CN115137313B

CN115137313B - Evaluation method and device for simultaneously aiming at sleep quality and myopia risk

Info

Publication number: CN115137313B
Application number: CN202211050549.2A
Authority: CN
Inventors: 康梦田; 刘勇; 张弛; 许文隽; 李仕明; 阿力木·达依木; 赵子贺; 王嘉琪; 陈君亮; 高硕�
Original assignee: Beijing Tongren Hospital
Current assignee: Beijing Tongren Hospital
Priority date: 2022-08-31
Filing date: 2022-08-31
Publication date: 2022-11-18
Anticipated expiration: 2042-08-31
Also published as: CN115137313A

Abstract

The application relates to the technical field of sleep quality and myopia risk assessment, and provides an assessment method and device for both sleep quality and myopia risk. The method comprises the following steps: acquiring wrist movement data, pulse blood oxygen saturation data, melatonin data and dopamine data, wherein the wrist movement data comprises at least two types of movement sub-data; generating cross ratio data based on the motion subdata and the pulse oximetry data; inputting the data into a pre-trained comprehensive evaluation model, generating a myopia risk evaluation result and sleep state information at the current time, and storing the sleep state information; and acquiring a plurality of sleep state information corresponding to each time in the target sleep cycle to generate a sleep quality evaluation result. According to the embodiment of the application, on the one hand, the accuracy of sleep quality assessment and myopia risk prediction is greatly increased, on the other hand, machine learning is carried out by leading the data into a preset comprehensive model, and the sleep quality and the myopia risk can be assessed and predicted at the same time.

Description

Evaluation method and device for simultaneously aiming at sleep quality and myopia risk

Technical Field

The application relates to the technical field of sleep quality and myopia risk assessment, in particular to a method and a device for assessing sleep quality and myopia risk simultaneously.

Background

On one hand, the myopia rate of teenagers in China is on the rise, and myopia is a main factor influencing the health of the teenagers. Myopia is mainly affected by factors such as close-range work, electronic product use, outdoor activities, indoor lighting, sleeping, diet conditions and the like. The technique of present prediction myopia risk is through the user wearing dedicated wearing equipment, collects eyeball working distance and eyes at ordinary times and receives illumination intensity data, excavates through the spatial data, realizes the quantization to myopia environment risk factor, aassessment user's future myopia risk.

On the other hand, sleep quality monitoring is an important component of human intelligent health monitoring, and with the rapid development of intelligent sensing and communication technologies, many commercially available portable sleep monitoring systems have appeared. The existing sleep quality monitoring is mainly realized by means of an intelligent bracelet, a body movement recorder in the bracelet records wrist movement conditions of a user during sleep by utilizing a three-axis acceleration sensor, and meanwhile, heart rate change conditions are monitored by utilizing a photoelectric sensor, and the sleep quality is evaluated based on wrist movement information and heart rate change information.

Because the existing myopia risk prediction technology and sleep quality evaluation do not consider the influence caused by melatonin, dopamine, pulse blood oxygen saturation and the like in the body, on one hand, the prediction precision and the sleep quality evaluation precision aiming at the myopia risk are poor, and on the other hand, the technology capable of monitoring the sleep and the myopia risk simultaneously does not exist.

Disclosure of Invention

In view of this, embodiments of the present application provide a method and an apparatus for evaluating sleep quality and myopia risk simultaneously, so as to solve the problem that in the prior art, influences caused by melatonin, dopamine, pulse blood oxygen saturation and the like in a body are not considered in a myopia risk prediction technology and sleep quality evaluation, so that on one hand, accuracy of myopia risk prediction and sleep quality evaluation is poor, and on the other hand, a technology capable of monitoring sleep and myopia risk simultaneously does not exist.

In a first aspect of the embodiments of the present application, a method for evaluating sleep quality and myopia risk simultaneously is provided, including:

acquiring wrist movement data, pulse blood oxygen saturation data, melatonin data and dopamine data at the current time, wherein the wrist movement data comprises at least two types of movement subdata;

generating cross ratio data based on the at least two kinds of motion sub-data and the pulse blood oxygen saturation data;

inputting the wrist movement data, the pulse blood oxygen saturation data, the melatonin data, the dopamine data and the cross ratio data into a pre-trained comprehensive evaluation model, generating a myopia risk evaluation result and sleep state information at the current time, and storing the sleep state information;

the method comprises the steps of obtaining a plurality of pieces of sleep state information corresponding to each time in a target sleep cycle, and generating a sleep quality evaluation result of the target sleep cycle based on the plurality of pieces of sleep state information, wherein the sleep cycle is a complete sleep cycle or an incomplete sleep cycle.

In a second aspect of the embodiments of the present application, there is provided an apparatus for evaluating sleep quality and myopia risk simultaneously, including:

the device comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring wrist movement data, pulse blood oxygen saturation data, melatonin data and dopamine data at the current time, and the wrist movement data comprises at least two types of movement subdata;

the first generation module is used for generating cross ratio data based on the at least two kinds of motion sub data and the pulse blood oxygen saturation data;

the second generation module is used for inputting the wrist movement data, the pulse blood oxygen saturation data, the melatonin data, the dopamine data and the cross ratio data into a pre-trained comprehensive evaluation model, generating a myopia risk evaluation result and sleep state information at the current time and storing the sleep state information;

the third generation module is configured to acquire a plurality of pieces of sleep state information corresponding to a target sleep cycle, and generate a sleep quality evaluation result of the target sleep cycle based on the plurality of pieces of sleep state information, where the sleep cycle is a complete sleep cycle or an incomplete sleep cycle.

In a third aspect of the embodiments of the present application, there is provided a wearable device including a wrist movement detection means, a pulse oximetry detection means, a melatonin detection means and a dopamine detection means in contact with skin, and a central processing device, wherein,

the wrist movement detection device is used for detecting wrist movement related signals of the wrist and sending the wrist movement related signals to the central processing equipment;

the pulse blood oxygen saturation detection device is used for detecting a pulse blood oxygen saturation signal and sending the pulse blood oxygen saturation signal to the central processing equipment;

the melatonin detection device is used for detecting a first oxidation-reduction current generated by an electrooxidation reaction through the electrooxidation reaction with melatonin in sweat, processing the first oxidation-reduction current to obtain melatonin data, and sending the melatonin data to the central processing equipment;

the dopamine detection device is used for detecting second redox current generated by electrooxidation reaction through the electrooxidation reaction with dopamine in sweat, processing the second redox current to obtain dopamine data and sending the dopamine data to the central processing equipment;

the central processing device is used for executing the steps of the method.

In a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, and the computer program realizes the steps of the above method when being executed by a processor.

Advantageous effects

Compared with the prior art, the embodiment of the application has the beneficial effects that at least: through to wrist motion data, pulse oxyhemoglobin saturation, melatonin, pulse oxyhemoglobin saturation and dopamine use multipurposely and predict sleep state information and near-sighted risk to combine the sleep state information that sleep process or sleep cycle correspond to carry out the sleep quality aassessment, greatly increased on the one hand to the precision of sleep quality aassessment with near-sighted risk prediction, on the other hand, through leading-in the predetermined integrated model with above-mentioned data and carrying out machine learning, can assess and predict sleep quality and near-sighted risk simultaneously.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

FIG. 1 is a schematic diagram of a scene of an assessment method for both sleep quality and myopia risk provided according to an embodiment of the present application;

FIG. 2 is a flow chart of some embodiments of a method for assessing both sleep quality and myopia risk provided in accordance with embodiments of the present application;

FIG. 3 is a flow chart of further embodiments of a method for simultaneously assessing sleep quality and myopia risk according to embodiments of the present application;

FIG. 4 is a schematic diagram of a simplified structure of an apparatus for evaluating both sleep quality and myopia risk according to an embodiment of the present application;

FIG. 5 is a schematic diagram of a central processing apparatus provided according to an embodiment of the present application;

fig. 6 is a schematic view of a bracelet provided according to an embodiment of the application.

Detailed Description

Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are for illustration purposes only and are not intended to limit the scope of the present application.

It should be noted that, for the convenience of description, only the parts relevant to the present application are shown in the drawings. The embodiments and features of the embodiments in the present application may be combined with each other without conflict.

It should be noted that the terms "first", "second", and the like in the present application are only used for distinguishing different systems, devices, modules or units, and are not used for limiting the order or interdependence of the functions executed by the systems, devices, modules or units.

It is noted that references to "a" or "an" modification in this application are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that references to "one or more" are intended to be exemplary unless the context clearly indicates otherwise.

The names of messages or information exchanged between a plurality of devices in the embodiments of the present application are for illustrative purposes only, and are not intended to limit the scope of the messages or information.

According to the law of human activity in sleep state, the whole falling asleep process of human body is divided into Wake period, REM period and Deep sleep period, wherein the Wake period is characterized in that the frequency and amplitude of the whole human activity are large and often accompanied by large-amplitude movement of wrists, the REM period is a shallow falling asleep period and is characterized in that weak activity signals are displayed, the frequency and amplitude of wrist activity are low and small, and Deep sleep period is provided, the human body keeps calm and basically has no activity information. A standard sleep cycle may have a standard duration of 1.5 hours, and the ratio of the Wake period, REM period, and Deep period in each sleep cycle has a standard ratio. The method and the device can predict and store the real-time sleep state (namely the sleep state belongs to a Wake period, a REM period or a Deep period in a sleep cycle) in the sleep process, call the stored data after the sleep is finished or the sleep cycle is finished, and compare a plurality of data corresponding to the sleep cycle or the complete sleep process with the standard sleep cycle, thereby obtaining the evaluation result of the sleep quality.

It should be noted that the sleep state partition also includes other kinds of partitions, such as Wake period, REM period, and NREM period (non-rapid eye movement period), which are similar to the disclosure and belong to the protection scope of the present application.

The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Fig. 1 is a schematic diagram of one application scenario of a method for simultaneous assessment of sleep quality and myopia risk according to some embodiments of the present application.

In the application scenario of fig. 1, first, the computing device 101 may acquire wrist movement data 102, pulse oximetry data 103, melatonin data 104, and dopamine data 105 for the current time;

second, computing device 101 may generate cross-ratio data 106 based on the wrist motion data 102 and pulse oximetry data 103;

again, the computing device 101 may input the wrist movement data 102, the pulse oximetry data 103, the melatonin data 104, the dopamine data 105, and the cross ratio data 106 into a pre-trained comprehensive assessment model 107, generate a myopia risk assessment result 108 and sleep state information 109 for the current time, and store the sleep state information 109;

finally, the computing device 101 may obtain a plurality of sleep state information 109 corresponding to each time in a target sleep cycle, and generate a sleep quality assessment result 110 of the target sleep cycle based on the plurality of sleep state information 109, where the sleep cycle is a complete sleep cycle or an incomplete sleep cycle.

The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules for providing distributed services, or as a single software or software module. And is not particularly limited herein.

It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as the implementation requires.

With continued reference to fig. 2, a flow 200 of some embodiments of a method for simultaneous assessment of sleep quality and myopia risk according to the present application is shown. The method may be performed by the computing device 101 of fig. 1. The method for simultaneously evaluating the sleep quality and the myopia risk comprises the following steps:

step 201, obtaining wrist movement data, pulse blood oxygen saturation data, melatonin data and dopamine data at the current time, wherein the wrist movement data comprises at least two types of movement sub-data.

In some embodiments, a subject (such as the computing device 101 shown in fig. 1) performing the method for simultaneously assessing sleep quality and myopia risk may connect to the target device through a wired connection or a wireless connection, and then obtain wrist movement data, pulse blood oxygen saturation data, melatonin data and dopamine data of the current time, wherein the wrist movement data includes at least two types of movement sub-data.

It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G/5G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, an UWB (ultra wideband) connection, and other wireless connection means now known or developed in the future.

The wrist movement data may refer to information about detected wrist movements during sleep of the human body. By way of example, wrist motion information may include yaw angle data, roll angle data, pitch angle data, horizontal axis acceleration data, vertical axis acceleration data, and the like for wrist motion. It is noted that the wrist motion data may include at least two kinds of motion sub data. The motion subdata may refer to one of data centers of yaw angle data, roll angle data, pitch angle data, horizontal axis acceleration data, vertical axis acceleration data and the like in the wrist motion information. Pulse oximetry data may refer to data relating to detected changes in the concentration of blood oxygen in the blood during sleep of a person. The pulse oximetry data may estimate oxygenation of the lungs and the hemoglobin oxygen carrying capacity. The ambient light intensity data may refer to data relating to the illumination intensity of the ambient light. Melatonin data may refer to data related to melatonin that may be detected as being present in the human body. Dopamine data may refer to data relating to the detectable presence of dopamine in the human body.

And 202, generating cross ratio data based on the at least two kinds of motion sub-data and the pulse blood oxygen saturation data.

In some embodiments, the execution subject may generate cross ratio data based on at least two motion sub-data of the wrist motion data and the pulse oximetry protection data. It should be noted that the cross ratio data may refer to data obtained by combining any two of the above-mentioned at least three kinds of data and calculating the ratio. By calculating the cross ratio data, the characteristic dimensionality of the data can be increased, so that a more accurate result can be predicted when machine learning is subsequently performed.

Step 203, inputting the wrist movement data, the pulse blood oxygen saturation data, the melatonin data, the dopamine data and the cross ratio data into a pre-trained comprehensive evaluation model, generating a myopia risk evaluation result and sleep state information at the current time, and storing the sleep state information.

In some embodiments, the executing subject may input the wrist movement data, the pulse blood oxygen saturation data, the melatonin data, the dopamine data and the cross ratio data into a pre-trained comprehensive evaluation model, generate and store the sleep state information of the current time. The evaluation model can be various machine learning models and is used for predicting the evaluation results of sleep quality and myopia risk based on wrist movement data, pulse blood oxygen saturation data, melatonin data, dopamine data and the cross ratio data. As an example, the evaluation model may be a model based on different algorithms, such as a decision tree model, a neural network model, a support vector machine model, or a clustering algorithm model. The result predicted by the evaluation model can be the sleep state and myopia evaluation result at the current time. The sleep state may be data in an integer, percentage, real number, or other representation form that conforms to a preset range, and the sleep state may be classified into different sleep states (Wake period, REM period, or Deep period) based on preset classification. The result may also be an indication based on a textual representation such as english, chinese, or a special symbol that may indicate a sleep state (Wake phase, REM phase, or Deep phase). The myopia risk assessment result may refer to the result predicted by the risk assessment model. The result may be data in a preset range based on expressions such as integer, percentage, real number, etc., and the result may also be a text expression converted based on integer, percentage, real number, etc., such as english, chinese, or special symbols, etc. The above-described setting is performed as needed, and is not particularly limited herein.

It should be noted that the myopia risk assessment result in this step is the target assessment result, and the sleep state information needs to be further processed based on the operation of step 204, so as to generate the assessment result for the sleep information.

In addition, when the sleep state information is saved, the sleep state information can be saved to a local storage structure, other storage structures connected in a wired or wireless connection mode, a cloud storage structure and other common storage structures.

Step 204, acquiring a plurality of pieces of sleep state information corresponding to each time in a target sleep cycle, and generating a sleep quality evaluation result of the target sleep cycle based on the plurality of pieces of sleep state information, wherein the sleep cycle is a complete sleep cycle or an incomplete sleep cycle.

In some embodiments, the execution subject may acquire a plurality of pieces of sleep state information corresponding to each time in a target sleep cycle, and generate a sleep quality evaluation result of the target sleep cycle based on the plurality of pieces of sleep state information, where the sleep cycle is a complete sleep cycle or an incomplete sleep cycle. The target sleep cycle may refer to a sleep cycle selected for evaluating sleep quality. It should be noted that, because the sleep cycle is interrupted due to a half-wake or sleep termination of the human sleep, and an incomplete cycle is generated, the target sleep cycle may be a complete sleep cycle or an incomplete sleep cycle.

The beneficial effects of one of the above embodiments of the present application at least include: the sleep state information and the myopia risk are predicted by comprehensively using wrist movement data, the pulse blood oxygen saturation, the melatonin, the pulse blood oxygen saturation and the dopamine, and the sleep quality evaluation is performed by combining the sleep state information corresponding to the sleep process or the sleep cycle, so that the accuracy of the sleep quality evaluation and the myopia risk prediction is greatly increased, and on the other hand, machine learning is performed by leading the data into a preset comprehensive model, and the sleep quality and the myopia risk can be evaluated and predicted at the same time.

In some embodiments, the wrist motion data comprises at least two motion subdata of yaw angle data, roll angle data, pitch angle data, lateral axis acceleration data, longitudinal axis acceleration data, and vertical axis acceleration data; the step of executing the subject to acquire the wrist movement data and the pulse oximetry data includes:

in the first step, the executing body can obtain pulse blood oxygen saturation signals and wrist movement signals, wherein the wrist movement signals comprise at least two motion sub-signals of yaw angle signals, roll angle signals, pitch angle signals, transverse axis acceleration signals, longitudinal axis acceleration signals and vertical axis acceleration signals. The yaw angle signal, the roll angle signal, the pitch angle signal, the horizontal axis acceleration signal, the vertical axis acceleration signal, and the vertical axis acceleration signal may be related signals of the acquired information about the wrist movement. The yaw angle signal, the roll angle signal, the pitch angle signal, the horizontal axis acceleration signal, the vertical axis acceleration signal, and the vertical axis acceleration signal are all common signals (data) for calculating relative motion information, and are not described in detail again. Since some of the motion sub-signals are lower weighted or even omitted when performing the model calculations, in some cases only part of them can be taken to fool the sub-signals.

And secondly, the executing body can perform Gaussian filtering processing on the pulse blood oxygen saturation degree signal and the at least two motion sub-signals to obtain at least two motion sub-signals after pulse blood oxygen saturation filtering.

Third, the execution main body may use the current time as a base point, and expand a first preset time length before the base point and a second preset time length after the base point to generate the first sampling window.

Fourthly, the executing body can calculate mean square deviation data, maximum positive value data and maximum negative value data of the waveform amplitude of the filtered pulse blood oxygen saturation signal in the first sampling window to obtain the pulse blood oxygen saturation data.

Fifthly, the executing body may calculate mean square difference data, maximum positive value data, and maximum negative value data of the waveform amplitudes of the at least two filtered motion sub-signals in the first sampling window, respectively, to obtain the wrist motion data.

The processing of at least two signals of the pulse blood oxygen saturation signal, the yaw angle signal, the roll angle signal, the pitch angle signal, the horizontal axis acceleration signal, the vertical axis acceleration signal and the vertical axis acceleration signal needs to be combined with data in a certain time window for comprehensive processing so as to obtain data with better data characteristics. In the embodiment, a time interval with a certain proportion is extended forwards or backwards by taking a current time point as a reference, and change data in the interval is calculated to obtain the data characteristic of the current time. The mean square difference data may be a variance value of the waveform amplitudes in a sampling window, and the maximum positive value data and the maximum negative value data may refer to the maximum positive value data and the maximum negative value data in the sampling window. By setting a fixed time window for calculation, better data characteristics can be obtained, and further more accurate prediction results can be obtained when model prediction is carried out.

In some embodiments, the executing body may generate the cross-ratio data based on the at least two kinds of motion sub-data and the pulse oximetry data by: screening at least one data pair from the pulse blood oxygen saturation data and the at least two types of motion subdata based on a preset data pair screening index, wherein each data pair is composed of two different types of data, and the different data pairs comprise the same or different data; and calculating the ratio of the two data in each data pair to obtain the cross ratio data.

Since the wrist movement data plus the pulse blood sample saturation data has at least three (or all seven) data, pairwise pairing can increase data dimensionality, but does not require any pairwise cross ratio data, as excessive data dimensionality can increase meaningless resource consumption. Therefore, one or more cross ratio data with larger weight (larger influence degree) can be obtained through experiments, the data pair screening index is set according to the one or more cross ratio data, and then the cross ratio data based on the screening index is obtained through calculation. Through the screening, can be increasing under the prerequisite of data characteristic, guarantee the prediction precision, the running efficiency of this application is promoted to the minimum resource consumption that tries to get one's best.

In some embodiments, the step of executing the subject to obtain the melatonin or dopamine data includes: acquiring a current signal generated by the reaction of the detection part and melatonin or dopamine in sweat; performing signal amplification processing on the current signal to obtain an amplified current signal; performing analog-to-digital conversion on the amplified current signal to obtain a digital melatonin or dopamine signal; and determining the melatonin or dopamine data of the current time in the digital melatonin or dopamine signals as the melatonin or dopamine data.

In some embodiments, after generating the cross ratio data based on the at least two kinds of motion sub-data and the pulse oximetry data, further comprising: calibrating melatonin data based on a preset first coefficient; calibrating the dopamine data based on a preset second coefficient; and coupling and denoising the periodic interference data and the Gaussian white noise data according to the wrist movement data, the pulse blood oxygen saturation data and the cross ratio data after calibration, and determining the wrist movement data, the pulse blood oxygen saturation data, the melatonin data, the dopamine data and the cross ratio data after coupling and denoising as input data of the pre-trained comprehensive evaluation model.

Since the data processing process involves amplification and other processing of the signal, resulting in proportional distortion of the generated melatonin data or dopamine data, correction of the generated melatonin data or dopamine data is required. The first coefficient or the second coefficient may refer to correction coefficients for melatonin and dopamine, respectively. The range of the correction coefficient of the first coefficient or the second coefficient is set according to the actual situation. The correction coefficient may be determined by experiment or by standard data in the field, and is not limited herein. By way of example, the first coefficient may range from 0.1 to 0.5. The second coefficient may range from 0.1 to 0.5. I.e. the generated melatonin data and/or dopamine data are multiplied by a first coefficient or a second coefficient. It should be noted that the first coefficient and the second coefficient may be the same or different.

In some embodiments, the pre-trained comprehensive evaluation model includes a Transformer model and a regression model, or the pre-trained comprehensive evaluation model is a comprehensive model with an output layer with a classification function added after the output of the Transformer model.

The comprehensive evaluation model is a combination of a Transformer model and a regression model. Data can be firstly imported into the Transformer model, and then the output result is imported into the regression model, so that the final prediction result, namely the sleep state information of the current time is obtained. When the model is selected, the calculation consumption and the accuracy of the model need to be considered, and through the test of various models (or model combinations), the Transformer model and the regression model are the better model combination aiming at the application, so that the required accuracy can be achieved, and the calculation consumption is reduced.

It should be noted that the regression model may be any one of the following regression models: a linear regression model, a stepwise regression model, a neural network model, and the like. The preferred regression model of the present application is a neural network model.

In some embodiments, the training step of the execution subject for the comprehensive evaluation model includes:

the method comprises the following steps that firstly, an executing body can obtain original sample data in a preset time interval of known sleep state information and a myopia risk assessment result, wherein the original sample data comprises wrist movement data, pulse blood oxygen saturation data, melatonin data, dopamine data and cross ratio data;

in the first step, the execution main body may embed preset sequence information into the original sample data to obtain embedded sample data. The preset sequence information may be identification information added to each original sample data by a pointer according to the preset sequence information. Since the order of the Transformer model is disturbed when the data is identified, the original sample data needs to be sorted in advance. Since the original sample data in the application all contain the time tag, the original sample data can be sequenced based on the time sequence.

Second, the execution subject may divide the embedded sample data into buried data and non-buried data based on a preset buried time interval. When sample data is processed, data in a certain time interval can be buried, the data in the time interval (namely, buried data) can be predicted by using the rest data (namely, data which is not buried), and the predicted result is compared with the buried data, so that the parameters of the transform model can be optimized. By optimizing the method, the acquisition of test data can be reduced, and therefore the running resources of the application are reduced.

And thirdly, the execution main body can lead the non-buried data into a preset original Transformer model to predict the buried data to obtain predicted buried data. The original Transformer model may refer to a pre-established model corresponding to the present application, with parameters not optimized. The predicted buried data may refer to data predicted for a buried time interval.

Fourthly, the executing body can optimize the original Transformer model based on the buried data and the predicted buried data.

And fifthly, the executing body can divide the embedded sample data into the buried data and the non-buried data based on the obtained new buried time interval, and the step of dividing the embedded sample data into the buried data and the non-buried data based on the new buried time interval is executed again until the prediction precision of the original Transformer model meets the preset requirement, so that the trained Transformer model is obtained.

And sixthly, the execution main body can lead the original sample data into the trained Transformer model to obtain predicted data.

And seventhly, the execution main body can lead the prediction data into a preset original regression model or a newly added original output layer with a classification function, and performs iterative training based on a preset loss function to obtain a trained regression model or an output layer with a classification function.

And eighthly, the executing body may obtain the trained comprehensive evaluation model based on the trained Transformer model and the trained regression model, or based on the trained Transformer model and the trained newly added original output layer with the classification function.

In some embodiments, the obtaining a plurality of pieces of sleep state information corresponding to respective times in a target sleep cycle and generating a sleep quality evaluation result of the target sleep cycle based on the plurality of pieces of sleep state information includes: acquiring a plurality of sleep state information corresponding to each time of a target sleep cycle and standard proportionality coefficients of a Wake period, a REM period and a Deep period in a standard sleep cycle; generating target proportional coefficients of a Wake period, an REM period and a Deep period in the target sleep cycle based on a plurality of pieces of sleep state information corresponding to the target sleep cycle; and calculating to obtain a sleep quality evaluation result of the target sleep cycle based on a preset proportion calculation formula, the target proportion coefficient and the standard proportion coefficient. It should be understood that the plurality of sleep state information corresponding to each time of the target sleep cycle may be sleep state information of the current time obtained at different times.

The preset proportion calculation formula may be a calculation formula which is pre-established and used for calculating a ratio between the ratio of each sleep state in the target sleep cycle and the ratio of each sleep state in the standard sleep cycle. For example, assume that the standard ratios are 33.33%, 44.44%, and 22.22% in the Wake phase, REM phase, and Deep phase, respectively. The proportions of each period in the target sleep cycle are 20%, 50% and 30%, respectively, then (10/33.33 +60/44.44+ 30/22.22)/3 = (0.3 +1.35+ 1.4)/3 = (1.02) can be calculated based on the calculation formula of the evaluation score = (target data of each stage/standard data of each stage)/3. It can be seen that the ratio of target sleep to standard sleep is 1.02. In addition, different weights can be added to different stages to calculate the scores, and the calculated scores can be multiplied by other preset coefficients. Alternatively, the occupation ratio of each sleep state in the target sleep cycle may be imported into a preset machine learning model for calculating a sleep evaluation result, and the like. If necessary, the method is not limited herein.

In some embodiments, the present application further comprises: and acquiring a sleep quality evaluation result of at least one sleep cycle in the complete sleep process, and generating a sleep quality evaluation result corresponding to the complete sleep process. After the target sleep cycle is calculated, the score of each sleep cycle in the complete sleep process may be processed (e.g., added, multiplied, given different calculation coefficients according to different time periods, or processed in other calculation manners) to obtain a composite score of the complete sleep process.

With continued reference to FIG. 3, a flow 300 of further embodiments of a method for assessing sleep quality and risk of myopia simultaneously according to the present application is shown and may be performed by computing device 101 of FIG. 1. The method for simultaneously evaluating the sleep quality and the myopia risk comprises the following steps:

step 301, obtaining wrist movement data, pulse blood oxygen saturation data, melatonin data and dopamine data of the current time, wherein the wrist movement data comprises at least two types of movement sub-data.

Step 302, generating cross ratio data based on the at least two kinds of motion sub-data and the pulse blood oxygen saturation data.

And 303, inputting the wrist movement data, the pulse blood oxygen saturation data, the melatonin data, the dopamine data and the cross ratio data into a pre-trained comprehensive evaluation model, generating a myopia risk evaluation result and sleep state information at the current time, and storing the sleep state information, wherein the comprehensive evaluation model comprises a Transformer model and a neural network regression model.

Step 304, acquiring a plurality of sleep state information corresponding to the target sleep cycle, and standard proportionality coefficients of a Wake period, a REM period and a Deep period in the standard sleep cycle.

Step 305, generating target proportional coefficients of a Wake period, a REM period and a Deep period in the target sleep cycle based on the plurality of sleep state information corresponding to the target sleep cycle.

And step 306, calculating to obtain a sleep quality evaluation result of the target sleep cycle based on a preset proportional calculation formula, the target proportional coefficient and the standard proportional coefficient.

Step 307, obtaining a sleep quality evaluation result of at least one sleep cycle in the complete sleep process, and generating a sleep quality evaluation result corresponding to the complete sleep process.

In some embodiments, the specific implementation of steps 301 to 307 and the technical effect brought by the implementation may refer to the steps in the embodiments corresponding to fig. 2, which are not described herein again.

All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.

The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.

With further reference to fig. 4, as an implementation of the above-described method for each of the above-described figures, the present application provides some embodiments of an assessment device for both sleep quality and myopia risk, which correspond to those method embodiments described above for fig. 2.

As shown in fig. 4, the apparatus 400 for evaluating sleep quality and myopia risk simultaneously according to some embodiments includes:

an obtaining module 401, configured to obtain wrist movement data, pulse blood oxygen saturation data, melatonin data, and dopamine data at a current time, where the wrist movement data includes at least two types of movement subdata;

a first generating module 402 for generating cross ratio data based on the at least two kinds of motion sub-data and the pulse oximetry data;

a second generating module 403, configured to input the wrist movement data, the pulse blood oxygen saturation data, the melatonin data, the dopamine data, and the cross ratio data into a pre-trained comprehensive evaluation model, generate a myopia risk evaluation result and sleep state information at the current time, and store the sleep state information;

a third generating module 404, configured to obtain multiple pieces of sleep state information corresponding to a target sleep cycle, and generate a sleep quality evaluation result of the target sleep cycle based on the multiple pieces of sleep state information, where the sleep cycle is a complete sleep cycle or an incomplete sleep cycle.

In some optional implementations of some embodiments, the wrist motion data includes at least two motion subdata of yaw angle data, roll angle data, pitch angle data, lateral axis acceleration data, longitudinal axis acceleration data, and vertical axis acceleration data; a step of acquiring said wrist movement data and said pulse oximetry data, comprising: acquiring pulse blood oxygen saturation signals and wrist movement signals, wherein the wrist movement signals comprise at least two movement sub-signals of yaw angle signals, roll angle signals, pitch angle signals, transverse axis acceleration signals, longitudinal axis acceleration signals and vertical axis acceleration signals; performing Gaussian filtering processing on the pulse blood oxygen saturation degree signal and the at least two motion sub-signals to obtain at least two motion sub-signals after filtering the pulse blood oxygen saturation degree signal; taking the current time as a base point, expanding a first preset time length before the base point, expanding a second preset time length after the base point, and generating a first sampling window; calculating mean square difference data, maximum positive value data and maximum negative value data of the waveform amplitude of the filtered pulse blood oxygen saturation signal in the first sampling window to obtain the pulse blood oxygen saturation data; and respectively calculating mean square difference data, maximum positive value data and maximum negative value data of the waveform amplitudes of the at least two filtered motion sub-signals in the first sampling window to obtain the wrist motion data.

In some optional implementations of some embodiments, the generating cross ratio data based on the at least two types of motion sub-data and pulse oximetry data comprises: screening at least one data pair from the pulse blood oxygen saturation data and the at least two types of motion subdata based on a preset data pair screening index, wherein each data pair is composed of two different types of data, and the different data pairs comprise the same or different data; and calculating the ratio of the two data in each data pair to obtain the cross ratio data.

In some optional implementations of some embodiments, the step of obtaining the melatonin data or dopamine data comprises: acquiring a current signal generated by the reaction of the detection part and melatonin or dopamine in sweat; performing signal amplification processing on the current signal to obtain an amplified current signal; performing analog-to-digital conversion processing on the amplified current signal to obtain a digital melatonin signal or a digital dopamine signal; and determining the melatonin data or the dopamine signal of the digital melatonin signal or the dopamine signal at the current time as the melatonin data or the dopamine data.

In some optional implementations of some embodiments, after generating the cross ratio data based on the at least two types of motion sub-data and the pulse oximetry data, further comprising: calibrating melatonin data based on a preset first coefficient; calibrating the dopamine data based on a preset second coefficient; and coupling denoising processing is carried out on the wrist movement data, the pulse blood oxygen saturation data and the cross ratio data as well as the calibrated melatonin data and the calibrated dopamine data according to the periodic interference data and the Gaussian white noise data, and the wrist movement data, the pulse blood oxygen saturation data, the melatonin data, the dopamine data and the cross ratio data after the coupling denoising processing are determined as input data of the pre-trained comprehensive evaluation model.

In some optional implementations of some embodiments, the pre-trained comprehensive evaluation model includes a Transformer model and a regression model, or the pre-trained comprehensive evaluation model is a comprehensive model in which an output layer with a classification function is added after the output of the Transformer model.

In some optional implementations of some embodiments, the training of the comprehensive assessment model includes: acquiring original sample data in a preset time interval of known sleep state information and a myopia risk evaluation result, wherein the original sample data comprises wrist movement data, pulse blood oxygen saturation data, melatonin data, dopamine data and cross ratio data; embedding preset sequence information into the original sample data to obtain embedded sample data; dividing the embedded sample data into buried data and non-buried data based on a preset buried time interval; importing the non-buried data into a preset original Transformer model to predict the buried data to obtain predicted buried data; optimizing the original transform model based on the buried data and the predicted buried data; dividing the embedded sample data into buried data and non-buried data based on the obtained new buried time interval, and re-executing the step until the prediction precision of the original Transformer model meets the preset requirement to obtain a trained Transformer model; importing the original sample data into the trained Transformer model to obtain predicted data; importing the prediction data into a preset original regression model or a newly-added original output layer with a classification function, and performing iterative training based on a preset loss function to obtain a trained regression model or an output layer with a classification function; and obtaining the trained comprehensive evaluation model based on the trained Transformer model and the trained regression model or based on the trained Transformer model and the trained newly-added original output layer with the classification function.

In some optional implementation manners of some embodiments, the acquiring multiple pieces of sleep state information corresponding to respective times in a target sleep cycle, and generating a sleep quality evaluation result of the target sleep cycle based on the multiple pieces of sleep state information includes: acquiring a plurality of sleep state information corresponding to a target sleep cycle and standard proportional coefficients of a Wake period, a REM period and a Deep period in a standard sleep cycle; generating target proportional coefficients of a Wake period, an REM period and a Deep period in the target sleep cycle based on a plurality of pieces of sleep state information corresponding to the target sleep cycle; and calculating to obtain a sleep quality evaluation result of the target sleep cycle based on a preset proportion calculation formula, the target proportion coefficient and the standard proportion coefficient.

In some optional implementations of some embodiments, the apparatus 400 for assessing sleep quality and myopia risk simultaneously further comprises: and the fourth generation module is used for acquiring the sleep quality evaluation result of at least one sleep cycle in the complete sleep process and generating the sleep quality evaluation result corresponding to the complete sleep process.

It is understood that the modules recited in the apparatus 400 correspond to the various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above for the method are also applicable to the apparatus 400 and the modules included therein, and are not described herein again.

As shown in fig. 5, the central processing apparatus 500 may include a processing device (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage device 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the central processing apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the central processing apparatus 500 to perform wireless or wired communication with other apparatuses to exchange data. While fig. 5 illustrates a central processing facility 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.

In particular, according to some embodiments of the present application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. Which when executed by the processing means 501 performs the above-described functions as defined in the methods of some embodiments of the present application.

It should be noted that the computer readable medium described above in some embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present application, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the apparatus; or may be separate and not incorporated into the central processing facility. The computer readable medium carries one or more programs which, when executed by the central processing apparatus, cause the central processing apparatus to:

Computer program code for carrying out operations for embodiments of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in some embodiments of the present application may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as:

the device comprises an acquisition module, a first generation module, a second generation module and a third generation module. For example, the acquisition module may also be described as a "module that acquires wrist movement data, pulse oximetry data, melatonin data, and dopamine data for the current time".

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.

The application also provides a wearable device, which is characterized by comprising a wrist movement detection device, a pulse blood oxygen saturation detection device, a melatonin detection device and a dopamine detection device which are in contact with the skin, and a central processing device, wherein,

the central processing device is configured to perform the steps in those embodiments corresponding to fig. 2.

It should be noted that the central processing device may be disposed in the wearable device, or may be disposed in another server or processing device that can be in wired or wireless communication with the wearable device. When the central processing device is disposed in the wearable device, the central processing device may be disposed in the same physical structure in the wearable device as any one or more of the wrist movement detection apparatus, the pulse oximetry detection apparatus, the melatonin detection apparatus, and the dopamine detection apparatus, or may be disposed in one of the physical structures in the wearable device separately. The central processing device may be an integrated arrangement or a distributed arrangement. The number of central processing devices may be 1 or more. The above-described setting is performed as needed, and is not particularly limited herein.

In addition, the wearable device may be a wearable device such as a bracelet, an arm ring, a neck ring, or a head ring, which can be attached to the skin, or a garment provided with a detection device corresponding to the skin, which is not limited herein.

In some embodiments, the melatonin detecting device comprises a first sensing patch, a first data amplifying unit and a first analog-to-digital conversion unit, wherein the first sensing patch is made of FeCo bimetallic alloy embedded carbon nano fibers; the first induction patch is used as a detection part to be in contact with skin and used for generating an electrooxidation reaction with melatonin in sweat and generating a first oxidation-reduction current between a positive electrode and a negative electrode of the first induction patch; the first data amplification unit is connected with the first induction patch and is used for amplifying the first oxidation-reduction current to obtain an amplified first oxidation-reduction current; the first analog-to-digital conversion unit is connected with the first data amplification unit and is used for performing analog-to-digital conversion on the amplified first redox current to obtain a digital melatonin signal, wherein the melatonin data at the current time in the digital melatonin signal is the melatonin data. It should be noted that the FeCo bimetal alloy may also be replaced by a mixture of two other metals or other materials, which have the same properties and can react with melatonin in sweat or be discovered in the future, and all of which fall within the scope of the present application.

In some embodiments, the dopamine detecting device comprises a second sensing patch, a second data amplifying unit and a second analog-to-digital conversion unit, wherein the second sensing patch is made by wrapping a Ni-MOF composite material or an AuNPs composite material on a carbon nano tube; the second induction patch is used as a detection part to be in contact with the skin and is used as a detection part to generate an electrooxidation reaction with dopamine in sweat, and a second redox current is generated between the positive electrode and the negative electrode of the second induction patch; the second data amplification unit is connected with the second sensing patch and is used for amplifying the second redox current to obtain an amplified second redox current; the second analog-to-digital conversion unit is connected with the second data amplification unit and is used for performing analog-to-digital conversion on the amplified second redox current to obtain a digital dopamine signal, and dopamine data of the current time in the digital dopamine signal is the dopamine data. It should be noted that the Ni-MOF or AuNPs composite may be replaced by other two metals or other materials that have the same properties and can react with dopamine in sweat, either now known or later discovered, and all fall within the scope of this application.

Referring to fig. 6, in some embodiments, the wearable device is a bracelet. Skin one side is pressed close to the bracelet can be provided with melatonin detection device array and the dopamine detection device array that interval set up in turn, wherein, melatonin detection device array includes two at least melatonin detection device, the dopamine detection device array includes two at least dopamine detection device. The same detection device can be for radially setting up along the bracelet inboard, also can be based on the inboard axial setting of bracelet, can also set up or the interval sets up etc. through the slant that accords with preset law, selects as required, does not do specific restriction here. Through setting up the detection device array, can increase the area that detects to improve the precision and the detection efficiency who detect.

The foregoing description is only exemplary of the preferred embodiments of the present application and is provided for the purpose of illustrating the general principles of the technology. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present application is not limited to the specific combination of the above-mentioned technical features, and other technical features formed by any combination of the above-mentioned technical features or their equivalents may be covered without departing from the inventive concept described above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present application are mutually replaced to form the technical solution.

Claims

1. An assessment device for both sleep quality and myopia risk, comprising:

an obtaining module, configured to obtain wrist movement data, pulse blood oxygen saturation data, melatonin data and dopamine data at a current time, where the wrist movement data includes at least two kinds of movement sub-data,

wherein the step of obtaining melatonin data or dopamine data comprises: acquiring a current signal generated by the reaction of the detection part and melatonin or dopamine in sweat; performing signal amplification processing on the current signal to obtain an amplified current signal; performing analog-to-digital conversion processing on the amplified current signal to obtain a digital melatonin signal or a digital dopamine signal; determining melatonin data or dopamine signals of the digital melatonin signals or dopamine signals at the current time as the melatonin data or dopamine data;

a first generation module, configured to generate cross ratio data based on the at least two types of motion sub-data and the pulse oximetry data; calibrating melatonin data based on a preset first coefficient; calibrating the dopamine data based on a preset second coefficient; performing coupling denoising processing on the wrist movement data, the pulse blood oxygen saturation data and the cross ratio data as well as the calibrated melatonin data and dopamine data aiming at the periodic interference data and the Gaussian white noise data, and determining the wrist movement data, the pulse blood oxygen saturation data, the melatonin data, the dopamine data and the cross ratio data after the coupling denoising processing as input data of a pre-trained comprehensive evaluation model;

a second generation module, configured to input the wrist movement data, pulse blood oxygen saturation data, melatonin data, dopamine data, and the cross ratio data into the pre-trained comprehensive evaluation model, generate a myopia risk evaluation result and sleep state information at the current time, and store the sleep state information,

the comprehensive evaluation model comprises a Transformer model and a regression model, or the pre-trained comprehensive evaluation model is a comprehensive model with an output layer with a classification function added after the output of the Transformer model;

and the training step of the comprehensive evaluation model comprises the following steps: acquiring original sample data in a preset time interval of known sleep state information and a myopia risk evaluation result, wherein the original sample data comprises wrist movement data, pulse blood oxygen saturation data, melatonin data, dopamine data and cross ratio data; embedding preset sequence information into the original sample data to obtain embedded sample data; dividing the embedded sample data into buried data and non-buried data based on a preset buried time interval; importing the non-buried data into a preset original Transformer model to predict the buried data to obtain predicted buried data; optimizing the original Transformer model based on the buried data and the predicted buried data; dividing the embedded sample data into buried data and non-buried data based on the obtained new buried time interval, and re-executing the step until the prediction precision of the original Transformer model meets the preset requirement to obtain a trained Transformer model; importing the original sample data into the trained Transformer model to obtain predicted data; importing the prediction data into a preset original regression model or a newly-added original output layer with a classification function, and performing iterative training based on a preset loss function to obtain a trained regression model or an output layer with a classification function; obtaining the trained comprehensive evaluation model based on the trained Transformer model and the trained regression model, or based on the trained Transformer model and the trained newly-added original output layer with the classification function;

2. The apparatus of claim 1, wherein the wrist motion data comprises at least two motion sub-data of yaw angle data, roll angle data, pitch angle data, lateral axis acceleration data, longitudinal axis acceleration data, and vertical axis acceleration data; a step of acquiring said wrist movement data and said pulse oximetry data, comprising:

acquiring pulse blood oxygen saturation signals and wrist movement signals, wherein the wrist movement signals comprise at least two movement sub-signals of yaw angle signals, roll angle signals, pitch angle signals, transverse axis acceleration signals, longitudinal axis acceleration signals and vertical axis acceleration signals;

performing Gaussian filtering processing on the pulse blood oxygen saturation degree signal and the at least two motion sub-signals to obtain at least two motion sub-signals after filtering pulse blood oxygen saturation;

the method comprises the steps that a current time is used as a base point, a first preset time length is expanded to the front of the base point, a second preset time length is expanded to the rear of the base point, and a first sampling window is generated;

calculating mean square difference data, maximum positive value data and maximum negative value data of the waveform amplitude of the filtered pulse blood oxygen saturation signal in the first sampling window to obtain the pulse blood oxygen saturation data;

and respectively calculating mean square difference data, maximum positive value data and maximum negative value data of the waveform amplitudes of the at least two filtered motion sub-signals in the first sampling window to obtain the wrist motion data.

3. The apparatus of claim 2, wherein the generating cross ratio data based on the at least two types of motion sub-data and pulse oximetry data comprises:

screening at least one data pair from the pulse blood oxygen saturation data and the at least two types of motion subdata based on a preset data pair screening index, wherein each data pair is composed of two different types of data, and the different data pairs comprise the same or different data;

and calculating the ratio of the two data in each data pair to obtain the cross ratio data.

4. The apparatus according to any one of claims 1 to 3, wherein the acquiring a plurality of sleep state information corresponding to each time in a target sleep cycle and generating a sleep quality assessment result of the target sleep cycle based on the plurality of sleep state information comprises:

acquiring a plurality of sleep state information corresponding to a target sleep cycle and standard proportional coefficients of a Wake period, a REM period and a Deep period in a standard sleep cycle;

generating target proportional coefficients of a Wake period, an REM period and a Deep period in the target sleep cycle based on a plurality of pieces of sleep state information corresponding to the target sleep cycle;

and calculating to obtain a sleep quality evaluation result of the target sleep cycle based on a preset proportion calculation formula, the target proportion coefficient and the standard proportion coefficient.

5. The apparatus of claim 4, further comprising:

and the second acquisition module is used for acquiring the sleep quality evaluation result of at least one sleep cycle in the complete sleep process and generating the sleep quality evaluation result corresponding to the complete sleep process.

6. A wearable device is characterized by comprising a wrist movement detection device, a pulse blood oxygen saturation detection device, a melatonin detection device and a dopamine detection device which are in contact with the skin, and a central processing device, wherein,

the melatonin detection device is used for detecting a first oxidation-reduction current generated by an electrooxidation reaction through the melatonin in sweat, processing the first oxidation-reduction current to obtain melatonin data and sending the melatonin data to the central processing equipment;

the central processing device comprises an assessment apparatus for both sleep quality and myopia risk as claimed in any of claims 1-5.

7. The wearing device of claim 6, wherein the melatonin detecting device comprises a first sensing patch, a first data amplifying unit and a first analog-to-digital converting unit, wherein the first sensing patch is made of FeCo bimetallic alloy embedded with carbon nano-fibers;

the first induction patch is used as a detection part to be in contact with skin and used for generating an electrooxidation reaction with melatonin in sweat and generating a first oxidation-reduction current between a positive electrode and a negative electrode of the first induction patch;

the first data amplification unit is connected with the first induction patch and is used for amplifying the first oxidation-reduction current to obtain an amplified first oxidation-reduction current;

the first analog-to-digital conversion unit is connected with the first data amplification unit and is used for performing analog-to-digital conversion on the amplified first redox current to obtain a digital melatonin signal, wherein the melatonin data of the current time in the digital melatonin signal is the melatonin data.

8. The wearing equipment of claim 6, wherein the dopamine detection device comprises a second sensing patch, a second data amplification unit and a second analog-to-digital conversion unit, and the second sensing patch is made of a Ni-MOF composite material or an AuNPs composite material wrapped on a carbon nano tube;

the second induction patch is used as a detection part to be in contact with the skin and used for generating an electrooxidation reaction with dopamine in sweat and generating a second redox current between the positive electrode and the negative electrode of the second induction patch;

the second data amplification unit is connected with the second induction patch and is used for amplifying the second redox current to obtain an amplified second redox current;

the second analog-to-digital conversion unit is connected with the second data amplification unit and is used for performing analog-to-digital conversion on the amplified second redox current to obtain a digital dopamine signal, and dopamine data of the current time in the digital dopamine signal is the dopamine data.

9. The wearable device according to any one of claims 6 to 8, wherein the wearable device is a bracelet.

10. The wearing device of claim 9, wherein the side of the bracelet close to the skin is provided with an array of melatonin detection devices and an array of dopamine detection devices alternately arranged at intervals, wherein the array of melatonin detection devices comprises at least two melatonin detection devices, and the array of dopamine detection devices comprises at least two dopamine detection devices.