CN111685731A - Sleep data processing method, device, equipment and storage medium - Google Patents
Sleep data processing method, device, equipment and storage medium Download PDFInfo
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- CN111685731A CN111685731A CN202010427322.XA CN202010427322A CN111685731A CN 111685731 A CN111685731 A CN 111685731A CN 202010427322 A CN202010427322 A CN 202010427322A CN 111685731 A CN111685731 A CN 111685731A
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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
- A61B5/4815—Sleep quality
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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
- A61B5/117—Identification of persons
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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
Abstract
The application relates to a sleep data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring sleep data of a current user; determining the identity information of the current user according to the sleep data; acquiring a physiological stage sleep standard corresponding to the identity information of the current user; and evaluating the sleep quality according to the sleep data of the current user and the sleep standard of the physiological stage. The method and the device are used for solving the problem that the accuracy of sleep quality assessment is low due to the fact that the correct physiological stage standard cannot be matched for the current user of the sleep monitoring equipment.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a sleep data processing method, apparatus, device, and storage medium.
Background
With the rapid development of society, the life rhythm of people gradually becomes fast, the facing life and working pressure is also increasingly greater, and the sleep quality is seriously influenced. A report of 2015 Chinese sleep index issued by the Chinese physician Association indicates that the incidence of insomnia in Chinese adults is as high as 38%. Sleep quality has become a luxury and sleep problems are also receiving increased attention.
Currently, devices for monitoring the signs of a user during sleep are such as: the sleep quality assessment system comprises a polysomnography instrument, an intelligent bracelet, a sleep detector, a detection belt and the like, wherein the sleep data of a user are acquired through a sensor arranged on the equipment, the related sleep data are analyzed, and the sleep quality of the user is assessed.
However, the evaluation criteria for persons at different physiological stages are different. In the prior art, in order to improve the accuracy of evaluation, the user identities of the monitoring devices are determined by a conventional method, i.e., a manual setting method, and the devices and the users are paired one by one. This approach severely limits the flexibility of sensor usage and the versatility of the monitoring device, and often results in the phenomenon that the set physiological stage sleep standard is applied to the real user sleep assessment, and then the wrong assessment result is obtained, resulting in a decrease in the accuracy of sleep assessment depending on different physiological stage sleep standards.
Disclosure of Invention
The application provides a sleep data processing method, a sleep data processing device, sleep data processing equipment and a storage medium, which are used for solving the problem of low accuracy of sleep quality evaluation caused by the fact that correct physiological stage standards cannot be matched for a current user of sleep monitoring equipment.
In a first aspect, an embodiment of the present application provides a sleep data processing method, including: acquiring sleep data of a current user; determining the identity information of the current user according to the sleep data; acquiring a physiological stage sleep standard corresponding to the identity information of the current user; and evaluating the sleep quality according to the sleep data of the current user and the sleep standard of the physiological stage.
Optionally, the acquiring sleep data of the current user includes: and acquiring sleep data of the current user through the sleep monitoring sensor.
Optionally, determining the identity information of the current user according to the sleep data includes: extracting characteristic parameters of the sleep data, inputting the characteristic parameters of the sleep data into a preset identity recognition model, and acquiring identity information of the current user output by the identity recognition model; the identity recognition model is obtained by training an original identity recognition model by adopting identity recognition sample data, wherein the identity recognition sample data comprises sleep sample data of N potential users within a set time length and sample identity information of the N potential users, and N is an integer greater than 1.
Optionally, the training process of the identity recognition model includes: acquiring the identity recognition sample data; extracting characteristic parameters of the identity recognition sample data; inputting the characteristic parameters of the identity identification sample data into the original identity identification model to obtain the respective predicted identity information of the identity identification sample data output by the original identity identification model; comparing the predicted identity information with the sample identity information, if the predicted identity information is inconsistent with the sample identity information, adjusting parameters of an original identity recognition model, and repeating the step of inputting the characteristic parameters of the identity recognition sample data into the original identity recognition model until the predicted identity information is consistent with the sample identity information, and taking the original identity recognition model as the final identity recognition model.
Optionally, performing sleep quality assessment according to the sleep data of the current user and the physiological stage sleep standard, including: obtaining a sleep index of the current user according to the sleep data of the current user; according to the sleep index and the physiological stage sleep standard, performing sleep staging to obtain the sleep state of the current user; and generating a sleep evaluation result of the current user according to the sleep index and the sleep state.
Optionally, performing sleep staging according to the sleep index and the physiological stage sleep standard to obtain the sleep state of the current user, including: extracting the characteristic parameters of the sleep index, inputting the characteristic parameters of the sleep index into a preset sleep staging model, and acquiring the sleep state of the current user output by the sleep staging model; the sleep stage model is obtained by training an original sleep stage model by using sleep stage sample data, wherein the sleep stage sample data comprises sample sleep indexes of M potential users and sample sleep states of the M potential users, and M is an integer greater than 1.
Optionally, the training process of the sleep staging model includes: acquiring the sleep stage sample data; extracting characteristic parameters of the sleep stage sample data; inputting the characteristic parameters of the sleep stage sample data into the original sleep stage model to obtain the respective predicted sleep states of the sleep stage sample data output by the original sleep stage model; comparing the predicted sleep state with the sample sleep state, if not, adjusting parameters of an original sleep staging model, repeating the step of inputting the characteristic parameters of the sleep staging sample data into the original sleep staging model, and taking the original sleep staging model as the final sleep staging model when the predicted sleep state is consistent with the sample sleep state.
In a second aspect, an embodiment of the present application provides a sleep data processing apparatus, including: the first acquisition module is used for acquiring sleep data of a current user, wherein the sleep data is acquired by a sleep monitoring sensor; the identity distinguishing module is used for determining the identity information of the current user according to the sleep data; the second acquisition module is used for acquiring the physiological stage sleep standard corresponding to the identity information of the current user; and the quality evaluation module is used for evaluating the sleep quality according to the sleep data of the current user and the sleep standard of the physiological stage.
In a third aspect, an embodiment of the present application provides an electronic device, including: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus; the memory for storing a computer program; the processor is used for executing the program stored in the memory and realizing the sleep data processing method.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the sleep data processing method.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages: the method provided by the embodiment of the application processes the sleep data, determines the identity information of the current user, matches the corresponding physiological stage sleep standard according to the identity information, and evaluates the sleep quality, so that the sleep quality evaluation standard adaptive to the identity information of the current user can be adopted to evaluate the sleep quality of the current user, the accuracy of the sleep quality evaluation is improved, even if different family members share equipment, the accuracy of the sleep quality evaluation can be ensured, the flexibility of the equipment use is improved, and a feasible solution is provided for the multi-user sharing of the equipment.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of a sleep data processing method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an identity recognition model training process according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an identity recognition model according to an embodiment of the present disclosure;
fig. 4 is a flowchart illustrating a method for obtaining a sleep index according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a sleep staging model training provided in an embodiment of the present application;
FIG. 6 is a schematic diagram of a classifier model construction as a vector machine according to an embodiment of the present application;
FIG. 7 is an exemplary graph of heart beat intervals (R-R intervals) provided by an embodiment of the present application;
fig. 8 is a schematic structural diagram of a sleep data processing apparatus according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a sleep data processing method, which can be applied to any electronic device, the electronic device can be integrated with a sleep monitoring sensor, or not integrated with the sleep monitoring sensor, and under the condition that the sleep monitoring sensor is not integrated, the electronic device can acquire sleep data transmitted by the sleep monitoring sensor in a wired or wireless mode.
As shown in fig. 1, the process of sleep data processing mainly includes the following steps:
In one embodiment, the sleep data of the current user is acquired by a sleep monitoring sensor which adopts a sensor with a PVDF piezoelectric film. Micro-motions such as breathing and heartbeat generated during sleeping of a person can generate pressure on the PVDF piezoelectric film, the sleep monitoring sensor further generates an analog voltage signal which changes along with the micro-motions, and the analog voltage signal is subjected to A/D conversion to obtain the sleep data of the current user.
The acquired sleep data includes respiration, heartbeat, body movement, noise and the like.
In one embodiment, the identity information of the current user is determined through machine learning, and the specific process includes: and extracting the characteristic parameters of the sleep data, and obtaining the current user identity information output by the identity identification model by setting the characteristic parameters of the sleep data to a preset identity identification model. The identity recognition model is obtained by training an original identity recognition model by adopting identity recognition sample data.
The characteristic parameters of the sleep data include, but are not limited to, frequency domain characteristic parameters and time domain characteristic parameters.
The parameters in the original identity recognition model are preset values at the beginning of training.
It should be noted that, this embodiment does not limit what kind of identity recognition model is specifically adopted, and any model that can be used to implement classification may be used in this embodiment, and the protection scope of this embodiment is not limited by what kind of identity recognition model is adopted.
As shown in fig. 2, the training process of the identity recognition model includes:
In one embodiment, the identity recognition model is implemented by a classifier, and the classifier response score g ═ f &duringthe identity recognition process. Wherein f is a characteristic parameter extracted from the sleep data, and h is a relevant classifier model. Where h ═ h1, h2, h3, hP), P is the total number of categories to which the identity information belongs; h1, …, hP respectively represent the relevant classification model parameters of the P different identity classes. And (4) judging the specific identity of the target according to the principle of maximum classifier correlation score or confidence value, namely determining the classifier model parameter with the highest response score to obtain corresponding identity information.
In one embodiment, the building of the identity recognition model, as shown in fig. 3, includes:
The identity information of the current user includes, but is not limited to, physiological characteristic information including age and weight.
People with different identities have different physiological characteristics, physiological stages can be determined through the physiological characteristics, and the sleep standards of the physiological stages corresponding to different physiological stages are different.
In one embodiment, the physiological stage sleep criteria define age stages, and the reference criteria for sleep duration for each month of 0-70 years are defined. The sleep time is recommended to be 14-17 hours in the newborn with the age stage from the first birth to 3 months; infants aged from 3 months to 11 months, with a recommended sleep time of 12-15 hours; the sleep time of infants aged from 1 to 2 years is recommended to be 11-14 hours; children aged 3 to 5 years, and recommended sleep time of 10-13 hours; the sleep time is recommended to be 9-11 hours for school-age children aged 6-13 years; a teenager aged 14 to 17 years is recommended to sleep for 8-10 hours; adults aged 18 to 64 years, with a recommended sleep time of 7-9 hours; the sleep time of the aged over 65 years old is recommended to be 7-8 hours.
And step 104, evaluating the sleep quality according to the sleep data of the current user and the sleep standard of the physiological stage.
In one embodiment, the specific process of sleep quality assessment is as follows: obtaining a sleep index of the current user according to the sleep data of the current user; according to the sleep index and the physiological stage sleep standard, performing sleep staging to obtain the sleep state of the current user; and generating a sleep evaluation result of the current user according to the sleep index and the sleep state.
Sleep metrics include, but are not limited to, physical activity level, heart rate, respiration rate, and heart rate variability, among others.
In one embodiment, the sleep index of the current user is obtained according to the sleep data of the current user by filtering and calculating the sleep data.
It should be noted that, the embodiment does not limit the method for obtaining the sleep index to filtering and calculating, any method capable of obtaining the sleep index may be used in the embodiment, and the protection range of the embodiment is not limited by which method is used to obtain the sleep index.
In one embodiment, a specific flow of a method for obtaining the body motion data, the heart rate and the respiratory rate through filtering and calculation is shown in fig. 4, and includes:
The calculation formula of the respiration rate and the heart rate is as follows: 60 (number of breaths B or number of heartbeats H per t seconds)/(time t) in units of: times per minute.
In one embodiment, the determination of the sleep state of the current user through machine learning includes: inputting the sleep index into a preset sleep staging model to obtain the sleep state of the current user output by the sleep staging model; the sleep stage model is obtained by training an original sleep stage model by adopting sleep stage sample data.
The parameters in the original sleep staging model are preset values at the beginning of the training.
It should be noted that, this embodiment does not limit what kind of sleep staging model is specifically used, and any model that can be used to implement classification may be used in this embodiment, and the scope of protection of this embodiment is not limited to what kind of sleep staging model is used.
As shown in fig. 5, the training process of the sleep staging model includes:
The characteristic parameters of the sleep stage sample data include, but are not limited to, frequency domain characteristic parameters and time domain characteristic parameters.
In one embodiment, when sleep staging is performed, the rule-like sleep staging model is built using the degree of physical movement BM, the heart rate HR, the Heart Rate Variability (HRV) and the respiration rate (BR). The sleep states of the user are classified into a wake state, a REM state (out-of-phase sleep), a light sleep state, and a deep sleep state.
The specific rule is as follows: and calculating and evaluating the values of BM, HR, HRV and BR of the user according to the time interval of T, wherein the optimal T is taken as 30 seconds. Rule: if the BM judges that no body movement exists, the HR is minimum and the HRV is close to 0, the BM judges that the BM is in a deep sleep state; if BM is obvious body movement and HR and BR detected after the body movement are maximum or 0, the state is judged to be an awake state; if the BM degree is weakened and the HR and the BR are reduced, the state of light sleep is judged; the remaining stages may be determined to be REM state.
The sleep staging model may also employ a support vector machine, a BP neural network, or a deep convolutional neural network.
In yet another embodiment, the sleep staging model employs a support vector machine when performing sleep staging. Specifically, the method comprises the following steps: the support vector machine is a classifier model for multiple classes, each class representing a sleep state. The sleep state is divided into 4 state periods: during waking, during rapid eye movement, during light sleep and during deep sleep. And inputting the sleep index of the current user into a preset support vector machine model to obtain the sleep state of the current user output by the support vector machine model.
As shown in fig. 6, the construction of the classifier model as a vector machine includes:
601, acquiring a sleep index of a user as sleep stage sample data;
and step 604, training the model by using the time domain characteristic parameters and the frequency domain characteristic parameters of the sleep index sample data.
In one embodiment, processing the heartbeat data includes: finding a peak value of the heartbeat from the heartbeat data; determining the heart beat interval (also called R-R interval) between every two adjacent N peaks, wherein N is a set value, and taking the respiration data and the heartbeat data of the corresponding interval as a set of data to be measured, as an example shown in FIG. 7; and extracting characteristic parameters of each group of data to be detected, and performing stage judgment by using a pre-trained sleep stage model. When the whole sleep time axis is traversed, the sleep stages of the whole night can be completed.
In one embodiment, generating a sleep evaluation result of the current user according to the sleep index and the sleep state includes: and comparing the sleep index and the sleep state of the current user with the corresponding physiological stage sleep standard, and obtaining the sleep evaluation score by combining the change trend of the sleep index of the current user. The sleep evaluation score is higher when the sleep index and the sleep state conform to the sleep standard more and the change of the sleep index is more stable.
The method provided by the embodiment of the application processes the sleep data, determines the identity information of the current user, matches the corresponding physiological stage sleep standard according to the identity information, and evaluates the sleep quality, so that the sleep quality evaluation standard adaptive to the identity information of the current user can be adopted to evaluate the sleep quality of the current user, the accuracy of the sleep quality evaluation is improved, even if different family members share equipment, the accuracy of the sleep quality evaluation can be ensured, the flexibility of the equipment use is improved, and a feasible solution is provided for the multi-user sharing of the equipment.
Based on the same concept, embodiments of the present application provide a sleep data processing apparatus, and specific implementation of the apparatus may refer to the description of the method embodiment, and repeated details are not repeated, as shown in fig. 8, the apparatus mainly includes:
a first obtaining module 801, configured to obtain sleep data of a current user, where the sleep data is obtained by a sleep monitoring sensor;
an identity recognition module 802, configured to determine identity information of the current user according to the sleep data;
a second obtaining module 803, configured to obtain a physiological stage sleep standard corresponding to the identity information of the current user;
a quality evaluation module 804, configured to perform sleep quality evaluation according to the sleep data of the current user and the physiological stage sleep standard.
Based on the same concept, an embodiment of the present application further provides an electronic device, as shown in fig. 9, the electronic device mainly includes: a processor 901, a communication interface 902, a memory 903 and a communication bus 904, wherein the processor 901, the communication interface 902 and the memory 903 are in communication with each other through the communication bus 904. The memory 903 stores a program executable by the processor 901, and the processor 901 executes the program stored in the memory 903, so as to implement the following steps: acquiring sleep data of a current user, wherein the sleep data is acquired by a sleep monitoring sensor; determining the identity information of the current user according to the sleep data; acquiring a physiological stage sleep standard corresponding to the identity information of the current user; and evaluating the sleep quality according to the sleep data of the current user and the sleep standard of the physiological stage.
The communication bus 904 mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 904 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 9, but this does not indicate only one bus or one type of bus.
The communication interface 902 is used for communication between the electronic apparatus and other apparatuses.
The Memory 903 may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Alternatively, the memory may be at least one storage device located remotely from the processor 901.
The Processor 901 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc., and may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components.
The electronic device in this embodiment includes any one of the following devices: a smart bracelet, a sleep detector, a polysomnography and other direct contact devices; indirect contact devices such as sleep boxes, sleep detection belts, and the like; microwave radar, and the like.
In one embodiment, the electronic device is an integrated terminal. The terminal can independently complete functions of sleep data acquisition, processing, evaluation result pushing, displaying and the like, such as an intelligent bracelet.
In yet another embodiment, the electronic device is a server. The server realizes the functions as follows: firstly, acquiring sleep data of a current user through a sleep monitoring sensor; then, the server (namely the cloud) receives the sleep data through the communication interface, processes the sleep data and completes identity identification and sleep quality evaluation; and finally, the server (namely the cloud) sends the evaluation result to terminal programs such as mobile phone apps or computer application programs through a communication interface, and the sleep quality evaluation result of the current user is displayed.
In still another embodiment of the present application, there is also provided a computer-readable storage medium having stored therein a computer program which, when run on a computer, causes the computer to execute the sleep data processing method described in the above-described embodiment.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The available media may be magnetic media (e.g., floppy disks, hard disks, tapes, etc.), optical media (e.g., DVDs), or semiconductor media (e.g., solid state drives), among others.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A sleep data processing method, the method comprising:
acquiring sleep data of a current user;
determining the identity information of the current user according to the sleep data;
acquiring a physiological stage sleep standard corresponding to the identity information of the current user;
and evaluating the sleep quality according to the sleep data of the current user and the sleep standard of the physiological stage.
2. The sleep data processing method as claimed in claim 1, wherein the acquiring the sleep data of the current user comprises:
and acquiring sleep data of the current user through the sleep monitoring sensor.
3. The sleep data processing method as claimed in claim 1, wherein determining the identity information of the current user according to the sleep data comprises:
extracting characteristic parameters of the sleep data, inputting the characteristic parameters of the sleep data into a preset identity recognition model, and acquiring identity information of the current user output by the identity recognition model;
the identity recognition model is obtained by training an original identity recognition model by adopting identity recognition sample data, wherein the identity recognition sample data comprises sleep sample data of N potential users within a set time length and sample identity information of the N potential users, and N is an integer greater than 1.
4. The sleep data processing method as claimed in claim 3, wherein the training process of the identity recognition model comprises:
acquiring the identity recognition sample data;
extracting characteristic parameters of the identity recognition sample data;
inputting the characteristic parameters of the identity identification sample data into the original identity identification model to obtain the respective predicted identity information of the identity identification sample data output by the original identity identification model;
comparing the predicted identity information with the sample identity information, if the predicted identity information is inconsistent with the sample identity information, adjusting parameters of an original identity recognition model, and repeating the step of inputting the characteristic parameters of the identity recognition sample data into the original identity recognition model until the predicted identity information is consistent with the sample identity information, and taking the original identity recognition model as the final identity recognition model.
5. The sleep data processing method as claimed in claim 1, wherein the performing of sleep quality assessment according to the sleep data of the current user and the physiological stage sleep criteria comprises:
obtaining a sleep index of the current user according to the sleep data of the current user;
according to the sleep index and the physiological stage sleep standard, performing sleep staging to obtain the sleep state of the current user;
and generating a sleep evaluation result of the current user according to the sleep index and the sleep state.
6. The sleep data processing method as claimed in claim 5, wherein the performing sleep staging according to the sleep index and the physiological stage sleep standard to obtain the sleep state of the current user comprises:
extracting the characteristic parameters of the sleep index, inputting the characteristic parameters of the sleep index into a preset sleep staging model, and acquiring the sleep state of the current user output by the sleep staging model;
the sleep stage model is obtained by training an original sleep stage model by using sleep stage sample data, wherein the sleep stage sample data comprises sample sleep indexes of M potential users and sample sleep states of the M potential users, and M is an integer greater than 1.
7. The sleep data processing method as claimed in claim 6, wherein the training process of the sleep staging model comprises:
acquiring the sleep stage sample data;
extracting characteristic parameters of the sleep stage sample data;
inputting the characteristic parameters of the sleep stage sample data into the original sleep stage model to obtain the respective predicted sleep states of the sleep stage sample data output by the original sleep stage model;
comparing the predicted sleep state with the sample sleep state, if not, adjusting parameters of an original sleep staging model, repeating the step of inputting the characteristic parameters of the sleep staging sample data into the original sleep staging model, and taking the original sleep staging model as the final sleep staging model when the predicted sleep state is consistent with the sample sleep state.
8. A sleep data processing apparatus, comprising:
the first acquisition module is used for acquiring sleep data of a current user, wherein the sleep data is acquired by a sleep monitoring sensor;
the identity distinguishing module is used for determining the identity information of the current user according to the sleep data;
the second acquisition module is used for acquiring the physiological stage sleep standard corresponding to the identity information of the current user;
and the quality evaluation module is used for evaluating the sleep quality according to the sleep data of the current user and the sleep standard of the physiological stage.
9. An electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory for storing a computer program;
the processor, configured to execute the program stored in the memory, and implement the sleep data processing method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the sleep data processing method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112674755A (en) * | 2020-12-09 | 2021-04-20 | 珠海格力电器股份有限公司 | Sleep detection system, method and storage medium |
| CN114588471A (en) * | 2022-03-28 | 2022-06-07 | 武汉工程大学 | Intelligent sleep assisting system, sleep state classification method and storage medium |
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| CN114588471A (en) * | 2022-03-28 | 2022-06-07 | 武汉工程大学 | Intelligent sleep assisting system, sleep state classification method and storage medium |
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