Sleep detection method, device, equipment and storage medium

Abstract

The present application provides a sleep detection method, device, equipment and storage medium, which belongs to the field of smart home technology. The method includes: in response to the detection target lying in bed, determining the weight level of the detection target; determining the current sleep detection mode based on the weight level, and the detection power of the sleep detector is different in different sleep detection modes; collecting the physiological signal data of the detection target in the current sleep detection mode; generating a sleep detection report of the detection target based on the physiological signal data. By obtaining the weight level of the detection target when lying in bed to determine the sleep detection mode, performing sleep detection in a suitable sleep mode, and adaptively adjusting the power of the sleep detector according to the weight of the detection target to obtain effective physiological signal data, the accuracy of sleep detection can be improved, a reliable sleep detection report can be provided, and the scope of application of sleep detection can be expanded.

Description

Technical Field

[0001] The present application relates to the field of smart home technology, and in particular to a sleep detection method, apparatus, device, and storage medium. Background Art

[0002] With the continuous advancement of sensor technology and information technology, every aspect of life is becoming more intelligent. People are no longer satisfied with traditional information transmission methods and are pursuing wireless, high-speed, secure, and convenient information transmission. This has led to the emergence of the Internet of Things, and smart beds have become a part of our lives. Sleep is a universal, natural state of rest for all living things. Given the fast pace of modern life and high work pressure, good rest and sleep are crucial for both health and career. People are increasingly paying attention to sleep health, and more and more people are purchasing sleep quality monitoring devices. Current sleep monitoring devices include wearable and non-wearable devices. Compared to wearable devices, non-wearable devices are mostly embedded in the mattress or placed under it, making them more user-friendly and comfortable. However, these devices can only monitor sleep within a certain weight range. If the user is underweight or overweight, the accuracy of the data analysis will be significantly reduced. Summary of the Invention

[0003] This application provides a sleep detection method, apparatus, device, and storage medium that can improve the accuracy of sleep detection and expand the scope of application of sleep detection. The technical solution is as follows:

[0004] In one aspect, an embodiment of the present application provides a sleep detection method, which is applied to a smart bedding equipped with a sleep detector, and includes:

[0005] In response to the detected subject being bedridden, determining a weight class of the detected subject;

[0006] determining a current sleep detection mode based on the weight level, wherein the detection power of the sleep detector is different in different sleep detection modes;

[0007] collecting physiological signal data of the detection target in the current sleep detection mode, wherein the physiological signal data is used to represent the physiological state of the detection target;

[0008] A sleep detection report of the detection target is generated based on the physiological signal data.

[0009] Optionally, pressure sensors are evenly distributed in the smart bedding;

[0010] In response to the detected target being bedridden, determining the weight level of the detected target includes:

[0011] In response to the detection target being in bed, determining a body mass index of the detection target based on a pressure area and an output current value of the pressure sensor;

[0012] In response to the body mass index being less than or equal to a lower body mass index limit, determining the weight level to be a first level;

[0013] In response to the body mass index being greater than the body mass index lower limit and less than the body mass index upper limit, determining the weight level to be the second level;

[0014] In response to the body mass index being greater than or equal to the body mass index upper limit, the weight level is determined to be the third level.

[0015] Optionally, determining the current sleep detection mode based on the weight level includes:

[0016] In response to the weight level being the second level, determining that the current sleep detection mode is a normal mode;

[0017] In response to the weight level being the first level, determining that the current sleep detection mode is a first super mode;

[0018] In response to the weight level being the third level, determining that the current sleep detection mode is a second super mode;

[0019] The detection power of the sleep detector in the normal mode is smaller than the detection power of the sleep detector in the first super mode and the second super mode.

[0020] Optionally, before determining the weight level of the detected target in response to the detected target being bedridden, the method further includes:

[0021] determining a pressed shape and a pressed position in response to the pressure value detected by the pressure sensor;

[0022] In response to the pressed position being within a preset range and the pressed shape conforming to a preset shape, it is determined that the detection target is in bed and the sleep detector is turned on.

[0023] Optionally, the smart bedding is provided with a movable guide rail;

[0024] The method further comprises:

[0025] determining a signal detection point of the detection target based on the pressure area and the pressure position detected by the pressure sensor;

[0026] The sleep detector is controlled to move on the movable guide rail to the coordinate corresponding to the signal detection point.

[0027] Optionally, the physiological signal data includes at least one of an electrocardiogram, heart rate, respiratory rate, and body temperature;

[0028] Generating a sleep detection report of the detection target based on the physiological signal data includes:

[0029] Combining the physiological signal data into a feature vector;

[0030] According to the chronological order of data collection, the feature vector is input into the long short-term memory network to obtain a sleep cycle detection report, where the sleep cycle includes wakefulness, light sleep and deep sleep. The long short-term memory network is trained based on a public sleep dataset and AASM sleep standards.

[0031] On the other hand, an embodiment of the present application provides a sleep detection device, comprising:

[0032] a first determining module, configured to determine a weight class of the detection target in response to the detection target being bedridden;

[0033] a second determining module, configured to determine a current sleep detection mode based on the weight level, wherein the detection power of the sleep detector is different in different sleep detection modes;

[0034] an acquisition module, configured to acquire physiological signal data of the detection target in the current sleep detection mode, wherein the physiological signal data is used to represent the physiological state of the detection target;

[0035] A generating module is used to generate a sleep detection report of the detection target based on the physiological signal data.

[0036] On the other hand, an embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, a sleep detection method as described in the above aspect is performed.

[0037] On the other hand, an embodiment of the present application provides a smart bedding, which includes the electronic device, sleep detector, and pressure sensor described in the above aspects, wherein the sleep detector is used to collect physiological signal data, and the pressure sensor is used to convert the pressure signal into an electrical signal. The electronic device is communicatively connected to the sleep detector and the pressure sensor, and is used to determine the weight level of the detection target based on the electrical signal of the pressure sensor, and adjust the sleep detection mode of the sleep detector based on the weight level to generate a sleep detection report.

[0038] On the other hand, an embodiment of the present application provides a computer-readable storage medium, which stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement a sleep detection method described in the above aspect.

[0039] On the other hand, an embodiment of the present application provides a computer program product, which runs on a processor of a computer device, so that the computer device executes a sleep detection method as described in the above aspects.

[0040] The technical solution provided by this application includes at least the following beneficial effects:

[0041] The present application provides a sleep detection method, apparatus, device, and storage medium. These methods determine a sleep detection mode by obtaining the weight level of a detection target while in bed, perform sleep detection in an appropriate sleep mode, and adaptively adjust the power of a sleep detector according to the weight of the detection target to obtain effective physiological signal data. This can improve the accuracy of sleep detection, provide reliable sleep detection reports, and expand the scope of application of sleep detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments.

[0043] Figure 1 is a flow chart of a sleep detection method provided by an exemplary embodiment of the present application;

[0044] Figure 2 is a flow chart of a sleep detection method provided by another exemplary embodiment of the present application;

[0045] Figure 3 is a flow chart of a sleep detection method provided by another exemplary embodiment of the present application;

[0046] Figure 4 This is a structural block diagram of a sleep detection device provided by an exemplary embodiment of the present application;

[0047] Figure 5 It is a structural block diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0049] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0050] If similar descriptions of "first\second\third" appear in the application documents, the following explanation will be added. In the following description, the terms "first\second\third" are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0052] Based on the problems existing in the related art, embodiments of the present application provide a sleep detection method, which is applied to an electronic device, which may be a mobile terminal, a computer, or the like. In some embodiments, the electronic device may be a controller for a smart bedding (e.g., a smart mattress), which includes: a controller, a pressure sensor, and a sleep detector, wherein the pressure sensor is used to convert a pressure signal into an electrical signal, and the sleep detector is used to collect physiological signal data. The controller is communicatively connected to the sleep detector and the pressure sensor, and is used to determine the weight level of the detection target based on the electrical signal of the pressure sensor, adjust the sleep detection mode of the sleep detector based on the weight level, and generate a sleep detection report.

[0053] The functions implemented by a sleep detection method provided in an embodiment of the present application can be implemented by a processor of an electronic device calling a program code, wherein the program code can be stored in a computer storage medium.

[0054] Please refer to Figure 1 , which shows a flowchart of a sleep detection method provided by an exemplary embodiment of the present application, the method is applied to a smart bedding equipped with a sleep detector. The method includes the following steps:

[0055] Step 101 : In response to a detection target being bedridden, determining the weight level of the detection target.

[0056] In one possible implementation, the smart bedding determines whether the subject is in bed. If the subject is determined to be in bed, the smart bedding does not directly initiate sleep detection, but first determines the subject's weight level and then implements a sleep detection mode corresponding to the weight level.

[0057] Illustratively, the detection target may be a person or an animal, and may be a specific object or a non-specific object.

[0058] Optionally, when a sleep detection operation is received, it is determined that the detection target is lying in bed, such as by turning on a sleep detection switch; or the smart bedding detects in real time whether there is a detection target lying in bed, such as by judging whether the pressure sensor receives pressure.

[0059] Illustratively, the weight level of the detection target can be set by the user, or a weight measuring device is provided in the smart bedding, and each time the smart bedding detects the detection target lying in bed, the current weight is measured by the weight measuring device to determine the weight level.

[0060] Step 102 : determining a current sleep detection mode based on the weight level, wherein the detection power of the sleep detector is different in different sleep detection modes.

[0061] The difficulty of acquiring physiological signals of detection targets of different weights varies. The physiological signals of detection targets that are too heavy or too light (such as infants and children) are weaker, and higher detection power is required to accurately capture the physiological signals.

[0062] Therefore, the smart bedding selects an appropriate sleep detection mode from the preset sleep detection modes based on the weight level of the detection target, determines it as the current sleep detection mode, and makes the sleep detector work in the current sleep detection mode. The sleep detector is used to collect physiological signal data of the detection target.

[0063] Step 103 : Acquire physiological signal data of the detection target in the current sleep detection mode. The physiological signal data is used to represent the physiological state of the detection target.

[0064] The smart bedding controls the sleep monitor to operate in the current sleep detection mode, collecting physiological signal data of the detection target. The physiological signal data is used to represent the physiological state of the detection target, for example, physiological signal data includes heart rate, body temperature, respiratory rate, etc.

[0065] The sleep detector has different detection powers in different sleep detection modes. In one possible implementation, the smart bedding is pre-configured with a correspondence between detection powers and sleep detection modes. The smart bedding queries the detection power based on the current sleep detection mode and sends a detection instruction to the sleep detector, causing the sleep detector to operate at the detection power.

[0066] Step 104 : Generate a sleep detection report of the detection target based on the physiological signal data.

[0067] The smart bedding generates a sleep monitoring report based on the physiological signal data collected by the sleep monitor. The sleep monitoring report includes the physiological signal data collected by the sleep monitor, and may also include other data generated by the smart bedding based on the physiological signal data analysis, such as various sleep cycles and the number of tossing and turning.

[0068] Optionally, the smart bedding can upload the sleep monitoring report to the cloud, and the user can access the sleep monitoring report from the cloud via other mobile devices (such as smartphones). Alternatively, the smart bedding can be provided with a display panel, allowing the user to view the sleep monitoring report directly through the smart bedding. Alternatively, the smart bedding can be connected to the user's mobile device (such as a Bluetooth connection) to send the sleep monitoring report to the mobile device for the user to view.

[0069] In summary, the sleep detection method provided in this application determines the sleep detection mode by obtaining the weight level of the detection target when lying in bed, performs sleep detection in a suitable sleep mode, and adaptively adjusts the power of the sleep detector according to the weight of the detection target to obtain effective physiological signal data. This can improve the accuracy of sleep detection, provide reliable sleep detection reports, and expand the scope of application of sleep detection.

[0070] Please refer to Figure 2 , which shows a flowchart of a sleep detection method provided by another exemplary embodiment of the present application, the method is applied to a smart bedding equipped with a sleep detector. The method includes the following steps:

[0071] Step 201 : In response to a pressure value detected by a pressure sensor, a pressed shape and a pressed position are determined.

[0072] In one possible implementation, smart bedding is equipped with pressure sensors. Because users typically sleep in a fluid position, small pressure sensors are evenly distributed throughout the smart bedding to facilitate detection. When a pressure sensor detects pressure, the smart bedding can determine the shape and location of the pressure based on the location of the pressure sensor, thereby determining whether the bed is the target bed.

[0073] Schematically, a pressure sensor is provided within every 0.04 square meters of the smart bedding.

[0074] Step 202 : In response to the pressure position being within a preset range and the pressure shape conforming to a preset shape, it is determined that the detection target is in bed and the sleep detector is turned on.

[0075] The smart bedding is configured with a preset range and shape for the target to be detected. If the pressure sensor's pressure location falls within the preset range, and the pressure shape corresponding to the pressure range matches the preset shape, the target is determined to be in a sleeping position. The pressure shape matches the shape of the contact surface of the smart bedding when the target is in a sleeping position, and the pressure range corresponds to the range the target typically resides in when in a sleeping position.

[0076] Correspondingly, if the pressure position does not fall within the preset range, or the pressure shape does not conform to the preset shape, it is determined that the person is not in bed, and the sleep detector is not started, or the sleep detector that is in the turned-on state is turned off.

[0077] When the pressure position is within the preset range and the pressure shape conforms to the preset shape, the target bed is determined to be detected. It can accurately control the automatic opening and closing of the sleep detector, and prevent other objects from being placed on the smart bedding to mistakenly start the sleep detection program, affect the sleep report results and consume electricity and equipment resources.

[0078] Optionally, the smart bedding is divided into three areas, namely, upper, middle and lower areas. When pressure is detected in all three areas and the shape of the pressed area is the shape of a human body, the target bed is determined to be detected.

[0079] Step 203 : In response to the detection target being in bed, the body weight index of the detection target is determined based on the pressure area and the output current value of the pressure sensor.

[0080] The pressure sensor converts the received pressure into an electric current, so the smart bedding can determine the body mass index based on the pressure area and the output current value of the pressure sensor. Generally speaking, the larger the current value and the larger the pressure area, the greater the weight.

[0081] In one possible implementation, different weights correspond to different current values. Technicians can determine the corresponding current values and possible pressure areas for each weight through experiments in advance, record them, and store them in the smart bedding. The smart bedding can then query the body mass index based on the pressure area and output current value.

[0082] Step 204 : In response to the body mass index being less than or equal to the body mass index lower limit, determining the weight level to be the first level.

[0083] The first level is a level with a relatively light weight. If the body mass index is less than or equal to the lower limit of the body mass index, the smart bedding determines that the weight level of the detection target is the first level.

[0084] For example, the lower limit of the body mass index is 30 kg. If the body mass index of the detection target is 25 kg, then the weight level is determined to be the first level.

[0085] Step 205 : In response to the body mass index being greater than the body mass index lower limit and less than the body mass index upper limit, determining the weight level to be the second level.

[0086] The second level is the normal weight level, corresponding to the weight range of most adults. If the subject's BMI is greater than the lower limit and less than the upper limit, the smart bedding will determine that their weight level is the second level.

[0087] For example, the lower limit of the body mass index is 30 kg and the upper limit of the body mass index is 80 kg. If the body mass index of the detected object is 70 kg, the smart bedding determines that its weight level is the second level.

[0088] Step 206 : In response to the body mass index being greater than or equal to the upper body mass index limit, determining the weight level to be the third level.

[0089] The third level is the overweight level. If the body mass index of the detected object is greater than or equal to the upper limit of the body mass index, the smart bedding determines that its weight level is the third level.

[0090] For example, the upper limit of the body mass index is 80 kg. If the body mass index of the detected object is 85 kg, the smart bedding determines that its weight level is the third level.

[0091] Step 207 : In response to the weight level being the second level, determining that the current sleep detection mode is the normal mode.

[0092] The second level corresponds to a body mass index with a stronger physiological signal strength, making it relatively easy for the sleep monitor to obtain its physiological signal data. The second level corresponds to normal mode. In normal mode, the sleep monitor's detection power is low.

[0093] Step 208 : In response to the weight level being the first level, determining that the current sleep detection mode is the first super mode.

[0094] Step 209 : In response to the weight level being the third level, determining that the current sleep detection mode is the second super mode.

[0095] The detection power of the sleep detector in the normal mode is less than the detection power of the sleep detector in the first super mode and the second super mode.

[0096] The first level of body mass index is lower than the lower limit of body mass index, and the third level of body mass index is higher than the upper limit of body mass index. The body mass indexes corresponding to the two are body mass indexes with relatively low physiological signal strength. It is relatively difficult for the sleep monitor to obtain its physiological signal data. The signal acquisition and amplification module needs to use high power for amplification in order to obtain sufficient and effective information. Therefore, the working mode of the sleep monitor at the first and third levels is the super strong mode. In the super strong mode, the detection power of the sleep monitor is higher. Specifically, the power of the first super strong detection mode and the second super strong detection mode is the same, or the detection power of the first super strong detection mode is greater than the detection power of the second super strong detection mode, or the detection power of the second super strong detection mode is greater than the detection power of the first super strong detection mode, or the power range of the first super strong detection mode and the second super strong detection mode overlap. Technicians can set the power range of each detection mode based on experimental results.

[0097] Step 210 : Acquire physiological signal data of the detection target in the current sleep detection mode. The physiological signal data is used to represent the physiological state of the detection target.

[0098] The specific implementation of step 210 can refer to the above step 103, and will not be repeated here in this embodiment of the present application.

[0099] Step 211: compose the physiological signal data into a feature vector.

[0100] Optionally, the physiological signal data includes, but is not limited to, at least one of an electrocardiogram (ECG), heart rate, respiratory rate, and body temperature. The smart bedding utilizes components such as an ECG detection module, a respiratory detection module, and an infrared temperature sensor integrated into the sleep monitor to detect and record the user's ECG, heart rate, respiratory rate, and body temperature while sleeping. The collected physiological signal data is then processed using a signal acquisition and amplification module and a noise reduction module.

[0101] In a possible implementation, the smart bedding combines various physiological signal data collected at the same time into a feature vector and uses a neural network to perform data analysis.

[0102] In step 212, the feature vector is input into the long short-term memory network according to the time sequence of data collection to obtain a sleep cycle detection report.

[0103] The smart bedding uses a long short-term memory network to analyze physiological signal data and generate a sleep cycle detection report. The LSTM network is a time-recurrent neural network. This network can effectively solve the problems of vanishing and exploding gradients in long sequence training, such as sleep duration. The LSTM network in the embodiment of the present application is trained based on a public sleep dataset and the AASM sleep standard. It can identify the significant waveforms of physiological signals in different sleep cycles and can estimate the user's three cycles of wakefulness, light sleep, and deep sleep by taking a small but sufficient amount of data.

[0104] Optionally, the smart bedding can also determine the number of turns based on the position changes of the pressure received by the pressure sensor, and determine the number of snoring based on the audio data collected by the sound collection module.

[0105] Smart bedding can also further realize home linkage based on the data in the sleep detection report, such as intervening in user snoring, creating a bedtime atmosphere, etc. It can also automatically turn off lights and close curtains after the user lies in bed based on the time the user spends in bed.

[0106] In this embodiment, the sleep monitor's power is adaptively adjusted based on the subject's weight. High power is used for underweight or overweight subjects to obtain valid physiological signal data, improving sleep detection accuracy and expanding its applicability. Furthermore, by determining whether the subject is in bed based on the shape and location of the pressure, sleep detection can be automatically activated without interference from other objects, preventing false activations.

[0107] The subject's movements during sleep, such as turning over, can cause the bed position to change, thereby affecting the data acquisition of the sleep monitor. Therefore, in one possible implementation, the method provided in the embodiment of the present application further includes the following steps:

[0108] Step 1: Determine the signal detection point of the detection target based on the pressure area and pressure position detected by the pressure sensor.

[0109] Step 2: Control the sleep detector to move on the moving guide rail to the coordinates corresponding to the signal detection point.

[0110] The sleep monitor is small in area and volume, while the smart bedding is large, and the movement of the user after falling asleep will also make signal acquisition difficult. Taking the detection target as the human body as an example, after the target is detected lying in bed, the pressure area of the smart bedding is obtained according to the data of the pressure sensor, and the position of the user's upper body and chest is easily obtained. The smart bedding takes the coordinates of the center point of the upper body figure and returns them to the guide rail to control the movement of the sleep monitor on the guide rail, track the user, and achieve more accurate monitoring. Optionally, the smart bedding is provided with two horizontal and vertical guide rails to enable the sleep monitor to move at any position of the smart bedding. Optionally, the smart bedding is only provided with one horizontal axis guide rail to realize the lateral movement of the sleep monitor. The technicians need to locate the approximate position of the user's chest when lying in bed during production.

[0111] Illustratively, in combination with the above embodiments, Figure 3 A flowchart of a sleep detection method using a human body as a detection target is shown. The method includes the following steps:

[0112] Step 301: determine whether the user is bedridden and assign a weight grade to the user.

[0113] Step 302: collecting the user's physiological signals in different modes according to the user's weight level.

[0114] Step 303: Identify the user's sleep cycle based on the collected signal.

[0115] Step 304 : Identify the user's sleeping position, and the sleep monitor automatically tracks and detects the position.

[0116] Step 305: Analyze the data and generate a sleep report.

[0117] Figure 4 This is a structural block diagram of a sleep detection device provided by an exemplary embodiment of the present application. The device includes the following structure:

[0118] A first determining module 401 is configured to determine a weight level of a detected target in response to the detected target being bedridden;

[0119] A second determining module 402 is configured to determine a current sleep detection mode based on the weight level, wherein the detection power of the sleep detector is different in different sleep detection modes;

[0120] An acquisition module 403 is configured to acquire physiological signal data of the detection target in the current sleep detection mode, wherein the physiological signal data is used to represent the physiological state of the detection target;

[0121] The generating module 404 is configured to generate a sleep detection report of the detection target based on the physiological signal data.

[0122] Optionally, pressure sensors are evenly distributed in the smart bedding;

[0123] The first determining module 401 is further configured to:

[0124] In response to the detection target being in bed, determining a body mass index of the detection target based on a pressure area and an output current value of the pressure sensor;

[0125] In response to the body mass index being less than or equal to a lower body mass index limit, determining the weight level to be a first level;

[0126] In response to the body mass index being greater than the body mass index lower limit and less than the body mass index upper limit, determining the weight level to be the second level;

[0127] In response to the body mass index being greater than or equal to the body mass index upper limit, the weight level is determined to be the third level.

[0128] Optionally, the second determining module 402 is further configured to:

[0129] In response to the weight level being the second level, determining that the current sleep detection mode is a normal mode;

[0130] In response to the weight level being the first level, determining that the current sleep detection mode is a first super mode;

[0131] In response to the weight level being the third level, determining that the current sleep detection mode is a second super mode;

[0132] The detection power of the sleep detector in the normal mode is smaller than the detection power of the sleep detector in the first super mode and the second super mode.

[0133] Optionally, the device further includes:

[0134] a third determining module, configured to determine a pressed shape and a pressed position in response to a pressure value detected by the pressure sensor;

[0135] The fourth determining module is configured to determine that the detection target is in bed and activate the sleep detector in response to the pressed position being within a preset range and the pressed shape being consistent with a preset shape.

[0136] Optionally, the smart bedding is provided with a movable guide rail;

[0137] The device further comprises:

[0138] a fifth determining module, configured to determine a signal detection point of the detection target based on the pressure area and the pressure position detected by the pressure sensor;

[0139] The control module is used to control the sleep detector to move on the movable guide rail to the coordinate corresponding to the signal detection point.

[0140] Optionally, the physiological signal data includes at least one of an electrocardiogram, heart rate, respiratory rate, and body temperature;

[0141] The generating module 404 is further configured to:

[0142] Combining the physiological signal data into a feature vector;

[0143] According to the chronological order of data collection, the feature vector is input into the long short-term memory network to obtain a sleep cycle detection report, where the sleep cycle includes wakefulness, light sleep and deep sleep. The long short-term memory network is trained based on a public sleep dataset and AASM sleep standards.

[0144] It should be noted that in the embodiment of the present application, if the above-mentioned sleep detection method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0145] Accordingly, an embodiment of the present application provides a storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of a sleep detection method provided in the above embodiment are implemented.

[0146] An embodiment of the present application provides an electronic device; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 5As shown, the electronic device 500 includes: a processor 501, at least one communication bus 502, a user interface 503, at least one external communication interface 504, and a memory 505. The communication bus 502 is configured to enable communication between these components. The user interface 503 may include a display screen, and the external communication interface 504 may include a standard wired interface and a wireless interface. The processor 501 is configured to execute a program stored in the memory for a method for recommending intelligent recipes, thereby implementing the steps of the sleep detection method provided in the above embodiment.

[0147] An embodiment of the present application provides a smart bedding, including an electronic device, a sleep monitor, and a pressure sensor, wherein the sleep monitor is used to collect physiological signal data, and the pressure sensor is used to convert the pressure signal into an electrical signal. The electronic device is communicatively connected to the sleep monitor and the pressure sensor, and is used to determine the weight level of the detection target based on the electrical signal of the pressure sensor, adjust the sleep detection mode of the sleep monitor based on the weight level, and generate a sleep detection report.

[0148] It should be noted that the descriptions of the above storage medium, electronic device, and smart bedding embodiments are similar to the descriptions of the above method embodiments and have similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0149] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0150] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, object, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, object, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of other identical elements in the process, method, object, or apparatus comprising the element.

[0151] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0152] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0153] In addition, all functional units in the embodiments of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the above-mentioned integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0154] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROMs), magnetic disks, optical disks, and other media that can store program codes.

[0155] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0156] The above is merely an embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A sleep detection method, characterized in that: The method is applied to a smart bedding equipped with a sleep detector, and the method comprises: In response to the detected subject being bedridden, determining a weight class of the detected subject; determining a current sleep detection mode based on the weight level, wherein the detection power of the sleep detector is different in different sleep detection modes; collecting physiological signal data of the detection target in the current sleep detection mode, wherein the physiological signal data is used to represent the physiological state of the detection target; A sleep detection report of the detection target is generated based on the physiological signal data.

2. The method according to claim 1, characterized in that Pressure sensors are evenly distributed in the smart bedding; In response to the detected target being bedridden, determining the weight level of the detected target includes: In response to the detection target being in bed, determining a body mass index of the detection target based on a pressure area and an output current value of the pressure sensor; In response to the body mass index being less than or equal to a lower body mass index limit, determining the weight level to be a first level; In response to the body mass index being greater than the body mass index lower limit and less than the body mass index upper limit, determining the weight level to be the second level; In response to the body mass index being greater than or equal to the body mass index upper limit, the weight level is determined to be the third level.

3. The method according to claim 2, characterized in that The determining of the current sleep detection mode based on the weight level includes: In response to the weight level being the second level, determining that the current sleep detection mode is a normal mode; In response to the weight level being the first level, determining that the current sleep detection mode is a first super mode; In response to the weight level being the third level, determining that the current sleep detection mode is a second super mode; The detection power of the sleep detector in the normal mode is smaller than the detection power of the sleep detector in the first super mode and the second super mode.

4. The method according to claim 2, characterized in that Before determining the weight level of the detected target in response to the detected target being bedridden, the method further includes: determining a pressed shape and a pressed position in response to the pressure value detected by the pressure sensor; In response to the pressed position being within a preset range and the pressed shape conforming to a preset shape, it is determined that the detection target is in bed and the sleep detector is turned on.

5. The method according to claim 2, characterized in that The intelligent bedding is provided with a movable guide rail; The method further comprises: determining a signal detection point of the detection target based on the pressure area and the pressure position detected by the pressure sensor; The sleep detector is controlled to move on the movable guide rail to the coordinate corresponding to the signal detection point.

6. The method according to any one of claims 1 to 5, characterized in that: The physiological signal data includes at least one of an electrocardiogram, heart rate, respiratory rate, and body temperature; Generating a sleep detection report of the detection target based on the physiological signal data includes: Combining the physiological signal data into a feature vector; According to the chronological order of data collection, the feature vector is input into the long short-term memory network to obtain a sleep cycle detection report, where the sleep cycle includes wakefulness, light sleep and deep sleep. The long short-term memory network is trained based on a public sleep dataset and AASM sleep standards.

7. A sleep detection device, characterized in that: The device comprises: a first determining module, configured to determine a weight class of the detection target in response to the detection target being bedridden; a second determining module, configured to determine a current sleep detection mode based on the weight level, wherein the detection power of the sleep detector is different in different sleep detection modes; an acquisition module, configured to acquire physiological signal data of the detection target in the current sleep detection mode, wherein the physiological signal data is used to represent the physiological state of the detection target; A generating module is used to generate a sleep detection report of the detection target based on the physiological signal data.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the sleep detection method according to any one of claims 1 to 6 is executed.

9. An intelligent bedding, characterized in that: The smart bedding includes the electronic device, a sleep detector, and a pressure sensor according to claim 8, wherein the sleep detector is used to collect physiological signal data, and the pressure sensor is used to convert the pressure signal into an electrical signal. The electronic device is communicatively connected to the sleep detector and the pressure sensor, and is used to determine the weight level of the detection target based on the electrical signal of the pressure sensor, and adjust the sleep detection mode of the sleep detector based on the weight level to generate a sleep detection report.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one instruction, at least one program, code set, or instruction set, and the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement a sleep detection method according to any one of claims 1 to 6.

11. A computer program product, characterized in that The computer program product runs on a processor of a computer device, so that the computer device executes the sleep detection method according to any one of claims 1 to 6.

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