WO2022021707A1

WO2022021707A1 - Sleep monitoring method and apparatus, and smart wearable device and readable storage medium

Info

Publication number: WO2022021707A1
Application number: PCT/CN2020/132294
Authority: WO
Inventors: 唐燕华
Original assignee: 歌尔股份有限公司
Priority date: 2020-07-30
Filing date: 2020-11-27
Publication date: 2022-02-03
Also published as: CN111839465A

Abstract

A sleep monitoring method and apparatus, and a smart wearable device (100) and a computer-readable storage medium. The method comprises: after it is determined that a user is in a sleep state, entering a sleep state monitoring mode (S101), and determining whether a motion behavior of the user is detected (S102); if a motion behavior is detected, determining whether the user is still in the sleep state (S103); if the user is still in the sleep state, acquiring physiological index data (S104); and determining a wearing state by using the physiological index data, and determining, according to the wearing state, whether to exit the sleep state monitoring mode (S105). By means of the method, by means of determining a motion behavior, a sleep state and physiological index data, a sleep state monitoring mode is exited if necessary, thereby avoiding the problem of incorrect determination of a sleep condition of a user.

Description

Sleep detection method, device, smart wearable device, and readable storage medium

This application claims the priority of the Chinese patent application filed on July 30, 2020 with the application number 202010752572.0 and the invention name "sleep detection method, device, smart wearable device and readable storage medium", the entire content of which is Incorporated herein by reference.

technical field

The present application relates to the technical field of smart wearable devices, and in particular, to a sleep detection method, a sleep detection device, a smart wearable device, and a computer-readable storage medium.

Background technique

At present, smart wearable devices are often used for sleep monitoring while users are sleeping, so that users can understand their own sleep conditions. When a smart wearable device performs sleep monitoring, it is necessary to determine whether the user is in a sleep state, and select the operation to perform according to the user's sleep state, such as acquiring and analyzing sleep monitoring data, or setting certain data items (such as alarm clock settings) .

In practical applications, users sometimes take off the smart wearable device because it is uncomfortable to wear the smart wearable device. In the related art, a proximity sensor or an ambient light sensor is generally used to detect whether the device is in a wearing state. If the user temporarily wakes up at night and removes the smart wearable device, due to the dark light at night, there is no significant difference in the light collected by the ambient light sensor between wearing and removing the device, so the change in the wearing state cannot be recognized, and it will be considered that it is still in the wearable state. wearing state. If the proximity sensor of the smart wearable device is blocked by an object (such as a pillow), it cannot recognize the change of the wearing state, and it will be considered that it is still in the wearing state. After the user takes off the smart wearable device, the smart wearable device will think that the user is continuously in a sleep state, thus causing a misjudgment of the user's sleep state. Therefore, the related art has the problem of inaccurate sleep detection.

Therefore, how to solve the problem of inaccurate sleep detection in the related art is a technical problem to be solved by those skilled in the art.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present application is to provide a sleep detection method, a sleep detection device, a smart wearable device and a computer-readable storage medium, which solve the problem of inaccurate sleep detection in the related art.

In order to solve the above technical problems, the present application provides a sleep detection method, including:

After determining that the user is in a sleep state, enter a sleep state detection mode, and determine whether the user's motion behavior is detected;

If the motion behavior is detected, determine whether the user is still in the sleep state;

If the user is still in the sleep state, acquiring physiological index data;

The wearing state is determined by using the physiological index data, and whether to exit the sleep state detection mode is determined according to the wearing state.

Optionally, the judging whether to detect the motion behavior of the user includes:

obtaining the first motion data of the user;

obtaining the first exercise intensity of the user according to the first exercise data;

determining whether the first exercise intensity is in a first low-intensity interval;

If the first exercise intensity is not within the first low intensity interval, it is determined that the exercise behavior is detected.

Optionally, the acquiring the first motion data of the user includes:

Acquire acceleration values along the X-axis direction, the Y-axis direction, and the Z-axis direction by using a motion sensor as the first motion data;

Correspondingly, the obtaining the first exercise intensity of the user according to the first exercise data includes:

The resultant acceleration data is obtained by using the acceleration value, and the resultant acceleration data is determined as the first exercise intensity.

Optionally, the judging whether the user is still in the sleep state includes:

Detecting the user by using a sleep algorithm to obtain a first detection result;

Whether the user is in the sleep state is determined by using the first detection result.

Optionally, the determining the wearing state using the physiological index data includes:

judging whether the physiological index data is in the personal index range corresponding to the user;

If it is not in the personal index interval, determine that the wearing state is not wearing;

If it is in the personal index interval, it is determined that the wearing state is worn.

Optionally, the judging whether to exit the sleep state detection mode according to the wearing state includes:

If the wearing state is the unworn state, exit the sleep state detection mode;

If the wearing state is the worn state, it is determined that the user is still in the sleeping state, and the sleep state detection mode is not exited.

Optionally, the process of determining that the user is in a sleep state includes:

obtaining second motion data of the user;

obtaining the second exercise intensity of the user according to the second exercise data;

determining whether the second exercise intensity is in a second low-intensity interval;

If the second exercise intensity is in the second low-intensity interval, use a sleep algorithm to detect the user to obtain a second detection result;

If the second detection result is that the user is in the sleep state, it is determined that the user is in the sleep state.

The present application also provides a sleep detection device, comprising:

a first judging module, configured to enter a sleep state detection mode after determining that the user is in a sleep state, and determine whether the motion behavior of the user is detected;

a second judgment module, configured to judge whether the user is still in the sleep state if the motion behavior is detected;

an acquisition module, configured to acquire physiological index data if the user is still in the sleep state;

The mode exit determination module is configured to use the physiological index data to determine the wearing state, and determine whether to exit the sleep state detection mode according to the wearing state.

The present application also provides a smart wearable device, including a memory and a processor, wherein:

the memory for storing computer programs;

The processor is configured to execute the computer program to implement the above sleep detection method.

The present application also provides a computer-readable storage medium for storing a computer program, wherein when the computer program is executed by a processor, the above-mentioned sleep detection method mode exit determination module is implemented.

In the sleep detection method provided by this application, after it is determined that the user is in a sleep state, it enters a sleep state detection mode, and determines whether the user's exercise behavior is detected; if the exercise behavior is detected, it is determined whether the user is still in a sleep state; If it is in a sleep state, obtain physiological index data; use the physiological index data to determine the wearing state, and judge whether to exit the sleep state detection mode according to the wearing state.

It can be seen that the method can enter the sleep state detection mode after determining that the user is in the sleep state, and determine the sleep state of the user by detecting the motion behavior of the user. If motion behavior is detected, it means that the user may wake up, or may be exercising in a sleep state, or may have removed the smart wearable device. Determine whether the user is in a sleep state. If the user is in a sleep state, it means that the user has not woken up, and the exercise behavior may be exercise in the sleep state, or may be the exercise of removing the smart wearable device. Therefore, the physiological index data is obtained, the wearing state is determined according to the physiological index data, and whether to exit the sleep state detection mode is determined according to the wearing state. That is, if the physiological index data indicates that the smart wearable device is still being worn, it can be determined that the user is in a sleep state, so there is no need to exit the sleep state detection mode; if the physiological index data indicates that the smart wearable device is removed. After it is determined that the smart wearable device is removed, it can be determined that the user's sleep state cannot be determined, so a sleep state detection mode can be introduced to avoid misjudgment of the user's sleep state. By judging the data of exercise behavior, sleep state and physiological indicators, it can accurately identify whether the smart wearable device has been removed, determine whether it can accurately determine whether the user is in a sleep state, and then exit when it is necessary to exit the sleep state detection mode, that is, when the user needs to exit the sleep state detection mode. It is impossible to accurately determine whether the user is in the sleep state and exit the sleep state detection mode, so as to avoid the problem of misjudging the user's sleep situation due to the inability to exit the sleep state detection mode, and solve the problem of inaccurate sleep detection in related technologies.

In addition, the present application also provides a sleep detection device, a smart wearable device and a computer-readable storage medium, which also have the above beneficial effects.

Description of drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only It is a part of the drawings of the present application. For those of ordinary skill in the art, other drawings can also be obtained from the provided drawings without any creative effort.

1 is a flowchart of a sleep detection method provided by an embodiment of the present application;

2 is a flowchart of a specific sleep detection method provided by an embodiment of the present application;

3 is a schematic structural diagram of a sleep detection device provided by an embodiment of the present application;

FIG. 4 is a schematic structural diagram of a smart wearable device according to an embodiment of the present application.

detailed description

In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments It is only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

In a possible implementation, please refer to FIG. 1 , which is a flowchart of a sleep detection method provided by an embodiment of the present application. The method includes:

Some or all of the steps in the embodiments of the present application may be performed by a smart wearable device, and the smart wearable device may be a smart watch, a smart bracelet, or the like. It should be noted that the purpose of this embodiment is to accurately determine the sleep status of the user after the user wears the smart wearable device and enters the sleep state, that is, to determine whether the user is in the sleep state, so as to prevent the user from being unable to exit after removing the smart wearable device. In the sleep state detection mode, it is still considered that the user is in a sleep state, causing misjudgment of the user's sleep state. Or it may affect subsequent operations that need to be performed according to the sleep state, such as the operation of acquiring sleep monitoring data, or the operation of setting an alarm clock reminder mode.

S101: Determine that the user is in a sleep state, and enter a sleep state detection mode.

It should be noted that, in this embodiment, the user's sleep state is determined after the user is in the sleep state. Therefore, it is first necessary to determine that the user is in the sleep state. The specific determination method is not limited in this embodiment. For example, the user's exercise data may be obtained, and the exercise intensity may be calculated according to the exercise data. If the exercise intensity is in a low intensity range, it means that the user may be in a sleeping state. At this time, the sleep state of the user can be judged in combination with the sleep state judgment algorithm, that is, the sleep state of the user is detected by using the sleep state algorithm. If the detection result indicates that the user is in a sleep state, it can be determined that the user is in a sleep state. The sleep state judging algorithm is a sleep algorithm of a wearable device, and the sleep algorithm of the wearable device may obtain the analysis result by analyzing the motion data, or may obtain the analysis result by analyzing the physiological monitoring data (ie, the physiological index data), Alternatively, it may be a combination of the above two (exercise data and physiological monitoring data). For details, reference may be made to related technologies, which will not be repeated here. In another embodiment. Whether it is currently in the user's sleep time can be determined according to historical data, and if it is in the sleep time and the user has not exercised for a long time, it can be determined that the user is in a sleep state. This embodiment does not limit the way of judging whether the user is in the sleep state, and the specific way of judging can be set according to the actual situation. After it is determined that the user is in a sleep state, the sleep state detection mode can be entered to continuously or periodically detect the user's sleep state, and perform corresponding operations according to the sleep state, such as obtaining sleep monitoring information (sleep duration, sleep quality, etc. ). Meanwhile, step S102 may be entered.

S102: Determine whether a user's motion behavior is detected.

Determining whether the motion behavior of the user is detected is judging whether the user has made a body motion that meets a predetermined condition. Motion behavior is detected based on motion data. According to different actual needs, the movement behavior can be any action made by the user that meets a predetermined condition, such as relatively vigorous body movement; or can be any body movement made by the user. In order to prevent the power consumption of the smart wearable device from being excessively large, in this embodiment, preferably, the exercise behavior can be any action made by the user that satisfies a predetermined condition. The predetermined condition for limiting the motion behavior may be a distance condition, an intensity condition, etc., which is not limited in this embodiment. According to different predetermined conditions, the way of detecting motion behavior is also different. For example, when the motion behavior is that the moving distance of the smart wearable device exceeds the predetermined distance, the positioning device can be used to determine whether the moving distance per unit time is greater than the predetermined distance. Then it is determined that the motion behavior of the user is detected. Or when the exercise behavior is that the exercise intensity of the smart wearable device is greater than the predetermined intensity, an acceleration sensor can be used to determine whether the exercise intensity is greater than the predetermined intensity, and if it is greater than the predetermined intensity, it is determined that the user's exercise behavior is detected.

If the motion behavior of the user is not detected, it means that the user does not perform motion indicating that the user may wake up and is still in a sleep state. If the motion behavior of the user is detected, it means that the user may have removed the smart wearable device, or may have performed other physical actions, so step S103 may be entered for further judgment. This embodiment does not limit the detection frequency of the motion behavior, for example, the detection may be performed in real time, or the detection may be performed according to a preset period. It should be noted that the frequency of motion behavior detection does not necessarily coincide with the frequency of acquiring motion data for motion behavior detection. In order to ensure the accuracy of the determination of the user's sleep state and take into account the energy consumption of the smart wearable device, in this embodiment, preferably, motion data for motion behavior detection can be acquired in real time, and according to a preset period, based on this period All motion data obtained are used to detect motion behavior. If motion behavior is detected, step S103 may be entered. This embodiment does not limit the content after the motion behavior is not detected. For example, step S106 may be entered, that is, a preset operation may be performed.

S103: Determine whether the user is still in a sleep state.

After the motion behavior is detected, it means that the user may have removed the smart wearable device, or may have performed physical movements in the sleep state, such as turning over, or may have woken up from the sleep state. In order to further determine the sleep condition of the user, it may be determined whether the user is in a sleep state. This embodiment does not limit the specific determination method, for example, a sleep algorithm different from that in step S101 may be used to determine the sleep state, or the sleep algorithm in step S101 may be used to determine the sleep state. The sleep algorithm is a sleep algorithm of a wearable device, and the sleep algorithm can be analyzed according to motion data, or can be analyzed according to physiological monitoring data (ie, physiological index data). This embodiment does not limit the content after the user is not in the sleep state. For example, step S106 may be entered, that is, a preset operation may be performed. After it is determined that the user is still in the sleep state, step S104 may be entered.

S104: Obtain physiological index data.

Since the operation power consumption of acquiring physiological index data is relatively large, in order to avoid the high power consumption of smart wearable devices, the physiological index data can be obtained after it is determined that exercise behavior occurs and the user is still in a sleep state, and use it to analyze the user's sleep status. further judgment. After it is determined that the user is in a sleep state, it is explained that the previous movement behavior may be the user's behavior of taking off the smart wearable device, or may be the user's other physical behavior in the sleep state. In order to further identify the specific content of the movement behavior, step S104 may be performed. The physiological index data is data that can reflect the physiological state of the user, for example, data such as temperature, heart rate, and breathing rate. The number of physiological index data is not limited, for example, it can be one or a combination of data such as temperature, heart rate, and respiration rate. After it is determined that the user is in a sleep state, in order to further identify whether the exercise behavior is specifically an action of the user taking off the smart wearable device, physiological index data may be obtained.

S105: Determine the wearing state using the physiological index data, and determine whether to exit the sleep state detection mode according to the wearing state.

After acquiring the physiological index data, it can be used to determine the wearing state, so as to determine the user's sleep situation according to the wearing state. In one embodiment, abnormality detection can be performed on the physiological index data, that is, it is determined whether the physiological index data is in a reasonable range, and a detection result is obtained. If the previous exercise behavior is the user's behavior of removing the smart wearable device, the physiological index data obtained after the smart wearable device is removed must not be reasonable data. However, after the smart wearable device is removed, the current sleep state of the user cannot be determined, so the detection of the user's sleep state can be stopped, that is, the user's sleep state is not detected and a detection result is obtained. After it is determined that the smart wearable device is removed, since it is impossible to determine whether the user is in a sleep state, the sleep state detection mode can be exited, so as to avoid the situation that the user cannot be considered to be in a sleep state for a long time without being able to exit the sleep state detection mode. It is assumed that the user is continuously in a sleep state, so as to avoid misjudging the user's sleep status.

The specific manner of using the physiological index data to determine the wearing state is not limited in this embodiment. For example, it can be compared with a preset normal index range to determine whether it is abnormal. For example, when the physiological indicator data is heart rate data, the user's real heart rate data cannot be obtained after the smart wearable device is removed, and the obtained heart rate data will be much lower than the normal heart rate. Or when the physiological index data is body temperature data, the user's real body temperature data cannot be obtained after the smart wearable device is removed, and the obtained body temperature data is also much lower than the normal body temperature. Or other methods can also be used to determine the wearing state according to the physiological index data. For example, when obtaining multiple physiological index data, it is determined whether the combination is reasonable. If it is not reasonable, the detection result is abnormal, and it can be determined that the smart wearable device has been removed.

After determining the wearing state of the smart wearable device by using the physiological index data, it can be determined whether to exit the sleep state detection mode according to the wearing state. When it is determined that the smart wearable device is removed, the wearing state is not worn, and the user's sleep status cannot be accurately judged at this time. Therefore, in order to ensure the accuracy of judging the user's sleep state, the sleep state detection mode can be exited, that is, the user's sleep state is no longer determined to be in the sleep state, so as to prevent misjudgment of the user's sleep state. You can also stop performing operations performed in the sleep state, or change settings that were changed in the sleep state. When it is determined that the smart wearable device is still being worn by the user, the wearing state is the wearing state. At this time, it can be determined that the user is still in the sleeping state, and the previous running behavior may be the user's turning over behavior or other similar behaviors, so it is not necessary to exit the sleeping state. detection mode. In a possible implementation manner, after the user's sleep condition is accurately determined, subsequent operations may be performed, such as an operation of acquiring sleep monitoring data, or an operation of setting an alarm clock reminder mode.

S106: Preset operation.

This embodiment does not limit the specific content of the preset operation, for example, it may be no operation, that is, no operation is performed. It should be noted that the preset operation performed after no motion behavior is detected in step S102 may be the same or different from the preset operation performed after step S103 detects that the user is not in a sleep state, and the specific content of the preset operation can be based on actual settings.

By applying the sleep detection method provided by the embodiment of the present application, after it is determined that the user is in a sleep state, the sleep state detection mode can be entered, and the user's sleep state can be determined by detecting the user's motion behavior. If motion behavior is detected, it means that the user may wake up, or may exercise in a sleep state, or may have removed the smart wearable device. Determine whether the user is in a sleep state. If the user is in a sleep state, it means that the user has not woken up, and the exercise behavior may be exercise in the sleep state, or may be the exercise of removing the smart wearable device. Therefore, the physiological index data is obtained, the wearing state is determined according to the physiological index data, and whether to exit the sleep state detection mode is determined according to the wearing state. That is, if the physiological index data indicates that the smart wearable device is still being worn, it can be determined that the user is in a sleep state, so there is no need to exit the sleep state detection mode; if the physiological index data indicates that the smart wearable device is removed. After it is determined that the smart wearable device is removed, it can be determined that the user's sleep state cannot be determined, so a sleep state detection mode can be introduced to avoid misjudgment of the user's sleep state. By judging the data of exercise behavior, sleep state and physiological indicators, it can accurately identify whether the smart wearable device has been removed, determine whether it can accurately determine whether the user is in a sleep state, and then exit when it is necessary to exit the sleep state detection mode, that is, when the user needs to exit the sleep state detection mode. It is impossible to accurately determine whether the user is in the sleep state and exit the sleep state detection mode, so as to avoid the problem of misjudging the user's sleep situation due to the inability to exit the sleep state detection mode, and solve the problem of inaccurate sleep detection in related technologies.

Based on the foregoing embodiments, this embodiment will specifically describe several steps in the foregoing embodiments. Specifically, step S101 may include:

Step 11: Acquire second motion data of the user.

Step 12: Obtain the second exercise intensity of the user according to the second exercise data.

Step 13: Determine whether the second exercise intensity is in the second low-intensity range.

Step 14: If the second exercise intensity is in the second low-intensity interval, use a sleep algorithm to detect the user to obtain a second detection result.

Step 15: If the second detection result is that the user is in a sleep state, it is determined that the user is in a sleep state.

In this embodiment, in order to ensure the accuracy of determining that the user is in a sleep state and prevent the execution of step S102 and subsequent steps when the user is not in a sleep state, it is possible to judge whether the user is in a sleep state from the aspect of exercise intensity; In another possible implementation, the judgment can be made from two aspects of exercise intensity and heart rate data. Specifically, the second exercise data of the user is obtained first, and the second exercise intensity of the user is obtained by using the second exercise data. The second motion intensity may represent the intensity of the user's limb motion. After the second exercise intensity is obtained, it can be determined whether it is in the second low intensity range. When the second exercise intensity is in the second low-intensity interval, it means that the user's limb movement is very slight, and the user may be in a sleep state, or in a low-intensity exercise state for a long time due to reasons such as watching a movie. In this case, the sleep condition of the user is detected by using the sleep algorithm, and a second detection result is obtained. For the specific content of the sleep algorithm, reference may be made to the related art, which will not be repeated here. If the second detection result is that the user is in a sleep state, it may be determined that the user is in a sleep state.

In a possible implementation, after it is determined that the user is already in a sleep state, further detection can be performed using heart rate data. For example, it can be determined whether the user's current heart rate is within the sleep state interval. It is further determined that the user is in a sleeping state and the detection accuracy is improved.

Based on the above embodiments, in one implementation, after it is determined that the user is in a sleep state, it can be determined whether a motion behavior is detected. In order to comprehensively detect the exercise behavior of the user, this embodiment judges whether the exercise behavior is detected by the first exercise intensity. Step S102 may include:

Step 21: Acquire the first motion data of the user.

Step 22: Obtain the first exercise intensity of the user according to the first exercise data.

Step 23: Determine whether the first exercise intensity is in the first low-intensity range.

Step 24: If the first exercise intensity is not in the first low-intensity interval, it is determined that an exercise behavior is detected.

It should be noted that when the first exercise intensity and the second exercise intensity are obtained in the same way, the difference between the first exercise data and the second exercise data is that the time of acquisition is different. The second motion data is acquired when it is not determined whether the user is in or out of the sleep state. When the sum of the first exercise intensity and the second exercise intensity is obtained in different ways, the difference between the first exercise data and the second exercise data is not only in the time of acquisition, but also in the content itself. The first motion data may specifically be acceleration data, speed data, and the like. After the first exercise data is obtained, the first exercise intensity of the user is obtained by calculating the first exercise intensity. The specific calculation method of the first exercise intensity is not limited in this embodiment, as long as the first exercise intensity obtained by using the first exercise data can represent the limbs of the user The intensity of the exercise is sufficient. After the first exercise intensity is obtained, it can be determined whether it is in the first low intensity interval. When the first exercise intensity is within the first low-intensity interval, it indicates that the user's limb movement is very slight, and cannot be used to indicate that the user's sleep situation has changed. For example, it cannot be used to indicate that the user has woken up, or that the user has taken under the smart wearable device. If the first exercise intensity is not in the first low-intensity interval, it means that the user's body movement is relatively intense, which may indicate that the user has made body movements (such as turning over) during sleep, or may indicate that the user has removed the smart wearable device. Therefore, it is determined that the motion behavior is detected. It should be noted that the size and upper and lower limit values of the first low-intensity interval and the second low-intensity interval may be the same or different.

Further, in order to accurately represent the first exercise intensity of the user, in this embodiment, preferably, acceleration data may be used as the first exercise data. At this point, step 21 may include:

Step 211: Acquire acceleration values along the X-axis direction, the Y-axis direction, and the Z-axis direction by using the motion sensor as the first motion data.

Correspondingly, step 22 may include:

Step 221 : Obtain the resultant acceleration data by using the acceleration value, and determine the resultant acceleration data as the first exercise intensity.

This embodiment does not limit the specific type of the motion sensor, for example, it may be a three-axis accelerometer. The three-axis accelerometer can acquire acceleration values along the X-axis, Y-axis, and Z-axis directions, and these three acceleration values can be combined into a combined acceleration. The resultant acceleration data can be the size of the resultant acceleration. For example, when the acceleration values along the three directions of the X-axis, Y-axis and Z-axis are x, y, and z, the resultant acceleration is calculated according to the calculation method of the space rectangular coordinate system. can be of size

The magnitude of the resultant acceleration can accurately represent the intensity of the movement, so the resultant acceleration data can be determined as the first movement intensity.

After it is determined that the motion behavior is detected, step S103 may include:

Step 31: Use the sleep algorithm to detect the user to obtain a first detection result.

Step 32: Use the first detection result to determine whether the user is in a sleep state.

It should be noted that the sleep algorithm may be an algorithm for detection based on parameters such as motion data and/or physiological index data, and its specific content is not limited, for example, a sleep algorithm in the related art may be used. In one embodiment, in order to reduce the power consumption of the smart wearable device, a sleep algorithm may be used to perform sleep condition detection on the first motion data, and there is no need to re-acquire the data used for sleep condition detection.

After the first detection result shows that the user is in a sleep state, physiological index data may also be obtained, the wearing state may be determined based on the physiological index data, and the user's sleeping state may be further determined according to the wearing state. That is, it is further accurately determined whether the user is in a sleep state. Step S105 may include:

Step 41: Determine whether the physiological index data is in the personal index range corresponding to the user.

Step 42: If it is not in the personal index range, determine that the wearing state is not wearing.

Step 43: If it is in the personal index range, determine that the wearing state is worn.

In one embodiment, after it is determined in step S101 that the user is in a sleep state, the smart wearable device may enter a sleep state detection mode. Correspondingly at this time, step S105 may further include:

Step 44: If the wearing state is not wearing, exit the sleep state detection mode.

Step 45: If the wearing state is worn, it is determined that the user is still in the sleeping state.

In order to make the judgment of the wearing state more accurate, the physiological index data and the personal index interval corresponding to the user can be used for judgment. The personal index interval corresponds to the user's own physical condition. Since there are certain differences in the physiological indicators of each person, the use of the personal index interval to judge the abnormality of the physiological index data can be more in line with the user's personal situation, and the obtained detection results are more accurate. high. In order to prevent misjudgment of the user's sleep status, that is, to avoid misjudgment of whether the user is in a sleep state, when it is determined that the wearing state is not wearing, the sleep state detection mode can be exited, and the user's sleep state is no longer determined. In order to be in a sleep state, it can avoid mistakenly thinking that the user is still in a sleep state, and the detection accuracy of the user's sleep state is improved.

In practical applications, when the user is in a sleep state, the smart wearable device can acquire sleep monitoring data corresponding to the user, so as to analyze the data to obtain an analysis result. Therefore, corresponding to the above embodiment, when it is determined that the wearing state is not worn, the smart wearable device cannot obtain accurate sleep monitoring data. In order to prevent the analysis result from being affected by inaccurate sleep monitoring data, the wearable device can When it is not worn, stop acquiring the sleep monitoring information corresponding to the user.

Based on the above embodiments, this embodiment will describe a specific sleep detection process. Please refer to FIG. 2 , which is a flowchart of a specific sleep detection method provided by an embodiment of the present application. After the start, calculate the exercise intensity V1 in the N1 time period. in:

Among them, i represents each moment in the N1 time period, and x(i), y(i), and z(i) respectively represent the acceleration values obtained by the three-axis accelerometer in three vertical directions. After obtaining V1, compare it with thre_s to determine whether there is running behavior in the N1 time period. If V1≥thre_s, it means that there is a motion behavior, and the user cannot be in a sleep state, so the acceleration value is re-acquired. If V1<thre_s, it means that there is no exercise behavior, and the user may fall asleep in the low-intensity range. Therefore, the sleep algorithm is used to detect whether the user is in a sleep state. If it is detected that the user is in a sleep state, the start time t1 of N1 is recorded as the sleep time start_t. At the same time, turn on heart rate detection to confirm whether to fall asleep. If it is determined to fall asleep, it can enter the sleep state detection mode, and send start_t to the sleep module so that the sleep module can record it. For the detection, reference may be made to the related art for details, and details are not described here.

After the user falls asleep, the exercise intensity V2 in the N2 time period is calculated, and the specific calculation method is the same as that of V1. At this time, different thresholds are used to judge the motion behavior. If V2≤thre_e, it means that there is no motion behavior, and the motion data is re-acquired. If V2>thre_e, it indicates that there is a motion behavior, and at this time, the start time t2 of N2 is recorded as end_t. At the same time, the sleep algorithm is used to detect whether the user is in a sleep state, and the detection time of the sleep algorithm detection is t3. If not in the sleep state, exit the sleep state detection mode. If it is in the sleep state, the heart rate detection is started at time t=t2+t3, that is, the heart rate data is obtained as the physiological index data. Use the heart rate data to determine the state of the smart wearable device, and then determine the user's sleep status according to the wearing state. If the heart rate data is normal data, it means that the smart wearable device has not been removed and is in a worn state, and it can be determined that the user is still in a sleeping state. , you can retrieve the motion data again, or perform other operations. If the heart rate data is abnormal data, it means that the smart wearable device has been removed and is in an unworn state. At this time, the user's sleep status cannot be accurately determined, so the user's sleep status is no longer determined to be in the sleep state, and end_t is sent to the sleep module for recording, and the sleep state detection mode is exited after the recording is completed.

The following describes the sleep detection apparatus provided by the embodiments of the present application, and the sleep detection apparatus described below and the sleep detection method described above may refer to each other correspondingly.

Please refer to FIG. 3. FIG. 3 is a schematic structural diagram of a sleep detection device provided by an embodiment of the present application, including:

The first judging module 110 is configured to enter the sleep state detection mode after determining that the user is in a sleep state, and determine whether the motion behavior of the user is detected;

The second judging module 120 is configured to judge whether the user is still in a sleep state if a motion behavior is detected;

an acquisition module 130, configured to acquire physiological index data if the user is still in a sleep state;

The mode exit determination module 140 is configured to use the physiological index data to determine the wearing state, and determine whether to exit the sleep state detection mode according to the wearing state.

Optionally, the first judgment module 110 includes:

a first data acquisition unit for acquiring the first motion data of the user;

a first intensity obtaining unit, configured to obtain the user's first exercise intensity according to the first exercise data;

a first intensity judging unit, configured to judge whether the first exercise intensity is in the first low-intensity interval;

The exercise behavior determination unit is configured to determine that the exercise behavior is detected if the first exercise intensity is not in the first low-intensity interval.

Optionally, the data acquisition unit, including:

an acceleration value acquiring subunit, used for acquiring acceleration values along the X-axis direction, the Y-axis direction and the Z-axis direction by using the motion sensor as the first motion data;

Correspondingly, the intensity acquisition unit includes:

The resultant acceleration data calculation subunit is used to obtain the resultant acceleration data by using the acceleration value, and determine the resultant acceleration data as the first exercise intensity.

Optionally, the second judgment module 120 includes:

a first sleep condition detection unit, configured to detect the user by using a sleep algorithm to obtain a first detection result;

The first sleep state determination unit is configured to use the first detection result to determine whether the user is in a sleep state.

Optionally, the mode exit determination module 140 includes:

an index judgment unit, used for judging whether the physiological index data is in the personal index range corresponding to the user;

The abnormality determination unit is used to determine that the wearing state is not wearing if it is not in the personal index range;

The normal determination unit is used to determine that the wearing state is worn if it is in the personal index range.

Optionally, the mode exit determination module 140 includes:

The mode exit unit is used to exit the sleep state detection mode if the wearing state is not worn;

The second sleep state determination unit is configured to determine that the user is still in the sleep state if the wearing state is worn, and not exit the sleep state detection mode.

Optionally, the first judgment module 110 includes:

a second data acquisition unit for acquiring the second movement data of the user;

a second intensity obtaining unit, configured to obtain the second exercise intensity of the user according to the second exercise data;

a second intensity judging unit, configured to judge whether the second exercise intensity is in the second low-intensity interval;

a second sleep condition detection unit, configured to detect the user by using a sleep algorithm to obtain a second detection result if the second exercise intensity is in the second low-intensity interval;

The third sleep state determining unit is configured to determine that the user is in the sleep state if the second detection result is that the user is in the sleep state.

The smart wearable device provided by the embodiments of the present application will be introduced below, and the smart wearable device described below and the sleep detection method described above may refer to each other correspondingly.

Please refer to FIG. 4 , which is a schematic structural diagram of a smart wearable device according to an embodiment of the present application. The smart wearable device 100 may include a processor 101 and a memory 102 , and may further include one or more of a multimedia component 103 , an information input/information output (I/O) interface 104 and a communication component 105 .

Wherein, the processor 101 is used to control the overall operation of the smart wearable device 100 to complete all or part of the steps in the above-mentioned sleep detection method; the memory 102 is used to store various types of data to support the operation of the smart wearable device 100, These data may include, for example, instructions for any application or method operating on the smart wearable device 100, as well as application-related data. The memory 102 may be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory) Erasable Programmable Read-Only Memory, EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (Read- One or more of Only Memory, ROM), magnetic memory, flash memory, magnetic disk or optical disk.

Multimedia components 103 may include screen and audio components. The screen can be, for example, a touch screen, and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 102 or transmitted through the communication component 105 . The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 104 provides an interface between the processor 101 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, a button, and the like. These buttons can be virtual buttons or physical buttons. The communication component 105 is used for wired or wireless communication between the smart wearable device 100 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC for short), 2G, 3G or 4G, or one or a combination of them, so the corresponding communication component 105 may include: Wi-Fi parts, Bluetooth parts, NFC parts.

The smart wearable device 100 may be implemented by one or more Application Specific Integrated Circuit (ASIC for short), Digital Signal Processor (DSP for short), Digital Signal Processing Device (DSPD for short) , Programmable Logic Device (PLD for short), Field Programmable Gate Array (FPGA for short), controller, microcontroller, microprocessor or other electronic components to implement the above implementation Example of sleep detection method given.

The computer-readable storage medium provided by the embodiments of the present application is introduced below, and the computer-readable storage medium described below and the sleep detection method described above may refer to each other correspondingly.

The present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned sleep detection method are implemented.

The computer-readable storage medium may include: a USB flash drive, a removable hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, etc. that can store program codes medium.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments may be referred to each other. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method.

Those skilled in the art may further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two, in order to clearly illustrate the hardware and software In the above description, the components and steps of each example have been generally described according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different approaches to implement the described functionality for each particular application, but such implementations should not be considered beyond the scope of this application.

The steps of a method or algorithm described in conjunction with the embodiments disclosed herein may be directly implemented in hardware, a software module executed by a processor, or a combination of the two. A software module can be placed in random access memory (RAM), internal memory, read only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other in the technical field. in any other known form of storage medium.

Finally, it should also be noted that, in this context, relationships such as first and second, etc., are used only to distinguish one entity or operation from another, and do not necessarily require or imply these entities or that there is any such actual relationship or sequence between operations. Furthermore, the terms including, comprising, or any other variation are intended to cover non-exclusive inclusion such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or Yes also includes elements inherent to such a process, method, article or apparatus.

The sleep detection method, sleep detection device, smart wearable device, and computer-readable storage medium provided by the present application have been introduced in detail above. The principles and implementations of the present application are described with specific examples. The description is only used to help understand the method of the present application and its core idea; meanwhile, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific embodiments and application scope. In summary, the above , the contents of this specification should not be construed as limiting the application.

Claims

A sleep detection method, comprising:

After determining that the user is in a sleep state, enter a sleep state detection mode, and determine whether the user's motion behavior is detected;

If the motion behavior is detected, determine whether the user is still in the sleep state;

If the user is still in the sleep state, acquiring physiological index data;

The wearing state is determined by using the physiological index data, and whether to exit the sleep state detection mode is determined according to the wearing state.
The sleep detection method according to claim 1, wherein the judging whether the motion behavior of the user is detected comprises:

obtaining the first motion data of the user;

obtaining the first exercise intensity of the user according to the first exercise data;

determining whether the first exercise intensity is in a first low-intensity interval;

If the first exercise intensity is not within the first low intensity interval, it is determined that the exercise behavior is detected.
The sleep detection method according to claim 2, wherein the acquiring the first motion data of the user comprises:

Acquire acceleration values along the X-axis direction, the Y-axis direction, and the Z-axis direction by using a motion sensor as the first motion data;

Correspondingly, the obtaining the first exercise intensity of the user according to the first exercise data includes:

The resultant acceleration data is obtained by using the acceleration value, and the resultant acceleration data is determined as the first exercise intensity.
The sleep detection method according to claim 1, wherein the judging whether the user is still in the sleep state comprises:

Detecting the user by using a sleep algorithm to obtain a first detection result;

Whether the user is in the sleep state is determined by using the first detection result.
The sleep detection method according to claim 1, wherein the determining the wearing state using the physiological index data comprises:

judging whether the physiological index data is in the personal index range corresponding to the user;

If it is not in the personal index interval, determine that the wearing state is not wearing;

If it is in the personal index interval, it is determined that the wearing state is worn.
The sleep detection method according to claim 5, wherein the determining whether to exit the sleep state detection mode according to the wearing state comprises:

If the wearing state is the unworn state, exit the sleep state detection mode;

If the wearing state is the worn state, it is determined that the user is still in the sleeping state, and the sleep state detection mode is not exited.
The sleep detection method according to any one of claims 1 to 6, wherein the process of determining that the user is in a sleep state comprises:

obtaining second motion data of the user;

obtaining the second exercise intensity of the user according to the second exercise data;

determining whether the second exercise intensity is in a second low-intensity interval;

If the second exercise intensity is in the second low-intensity interval, use a sleep algorithm to detect the user to obtain a second detection result;

If the second detection result is that the user is in the sleep state, it is determined that the user is in the sleep state.
A sleep detection device, characterized in that it includes:

a first judging module, configured to enter a sleep state detection mode after determining that the user is in a sleep state, and determine whether the motion behavior of the user is detected;

a second judgment module, configured to judge whether the user is still in the sleep state if the motion behavior is detected;

an acquisition module, configured to acquire physiological index data if the user is still in the sleep state;

The mode exit determination module is configured to use the physiological index data to determine the wearing state, and determine whether to exit the sleep state detection mode according to the wearing state.
A smart wearable device, comprising a memory and a processor, wherein:

the memory for storing computer programs;

The processor is configured to execute the computer program to implement the sleep detection method according to any one of claims 1 to 7.
A computer-readable storage medium, characterized in that it is used for storing a computer program, wherein when the computer program is executed by a processor, the sleep detection method according to any one of claims 1 to 7 is implemented.