CN111657890A - Sleep state monitoring method and device, intelligent mattress and medium - Google Patents
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Abstract
The embodiment of the invention discloses a method for monitoring a sleep state, which comprises the following steps: acquiring sleep parameter data of a user to be detected, wherein the sleep parameter data comprises one or more of heart rate data, breathing data, user temperature data and user movement data; according to a preset feature extraction algorithm, respectively extracting sleep features of each sleep parameter data; and determining the target sleep state of the user to be detected according to the sleep characteristics according to a preset target sleep state calculation model. The embodiment of the invention also discloses a sleep state monitoring device, a storage medium and an intelligent mattress. By adopting the invention, the accuracy of sleep state monitoring can be improved, and the effectiveness based on sleep treatment can be improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a sleep state monitoring method and device, an intelligent mattress and a computer readable storage medium.
Background
Approximately 1/3 hours of a person's lifetime spent asleep. When people are in a sleeping state, the brain and the body of people can be rested, rested and recovered, and a proper amount of sleep is helpful for daily work and study of people. Scientifically improve the sleep quality, and is the guarantee for normal work, study and life of people. With the improvement of modern life quality, more and more people begin to pay attention to the sleep quality of the people, and good sleep is very important for stabilizing emotion, balancing mind and restoring energy.
Sleep monitoring has been widely used in mobile terminals as a monitoring means for effectively recording monitoring exercises. The sleep monitoring can help the user to know the sleep condition of the user and adjust the sleep condition in time, or the sleep monitoring can be used as a part of user state detection in sleep treatment schemes such as insomnia and the like, so that corresponding treatment or other control schemes can be determined according to the sleep monitoring result.
However, in the prior art, the accuracy of sleep monitoring is insufficient, and the misjudgment is more.
Disclosure of Invention
In view of the above, it is necessary to provide a sleep state monitoring method, device, smart mattress and computer readable storage medium.
A method of sleep state monitoring, the method comprising:
acquiring sleep parameter data of a user to be detected, wherein the sleep parameter data comprises one or more of heart rate data, breathing data, user temperature data and user movement data;
according to a preset feature extraction algorithm, respectively extracting sleep features of each sleep parameter data;
and determining the target sleep state of the user to be detected according to the sleep characteristics according to a preset target sleep state calculation model.
After the step of determining the target sleep state of the user to be tested according to the sleep characteristics according to the preset target sleep state calculation model, the method further includes:
and acquiring a preset target control strategy corresponding to the target sleep state and executing the preset target control strategy.
Wherein, the step of obtaining the sleep parameter data of the user to be tested further comprises:
acquiring heart rate data of the user to be detected through a heart rate sensor;
acquiring user temperature data of the user to be detected through a temperature sensor;
acquiring user motion data of the user to be detected through a motion sensor and/or a pressure sensor;
the heart rate sensor motion sensor sets up on a wearable equipment, pressure sensor sets up on intelligent mattress.
The step of determining the target sleep state of the user to be tested according to the sleep characteristics according to the preset target sleep state calculation model further comprises the following steps:
inputting the sleep characteristics into the target sleep state calculation model, and acquiring an output result of the target sleep state calculation model as the target sleep state;
or the like, or, alternatively,
and determining the sleep state corresponding to the sleep characteristics as the target sleep state according to the corresponding relation between the sleep characteristics and the sleep state contained in the target sleep state calculation model.
Wherein the target sleep state calculation model is a neural network model.
The sleep parameter data also comprises sound data, and the sound data is used for representing the sound production conditions of the user in the states of speaking, calling and the like.
Before the step of determining the target sleep state of the user to be tested according to the sleep characteristics according to the preset target sleep state calculation model, the method further includes:
acquiring body state data corresponding to the user to be detected, wherein the body state data comprises one or more of physical examination data, medication data and basic data, and the basic data comprises one or more of age, sex, weight and height;
and correcting a preset initial sleep state calculation model according to the body state data, and taking the corrected sleep state calculation model as the target sleep state calculation model.
An apparatus for sleep state monitoring, the apparatus comprising:
the data detection module is used for acquiring sleep parameter data of a user to be detected, wherein the sleep parameter data comprises one or more of heart rate data, respiration data, user temperature data and user motion data;
the characteristic extraction module is used for respectively extracting the sleep characteristics of each sleep parameter data according to a preset characteristic extraction algorithm;
and the sleep state calculation module is used for determining the target sleep state of the user to be detected according to the sleep characteristics according to a preset target sleep state calculation model.
A smart mattress comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of:
acquiring sleep parameter data of a user to be detected, wherein the sleep parameter data comprises one or more of heart rate data, breathing data, user temperature data and user movement data;
according to a preset feature extraction algorithm, respectively extracting sleep features of each sleep parameter data;
and determining the target sleep state of the user to be detected according to the sleep characteristics according to a preset target sleep state calculation model.
A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
acquiring sleep parameter data of a user to be detected, wherein the sleep parameter data comprises one or more of heart rate data, breathing data, user temperature data and user movement data;
according to a preset feature extraction algorithm, respectively extracting sleep features of each sleep parameter data;
and determining the target sleep state of the user to be detected according to the sleep characteristics according to a preset target sleep state calculation model.
The embodiment of the invention has the following beneficial effects:
after the monitoring method and device for the sleep state, the intelligent mattress and the computer readable storage medium are adopted, under the condition that the sleep state of the user needs to be detected or monitored, various sleep parameter data of the user are respectively measured through various sensors, corresponding sleep characteristics are respectively extracted, and then the target sleep state of the user is calculated according to a preset target sleep state calculation model. Compared with the technical scheme that the sleep state of the user is determined only by the heart rate data or the call data in the related technical scheme, the sleep state of the user is determined comprehensively by the plurality of sleep parameter data, so that the accuracy of sleep state detection can be improved, the effectiveness of subsequent control or treatment based on the sleep state monitoring result is improved, and the user experience is greatly improved.
Drawings
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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
FIG. 1 is a flow diagram illustrating a method for sleep state monitoring according to one embodiment;
FIG. 2 is a schematic diagram of an embodiment of a sleep state monitoring apparatus;
fig. 3 is a schematic structural diagram of a computer device for executing the sleep state monitoring method in one embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
In this embodiment, in order to improve the accuracy of monitoring the sleep state, a sleep state monitoring method is provided. The sleep state monitoring method may be implemented based on a computer device including a plurality of sensors (e.g., heart rate sensors and other sensors) for collecting various states or data of a user, for example, the sleep state monitoring method may be implemented based on an intelligent mattress, on which a plurality of sensors are integrated, and the state or data of the user (hereinafter, the user to be detected) placed on the intelligent mattress may be collected, and the intelligent mattress further includes a processor, and the sleep state determination and the subsequent control process may be performed according to the collected data (the step may also be performed based on a computer device connected to the intelligent mattress, for example, a background server).
Specifically, the method for monitoring the sleep state includes steps S102 to S106 shown in fig. 1:
step S102: the method comprises the steps of obtaining sleep parameter data of a user to be detected, wherein the sleep parameter data comprise one or more of heart rate data, breathing data, user temperature data and user movement data.
In this step, in order to evaluate and determine the sleep state of the user to be tested, various physiological parameters of the user to be tested, such as the heart rate, need to be detected first. Specifically, various sleep parameter data of the user to be detected are collected through various sensors, wherein the sleep parameter data may include one or more of heart rate data, respiration data, user temperature data, user movement data and the like, and may further include other sleep parameter data.
In a specific implementation, the heart rate data of the user to be tested is acquired by a heart rate sensor, for example, a PPG signal and an ECG signal of the user to be tested are acquired by a PPG sensor or an ECG sensor, and then the heart rate data of the user to be tested is determined according to the ECG signal and/or the PPG signal.
User temperature data of the user to be detected is acquired through the temperature sensor, for example, the body surface temperature or the core body temperature of the user to be detected is acquired through the infrared temperature sensor or other temperature sensors. Optionally, the body temperature data (for example, wrist temperature, forehead temperature, etc.) of a user to be measured at a plurality of positions may also be collected, and then the body temperature condition of the user (that is, user temperature data) is calculated according to the body temperature data at the plurality of positions according to a preset algorithm.
The user motion data of the user to be detected is acquired through the motion sensor and/or the pressure sensor, so that whether the user has the actions of non-sleep motion or turning over or the like at present is known.
Furthermore, the heart rate sensor and the motion sensor can be arranged on the wearable device, and under the condition that the user to be detected sleeps, the wearable device is used for measuring various sleep parameter data of the user to be detected. In another embodiment, the sensor and the pressure sensor are also arranged on the intelligent mattress, and under the condition that the user to be detected is placed on the intelligent mattress, the sensor arranged on the intelligent mattress can be used for detecting various items of sleep parameter data of the user to be detected.
In addition to the above-mentioned items of sleep parameter data, sound data may be included, for example, corresponding sound data may be acquired by a microphone device on the wearable device or the smart mattress, where the sound data may be related sound data that identifies the user speaking or making a call, or the environment.
Step S104: and respectively extracting the sleep characteristics of each sleep parameter data according to a preset characteristic extraction algorithm.
After the various sleep parameter data are acquired, further feature extraction needs to be performed on the sleep parameter data, that is, features corresponding to each kind of sleep parameter data, that is, sleep features, are respectively extracted according to a preset feature extraction algorithm. The sleep characteristics are used to identify characteristics related to the sleep state in each item of sleep parameter data, for example, when the heart rate parameter data is a PPG signal, the corresponding sleep characteristics are the heart rate calculated according to the PPG signal, and then the sleep state in which the user is located is determined according to the heart rate. In the present embodiment, a feature extraction algorithm of sleep features will not be described in detail.
Step S106: and determining the target sleep state of the user to be detected according to the sleep characteristics according to a preset target sleep state calculation model.
Under the condition that various sleep parameter data of the user to be detected are detected and the corresponding sleep characteristics are extracted, the corresponding sleep state can be calculated according to a preset target sleep state calculation model to serve as the target sleep state corresponding to the user to be detected.
Specifically, in an embodiment, a corresponding relationship is established in advance according to the sleep characteristics and the sleep state, for example, when the value of each sleep characteristic is a preset value, the corresponding sleep state is the first sleep state. In specific implementation, according to a correspondence relationship between the sleep characteristics and the sleep states which are constructed in advance, the sleep state corresponding to the sleep characteristics is determined as a target sleep state.
In another embodiment, the sleep characteristics may be input into the target sleep state calculation model, and then the output result of the target sleep state calculation model is obtained as the target sleep state; wherein, the target sleep state calculation model is a neural network model. In specific implementation, the sleep characteristics and the sleep state need to be collected in advance, and then training is performed on the sleep characteristics and the sleep state with a preset neural network model, so that the corresponding neural network model has the capability of predicting the sleep state according to the sleep characteristics.
It should be noted that the sleep states include an awake state and a sleep state, and the sleep states further include a deep sleep state and a light sleep state, and in other embodiments, a plurality of different sleep states may be defined according to different states to represent different sleep states of the user, so as to subdivide the sleep states more accurately.
The target sleep state calculation model, whether the corresponding relationship between the sleep characteristics and the sleep state is established or the neural network model, is related to the state of the user, for example, the sleep states of users of different ages or sexes can be obviously different. Therefore, in this embodiment, a personalized target sleep state calculation model may also be determined according to the relevant data of the user, so as to improve the accuracy of sleep state determination.
Specifically, before performing sleep monitoring, body state data corresponding to the user to be detected also needs to be acquired, and the acquisition of the body state data may be input by the user through a preset input device (for example, a connected smart terminal such as a mobile phone) or directly acquired by being connected with a corresponding database (for example, a hospital database is connected to acquire corresponding data). The physical status data comprises one or more of physical examination data, medication data and basic data, and the basic data comprises one or more of age, sex, weight and height.
Then, the preset initial sleep state calculation model is corrected or modified according to the body state data to obtain a model corresponding to the individuation of the user to be tested, the corrected sleep state calculation model is used as a target sleep state calculation model, and then the model is used for the calculation of the sleep state in the step S106.
In this embodiment, the method for monitoring a sleep state further includes, in addition to the steps S102 to S106:
step S108: and acquiring a preset target control strategy corresponding to the target sleep state and executing the preset target control strategy.
That is to say, after the target sleep state of the user to be tested is determined, the target control strategy corresponding to the target sleep state is determined and executed according to the control required by the current application scene, so that the control based on accurate sleep state monitoring is realized, and the accuracy and effectiveness of the control are improved.
For example, in the case that the monitoring method of the sleep state is based on sleep assistance and insomnia treatment of the smart mattress, a preset corresponding control strategy is obtained according to whether the detected sleep state is a wake state, a shallow sleep state or a deep sleep state, and then corresponding modules in the smart mattress are controlled to execute corresponding operations, so as to implement the sleep assistance and the insomnia treatment.
Further, in another embodiment, as shown in fig. 2, a sleep state monitoring apparatus is also provided.
Specifically, referring to fig. 2, the sleep state monitoring apparatus includes:
the data detection module 102 is configured to acquire sleep parameter data of a user to be detected, where the sleep parameter data includes one or more of heart rate data, respiration data, user temperature data, and user motion data;
the feature extraction module 104 is configured to extract sleep features of each piece of sleep parameter data according to a preset feature extraction algorithm;
and the sleep state calculation module 106 is configured to determine a target sleep state of the user to be tested according to the sleep characteristics according to a preset target sleep state calculation model.
In an embodiment, as shown in fig. 2, the monitoring apparatus for sleep states further includes a control module 108, configured to acquire and execute a preset target control policy corresponding to the target sleep state.
In one embodiment, the data detection module 102 is further configured to acquire heart rate data of the user to be detected through a heart rate sensor; acquiring user temperature data of the user to be detected through a temperature sensor; acquiring user motion data of the user to be detected through a motion sensor and/or a pressure sensor; the heart rate sensor, the heart rate sensor and the motion sensor are arranged on a wearable device, and the pressure sensor is arranged on the intelligent mattress.
In one embodiment, the sleep state calculation module 106 is further configured to input the sleep characteristics into the target sleep state calculation model, and obtain an output result of the target sleep state calculation model as the target sleep state; or determining the sleep state corresponding to the sleep characteristics as the target sleep state according to the corresponding relation between the sleep characteristics and the sleep states contained in the target sleep state calculation model.
In one embodiment, the target sleep state computational model is a neural network model.
In one embodiment, the sleep parameter data further comprises voice data representing utterances of the user in a speaking, calling, or the like state.
In one embodiment, the sleep state calculation module 106 is further configured to obtain body state data corresponding to the user to be tested, where the body state data includes one or more of physical examination data, medication data, and basic data, and the basic data includes one or more of age, gender, weight, and height; and correcting a preset initial sleep state calculation model according to the body state data, and taking the corrected sleep state calculation model as the target sleep state calculation model.
FIG. 3 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may specifically be a terminal, and may also be a server. As shown in fig. 3, the computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program which, when executed by the processor, causes the processor to implement the sleep state monitoring method. The internal memory may also have a computer program stored therein, which when executed by the processor, causes the processor to perform a sleep state monitoring method. Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a smart mattress is proposed, comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
acquiring sleep parameter data of a user to be detected, wherein the sleep parameter data comprises one or more of heart rate data, breathing data, user temperature data and user movement data;
according to a preset feature extraction algorithm, respectively extracting sleep features of each sleep parameter data;
and determining the target sleep state of the user to be detected according to the sleep characteristics according to a preset target sleep state calculation model.
In one embodiment, a computer-readable storage medium is proposed, in which a computer program is stored which, when executed by a processor, causes the processor to carry out the steps of:
acquiring sleep parameter data of a user to be detected, wherein the sleep parameter data comprises one or more of heart rate data, breathing data, user temperature data and user movement data;
according to a preset feature extraction algorithm, respectively extracting sleep features of each sleep parameter data;
and determining the target sleep state of the user to be detected according to the sleep characteristics according to a preset target sleep state calculation model.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for monitoring a sleep state, the method comprising:
acquiring sleep parameter data of a user to be detected, wherein the sleep parameter data comprises one or more of heart rate data, breathing data, user temperature data and user movement data;
according to a preset feature extraction algorithm, respectively extracting sleep features of each sleep parameter data;
and determining the target sleep state of the user to be detected according to the sleep characteristics according to a preset target sleep state calculation model.
2. The method for monitoring a sleep state according to claim 1, wherein after the step of determining the target sleep state of the user to be tested according to the sleep characteristics according to the preset target sleep state calculation model, the method further comprises:
and acquiring a preset target control strategy corresponding to the target sleep state and executing the preset target control strategy.
3. The method for monitoring the sleep state according to claim 1, wherein the step of obtaining the sleep parameter data of the user to be tested further comprises:
acquiring heart rate data of the user to be detected through a heart rate sensor;
acquiring user temperature data of the user to be detected through a temperature sensor;
acquiring user motion data of the user to be detected through a motion sensor and/or a pressure sensor;
the heart rate sensor motion sensor sets up on a wearable equipment, pressure sensor sets up on intelligent mattress.
4. The method for monitoring a sleep state according to claim 1, wherein the step of determining the target sleep state of the user to be tested according to the sleep characteristics according to the preset target sleep state calculation model further comprises:
inputting the sleep characteristics into the target sleep state calculation model, and acquiring an output result of the target sleep state calculation model as the target sleep state;
or the like, or, alternatively,
and determining the sleep state corresponding to the sleep characteristics as the target sleep state according to the corresponding relation between the sleep characteristics and the sleep state contained in the target sleep state calculation model.
5. The method of claim 1, wherein the target sleep state computational model is a neural network model.
6. The method for monitoring the sleep state according to claim 1, wherein the sleep parameter data further comprises voice data, and the voice data is used for representing the voice production of the user in the states of speaking, calling and the like.
7. The method for monitoring a sleep state according to claim 1, wherein before the step of determining the target sleep state of the user to be tested according to the sleep characteristics according to the preset target sleep state calculation model, the method further comprises:
acquiring body state data corresponding to the user to be detected, wherein the body state data comprises one or more of physical examination data, medication data and basic data, and the basic data comprises one or more of age, sex, weight and height;
and correcting a preset initial sleep state calculation model according to the body state data, and taking the corrected sleep state calculation model as the target sleep state calculation model.
8. An apparatus for monitoring sleep states, the apparatus comprising:
the data detection module is used for acquiring sleep parameter data of a user to be detected, wherein the sleep parameter data comprises one or more of heart rate data, respiration data, user temperature data and user motion data;
the characteristic extraction module is used for respectively extracting the sleep characteristics of each sleep parameter data according to a preset characteristic extraction algorithm;
and the sleep state calculation module is used for determining the target sleep state of the user to be detected according to the sleep characteristics according to a preset target sleep state calculation model.
9. A computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to carry out the steps of the method according to any one of claims 1 to 7.
10. A smart mattress comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of the method according to any one of claims 1 to 7.
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