CN110974195A - Method, device and storage medium for adjusting sleep environment - Google Patents
Method, device and storage medium for adjusting sleep environment Download PDFInfo
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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- A—HUMAN NECESSITIES
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- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
- A61M21/02—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis for inducing sleep or relaxation, e.g. by direct nerve stimulation, hypnosis, analgesia
Abstract
The disclosure relates to the technical field of smart home, in particular to a method, a device and a storage medium for adjusting a sleep environment, which are used for solving the technical problem of how to improve the sleep quality of a user in the related art. The method for adjusting the sleep environment comprises the following steps: acquiring a micro-motion signal when a human body is in a sleep state; inputting the micro-motion signal into a trained sleep quality evaluation model to output the current sleep grade of the human body; and adjusting the sleeping environment parameters of the human body by controlling household appliances according to the current sleeping grade of the human body.
Description
Technical Field
The present disclosure relates to the field of smart home technologies, and in particular, to a method and an apparatus for adjusting a sleep environment, and a storage medium.
Background
At present, with the improvement of living standard of people, the living rhythm is faster and faster, and the sleeping problem is more and more serious. Long-term sleep disturbance can not only reduce the quality of life and affect the working life of an individual, but also cause diseases such as cardiovascular diseases, depression and the like, and even endanger life. Therefore, the improvement of sleep quality becomes an increasing concern.
Disclosure of Invention
The present disclosure provides a method, an apparatus, and a storage medium for adjusting a sleep environment to solve a technical problem of how to improve sleep quality of a user in related art.
To achieve the above object, in a first aspect of the embodiments of the present disclosure, there is provided a method for adjusting a sleep environment, the method including:
acquiring a micro-motion signal when a human body is in a sleep state;
inputting the micro-motion signal into a trained sleep quality evaluation model to output the current sleep grade of the human body;
and adjusting the sleeping environment parameters of the human body by controlling household appliances according to the current sleeping grade of the human body.
Optionally, acquiring the micro-motion signal when the human body is in the sleep state includes:
receiving a millimeter wave feedback signal, wherein the millimeter wave feedback signal is obtained when a millimeter wave detection signal transmitted by a millimeter wave radar is fed back by a human body;
and extracting a micro-motion signal when the human body is in a sleep state from the millimeter wave feedback signal.
Optionally, the sleep quality evaluation model is a decision tree model using an ID3 algorithm;
inputting the micro-motion signal into a trained sleep quality evaluation model to output the current sleep quality grade of the human body, wherein the method comprises the following steps:
converting the micro-motion signal into discrete data to be detected;
and inputting the data to be detected into the decision tree model so as to output the current sleep quality grade of the human body.
Optionally, the human body micro-motion signal comprises a human body posture change signal, a respiration signal and a heartbeat signal; converting the micro-motion signal into discrete data to be detected, comprising:
selecting unit time, and converting the human body posture change signal, the respiration signal and the heartbeat signal into discrete data to be detected, wherein the discrete data to be detected comprises the human body posture change rate, the respiration rate and the heartbeat rate.
Optionally, the method further comprises:
collecting training data containing human body posture change rate, respiration rate and heartbeat rate;
and training a decision tree model by using the training data to obtain a trained sleep quality evaluation model.
Optionally, the method further comprises:
collecting historical environment parameters when the human body is at the highest sleep level;
according to the current sleep grade of the human body, adjusting the sleep environment parameters of the human body by controlling household appliances, wherein the sleep environment parameters comprise:
when the current sleep quality level of the human body is not the highest sleep level, adjusting the sleep environment parameters of the human body by controlling household appliances so as to enable the adjusted sleep environment parameters of the human body to be consistent with the historical environment parameters of the human body in the highest sleep level.
Optionally, the historical environmental parameters include a temperature parameter, an illumination parameter, and a humidity parameter:
adjusting the sleep environment parameters of the human body by controlling household electrical appliance equipment so as to make the adjusted sleep environment parameters of the human body consistent with the historical environment parameters of the human body at the highest sleep level, comprising:
and adjusting the temperature parameter, the illumination parameter and the humidity parameter of the human body by controlling air conditioning equipment, lighting equipment and humidifying equipment so as to enable the sleep environment parameter of the human body to be adjusted to be consistent with the historical environment parameter of the human body at the highest sleep level.
In a second aspect of the embodiments of the present disclosure, there is provided an apparatus for adjusting a sleep environment, including:
the acquisition module is configured to acquire a micro-motion signal when the human body is in a sleep state;
an evaluation module configured to input the micro-motion signal into a trained sleep quality evaluation model to output a current sleep grade of the human body;
and the adjusting module is configured to adjust the sleep environment parameters of the human body by controlling household appliances according to the current sleep grade of the human body.
In a third aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the steps of the method of any one of the above first aspects.
In a fourth aspect of the embodiments of the present disclosure, an apparatus for adjusting a sleep environment is provided, including:
a memory having a computer program stored thereon; and
a processor for executing the computer program in the memory to implement the steps of the method of any of the first aspects above.
By adopting the technical scheme, the following technical effects can be at least achieved:
according to the sleep quality evaluation method and device, the micro signals of the human body are input into the trained sleep quality evaluation model, the sleep quality evaluation model can output the current sleep grade of the human body according to the sleep quality evaluation model, and then the sleep environment parameters of the human body can be adjusted by controlling the household appliance according to the current sleep grade of the human body, so that the technical problem of how to improve the sleep quality of the user in the related technology is solved.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
fig. 1 is a flowchart illustrating a method for adjusting a sleep environment according to an exemplary embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating another method for adjusting a sleep environment according to an exemplary embodiment of the present disclosure.
Fig. 3 is a flow chart illustrating sleep level evaluation in an exemplary embodiment of the present disclosure.
Fig. 4 is a diagram illustrating a decision tree model evaluating sleep level evaluation according to an exemplary embodiment of the present disclosure.
Fig. 5 is a block diagram illustrating an apparatus for adjusting a sleep environment according to an exemplary embodiment of the present disclosure.
Fig. 6 is a block diagram illustrating another apparatus for adjusting a sleep environment according to an exemplary embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in detail with reference to the accompanying drawings and examples, so that how to apply technical means to solve technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments and various features in the embodiments of the present application can be combined with each other without conflict, and the formed technical solutions are all within the protection scope of the present disclosure.
Example one
Fig. 1 is a flowchart illustrating a method for adjusting a sleep environment according to an exemplary embodiment of the present disclosure, so as to solve a technical problem of how to improve sleep quality of a user in the related art. As shown in fig. 1, the method for adjusting a sleep environment includes:
and S11, acquiring the micro-motion signal when the human body is in the sleep state.
And S12, inputting the micro-motion signal into a trained sleep quality evaluation model to output the current sleep grade of the human body.
And S13, adjusting the sleep environment parameters of the human body by controlling household appliances according to the current sleep grade of the human body.
In step S11, the inching signal when the human body is in the sleep state may be obtained by: the method comprises the steps of utilizing a millimeter wave radar to emit single-frequency continuous waves, namely millimeter wave detection signals, feeding back a millimeter wave feedback signal through a human body by the millimeter wave detection signals, and extracting a micro-motion signal when the human body is in a sleep state from the millimeter wave feedback signal after receiving the millimeter wave feedback signal.
Wherein, the micro-motion signal of the human body in the sleep state can be extracted from the millimeter wave feedback signal in the following way: the method of orthogonal phase detection is used for selecting the wavelength and the frequency of the frequency band of the human posture change signal, the human respiration signal and the heartbeat signal to obtain the included human micromotion signal, namely the Doppler characteristic. The human posture change signal can represent the micro-actions of turning over the human body or other limb parts in sleep and the like. In the quadrature phase detection, two synchronous carrier signals in the directions of 90 degrees to each other are used for synchronous detection, and information carried in the two directions of the carrier is detected. It should be noted that, since the human posture change signal, the human respiration signal and the heartbeat signal are two key parameters capable of reflecting the sleep quality of the human body, the two parameters are set forth herein. Of course, in other embodiments, other parameters that enable the quality of human sleep are also possible.
After acquiring the micro-motion signal when the human body is in the sleep state, step S12 is executed, and the micro-motion signal is input into the trained sleep quality evaluation model to output the current sleep level of the human body. The sleep quality evaluation model is a decision tree model adopting an ID3 algorithm, and when the micro-motion signal is input into the trained decision tree model, the micro-motion signal needs to be converted into discrete data to be detected.
The method comprises the steps of selecting a human posture change signal, a respiration signal and a heartbeat signal in the micro-motion signal, selecting unit time, and sampling the human posture change signal, the respiration signal and the heartbeat signal into discrete data to be detected, wherein the discrete data to be detected comprises a human posture change rate, a respiration rate and a heartbeat rate. And then inputting the data to be detected into the trained decision tree model to output the current sleep quality grade of the human body. The sleep quality level can be classified according to the user requirement, such as the following two levels: a good grade, indicating good sleep quality; poor grade, indicating poor sleep quality; the quality grade is greater than the quality grade.
Optionally, the sleep quality evaluation model needs to be obtained by the following training mode: firstly, training data comprising human posture change rate, respiration rate and heartbeat rate needs to be collected; then, the training data is put into a decision tree model to be trained for training, and a trained sleep quality evaluation model can be obtained through training of a large amount of data.
Because the decision tree algorithm divides continuous data into data segments and converts the data segments into discrete data types when training discrete data according to the algorithm model, each segment of data corresponds to different classification labels of sleep effects, and the algorithm is different from other algorithms (such as a BP neural network algorithm) in the sleep grade evaluation field, the method has high speed and high accuracy when processing the data training model.
And after the sleep quality evaluation model outputs the current sleep grade of the human body, executing step S13, and adjusting the sleep environment parameters of the human body by controlling household appliances according to the current sleep grade of the human body. The sleep environment parameters can comprise temperature parameters, illumination parameters and humidity parameters, and correspondingly, the household appliance can comprise air conditioning equipment, lighting equipment and humidifying equipment. The temperature parameter can be obtained by sending a remote control instruction to the air conditioning equipment so that the air conditioning equipment operates to adjust the current ambient temperature. The illumination parameters may be generated by sending remote control instructions to the lighting device to cause the lighting device to operate to adjust the illumination intensity of the current environment. The humidity parameter can be obtained by sending a remote control instruction to the humidifying equipment so that the humidifying equipment operates to adjust the current ambient humidity.
Optionally, before the sleep environment parameter of the human body is adjusted by controlling the home appliance device, it is required to determine whether the human body is at the highest sleep level, and if so, the home appliance device is not required to be controlled, and only the current working state of the home appliance device is required to be maintained. If the human body is not in the highest sleep level, the sleep environment parameters of the human body are required to be adjusted by controlling the household appliance.
Optionally, the adjusted parameters may be obtained by collecting historical environment parameters when the human body is at the highest sleep level, and when the current sleep quality level of the human body is not the highest sleep level, adjusting the sleep environment parameters of the human body by controlling a home appliance device, so that the adjusted sleep environment parameters of the human body are consistent with the historical environment parameters when the human body is at the highest sleep level.
According to the sleep quality evaluation method and device, the micro signals of the human body are input into the trained sleep quality evaluation model, the sleep quality evaluation model can output the current sleep grade of the human body according to the sleep quality evaluation model, and then the sleep environment parameters of the human body can be adjusted by controlling the household appliance according to the current sleep grade of the human body, so that the technical problem of how to improve the sleep quality of the user in the related technology is solved.
It should be noted that the method embodiment shown in fig. 1 is described as a series of acts or combinations for simplicity of description, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts or steps described. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required in order to implement the disclosure.
Example two
Fig. 2 is a flowchart illustrating another method for adjusting a sleep environment according to an exemplary embodiment of the present disclosure, and fig. 3 is a flowchart illustrating sleep level evaluation according to an exemplary embodiment of the present disclosure to solve a technical problem of how to improve sleep quality of a user in the related art. As shown in fig. 2 and 3, the method for adjusting a sleep environment may include the steps of:
firstly, the millimeter wave radar is used for identifying and positioning the human body, and heartbeat and breathing micro-motion vital sign signals generated by the human body are extracted due to the difference of micro-Doppler frequency spectrum frequencies generated by the human body and the surrounding static environment.
Next, as shown in fig. 2 and fig. 3, in the detection of the human body micro-motion signal, the sleep quality of the human body is evaluated by using a millimeter wave radar detection technology and a machine learning algorithm. Aiming at the collected millimeter radar waves, a single-frequency continuous wave and orthogonal phase detection mode is adopted to obtain the micro Doppler characteristics of the millimeter radar waves, and the micro Doppler spectrum is obtained by the collected time domain echoes through a short-time Fourier analysis method.
Based on the actual condition that the sleep grade is in the test range and the advantages of high data processing speed and high data processing efficiency of the decision tree classifier, proper unit time is selected from the obtained time-frequency domain waveform, corresponding breathing and heartbeat frequencies are collected, continuous signal data are converted into discrete test data, and the discrete unit time-frequency data are used as test samples. In this embodiment, the model selected for data classification is a decision tree classifier, the selected time-frequency discrete test data is trained, and an optimal decision tree is obtained according to the information gain maximization principle. Therefore, the micro-motion vital signal in the test sample can correspond to a sleep data interval to obtain a corresponding sleep grade.
The input factors applied to the decision tree model in the invention are as follows: micro-actions such as turning over the human body and the like, the detected respiratory rate and the heart rate are used for learning and training the acquired sleep data of different people at the same time point or different time points of the same person according to the three related factors to obtain the corresponding sleep grade, such as
FIG. 4 shows: where sleep level a < sleep level B < sleep level C is assumed. Of course, there may be a plurality of sleep factors in the sleep level determination process, and all the sleep factors are determined according to the sample parameters that can be collected and can be used in the training model of this time.
In the related technology, the sleep monitoring method mainly uses a sensor sensing technology, and has lower accuracy due to more serious interference of external factors; the millimeter wave radar is selected to acquire the micro Doppler spectrum of the human body, namely, the millimeter wave radar is adopted to collect the human body respiration and heartbeat weak vital sign signals to judge the sleep depth level of the human body, and then the self-adaptive optimization control of the sleep environment can be carried out through the sleep quality data of the human body, the healthy living state of the human body is ensured, and the problem of lower accuracy of sleep quality detection in the related technology is solved. The wave radar is adopted to collect data, so that the privacy of a user is not invaded, and the action of a human body is not interfered.
In addition, the decision tree model using the ID3 algorithm is selected for evaluating the respiration and heartbeat data, and when the discrete data are trained according to the algorithm model, the data processing is fast and the accuracy rate is high.
EXAMPLE III
Fig. 5 is a block diagram of an apparatus for adjusting a sleep environment according to an exemplary embodiment of the present disclosure, so as to solve the technical problem of how to improve the sleep quality of a user in the related art. As shown in fig. 5, the apparatus 300 for adjusting a sleep environment includes:
an acquiring module 310 configured to acquire a micro-motion signal when the human body is in a sleep state;
an evaluation module 320 configured to input the micro-motion signal into a trained sleep quality evaluation model to output a current sleep level of the human body;
an adjusting module 330 configured to adjust the sleep environment parameter of the human body by controlling a home appliance device according to the current sleep level of the human body.
The present disclosure also provides another preferred embodiment of the apparatus for adjusting a sleep environment, in which the apparatus for adjusting a sleep environment includes: a processor, wherein the processor is configured to execute the following program modules stored in the memory: the acquisition module is configured to acquire a micro-motion signal when the human body is in a sleep state; an evaluation module configured to input the micro-motion signal into a trained sleep quality evaluation model to output a current sleep grade of the human body; and the adjusting module is configured to adjust the sleep environment parameters of the human body by controlling household appliances according to the current sleep grade of the human body.
According to the sleep quality evaluation method and device, the micro signals of the human body are input into the trained sleep quality evaluation model, the sleep quality evaluation model can output the current sleep grade of the human body according to the sleep quality evaluation model, and then the sleep environment parameters of the human body can be adjusted by controlling the household appliance according to the current sleep grade of the human body, so that the technical problem of how to improve the sleep quality of the user in the related technology is solved.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Example four
The present disclosure also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method steps of any of the alternative embodiments described above.
For example, the method implemented when the computer program running on the processor is executed may refer to a specific embodiment of the method for adjusting a sleep environment of the present disclosure, and details are not described here.
The processor may be an integrated circuit chip having information processing capabilities. The processor may be a general-purpose processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like.
EXAMPLE five
The present disclosure also provides a device for adjusting a sleep environment, including:
a memory having a computer program stored thereon; and
a processor for executing the computer program in the memory to perform the method steps of any of the alternative embodiments described above.
Fig. 6 is a block diagram illustrating an apparatus 400 for adjusting a sleep environment according to an example embodiment. As shown in fig. 6, the apparatus 400 may include: a processor 401, a memory 402, a multimedia component 403, an input/output (I/O) interface 404, and a communication component 405.
The processor 401 is configured to control the overall operation of the apparatus 400, so as to complete all or part of the steps in the method for adjusting the sleep environment. The memory 402 is used to store various types of data to support operation of the apparatus 400, and such data may include, for example, instructions for any application or method operating on the apparatus 400, as well as application-related data. The Memory 402 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 403 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 402 or transmitted through the communication component 405. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 404 provides an interface between the processor 401 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 405 is used for wired or wireless communication between the apparatus 400 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding Communication component 405 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the apparatus 400 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above method of adjusting a sleep environment.
In another exemplary embodiment, a computer readable storage medium, such as the memory 402, comprising program instructions executable by the processor 401 of the apparatus 400 to perform the above-described method of adjusting a sleep environment is also provided.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (10)
1. A method of adjusting a sleep environment, the method comprising:
acquiring a micro-motion signal when a human body is in a sleep state;
inputting the micro-motion signal into a trained sleep quality evaluation model to output the current sleep grade of the human body;
and adjusting the sleeping environment parameters of the human body by controlling household appliances according to the current sleeping grade of the human body.
2. The method of claim 1, wherein acquiring the micro-motion signal while the human body is in the sleep state comprises:
receiving a millimeter wave feedback signal, wherein the millimeter wave feedback signal is obtained when a millimeter wave detection signal transmitted by a millimeter wave radar is fed back by a human body;
and extracting a micro-motion signal when the human body is in a sleep state from the millimeter wave feedback signal.
3. The method of claim 2, wherein the sleep quality assessment model is a decision tree model employing the ID3 algorithm;
inputting the micro-motion signal into a trained sleep quality evaluation model to output the current sleep quality grade of the human body, wherein the method comprises the following steps:
converting the micro-motion signal into discrete data to be detected;
and inputting the data to be detected into the decision tree model so as to output the current sleep quality grade of the human body.
4. The method of claim 3, wherein the body micromotion signal comprises a body posture change signal, a respiration signal, and a heartbeat signal; converting the micro-motion signal into discrete data to be detected, comprising:
selecting unit time, and converting the human body posture change signal, the respiration signal and the heartbeat signal into discrete data to be detected, wherein the discrete data to be detected comprises the human body posture change rate, the respiration rate and the heartbeat rate.
5. The method of claim 4, further comprising:
collecting training data containing human body posture change rate, respiration rate and heartbeat rate;
and training a decision tree model by using the training data to obtain a trained sleep quality evaluation model.
6. The method of claim 1, further comprising:
collecting historical environment parameters when the human body is at the highest sleep level;
according to the current sleep grade of the human body, adjusting the sleep environment parameters of the human body by controlling household appliances, wherein the sleep environment parameters comprise:
when the current sleep quality level of the human body is not the highest sleep level, adjusting the sleep environment parameters of the human body by controlling household appliances so as to enable the adjusted sleep environment parameters of the human body to be consistent with the historical environment parameters of the human body in the highest sleep level.
7. The method of claim 6, wherein the historical environmental parameters include a temperature parameter, a lighting parameter, and a humidity parameter:
adjusting the sleep environment parameters of the human body by controlling household electrical appliance equipment so as to make the adjusted sleep environment parameters of the human body consistent with the historical environment parameters of the human body at the highest sleep level, comprising:
and adjusting the temperature parameter, the illumination parameter and the humidity parameter of the human body by controlling air conditioning equipment, lighting equipment and humidifying equipment so as to enable the sleep environment parameter of the human body to be adjusted to be consistent with the historical environment parameter of the human body at the highest sleep level.
8. An apparatus for adjusting a sleep environment, comprising:
the acquisition module is configured to acquire a micro-motion signal when the human body is in a sleep state;
an evaluation module configured to input the micro-motion signal into a trained sleep quality evaluation model to output a current sleep grade of the human body;
and the adjusting module is configured to adjust the sleep environment parameters of the human body by controlling household appliances according to the current sleep grade of the human body.
9. An apparatus for adjusting a sleep environment, comprising:
a memory having a computer program stored thereon; and
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 7.
10. A storage medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| CN113545752A (en) * | 2021-07-21 | 2021-10-26 | 深圳市联美智能家居有限公司 | Sleep quality monitoring method and system of intelligent bed |
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