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WO2022021707A1 - 睡眠检测方法、装置、智能穿戴设备及可读存储介质 - Google Patents

睡眠检测方法、装置、智能穿戴设备及可读存储介质 Download PDF

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Publication number
WO2022021707A1
WO2022021707A1 PCT/CN2020/132294 CN2020132294W WO2022021707A1 WO 2022021707 A1 WO2022021707 A1 WO 2022021707A1 CN 2020132294 W CN2020132294 W CN 2020132294W WO 2022021707 A1 WO2022021707 A1 WO 2022021707A1
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Prior art keywords
user
sleep
sleep state
state
data
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English (en)
French (fr)
Inventor
唐燕华
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Goertek Inc
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Goertek Inc
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1118—Determining activity level
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4815—Sleep quality
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface

Definitions

  • 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.
  • smart wearable devices are often used for sleep monitoring while users are sleeping, so that users can understand their own sleep conditions.
  • 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) .
  • 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.
  • 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.
  • the present application provides a sleep detection method, including:
  • 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 judging whether to detect the motion behavior of the user includes:
  • the acquiring the first motion data of the user includes:
  • 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 judging whether the user is still in the sleep state includes:
  • Whether the user is in the sleep state is determined by using the first detection result.
  • the determining the wearing state using the physiological index data includes:
  • the judging whether to exit the sleep state detection mode according to the wearing state includes:
  • 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 process of determining that the user is in a sleep state includes:
  • 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.
  • the sleep detection method 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.
  • 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.
  • 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.
  • a sleep state detection mode can be introduced to avoid misjudgment of the user's sleep state.
  • 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.
  • FIG. 1 is a flowchart of a sleep detection method provided by an embodiment of the present application.
  • FIG. 2 is a flowchart of a specific sleep detection method provided by an embodiment of the present application.
  • FIG. 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.
  • FIG. 1 is a flowchart of a sleep detection method provided by an embodiment of the present application.
  • the method includes:
  • a smart wearable device may be a smart watch, a smart bracelet, or the like.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • step S101 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • the physiological index data 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • a sleep state detection mode can be introduced to avoid misjudgment of the user's sleep state.
  • 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.
  • 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.
  • the sleep condition of the user is detected by using the sleep algorithm, and a second detection result is obtained.
  • the sleep algorithm 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • acceleration data may be used as the first exercise data.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • a sleep algorithm 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the smart wearable device 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.
  • FIG. 2 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:
  • i represents each moment in the N1 time period
  • x(i), y(i), and z(i) respectively represent the acceleration values obtained by the three-axis accelerometer in three vertical directions.
  • 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.
  • 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.
  • the start time t1 of N1 is recorded as the sleep time start_t.
  • 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.
  • start_t For the detection, reference may be made to the related art for details, and details are not described here.
  • the exercise intensity V2 in the N2 time period is calculated, and the specific calculation method is the same as that of V1.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • FIG. 4 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 .
  • a multimedia component 103 may be included in the smart wearable device 100 .
  • I/O information input/information output
  • 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.
  • SRAM Static Random Access Memory
  • EEPROM Electrically Erasable Programmable Read-Only Memory
  • EPROM Erasable Programmable Read-Only Memory
  • PROM Programmable Read-Only Memory
  • Read- One or more of Only Memory ROM
  • magnetic memory flash 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.
  • 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.
  • ASIC Application Specific Integrated Circuit
  • DSP Digital Signal Processor
  • DSPD Digital Signal Processing Device
  • PLD Programmable Logic Device
  • FPGA Field Programmable Gate Array
  • 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.
  • 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.
  • RAM random access memory
  • ROM read only memory
  • 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.

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Abstract

一种睡眠检测方法、装置、智能穿戴设备（100）及计算机可读存储介质，该方法包括：在确定用户处于睡眠状态后，进入睡眠状态检测模式（S101），并判断是否检测到用户的运动行为（S102）；若检测到运动行为，则判断用户是否仍处于睡眠状态（S103）；若用户仍处于睡眠状态，则获取生理指标数据（S104）；利用生理指标数据确定穿戴状态，并根据穿戴状态判断是否退出睡眠状态检测模式（S105）；该方法通过对运动行为、睡眠状态以及生理指标数据进行判断，在需要退出睡眠状态检测模式时退出，避免出现对用户睡眠情况造成误判的问题。

Description

睡眠检测方法、装置、智能穿戴设备及可读存储介质
本申请要求于2020年07月30日提交中国专利局、申请号为202010752572.0、发明名称为“睡眠检测方法、装置、智能穿戴设备及可读存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及智能穿戴设备技术领域，特别涉及一种睡眠检测方法、睡眠检测装置、智能穿戴设备及计算机可读存储介质。
背景技术
目前，智能穿戴设备常被用于在用户睡眠时进行睡眠监测，以便用户对自己的睡眠情况进行了解。在智能穿戴设备进行睡眠监测时，需要确定用户是否处于睡眠状态，根据用户的睡眠状态选择执行的操作，例如获取并分析睡眠监测数据，或者对某些数据项进行怎样的设置(例如闹钟设置)。
实际应用中，用户有时会因穿戴智能穿戴设备不舒服而取下智能穿戴设备。相关技术一般采用接近传感器或者环境光传感器检测是否处于穿戴状态。若用户夜里暂时醒来，取下智能穿戴设备，由于夜间光线较暗，穿戴与取下两种情况环境光传感器采集到的光线没有较大差异，因此无法识别到穿戴状态变化，会认为仍然处于穿戴状态。若智能穿戴设备的接近传感器被物体(例如枕头)遮挡，也无法识别到穿戴状态变化，会认为仍然处于穿戴状态。而在用户取下智能穿戴设备后，智能穿戴设备会认为用户持续处于睡眠状态，因此就造成了对用户睡眠状态的误判。因此相关技术存在睡眠检测不准确的问题。
因此，如何解决相关技术存在睡眠检测不准确的问题，是本领域技术人员需要解决的技术问题。
发明内容
有鉴于此，本申请的目的在于提供一种睡眠检测方法、睡眠检测装置、智能穿戴设备及计算机可读存储介质，解决了相关技术存在睡眠检测不准确的问题。
为解决上述技术问题，本申请提供了一种睡眠检测方法，包括：
在确定用户处于睡眠状态后，进入睡眠状态检测模式，并判断是否检测到所述用户的运动行为；
若检测到所述运动行为，则判断所述用户是否仍处于所述睡眠状态；
若所述用户仍处于所述睡眠状态，则获取生理指标数据；
利用所述生理指标数据确定穿戴状态，并根据所述穿戴状态判断是否退出所述睡眠状态检测模式。
可选地，所述判断是否检测到所述用户的运动行为，包括：
获取所述用户的第一运动数据；
根据所述第一运动数据得到所述用户的第一运动强度；
判断所述第一运动强度是否处于第一低强度区间；
若所述第一运动强度未处于所述第一低强度区间，则确定检测到所述运动行为。
可选地，所述获取所述用户的第一运动数据，包括：
利用运动传感器获取沿X轴方向、Y轴方向和Z轴方向的加速度值作为所述第一运动数据；
相应的，所述根据所述第一运动数据得到所述用户的第一运动强度，包括：
利用所述加速度值得到合加速度数据，并将所述合加速度数据确定为所述第一运动强度。
可选地，所述判断所述用户是否仍处于所述睡眠状态，包括：
利用睡眠算法对所述用户进行检测，得到第一检测结果；
利用所述第一检测结果判断所述用户是否处于所述睡眠状态。
可选地，所述利用所述生理指标数据确定穿戴状态，包括：
判断所述生理指标数据是否处于所述用户对应的个人指标区间；
若不处于所述个人指标区间，则确定所述穿戴状态为未穿戴；
若处于所述个人指标区间，则确定所述穿戴状态为已穿戴。
可选地，所述根据所述穿戴状态判断是否退出所述睡眠状态检测模式，包括：
若所述穿戴状态为所述未穿戴，则退出所述睡眠状态检测模式；
若所述穿戴状态为所述已穿戴，则确定所述用户仍处于睡眠状态，不退出所述睡眠状态检测模式。
可选地，确定用户处于睡眠状态的过程，包括：
获取所述用户的第二运动数据；
根据所述第二运动数据得到所述用户的第二运动强度；
判断所述第二运动强度是否处于第二低强度区间；
若所述第二运动强度处于所述第二低强度区间，则利用睡眠算法对所述用户进行检测，得到第二检测结果；
若所述第二检测结果为处于睡眠状态，则确定所述用户处于所述睡眠状态。
本申请还提供了一种睡眠检测装置，包括：
第一判断模块，用于在确定用户处于睡眠状态后，进入睡眠状态检测模式，并判断是否检测到所述用户的运动行为；
第二判断模块，用于若检测到所述运动行为，则判断所述用户是否仍处于所述睡眠状态；
获取模块，用于若所述用户仍处于所述睡眠状态，则获取生理指标数据；
模式退出确定模块，用于利用所述生理指标数据确定穿戴状态，并根据所述穿戴状态判断是否退出所述睡眠状态检测模式。
本申请还提供了一种智能穿戴设备，包括存储器和处理器，其中：
所述存储器，用于保存计算机程序；
所述处理器，用于执行所述计算机程序，以实现上述的睡眠检测方法。
本申请还提供了一种计算机可读存储介质，用于保存计算机程序，其中，所述计算机程序被处理器执行时实现上述的睡眠检测方法模式退出确定模块。
本申请提供的睡眠检测方法，在确定用户处于睡眠状态后，进入睡眠状态检测模式，并判断是否检测到用户的运动行为；若检测到运动行为，则判 断用户是否仍处于睡眠状态；若用户仍处于睡眠状态，则获取生理指标数据；利用生理指标数据确定穿戴状态，并根据穿戴状态判断是否退出睡眠状态检测模式。
可见，该方法在确定用户处于睡眠状态后，即可进入睡眠状态检测模式，并通过检测用户的运动行为确定用户的睡眠情况。若检测到运动行为，则说明用户可能醒来，或者可能在睡眠状态下运动，或者可能取下了智能穿戴设备。判断用户是否处于睡眠状态，若用户处于睡眠状态，则说明用户没有醒来，运动行为可能为睡眠状态下的运动，或者可能为取下智能穿戴设备的运动。因此获取生理指标数据，根据生理指标数据确定穿戴状态，并根据穿戴状态确定是否退出睡眠状态检测模式。即，若生理指标数据表示智能穿戴设备仍然被穿戴，此时可以确定用户处于睡眠状态，因此无需退出睡眠状态检测模式；若生理指标数据表示智能穿戴设备被取下。在确定智能穿戴设备被取下后，可以确定无法判断用户的睡眠情况，因此可以推出睡眠状态检测模式，避免造成对用户睡眠状态的误判。通过对运动行为、睡眠状态以及生理指标数据进行判断，可以准确地识别智能穿戴设备是否被取下，确定是否能够准确判断用户是否处于睡眠状态，进而在需要退出睡眠状态检测模式时退出，即在无法准确确定用户是否处于睡眠状态时退出睡眠状态检测模式，避免因无法退出睡眠状态检测模式而出现对用户睡眠情况造成误判的问题，解决了相关技术存在的睡眠检测不准确的问题。
此外，本申请还提供了一种睡眠检测装置、智能穿戴设备及计算机可读存储介质，同样具有上述有益效果。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一部分附图，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据提供的附图获得其他的附图。
图1为本申请实施例提供的一种睡眠检测方法流程图；
图2为本申请实施例提供的一种具体的睡眠检测方法流程图；
图3为本申请实施例提供的一种睡眠检测装置的结构示意图；
图4为本申请实施例提供的一种智能穿戴设备的结构示意图。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
在一种可能的实施方式中，请参考图1，图1为本申请实施例提供的一种睡眠检测方法流程图。该方法包括：
本申请实施例中的部分或全部步骤可以由智能穿戴设备执行，智能穿戴设备可以为智能手表、智能手环等。需要说明的是，本实施例的目的在于在用户佩戴着智能穿戴设备进入睡眠状态后，准确地判断用户的睡眠情况，即判断用户是否处于睡眠状态，防止在用户取下智能穿戴设备后无法退出睡眠状态检测模式，仍然认为用户处于睡眠状态，对用户的睡眠情况造成误判。或者对需要根据睡眠状态执行的后续操作造成影响，例如获取睡眠监测数据的操作，或者设定闹钟提醒方式的操作。
S101：确定用户处于睡眠状态，进入睡眠状态检测模式。
需要说明的是，本实施例在用户处于睡眠状态后对用户的睡眠状态进行判断，因此首先需要确定用户已处于睡眠状态。具体的确定方法本实施例不做限定，例如可以获取用户的运动数据，根据运动数据计算运动强度，若运动强度处于低强度区间，则说明用户可能处于睡眠状态。此时可以结合睡眠状态判断算法对用户的睡眠情况进行判断，即利用睡眠算法对用户进行睡眠情况检测，若检测结果表示用户处于睡眠状态，则可以确定用户处于睡眠状态。该睡眠状态判断算法为可穿戴设备的睡眠算法，该可穿戴设备的睡眠算法可以是根据运动数据进行分析得到分析结果，或者可以是根据生理监测数据(即生理指标数据)进行分析得到分析结果，或者可以为上述两者(运动数据和生理监测数据)的结合，具体可以参考相关技术，在此不再赘述。在另一种实施方式中。可以根据历史数据，判断当前是否处于用户的睡眠时间，若处于睡眠时间，且用户长时间未运动，则可以确定用户处于睡眠状态。本 实施例并不对用户是否处于睡眠状态的判断方式进行限定，可以根据实际情况对具体判断方式进行设定。在确定用户处于睡眠状态后，可以进入睡眠状态检测模式，以便持续或周期性地对用户的睡眠情况进行检测，并根据睡眠状态执行对应的操作，例如获取睡眠监测信息(睡眠时长、睡眠质量等)。同时，可以进入S102步骤。
S102：判断是否检测到用户的运动行为。
判断是否检测到用户的运动行为，即为判断用户是否做出了符合预定条件的肢体运动。运动行为基于运动数据进行检测。根据实际需要的不同，运动行为可以为用户做出的满足预定条件的任何动作，例如可以较为剧烈的肢体运动；或者可以为用户做出的任意肢体运动。为了防止智能穿戴设备的功耗过大，本实施例优选的，运动行为可以为用户做出的满足预定条件的任何动作。限定运动行为的预定条件可以为距离条件、强度条件等，本实施例对此不做限定。根据预定条件的不同，检测运动行为的方式也不同，例如当运动行为为智能穿戴设备的移动距离超过预定距离的运动时，可以利用定位设备判断在单位时间内的移动距离是否大于预定距离，若是则确定检测到用户的运动行为。或者当运动行为为智能穿戴设备的运动强度大于预定强度的运动时，可以利用加速度传感器判断运动强度是否大于预定强度，若大于预定强度则确定检测到用户的运动行为。
若未检测到用户的运动行为，则说明用户没有进行表示用户可能醒来的运动，仍然处于睡眠状态。若检测到了用户的运动行为，说明用户可能取下了智能穿戴设备，或者可能做出了其他肢体动作，因此可以进入S103步骤进行进一步判断。本实施例并不限定对运动行为的检测频率，例如可以实时检测，或者可以按照预设周期检测。需要说明的是，运动行为的检测频率，与获取用于进行运动行为检测的运动数据的频率不一定一致。为了保证对用户睡眠状态的确定的准确性，同时兼顾智能穿戴设备的能耗情况，本实施例优选的，可以实时获取用于进行运动行为检测的运动数据，按照预设周期，基于本周期内获得的全部运动数据对运动行为进行检测。若检测到了运动行为可以进入S103步骤。本实施例并不限定未检测到运动行为后的内容，例如可以进入S106步骤，即执行预设操作。
S103：判断用户是否仍处于睡眠状态。
在检测到运动行为后，说明用户可能取下了智能穿戴设备，或者可能在睡眠状态下做出了肢体运动，例如翻身，或者可能从睡眠状态中苏醒。为了进一步确定用户的睡眠情况，可以判断用户是否处于睡眠状态。本实施例并不限定具体的判断方式，例如可以采用与S101步骤中不同的睡眠算法对睡眠状态进行判断，或者可以采用S101步骤中的睡眠算法对睡眠状态进行判断。该睡眠算法为可穿戴设备的睡眠算法，该睡眠算法可以根据运动数据进行分析，或者可以根据生理监测数据(即生理指标数据)进行分析，具体可以参考相关技术，在此不再赘述。本实施例并不限定用户未处于睡眠状态后的内容，例如可以进入S106步骤，即执行预设操作。在确定用户仍处于睡眠状态后，可以进入S104步骤。
S104：获取生理指标数据。
由于获取生理指标数据的操作功耗较大，因此为了避免智能穿戴设备功耗较高，可以在确定发生运动行为且用户仍处于睡眠状态后获取生理指标数据，并利用其对用户的睡眠情况进行进一步判断。在确定用户处于睡眠状态后，说明先前的运动行为可能为用户取下智能穿戴设备的行为，或者可能为用户在睡眠状态下的其他肢体行为。为了进一步对运动行为的具体内容进行甄别，可以执行S104步骤。生理指标数据为可以体现用户生理状态的数据，例如可以为温度、心率、呼吸速率等数据。生理指标数据的数量不做限定，例如可以为温度、心率、呼吸速率等数据中的一项或多项的组合。在确定用户处于睡眠状态后，为了进一步甄别运动行为具体是否为用户取下智能穿戴设备的动作，可以获取生理指标数据。
S105：利用生理指标数据确定穿戴状态，并根据穿戴状态判断是否退出睡眠状态检测模式。
在获取生理指标数据后，可以利用其确定穿戴状态，以便根据穿戴状态确定用户的睡眠情况。在一种实施方式中，可以对生理指标数据进行异常检测，即判断生理指标数据是否处于合理区间，得到检测结果。若先前的运动行为为用户取下智能穿戴设备的行为，则智能穿戴设备被取下后获取的生理指标数据必然不是合理的数据。而在智能穿戴设备被取下后，则无法确定用户当前的睡眠状态，因此可以停止检测用户的睡眠情况，即不对用户的睡眠状态进行检测并得到检测结果。在确定智能穿戴设备被取下后，由于无法判 断用户是否处于睡眠状态，因此可以退出睡眠状态检测模式，避免出现无法退出睡眠状态检测模式，长时间认为用户处于睡眠状态的情况，因此不会误认为用户持续处于睡眠状态，避免对用户的睡眠情况进行误判。
利用生理指标数据判断穿戴状态的具体方式本实施例不做限定，例如可以将其与预设的正常指标范围进行比对，进而确定是否异常。例如当生理指标数据为心率数据时，在智能穿戴设备被取下后就无法获取用户的真正心率数据，获取到的心率数据将会比正常心率低很多。或者当生理指标数据为体温数据时，在智能穿戴设备被取下后就无法获取用户的真正体温数据，获取到的体温数据也比正常体温低很多。或者还可以采用其他方法根据生理指标数据确定穿戴状态，例如在获取多项生理指标数据时判断其组合是否合理，若不合理，则检测结果为异常，可以确定智能穿戴设备被取下。
在利用生理指标数据确定智能穿戴设备的穿戴状态后，可以根据穿戴状态判断是否退出睡眠状态检测模式。当确定智能穿戴设备被取下时，穿戴状态即为未穿戴，此时无法准确地判断用户的睡眠情况。因此，为了保证对用户睡眠情况的判断准确性，可以退出睡眠状态检测模式，即不再将用户的睡眠情况判定为处于睡眠状态，防止造成对用户睡眠情况的误判。同时还可以停止执行在睡眠状态中执行的操作，或更改在睡眠状态中被更改的设置。当确定智能穿戴设备仍然被用户佩戴时，穿戴状态即为已穿戴，此时可以确定用户仍然处于睡眠状态，而先前的运行行为可能为用户的翻身行为或其他类似行为，因此可以不退出睡眠状态检测模式。在一种可能的实施方式中，在准确确定用户的睡眠情况后，可以执行后续操作，例如获取睡眠监测数据的操作，或者设定闹钟提醒方式的操作。
S106：预设操作。
本实施例并不限定预设操作的具体内容，例如可以为无操作，即不执行任何操作。需要说明的是，S102步骤未检测到运动行为后执行的预设操作，与S103步骤检测到用户未处于睡眠状态后执行的预设操作可以相同，也可以不同，预设操作的具体内容可以根据实际情况进行设置。
应用本申请实施例提供的睡眠检测方法，在确定用户处于睡眠状态后，即可进入睡眠状态检测模式，并通过检测用户的运动行为确定用户的睡眠情况。若检测到运动行为，则说明用户可能醒来，或者可能在睡眠状态下运动， 或者可能取下了智能穿戴设备。判断用户是否处于睡眠状态，若用户处于睡眠状态，则说明用户没有醒来，运动行为可能为睡眠状态下的运动，或者可能为取下智能穿戴设备的运动。因此获取生理指标数据，根据生理指标数据确定穿戴状态，并根据穿戴状态确定是否退出睡眠状态检测模式。即，若生理指标数据表示智能穿戴设备仍然被穿戴，此时可以确定用户处于睡眠状态，因此无需退出睡眠状态检测模式；若生理指标数据表示智能穿戴设备被取下。在确定智能穿戴设备被取下后，可以确定无法判断用户的睡眠情况，因此可以推出睡眠状态检测模式，避免造成对用户睡眠状态的误判。通过对运动行为、睡眠状态以及生理指标数据进行判断，可以准确地识别智能穿戴设备是否被取下，确定是否能够准确判断用户是否处于睡眠状态，进而在需要退出睡眠状态检测模式时退出，即在无法准确确定用户是否处于睡眠状态时退出睡眠状态检测模式，避免因无法退出睡眠状态检测模式而出现对用户睡眠情况造成误判的问题，解决了相关技术存在的睡眠检测不准确的问题。
基于上述实施例，本实施例将对上述实施例中的若干步骤进行具体的阐述。具体的，S101步骤可以包括：
步骤11：获取用户的第二运动数据。
步骤12：根据第二运动数据得到用户的第二运动强度。
步骤13：判断第二运动强度是否处于第二低强度区间。
步骤14：若第二运动强度处于第二低强度区间，则利用睡眠算法对用户进行检测，得到第二检测结果。
步骤15：若第二检测结果为处于睡眠状态，则确定用户处于睡眠状态。
本实施例中，为了保证确定用户处于睡眠状态的准确性，防止在用户没有处于睡眠状态的情况下执行S102步骤及后续步骤，可以从运动强度这一方面对用户是否处于睡眠状态进行判断；在另一种可能的实施方式中，可以从运动强度和心率数据两个方面进行判断。具体的，首先获取用户的第二运动数据，并利用该第二运动数据得到用户的第二运动强度。第二运动强度可以表示用户肢体运动的剧烈程度。在得到第二运动强度后，可以判断其是否处于第二低强度区间。当第二运动强度处于第二低强度区间内时，说明用户的肢体运动很轻微，可能处于睡眠状态，或者因看电影等原因而长时间处于低 强度运动状态。此时利用睡眠算法对用户进行睡眠情况检测，得到第二检测结果。睡眠算法的具体内容可以参考相关技术，在此不做赘述。若第二检测结果为处于睡眠状态，则可以确定用户处于睡眠状态。
在一种可能的实施方式中，在确定用户已经处于睡眠状态后，还可以利用心率数据进行进一步检测，例如可以判断用户当前的心率是否处于睡眠状态区间内，若处于睡眠状态区间内，则可以进一步确定用户处于睡眠状态，提高检测准确性。
基于上述实施例，在一种实施方式中，在确定用户处于睡眠状态后，可以判断是否检测到运动行为。为了全面地对用户的运动行为进行检测，本实施例通过第一运动强度判断是否检测到运动行为。S102步骤可以包括：
步骤21：获取用户的第一运动数据。
步骤22：根据第一运动数据得到用户的第一运动强度。
步骤23：判断第一运动强度是否处于第一低强度区间。
步骤24：若第一运动强度未处于第一低强度区间，则确定检测到运动行为。
需要说明的是，在采用相同的方式得到第一运动强度和第二运动强度时，第一运动数据与第二运动数据的区别在于获取的时间不同，第一运动数据在确定用户处于睡眠状态后获取，第二运动数据在未确定用户是否出入睡眠状态时获取。在采用不同的方式得到第一运动强度和第二运动强度和，第一运动数据与第二运动数据的区别不仅在于获取的时间不同，其内容本身可以不同。第一运动数据具体可以为加速度数据、速度数据等。在得到第一运动数据后，利用其计算得到用户的第一运动强度，第一运动强度的具体计算方式本实施例不做限定，只要利用第一运动数据得到的第一运动强度可以表示用户肢体运动的剧烈程度即可。在得到第一运动强度后，可以判断是否处于第一低强度区间。当第一运动强度处于第一低强度区间内时，说明用户的肢体运动很轻微，不能用于说明用户的睡眠情况发生改变，例如不能用于说明用户已经醒来，或者不能用于说明用户取下了智能穿戴设备。若第一运动强度未处于第一低强度区间，则说明用户的肢体运动较剧烈，可能表示用户在睡眠状态中做出了肢体运动(例如翻身)，或者可能表示用户取下了智能穿戴设备，因此确定检测到了运动行为。需要说明的是，第一低强度区间和第二 低强度区间的大小和上下限值可以相同也可以不同。
进一步，为了准确的表示用户的第一运动强度，本实施例优选的，可以利用加速度数据作为第一运动数据。此时，步骤21可以包括：
步骤211：利用运动传感器获取沿X轴方向、Y轴方向和Z轴方向的加速度值作为第一运动数据。
相应的，步骤22可以包括：
步骤221：利用加速度值得到合加速度数据，并将合加速度数据确定为第一运动强度。
本实施例并不限定运动传感器的具体类型，例如可以为三轴加速度计。三轴加速度计可以获取沿X轴方向、Y轴方向和Z轴方向三个方向上的加速度值，利用这三个加速度值可以合并为一个合加速度。合加速度数据可以为合加速度的大小，例如当沿X轴方向、Y轴方向和Z轴方向三个方向上的加速度值为x、y、z时，根据空间直角坐标系的计算方式，合加速度的大小可以为
Figure PCTCN2020132294-appb-000001
合加速度的大小可以准确地表示运动的强度，因此可以将合加速度数据确定为第一运动强度。
在确定检测到运动行为后，S103步骤可以包括：
步骤31：利用睡眠算法对用户进行检测，得到第一检测结果。
步骤32：利用第一检测结果判断用户是否处于睡眠状态。
需要说明的是，睡眠算法可以为基于运动数据和/或生理指标数据等参数进行检测的算法，其具体内容不做限定，例如可以采用相关技术中的睡眠算法。在一种实施方式中，为了减少智能穿戴设备的功耗，可以利用睡眠算法对第一运动数据进行睡眠情况检测，无需重新获取用于进行睡眠情况检测的数据。
在第一检测结果显示用户处于睡眠状态后，还可以获取生理指标数据，并基于生理指标数据确定穿戴状态，进一步根据穿戴状态确定用户的睡眠情况。即，进一步准确判断用户是否处于睡眠状态。S105步骤可以包括：
步骤41：判断生理指标数据是否处于用户对应的个人指标区间。
步骤42：若不处于个人指标区间，则确定穿戴状态为未穿戴。
步骤43：若处于个人指标区间，则确定穿戴状态为已穿戴。
在一种实施方式中，在S101步骤确定用户处于睡眠状态后，智能穿戴设 备可以进入睡眠状态检测模式。此时相应的，S105步骤还可以包括：
步骤44：若穿戴状态为未穿戴，则退出睡眠状态检测模式。
步骤45：若穿戴状态为已穿戴，则确定用户仍处于睡眠状态。
为了使穿戴状态判断更加准确，可以利用生理指标数据与用户对应的个人指标区间进行判断。个人指标区间与用户自身的身体情况相对应，由于每个人的生理指标存在一定差异，因此利用个人指标区间对生理指标数据进行异常判断，可以更符合用户个人的情况，得到的检测结果准确性更高。为了防止对用户的睡眠情况做出误判，即为了避免对用户是否处于睡眠状态做出误判，在确定穿戴状态为未穿戴时，可以退出睡眠状态检测模式，不再将用户的睡眠情况判定为处于睡眠状态，因此可以避免错误地认为用户仍然处于睡眠状态，提高了对用户睡眠状态的检测准确性。
在实际应用中，当用户处于睡眠状态时，智能穿戴设备可以获取用户对应的睡眠监测数据，以便对该数据进行分析得到分析结果。因此与上述实施方式相对应的，在确定穿戴状态为未穿戴时，智能穿戴设备已经无法获取到准确的睡眠监测数据，为了防止分析结果受到不准确的睡眠监测数据的影响，可以在确定穿戴设备为未穿戴时，停止获取用户对应的睡眠监测信息。
基于上述实施例，本实施例将说明一种具体的睡眠检测过程。请参考图2，图2为本申请实施例提供的一种具体的睡眠检测方法流程图。在开始后，计算N1时间段内的运动强度V1。其中：
Figure PCTCN2020132294-appb-000002
其中，i表示N1时间段内的各个时刻，x(i)、y(i)、z(i)分别表示三轴加速度计在三个垂直方向上获取到的加速度值。在得到V1后，将其与thre_s比对，判断N1时间段内是否存在运行行为。若V1≥thre_s，说明存在运动行为，用户不可能处于睡眠状态，因此重新获取加速度值。若V1<thre_s，说明不存在运动行为，处于低强度区间，用户可能入睡，因此利用睡眠算法检测用户是否处于睡眠状态。若检测到用户处于睡眠状态，则将N1的开始时刻t1记录为入睡时刻start_t。同时开启心率检测，确认是否入睡。若确定入睡，则可以进入睡眠状态检测模式，且将start_t发送给睡眠模块，以便睡眠模块进行记录，睡眠模块可以用于对用户的睡眠时间进行统计，或者可以执行其他操作，例如 对用户睡眠深浅进行检测，具体可以参考相关技术，在此不做赘述。
在用户入睡后，计算N2时间段的运动强度V2，具体计算方式与V1相同。此时采用了不同的阈值对运动行为进行判断，若V2≤thre_e，说明不存在运动行为，重新获取运动数据。若V2>thre_e，说明存在运动行为，此时将N2的开始时刻t2记录为end_t。同时利用睡眠算法检测是否用户是否处于睡眠状态，睡眠算法检测的检测用时为t3。若不处于睡眠状态，则退出睡眠状态检测模式。若处于睡眠状态，则在t＝t2+t3时刻开启心率检测，即获取心率数据作为生理指标数据。利用心率数据确定智能穿戴设备的状态状态，进而根据穿戴状态确定用户的睡眠情况，若心率数据为正常数据，则说明智能穿戴设备未被取下，处于已穿戴状态，可以确定用户仍处于睡眠状态，此时可以重新获取运动数据，或执行其他操作。若心率数据为异常数据，则说明智能穿戴设备被取下，处于未穿戴状态。此时无法准确地判断用户的睡眠情况，因此不再将用户的睡眠情况判定为处于睡眠状态，将end_t发送给睡眠模块以便记录，在记录完毕后退出睡眠状态检测模式。
下面对本申请实施例提供的睡眠检测装置进行介绍，下文描述的睡眠检测装置与上文描述的睡眠检测方法可相互对应参照。
请参考图3，图3为本申请实施例提供的一种睡眠检测装置的结构示意图，包括：
第一判断模块110，用于在确定用户处于睡眠状态后，进入睡眠状态检测模式，并判断是否检测到用户的运动行为；
第二判断模块120，用于若检测到运动行为，则判断用户是否仍处于睡眠状态；
获取模块130，用于若用户仍处于睡眠状态，则获取生理指标数据；
模式退出确定模块140，用于利用生理指标数据确定穿戴状态，并根据穿戴状态判断是否退出睡眠状态检测模式。
可选地，第一判断模块110，包括：
第一数据获取单元，用于获取用户的第一运动数据；
第一强度获取单元，用于根据第一运动数据得到用户的第一运动强度；
第一强度判断单元，用于判断第一运动强度是否处于第一低强度区间；
运动行为确定单元，用于若第一运动强度未处于第一低强度区间，则确定检测到运动行为。
可选地，数据获取单元，包括：
加速度值获取子单元，用于利用运动传感器获取沿X轴方向、Y轴方向和Z轴方向的加速度值作为第一运动数据；
相应的，强度获取单元，包括：
合加速度数据计算子单元，用于利用加速度值得到合加速度数据，并将合加速度数据确定为第一运动强度。
可选地，第二判断模块120，包括：
第一睡眠情况检测单元，用于利用睡眠算法对用户进行检测，得到第一检测结果；
第一睡眠状态确定单元，用于利用第一检测结果判断用户是否处于睡眠状态。
可选地，模式退出确定模块140，包括：
指标判断单元，用于判断生理指标数据是否处于用户对应的个人指标区间；
异常确定单元，用于若不处于个人指标区间，则确定穿戴状态为未穿戴；
正常确定单元，用于若处于个人指标区间，则确定穿戴状态为已穿戴。
可选地，模式退出确定模块140，包括：
模式退出单元，用于若穿戴状态为未穿戴，则退出睡眠状态检测模式；
第二睡眠状态确定单元，用于若穿戴状态为已穿戴，则确定用户仍处于睡眠状态，不退出睡眠状态检测模式。
可选地，第一判断模块110，包括：
第二数据获取单元，用于获取用户的第二运动数据；
第二强度获取单元，用于根据第二运动数据得到用户的第二运动强度；
第二强度判断单元，用于判断第二运动强度是否处于第二低强度区间；
第二睡眠情况检测单元，用于若第二运动强度处于第二低强度区间，则利用睡眠算法对用户进行检测，得到第二检测结果；
第三睡眠状态确定单元，用于若第二检测结果为处于睡眠状态，则确定用户处于睡眠状态。
下面对本申请实施例提供的智能穿戴设备进行介绍，下文描述的智能穿戴设备与上文描述的睡眠检测方法可相互对应参照。
请参考图4，图4为本申请实施例提供的一种智能穿戴设备的结构示意图。其中智能穿戴设备100可以包括处理器101和存储器102，还可以进一步包括多媒体组件103、信息输入/信息输出(I/O)接口104以及通信组件105中的一种或多种。
其中，处理器101用于控制智能穿戴设备100的整体操作，以完成上述的睡眠检测方法中的全部或部分步骤；存储器102用于存储各种类型的数据以支持在智能穿戴设备100的操作，这些数据例如可以包括用于在该智能穿戴设备100上操作的任何应用程序或方法的指令，以及应用程序相关的数据。该存储器102可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，例如静态随机存取存储器(Static Random Access Memory，SRAM)、电可擦除可编程只读存储器(Electrically Erasable Programmable Read-Only Memory，EEPROM)、可擦除可编程只读存储器(Erasable Programmable Read-Only Memory，EPROM)、可编程只读存储器(Programmable Read-Only Memory，PROM)、只读存储器(Read-Only Memory，ROM)、磁存储器、快闪存储器、磁盘或光盘中的一种或多种。
多媒体组件103可以包括屏幕和音频组件。其中屏幕例如可以是触摸屏，音频组件用于输出和/或输入音频信号。例如，音频组件可以包括一个麦克风，麦克风用于接收外部音频信号。所接收的音频信号可以被进一步存储在存储器102或通过通信组件105发送。音频组件还包括至少一个扬声器，用于输出音频信号。I/O接口104为处理器101和其他接口模块之间提供接口，上述其他接口模块可以是键盘，鼠标，按钮等。这些按钮可以是虚拟按钮或者实体按钮。通信组件105用于智能穿戴设备100与其他设备之间进行有线或无线通信。无线通信，例如Wi-Fi，蓝牙，近场通信(Near Field Communication，简称NFC)，2G、3G或4G，或它们中的一种或几种的组合，因此相应的该通信组件105可以包括：Wi-Fi部件，蓝牙部件，NFC部件。
智能穿戴设备100可以被一个或多个应用专用集成电路(Application Specific Integrated Circuit，简称ASIC)、数字信号处理器(Digital Signal  Processor，简称DSP)、数字信号处理设备(Digital Signal Processing Device，简称DSPD)、可编程逻辑器件(Programmable Logic Device，简称PLD)、现场可编程门阵列(Field Programmable Gate Array，简称FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述实施例给出的睡眠检测方法。
下面对本申请实施例提供的计算机可读存储介质进行介绍，下文描述的计算机可读存储介质与上文描述的睡眠检测方法可相互对应参照。
本申请还提供一种计算机可读存储介质，计算机可读存储介质上存储有计算机程序，计算机程序被处理器执行时实现上述的睡眠检测方法的步骤。
该计算机可读存储介质可以包括：U盘、移动硬盘、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其它实施例的不同之处，各个实施例之间相同或相似部分互相参见即可。对于实施例公开的装置而言，由于其与实施例公开的方法相对应，所以描述的比较简单，相关之处参见方法部分说明即可。
本领域技术人员还可以进一步意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件的方式来执行，取决于技术方案的特定应用和设计约束条件。本领域技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应该认为超出本申请的范围。
结合本文中所公开的实施例描述的方法或算法的步骤可以直接用硬件、处理器执行的软件模块，或者二者的结合来实施。软件模块可以置于随机存储器(RAM)、内存、只读存储器(ROM)、电可编程ROM、电可擦除可编程ROM、寄存器、硬盘、可移动磁盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质中。
最后，还需要说明的是，在本文中，诸如第一和第二等之类的关系属于仅仅用来将一个实体或者操作与另一个实体或者操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语包括、包含或者其他任何变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。
以上对本申请所提供的睡眠检测方法、睡眠检测装置、智能穿戴设备和计算机可读存储介质进行了详细介绍，本文中应用了具体个例对本申请的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本申请的方法及其核心思想；同时，对于本领域的一般技术人员，依据本申请的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本申请的限制。

Claims (10)

  1. 一种睡眠检测方法，其特征在于，包括：
    在确定用户处于睡眠状态后，进入睡眠状态检测模式，并判断是否检测到所述用户的运动行为；
    若检测到所述运动行为，则判断所述用户是否仍处于所述睡眠状态；
    若所述用户仍处于所述睡眠状态，则获取生理指标数据；
    利用所述生理指标数据确定穿戴状态，并根据所述穿戴状态判断是否退出所述睡眠状态检测模式。
  2. 根据权利要求1所述的睡眠检测方法，其特征在于，所述判断是否检测到所述用户的运动行为，包括：
    获取所述用户的第一运动数据；
    根据所述第一运动数据得到所述用户的第一运动强度；
    判断所述第一运动强度是否处于第一低强度区间；
    若所述第一运动强度未处于所述第一低强度区间，则确定检测到所述运动行为。
  3. 根据权利要求2所述的睡眠检测方法，其特征在于，所述获取所述用户的第一运动数据，包括：
    利用运动传感器获取沿X轴方向、Y轴方向和Z轴方向的加速度值作为所述第一运动数据；
    相应的，所述根据所述第一运动数据得到所述用户的第一运动强度，包括：
    利用所述加速度值得到合加速度数据，并将所述合加速度数据确定为所述第一运动强度。
  4. 根据权利要求1所述的睡眠检测方法，其特征在于，所述判断所述用户是否仍处于所述睡眠状态，包括：
    利用睡眠算法对所述用户进行检测，得到第一检测结果；
    利用所述第一检测结果判断所述用户是否处于所述睡眠状态。
  5. 根据权利要求1所述的睡眠检测方法，其特征在于，所述利用所述生理指标数据确定穿戴状态，包括：
    判断所述生理指标数据是否处于所述用户对应的个人指标区间；
    若不处于所述个人指标区间，则确定所述穿戴状态为未穿戴；
    若处于所述个人指标区间，则确定所述穿戴状态为已穿戴。
  6. 根据权利要求5所述的睡眠检测方法，其特征在于，所述根据所述穿戴状态判断是否退出所述睡眠状态检测模式，包括：
    若所述穿戴状态为所述未穿戴，则退出所述睡眠状态检测模式；
    若所述穿戴状态为所述已穿戴，则确定所述用户仍处于睡眠状态，不退出所述睡眠状态检测模式。
  7. 根据权利要求1至6任一项所述的睡眠检测方法，其特征在于，确定用户处于睡眠状态的过程，包括：
    获取所述用户的第二运动数据；
    根据所述第二运动数据得到所述用户的第二运动强度；
    判断所述第二运动强度是否处于第二低强度区间；
    若所述第二运动强度处于所述第二低强度区间，则利用睡眠算法对所述用户进行检测，得到第二检测结果；
    若所述第二检测结果为处于睡眠状态，则确定所述用户处于所述睡眠状态。
  8. 一种睡眠检测装置，其特征在于，包括：
    第一判断模块，用于在确定用户处于睡眠状态后，进入睡眠状态检测模式，并判断是否检测到所述用户的运动行为；
    第二判断模块，用于若检测到所述运动行为，则判断所述用户是否仍处于所述睡眠状态；
    获取模块，用于若所述用户仍处于所述睡眠状态，则获取生理指标数据；
    模式退出确定模块，用于利用所述生理指标数据确定穿戴状态，并根据所述穿戴状态判断是否退出所述睡眠状态检测模式。
  9. 一种智能穿戴设备，其特征在于，包括存储器和处理器，其中：
    所述存储器，用于保存计算机程序；
    所述处理器，用于执行所述计算机程序，以实现如权利要求1至7任一项所述的睡眠检测方法。
  10. 一种计算机可读存储介质，其特征在于，用于保存计算机程序，其中，所述计算机程序被处理器执行时实现如权利要求1至7任一项所述的睡眠检测方法。
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