal of the user and correcting the sleep stage result according to the adjusted action intensity threshold value comprises:

8. The method as recited in claim 7, further comprising:

obtaining a heart rate signal of the user in a period corresponding to the shallow sleep phase and/or the rapid eye movement phase;

it is determined whether the current period is a fast eye movement phase based on a change in the heart rate signal of the user.

9. An apparatus for monitoring sleep comprising a processor and a memory storing program instructions, wherein the processor is configured to perform the method for monitoring sleep of any one of claims 1 to 8 when the program instructions are executed.

10. A monitoring device, comprising:

a body movement signal detection device; and, a step of, in the first embodiment,

the apparatus for monitoring sleep of claim 9.