CN118059370A - Sleep environment light self-adjustment system and method based on sleep stage assessment - Google Patents
Sleep environment light self-adjustment system and method based on sleep stage assessment Download PDFInfo
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Abstract
The invention is suitable for the technical field of intelligent home and medical equipment, and provides a sleep environment light self-adjusting system and a sleep environment light self-adjusting method based on sleep stage assessment, wherein the system comprises the following steps: the intelligent mattress comprises a sleep-aiding lamp, a controller and an intelligent mattress, wherein the sleep-aiding lamp is in communication connection with the controller and the intelligent mattress; the sleep-aiding lamp comprises an LED light source, a data processing unit, a data transmission unit and a database; the database stores historical physiological characteristic data of a user in a sleep state, and can train the deep learning model based on a specified deep learning model by taking the historical physiological characteristic data as a training set to obtain a trained sleep stage model. According to the invention, through preset parameters, feedback parameters and real-time physiological characteristic data, the sleep stage model is input to carry out sleep stage assessment; and then generating personalized sleep light environment, and providing personalized sleep full-stage light environment regulation and control.
Description
Technical Field
The invention belongs to the technical field of intelligent home and medical equipment, and particularly relates to a sleep environment light self-adjusting system and method based on sleep stage assessment.
Background
The treatment method of insomnia is mainly divided into drug intervention and non-drug intervention. Wherein the therapeutic effect of the drug intervention is rapid and obvious, but the side effect of the drug is difficult to avoid, and the drug dependence is easy to form; but the non-drug intervention has slower effect and longer treatment period, but has high safety, easy implementation and good patient compliance, and can play a role in relieving and treating partial sleep disorder diseases for which no effective drugs are developed.
Visible light intervention is an important component of non-pharmaceutical intervention means, and researches show that the secretion of melatonin is closely related to the sleeping activity of an organism, and the light rays with different wave bands and intensities can influence the activity of nuclei on hypothalamic visual intersection by stimulating intrinsic photosensitive retinal ganglion cells, so that the secretion cycle mode of the melatonin is adjusted, thereby changing the sleeping circadian rhythm of the organism, adjusting sleep disorder and improving sleeping quality. Researches show that the light in the red light wave band of 620-780nm can promote the secretion of melatonin, and the light in the blue light wave band of 450-490nm can inhibit the secretion of melatonin. Meanwhile, the light rays with different colors act on retina, light information is transmitted into brain through optic nerve, and the brain is associated by combining thinking and past experience of people, so that a series of color psychological reactions are formed, the emotion of people is influenced, and mental stress and anxiety are relieved.
The visible light intervention is mainly realized through the lamp, a plurality of related lamps for assisting sleep are available in the market at present, but the existing lamp for assisting sleep mainly intervenes before sleep, namely, background light is irradiated for a certain period of time before the user falls asleep, the intervention mode is too single, and the transition stage before sleep only occupies a small proportion in the whole night sleep process, so that the effect of assisting sleep is limited; improvements are needed.
Disclosure of Invention
The embodiment of the invention aims to provide a sleep environment light self-adjusting system based on sleep stage assessment, which can solve the problems of single intervention mode, shorter intervention process and lack of personalized intervention means of a sleep-assisting lamp in the prior art.
The embodiment of the invention is realized in such a way that the sleep environment light self-adjusting system based on sleep stage assessment comprises a sleep-aiding lamp, a controller and an intelligent mattress, wherein the sleep-aiding lamp is in communication connection with the controller and the intelligent mattress;
The sleep-aiding lamp comprises an LED light source, a data processing unit, a data transmission unit and a database; the database stores historical physiological characteristic data of a user in a sleep state, and can be used for training the deep learning model based on a specified deep learning model by taking the historical physiological characteristic data as a training set to obtain a trained sleep stage model;
the controller is used for setting preset parameters for driving the LED light sources so as to generate different sleeping light environments and obtaining feedback parameters of the sleeping environments of users;
the intelligent mattress is used for acquiring real-time physiological characteristic data of a user in a sleep state;
The data processing unit can receive the preset parameters, the feedback parameters and the real-time physiological characteristic data through the data transmission unit, and input the sleep stage model for sleep stage assessment; and controlling the LED light source to generate a corresponding sleep light environment according to the sleep stage evaluation result so as to realize sleep assistance;
Wherein, the historical physiological characteristic data and the real-time physiological characteristic data comprise heart rate signals, respiratory signals and body movement signals.
Another object of the embodiments of the present invention is to provide a sleep environment light self-adjusting method based on sleep stage assessment, which is used for the sleep environment light self-adjusting system based on sleep stage assessment, and includes the following steps:
Invoking the database to obtain historical physiological characteristic data of a user in a sleep state, and training the deep learning model by taking the historical physiological characteristic data as a training set based on a specified deep learning model to obtain a trained sleep stage model;
setting preset parameters for driving the LED light sources to generate different sleeping light environments and acquiring feedback parameters of the sleeping environments of users;
acquiring real-time physiological characteristic data of a user in a sleep state;
receiving the preset parameters, the feedback parameters and the real-time physiological characteristic data through the data transmission unit, and inputting the sleep stage model to carry out sleep stage assessment;
and controlling the LED light source to generate a corresponding sleep light environment according to the sleep stage evaluation result, so as to realize sleep assistance.
Compared with the sleep environment light self-adjusting system based on sleep stage assessment in the prior art, the sleep stage self-adjusting system provided by the embodiment of the invention is realized by only acquiring the body dynamic signals, and has the following beneficial effects: the data transmission unit of the sleep-aiding lamp acquires feedback parameters, preset parameters and physiological characteristic parameters from the controller and the intelligent mattress, the parameters are transmitted to the data processing unit for analysis and processing, a sleep stage model is built and stored in the database, and meanwhile, the LED light source is driven to generate a corresponding sleep light environment according to the output result of the sleep stage model. Thus, a personalized sleep light environment is generated, and personalized light environment regulation and control of the whole sleep stage is provided; the problems of single intervention mode, shorter intervention process and lack of personalized intervention means of the sleep-aiding lamp in the prior art are solved.
Drawings
Fig. 1 is a schematic structural diagram of a sleep environment light self-adjusting system based on sleep stage assessment according to an embodiment of the present invention;
FIG. 2 is a block diagram of a sleep environment light self-adjusting system based on sleep stage assessment according to an embodiment of the present invention;
FIG. 3 is a flow chart diagram of a sleep environment light self-adjusting method based on sleep stage assessment according to an embodiment of the present invention;
fig. 4 is a flowchart of a sleep environment light self-adjustment method based on sleep stage assessment according to an embodiment of the present invention.
In the accompanying drawings: 100-sleeping-aid lamp; 110-LED light source; 120-database; 130-a data processing unit; 140-a data transmission unit; 150-a switch; 151-first interface; 152-a second interface; 153-third interface; 200-a controller; 210-a feedback unit; 211-illumination intensity detection points; 212-spectroscopic analysis detection points; 220-a human-computer interaction unit; 221-a touch screen; 300-intelligent mattress; 310-a signal processing unit; 320-pressure sensor unit.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a block diagram of a sleep environment light self-adjusting system based on sleep stage assessment according to an embodiment of the present invention, and fig. 2 is a block diagram of a sleep environment light self-adjusting system based on sleep stage assessment according to an embodiment of the present invention; the intelligent mattress comprises a sleep-aiding lamp 100, a controller 200 and an intelligent mattress 300, wherein the sleep-aiding lamp 100 is in communication connection with the controller 200 and the intelligent mattress 300;
The sleep-aiding lamp 100 comprises an LED light source 110, a data processing unit 130, a data transmission unit 140 and a database 120; the database 120 stores historical physiological characteristic data of a user in a sleep state, and is capable of training the deep learning model based on a specified deep learning model by taking the historical physiological characteristic data as a training set to obtain a trained sleep stage model;
The controller 200 is configured to set preset parameters for driving the LED light source 110 to generate different sleep light environments, and obtain feedback parameters of the sleep environment of the user;
the intelligent mattress 300 is configured to obtain real-time physiological characteristic data of a user in a sleep state;
the data processing unit 130 can receive the preset parameters, the feedback parameters and the real-time physiological characteristic data through the data transmission unit 140, and input the sleep stage model for sleep stage assessment; and controlling the LED light source 110 to generate a corresponding sleep light environment according to the sleep stage evaluation result, so as to realize sleep assistance;
Wherein, the historical physiological characteristic data and the real-time physiological characteristic data comprise heart rate signals, respiratory signals and body movement signals.
In this embodiment, the database 120 is disposed inside the sleep-aiding lamp 100, and may use a storage device such as a solid state disk to store preset parameters, physiological characteristic data or data such as physiological characteristic parameters and sleep stage models; the database 120 may be stored using a network platform such as a cloud server. The data transmission unit 140 of the sleep-aiding lamp 100 obtains feedback parameters, preset parameters and physiological characteristic parameters from the controller 200 and the intelligent mattress 300, the parameters are transmitted to the data processing unit 130 for analysis and processing, a sleep stage model can be built in an offset learning mode and stored in the database 120, and meanwhile, the LED light source 110 is driven to generate a corresponding sleep light environment according to the output result of the sleep stage model. During the monitoring process of the sleep of the user through the intelligent mattress 300, physiological characteristic parameters such as body movement, heart rate, respiratory rate and the like are extracted, a sleep stage evaluation model can be input in real time, the sleep stage of the user is analyzed, and according to the control strategy of the LED light source 110 corresponding to the sleep stage, control instructions of all units are generated, so that a personalized sleep light environment is generated; meanwhile, aiming at the sleep stage model, the sleep stage model can be trained in advance, namely, based on the pressure distribution information of the body of the user and the intelligent mattress 300, physiological characteristic parameters such as body movement, heart rate, respiratory rate and the like are extracted to obtain real-time physiological characteristic data, and on the basis, the sleep stage model is built by fusing clinical expert diagnosis and treatment knowledge, and the LED light source 110 is driven to generate a personalized sleep light environment so as to provide personalized light environment regulation of the whole sleep stage; therefore, the embodiment of the invention can realize the intellectualization of the sleep-aiding device based on light intervention, is used for providing personalized sleep light environment and helps the user to improve sleep.
It is understood that the division of sleep stages includes, but is not limited to, a wake stage, a light sleep stage, a deep sleep stage, and a fast eye movement stage, for example, the transition stage seat of the light sleep stage, the deep sleep stage may be further subdivided, but the embodiment is not limited thereto.
In an example of this embodiment, the communication connection between the sleep-aiding light fixture 100 and the controller 200 and the intelligent mattress 300 may be implemented by setting up an existing communication device, or may be implemented by sharing a wireless or wired network through an existing home network.
In one example of the present embodiment, the data processing unit 130 can receive the preset parameter, the feedback parameter and the real-time physiological characteristic data through the data transmission unit 140, and input the sleep stage model for sleep stage assessment; and controlling the LED light source 110 to generate a corresponding sleep light environment according to the sleep stage evaluation result, so as to realize sleep assistance; the preset parameters include a preset driving control instruction of the LED light source or a key set, and may include information that can identify, mark, etc. the user identity may be realized by the controller 200; and the real-time physiological characteristic data is used as input of sleep stage assessment of the sleep stage model, and the feedback parameters are mainly used as feedback of the generation result of the sleep light environment of the LED light source in the sleep monitoring process.
In some examples, the existing communication device may include a WIFI module, a network port, a serial port, and a USB port, where the WIFI module and the network port are used for data communication of a local area network or the internet, and the serial port and the USB port are communicatively connected with the data transmission unit 140, the controller 20, or the intelligent mattress 300 through data acquisition and transmission components (such as a USB data line), and may also perform data communication with other components such as a keyboard, an image recognition device, a display, or the like.
In one example, the specified deep learning model described above is a common deep learning neural network, including, but not limited to, a VGG model, a residual convolutional neural network model (ResNet), a convolutional neural network model (CNN), a Mobilenet model, a DenseNet model, and the like; the embodiment preferably uses a convolutional neural network model; in this example, the deep learning model generated using the transfer learning is a prior art, and will not be described in detail here.
In one embodiment, the data transmission unit 140 includes a switch 150 and a transmission interface, where the switch 150 connects the LED light source 110, the controller 200 and the intelligent mattress 300 through a plurality of transmission interfaces, respectively, so as to implement communication between the LED light source 110 and the controller 200 and the intelligent mattress 300.
For example, as shown in fig. 1, the transmission interfaces to which the switch 150 is connected are respectively: a first interface 151, a second interface 152, and a third interface 153, where the first interface 151 connects the sleep-aiding light fixture 100 and the switch 150, specifically connects the LED light source 110 and the switch 150 of the sleep-aiding light fixture 100; a second interface 152 connects the switch 150 with the controller 200; the third interface 153 connects the switch 150 with the intelligent mattress 300. The first interface 151, the second interface 152 and the third interface 153 all belong to ethernet ports, and adopt the communication protocol of the ethernet card.
In one example of the present embodiment, the data processing unit 130 obtains real-time physiological characteristic data based on the information of the body of the user and the pressure distribution of the intelligent mattress 300, and extracts physiological characteristic parameters such as body movement, heart rate, respiratory rate, etc. based on the intelligent mattress 300; wherein the respiration rate characterizes the change in the respiration rate of the user at various stages of sleep, such as: fluctuation of respiration, variation of respiratory rate, and the like; the data processing unit 130 may be provided in the sleep aiding luminaire 100.
Thus, to increase the implementation convenience of the system, in one example, one self-adjusting approach to the sleeping light environment is: the brightness change of the LED light source corresponds to fluctuation change of breathing of a user, and the flicker frequency change of the LED light source corresponds to breathing frequency change of each sleeping stage of the user; the method comprises the steps that when a user falls asleep and wakes up, the flicker frequency is gradually changed from the respiratory frequency in the wakening period to the respiratory frequency in the shallow sleeping period, when the user falls in the shallow sleeping period, the flicker frequency is gradually changed from the respiratory frequency in the shallow sleeping period to the respiratory frequency in the deep sleeping period, and when the user falls in the deep sleeping period, the flicker frequency is kept to be the respiratory frequency in the deep sleeping period, so that the user is helped to quickly transition the wakening period and the shallow sleeping period, and the deep sleeping period is prolonged; the method has individuation and pertinence to the sleep aiding of different users.
Above-mentioned, the light environment regulation and control process of sleep full stage includes: generating a fixed sleeping light environment when a user is in a wake period before sleeping; when the user is in a light sleep period and a deep sleep period or when a wake condition occurs after the user falls asleep, a changed sleep light environment is generated, compared with the wake period before sleeping, the light source brightness is reduced, and the change of the light source brightness and the flicker frequency corresponds to the change of the physiological characteristic number (also called physiological characteristic parameter) of the sleep stage where the user is positioned; when the user is in the rapid eye movement period, the LED light source 110 is turned off.
In one example, to avoid over-illumination of the light source, it is necessary to define it; the threshold value of the light source brightness and the default value of the flicker frequency are obtained by the system by combining the sleeping light environment of the user, sleeping stage history data in a database and clinical expert diagnosis and treatment knowledge, and the data are analyzed by a data processing unit; the spectrum distribution of the sleeping environment light defaults to a red light wave band of 620-780 nm; wherein the sleeping light environment may also be adjusted by the controller in combination with the subjective perception of the individual by the user, the present example is not limited thereto.
In one embodiment, the LED light source 110 may use one or more groups of LED light sources, wherein LEDs with different colors in the LED light sources provide a light source with the full wavelength band (380-780 nm) of visible light, and one reference of the spectrum band of the LEDs is divided into purple light 380-420nm, indigo light 420-450nm, blue light 450-490nm, green light 490-560nm, yellow light 560-590nm, orange light 590-620nm and red light 620-780nm; the LED lamp group is directly controlled by the data processing unit 130, and a corresponding sleep light environment is generated according to the output result of the sleep stage model; the LED lamp group not only can provide a single-band sleeping light environment, but also can provide a multi-band mixed sleeping light environment, and can be flexibly adjusted and has higher adaptability for different users.
As shown in fig. 2, in one embodiment, the intelligent mattress 300 includes a mattress main body, a pressure sensor unit 320 built in the mattress main body, and a signal processing unit 310;
the pressure sensor unit 320 is configured to acquire a pressure signal of a sleeping state of a user;
The signal processing unit 310 connects the data transmission unit 140 with the pressure sensor unit 320; the signal processing unit 310 may be configured to extract physiological characteristic parameters of the user by using a specified characteristic extraction algorithm after filtering and amplifying the pressure signal, so as to form the real-time physiological characteristic data.
In this embodiment, the pressure sensor unit 320 may be a flexible array force sensor integrated inside the intelligent mattress 300, and the flexible array force sensor may collect an original pressure signal (or pressure signal) of the user after waiting for the user to get on the bed; the signal processing unit 310 may adopt a signal processing circuit, where the signal processing circuit includes a pre-amplifying circuit and a MCU (microprocessor), the pre-amplifying circuit amplifies and filters an original pressure signal, and then the MCU uses a time-frequency analysis, a nonlinear analysis and other specified feature extraction algorithms to extract physiological feature parameters of a user; the physiological characteristic parameters comprise heart rate, respiratory rate, body movement and the like, and the mattress main body provides support for undisturbed monitoring, so that the interference of the sleep monitoring process on the sleep of a user can be greatly reduced.
In one embodiment, the division of sleep stages includes a wake stage, a light sleep stage, a deep sleep stage, and a fast eye movement stage; the step of controlling the LED light source 110 to generate a corresponding sleep light environment according to the result of sleep stage assessment to achieve sleep assistance specifically includes the following steps:
when the user wakes up after falling asleep, controlling the flickering frequency of the LED light source 110 to gradually change from the breathing frequency of the wakefulness period to the breathing frequency of the shallow sleep period;
When the user is in the light sleep period, controlling the flicker frequency of the LED light source 110 to gradually change from the breathing frequency in the light sleep period to the breathing frequency in the deep sleep period;
when the user is in the deep sleep period, the flicker frequency of the LED light source 110 is controlled to be kept at the respiratory frequency of the deep sleep period, so that the user is helped to quickly transition the wake period and the shallow sleep period, and the deep sleep period is prolonged.
As shown in fig. 2, in one embodiment, the controller 200 is configured with a feedback unit 210 and a man-machine interaction unit 220;
The feedback unit 210 is configured to obtain feedback parameters of light of the sleeping environment of the user, where the feedback parameters include at least illumination intensity, spectral distribution, and flicker frequency;
The man-machine interaction unit 220 is configured to set preset parameters for driving the LED light source to generate different sleeping light environments.
In this embodiment, the feedback unit 210 may use an illuminometer and a spectrum analyzer to detect the environment where the user is located, where the feedback parameters include illumination intensity, spectral distribution, flicker frequency, and the like; the illuminometer and the spectrum analyzer are arranged inside the controller 200, and a window opening area, namely an illumination intensity detection point 211 and a spectrum analysis detection point 212 is reserved on the outer shell of the controller 200.
The man-machine interaction unit may use a touch screen 221, and is configured to receive input information of a user, where the input information includes identity information of the user, preset parameters for driving the LED light source, and the like; and the current working state of the system can be displayed; in addition, the touch screen 221 rests on the screen after waiting for a period of time, without affecting the sleep of the user.
In one embodiment, the system further comprises: the data preprocessing unit is configured to preprocess the real-time physiological characteristic data and then transmit the preprocessed real-time physiological characteristic data to the data transmission unit 140, so as to compress a data amount of the real-time physiological characteristic data and improve a transmission efficiency of the data transmission unit 140;
the data preprocessing unit 130 specifically includes: a feature comparison subunit, a feature decomposition subunit and a feature fusion subunit;
The characteristic comparison subunit is used for comparing the physiological characteristic in the real-time physiological characteristic data of the current period with the physiological characteristic difference value in the real-time physiological characteristic data of the previous period based on time sequence;
The characteristic decomposition subunit is used for decomposing and extracting the difference between the physiological characteristic in the real-time physiological characteristic data of the current period and the physiological characteristic in the real-time physiological characteristic data of the previous period if the difference value is larger than or equal to a preset difference threshold value, generating an equivalent characteristic and a difference characteristic, and transmitting only the difference characteristic;
and the characteristic fusion subunit is used for fusing the difference characteristic as a basic characteristic based on the physiological characteristic in the real-time physiological characteristic data of the previous period to obtain new real-time physiological characteristic data.
In this embodiment, the physiological features in the real-time physiological feature data include: body movement, heart rate, and respiration rate; taking the respiratory rate as an example, segmenting a frequency time diagram of the respiratory rate in a fixed period in a time sequence to generate a plurality of sections of change curves with the time sequence, representing the respiratory rate, comparing the change curves of the frequency time diagrams of any two adjacent sections, determining the difference between the two sections, and if the difference value of the difference is greater than or equal to a preset difference threshold value, indicating that the stage of the sleep stage changes, and the change belongs to two of a wake stage, a shallow sleep stage, a deep sleep stage and a rapid eye movement stage; then, the difference between the physiological characteristics in the real-time physiological characteristic data of the current period and the physiological characteristics in the real-time physiological characteristic data of the previous period is decomposed and extracted, the difference characteristics are generated and transmitted, and finally, the characteristics are fused, so that new real-time physiological characteristic data, namely the real-time physiological characteristic data of the current period, is obtained.
When the method is implemented, different data transmission and processing modes are performed aiming at different stages of sleep stage, so that lower time delay in the data transmission process can be ensured, data details can be sent and displayed as required, and resources can be saved and the speed can be increased.
As shown in fig. 3 and 4, in another embodiment, a sleep environment light self-adjusting method based on sleep stage assessment is used in the sleep environment light self-adjusting system based on sleep stage assessment, and includes the following steps S401 to S409:
s401, calling the database 120, acquiring historical physiological characteristic data of a user in a sleep state, and training the deep learning model by taking the historical physiological characteristic data as a training set based on a specified deep learning model to obtain a trained sleep stage model;
In this step, the sleep stage model may employ a conventional machine learning network; improvements may also be made based on the machine learning network; the historical physiological characteristic data may be: the pressure signal during sleeping of the user is continuously collected for a certain number of days through the pressure sensor unit 320, and after the pressure signal is filtered and amplified through the signal processing unit 310, the physiological characteristic parameters of the user are calculated and extracted by using algorithms such as time-frequency analysis, nonlinear analysis and the like.
S403, setting preset parameters for driving the LED light source 110 to generate different sleeping light environments and obtaining feedback parameters of the sleeping environments of the user;
In one scenario, preset parameters are used to control the LED light source 110 to emit light with a specified wavelength band, so as to achieve sleep-aiding; the spectrum distribution of the ambient light of the sleeping light environment defaults to a red light wave band of 620-780 nm; of course, the sleeping light environment may also be adjusted by the human-computer interaction unit 220 in combination with the subjective feeling of the individual, which will not be described in detail here.
In one scenario, the preset parameters may also control the flickering and variation of the light of the specified wavelength band emitted by the LED light source 110; the brightness change of the brightness corresponds to the fluctuation change of the breathing of the user, and the change of the flicker frequency corresponds to the breathing frequency change of each sleeping stage of the user; the flicker frequency is gradually changed from the respiratory frequency of the wake period to the respiratory frequency of the light sleep period when the user falls asleep and gradually changed from the respiratory frequency of the light sleep period to the respiratory frequency of the deep sleep period when the user falls asleep, and the flicker frequency is kept to be the respiratory frequency of the deep sleep period when the user falls in the deep sleep period, so that the user is helped to quickly transition the wake period and the light sleep period, and the deep sleep period is prolonged. Meanwhile, when the user is in a light sleep period and a deep sleep period or when a wake condition occurs after the user falls asleep, a changed sleep light environment is generated, and compared with the wake period before sleeping, the brightness of the light source is reduced, so that the user is helped to fall asleep quickly.
S405, acquiring real-time physiological characteristic data of a user in a sleep state;
in this step, physiological characteristic data including physiological characteristic parameters such as body movement, heart rate, respiration rate, etc. may be obtained by the controller 200 and the intelligent mattress 300; the user sleep state includes a state in which the user falls asleep for a period of time before getting into bed and a period of time after getting into bed.
S407, receiving the preset parameters, the feedback parameters and the real-time physiological characteristic data through the data transmission unit 140, and inputting the sleep stage model for sleep stage assessment;
This step can be divided into a number of sub-steps: receiving the preset parameters, controlling the LED light source 110 to enter initialization, and starting to work; receiving the real-time physiological characteristic data, and inputting the sleep stage model to perform sleep stage assessment; receiving the feedback parameters and adjusting the sleeping light environment; this sub-step may be performed at any stage in order to provide a more personalized sleep-aiding service for the user.
S409, controlling the LED light source 110 to generate a corresponding sleep light environment according to the sleep stage evaluation result, so as to realize sleep assistance.
In this step, the sleep light environment is adjusted, and the closed-loop control of monitoring, adjusting and feedback is implemented through the feedback unit 210, the data processing unit 130 and the pressure sensor unit 320; flexible adjustment and individuality.
In one embodiment, the step of acquiring real-time physiological characteristic data of the sleeping state of the user specifically includes:
Acquiring a pressure signal of a user in a sleep state;
and after filtering and amplifying the pressure signal, extracting physiological characteristic parameters of a user by using a specified characteristic extraction algorithm to form the real-time physiological characteristic data.
In this embodiment, the specified feature extraction algorithm includes, but is not limited to, time-frequency analysis, nonlinear analysis, and the like, and a combination of a plurality of algorithms, and the like.
In one embodiment, the step of controlling the LED light source 110 to generate a corresponding sleep light environment according to the result of the sleep stage evaluation to achieve sleep assistance specifically includes the following steps:
when the user wakes up after falling asleep, controlling the flickering frequency of the LED light source 110 to gradually change from the breathing frequency of the wakefulness period to the breathing frequency of the shallow sleep period;
When the user is in the light sleep period, controlling the flicker frequency of the LED light source 110 to gradually change from the breathing frequency in the light sleep period to the breathing frequency in the deep sleep period;
when the user is in the deep sleep period, the flicker frequency of the LED light source 110 is controlled to be kept at the respiratory frequency of the deep sleep period, so that the user is helped to quickly transition the wake period and the shallow sleep period, and the deep sleep period is prolonged.
In one embodiment, the method further comprises:
Whether the user leaves the bed is judged by the intelligent mattress 300, if yes, the sleep aiding of the sleep aiding lamp 100 is ended.
As shown in fig. 3, in one example of the present embodiment, the sleep environment light self-adjustment method based on sleep stage estimation may include some or all of the following steps S101 to S108:
S101, acquiring physiological characteristic parameters of a user in the whole night sleeping process through an intelligent mattress 300, storing the physiological characteristic parameters in a database 120, and continuously acquiring for a certain number of days (for example, 5, 7 or 14 days) until the data volume meets the basic requirement of a sleep stage model, and completing the establishment of the sleep stage model;
S102, a user inputs preset parameters through the man-machine interaction unit 220, and places the controller 200 at a position close to the pillow edge of the mattress main body; the user needs to ensure that the sleeping environment is in a dark environment, so that the interference of external light is avoided;
s103, the intelligent mattress 300 judges that the user is in a bed state and then starts sleep aiding service, and physiological characteristic parameters of the user are uploaded in real time; the data processing unit 130 evaluates the sleep stage in which the user is located according to the sleep stage model;
s104, generating a fixed sleeping light environment when the user is in a wake period before sleeping through the LED light source 110;
S105, generating a changed sleeping light environment when the user is in a light sleep period and a deep sleep period or when the user is in a wake state after falling asleep through the LED light source 110;
s106, when the user is in the rapid eye movement period, the LED light source is turned off;
S107, uploading feedback parameters of the environment where the user is located in real time in the sleeping process through the feedback unit 210, and adjusting the LED light source 110 according to the feedback parameters to ensure that the sleeping light environment where the user is located is consistent with the system setting;
s108, after the intelligent mattress 300 judges that the user leaves the bed, the sleep aiding service is finished, and white light illumination is started.
The embodiment has the following beneficial effects: by collecting physiological characteristic parameters of the user, a sleep stage model aiming at the user individual is established, and then a personalized sleep light environment can be provided according to a sleep stage evaluation result. The method has the advantages that not only is the user intervened before sleeping, but also after the user falls asleep, the sleeping light environment where the user is located is dynamically changed, so that the change of brightness and flicker frequency corresponds to the change of physiological characteristic parameters of the sleeping stage where the user is located, the user is helped to transition to the deep sleep stage, the duration of the deep sleep stage is prolonged, the sleeping quality is improved, and the intervention of the whole sleeping process is realized. By establishing a personalized and full-stage sleep light environment, a basic platform is provided for medical research related to sleep disorder rehabilitation, and a convenient medical means is provided for insomnia patients, so that social resources are saved, and social medical cost is controlled.
In another embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to perform steps S401 to S409 of the method as described above;
S401, calling the database to acquire historical physiological characteristic data of a user in a sleep state, and training the deep learning model by taking the historical physiological characteristic data as a training set based on a specified deep learning model to obtain a trained sleep stage model;
S403, setting preset parameters for driving the LED light sources to generate different sleeping light environments and obtaining feedback parameters of the sleeping environments of the users;
S405, acquiring real-time physiological characteristic data of a user in a sleep state;
S407, receiving the preset parameters, the feedback parameters and the real-time physiological characteristic data through the data transmission unit, and inputting the sleep stage model for sleep stage assessment;
S409, controlling the LED light source to generate a corresponding sleep light environment according to the sleep stage evaluation result, so as to realize sleep assistance.
As described above, in the sleep environment light self-adjusting system based on sleep stage assessment provided in this embodiment, feedback parameters, preset parameters and physiological characteristic parameters are obtained from the controller 200 and the intelligent mattress 300, and these parameters are transmitted to the data processing unit 130 for analysis and processing, a sleep stage model is built based on the database 120 and stored in the database 120, and meanwhile, the data processing unit 130 drives the LED light source 110 to generate a corresponding sleep light environment according to the output result of the sleep stage model, i.e., according to the estimated wake-up period, light sleep period, deep sleep period and rapid eye movement period; in this way, a personalized sleep light environment may be generated, such as: generating a fixed sleeping light environment when a user is in a wake period before sleeping; generating a changing sleeping light environment when the user is in a light sleep period and a deep sleep period or when an awake state occurs after the user falls asleep; when the user is in the rapid eye movement period, the LED light source and the like are turned off; providing individualized sleep full-stage light environment regulation; the problems of single intervention mode, shorter intervention process and lack of personalized intervention means of the sleep-aiding lamp in the prior art are solved.
The computer device comprises a processor, a memory, a network interface, an input device and a display screen which are connected through a system bus. The memory includes a nonvolatile 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 that, when executed by the processor, causes the processor to implement a sleep environment light self-regulating method based on sleep stage assessment. The internal memory may also have stored therein a computer program which, when executed by the processor, causes the processor to perform a sleep environment light self-regulating method based on sleep stage assessment. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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 (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The sleep environment light self-adjusting system based on sleep stage assessment comprises a sleep-aiding lamp, a controller and an intelligent mattress, wherein the sleep-aiding lamp is in communication connection with the controller and the intelligent mattress; it is characterized in that the method comprises the steps of,
The sleep-aiding lamp comprises an LED light source, a data processing unit, a data transmission unit and a database; the database stores historical physiological characteristic data of a user in a sleep state, and can be used for training the deep learning model based on a specified deep learning model by taking the historical physiological characteristic data as a training set to obtain a trained sleep stage model;
the controller is used for setting preset parameters for driving the LED light sources so as to generate different sleeping light environments and obtaining feedback parameters of the sleeping environments of users;
the intelligent mattress is used for acquiring real-time physiological characteristic data of a user in a sleep state;
The data processing unit can receive the preset parameters, the feedback parameters and the real-time physiological characteristic data through the data transmission unit, and input the sleep stage model for sleep stage assessment; and controlling the LED light source to generate a corresponding sleep light environment according to the sleep stage evaluation result so as to realize sleep assistance;
Wherein, the historical physiological characteristic data and the real-time physiological characteristic data comprise heart rate signals, respiratory signals and body movement signals.
2. The sleep stage assessment based sleep environment light self-regulating system according to claim 1, wherein the data transmission unit comprises a switch and a transmission interface, wherein the switch is respectively connected with the LED light source, the controller and the intelligent mattress through a plurality of the transmission interfaces so as to realize communication between the LED light source and the controller and the intelligent mattress.
3. Sleep environment light self-regulating system based on sleep stage assessment according to claim 1 or 2, characterized in that the system further comprises: the data preprocessing unit is used for preprocessing the real-time physiological characteristic data and then transmitting the preprocessed real-time physiological characteristic data to the data transmission unit;
The data preprocessing unit specifically comprises: a feature comparison subunit, a feature decomposition subunit and a feature fusion subunit;
The characteristic comparison subunit is used for comparing the physiological characteristic in the real-time physiological characteristic data of the current period with the physiological characteristic difference value in the real-time physiological characteristic data of the previous period based on time sequence;
the characteristic decomposition subunit is used for decomposing and extracting the difference between the physiological characteristic in the real-time physiological characteristic data of the current period and the physiological characteristic in the real-time physiological characteristic data of the previous period if the difference value is larger than or equal to a preset difference threshold value, and generating a difference characteristic for transmission;
and the characteristic fusion subunit is used for fusing the difference characteristic as a basic characteristic based on the physiological characteristic in the real-time physiological characteristic data of the previous period to obtain new real-time physiological characteristic data.
4. The sleep stage assessment based sleep environment light self-regulating system according to claim 1, wherein the controller is configured with a feedback unit and a human-computer interaction unit;
the feedback unit is used for acquiring feedback parameters of the sleeping environment of the user, wherein the feedback parameters at least comprise illumination intensity, spectral distribution and flicker frequency;
the man-machine interaction unit is used for setting preset parameters for driving the LED light source so as to generate different sleeping light environments.
5. The sleep stage assessment based sleep ambient light self-regulating system according to claim 1, wherein the intelligent mattress comprises a mattress body, a pressure sensor unit built into the mattress body, and a signal processing unit;
The pressure sensor unit is used for acquiring a pressure signal of a user in a sleep state;
The signal processing unit is connected with the data transmission unit and the pressure sensor unit; and the signal processing unit can be used for extracting physiological characteristic parameters of a user by using a specified characteristic extraction algorithm after filtering and amplifying the pressure signal to form the real-time physiological characteristic data.
6. The sleep environment light self-regulating system based on sleep stage assessment according to claim 1, wherein the divisions of the sleep stage include wake phase, light sleep phase, deep sleep phase and fast eye movement phase; and controlling the LED light source to generate a corresponding sleep light environment according to the sleep stage evaluation result, so as to realize sleep aiding, and specifically comprising the following steps:
When the user wakes up after falling asleep, controlling the flicker frequency of the LED light source to gradually change from the respiratory frequency in the wake-up period to the respiratory frequency in the shallow sleep period;
when the user is in the light sleep period, controlling the flicker frequency of the LED light source to gradually change from the breathing frequency in the light sleep period to the breathing frequency in the deep sleep period;
When the user is in the deep sleep period, the flicker frequency of the LED light source is controlled to be kept to be the respiratory frequency of the deep sleep period, so that the user is helped to quickly transition the wake period and the shallow sleep period, and the deep sleep period is prolonged.
7. A sleep environment light self-adjusting method based on sleep stage assessment, characterized in that the method is used for the sleep environment light self-adjusting system based on sleep stage assessment as claimed in any one of claims 1-6, comprising the following steps:
Invoking the database to obtain historical physiological characteristic data of a user in a sleep state, and training the deep learning model by taking the historical physiological characteristic data as a training set based on a specified deep learning model to obtain a trained sleep stage model;
setting preset parameters for driving the LED light sources to generate different sleeping light environments and acquiring feedback parameters of the sleeping environments of users;
acquiring real-time physiological characteristic data of a user in a sleep state;
receiving the preset parameters, the feedback parameters and the real-time physiological characteristic data through the data transmission unit, and inputting the sleep stage model to carry out sleep stage assessment;
and controlling the LED light source to generate a corresponding sleep light environment according to the sleep stage evaluation result, so as to realize sleep assistance.
8. The sleep stage assessment based sleep environment light self-regulating method according to claim 7, wherein said step of acquiring real-time physiological characteristic data of a user in a sleep state comprises:
Acquiring a pressure signal of a user in a sleep state;
and after filtering and amplifying the pressure signal, extracting physiological characteristic parameters of a user by using a specified characteristic extraction algorithm to form the real-time physiological characteristic data.
9. The sleep environment light self-adjusting method based on sleep stage assessment according to claim 7, wherein the step of controlling the LED light source to generate a corresponding sleep light environment according to the result of the sleep stage assessment to achieve sleep assistance specifically comprises the following steps:
When the user wakes up after falling asleep, controlling the flicker frequency of the LED light source to gradually change from the respiratory frequency in the wake-up period to the respiratory frequency in the shallow sleep period;
when the user is in the light sleep period, controlling the flicker frequency of the LED light source to gradually change from the breathing frequency in the light sleep period to the breathing frequency in the deep sleep period;
When the user is in the deep sleep period, the flicker frequency of the LED light source is controlled to be kept to be the respiratory frequency of the deep sleep period, so that the user is helped to quickly transition the wake period and the shallow sleep period, and the deep sleep period is prolonged.
10. The sleep stage assessment based sleep environment light self-regulating method according to claim 9, wherein the method further comprises:
judging whether the user leaves the bed or not through the intelligent mattress, and if so, ending the sleep aiding of the sleep aiding lamp.
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