CN116269199A - Detection and quantification method, system and device for eye fatigue recovery level - Google Patents
Detection and quantification method, system and device for eye fatigue recovery level Download PDFInfo
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Abstract
The invention provides a detection and quantification method for eye fatigue recovery level, which comprises the following steps: detecting, collecting and processing signals of eye physiological states of a user in a task state, a resting state and a sleeping state to generate polymorphic eye physiological state signals, and extracting and generating task eye physiological characteristics and resting eye physiological characteristics; generating sleep state eye physiological characteristics according to the polymorphic eye physiological state signals and the sleep time phase state of the user; extracting polymorphic eyestrain recovery level indexes according to the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics and the sleep state eyestrain physiological characteristics, and obtaining an eyestrain recovery comprehensive evaluation index through fitting sequence data; repeating the steps to obtain a polymorphic eyestrain recovery level curve and an eyestrain recovery comprehensive evaluation curve, generating a user eyestrain recovery level quantitative report according to the need, and establishing a tracking database. The invention can realize accurate baseline assessment and dynamic trend analysis of the individual eye fatigue degree or recovery level of the user.
Description
Technical Field
The invention relates to the field of detection and quantification of eye fatigue recovery levels, in particular to a detection and quantification method, a detection and quantification system and a detection and quantification device of eye fatigue recovery levels.
Background
The eye is one of the most important sensory organs of humans, and takes on the task and behavior of human full visual perception. The eye fatigue states such as eye distension, burning, eye trapping, dryness, lacrimation, eye creatinine pain, double vision, dizziness and the like can occur due to factors such as continuous long-time use of eyes, ametropia, reduced ciliary muscle regulation capability of eyes, poor eye use behaviors, poor light environment and the like, and especially in the long-time overload eye use groups such as students, drivers, programmers, literature and video workers. In addition, mental stress, aging, diseases, accidental infections and other factors may bring about eyestrain states of the relevant people.
The eye fatigue degree is obtained through subjective experience reports of people and can also be obtained through clinical medical observation such as fundus examination, intraocular pressure examination, extraocular muscle function examination and the like, but the dimension considered by the means is single, and more objective and comprehensive quantification means and data reports are lacking, for example, only continuous time and the like when eyes are used are inspected. Meanwhile, under different large scene states, the eye fatigue degree and the recovery level of a person depend on the change of a long-term continuous physiological basis of the user, for example, the eye fatigue state of a pupil and a pupil with learning school burden and myopia factors can last for many years repeatedly, and the person can get a better evaluation basis only by continuous tracking, quantization, analysis and evaluation.
The physiological progress and the physiological meaning value of eye fatigue and eye recovery in different crowds and different behavioral states are different. In the resting period, the eyes can be intermittently restored by closing the eye behaviors; during sleep, eyes can be restored for a long time; in addition, in task states with different intensities, eye metabolism or eye recovery can balance the eye fatigue level, and the eye recovery pressure brought by different task intensities is different; more importantly, the eye fatigue and the eye recovery are mutually opposite, and the better eye recovery capacity or level can work for the eye fatigue task with higher intensity and longer time. In the prior art, even though multi-mode eye physiological monitoring is adopted, eye fatigue in a single working process or a single sleeping process is always considered, fatigue operation reminding and the like are carried out, and the system concept is lacking, so that the behavior that the eye condition of a human body is continuous is ignored, and the fatigue state and the recovery level are not only simultaneously existed in the operations of different task states, but also can be recovered to different degrees in the sleeping process. However, the level or degree of recovery from eye fatigue is not disclosed or noted in the prior art, nor is an effective solution for overdosing the level of recovery.
The eye fatigue degree can bring about remarkable change of eye physiological function states, and scientific detection quantification and long-term tracking analysis of the eye fatigue degree or recovery level are further realized through the eye physiological state changes, so that accurate baseline evaluation and dynamic trend analysis of the eye fatigue degree or recovery level of different individuals are further realized, and the formulation and execution of an eye fatigue relieving scheme or measure are assisted. The method is also a problem or a difficult problem which is not solved by an explicit technical scheme at home and abroad at present.
Disclosure of Invention
Aiming at the defects and improvement demands of the existing method, the invention aims to provide a detection quantification method of the eye fatigue recovery level, which is characterized in that the task state, the resting state and the eye physiological state of a user in a sleep state of a continuous time window in a preset time period are detected, collected, processed and analyzed by features, a polymorphic eye fatigue recovery level index and an eye fatigue recovery comprehensive evaluation index are further calculated and extracted, a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve are generated, and finally, the on-demand generation of a user eye fatigue recovery level quantification report is completed, and the establishment and the continuous update of a user personalized eye fatigue recovery level tracking database are further realized, so that the accurate baseline evaluation and the dynamic trend analysis of the individual eye fatigue degrees or recovery levels of the user are further realized. The invention also provides a detection and quantification system for the eye fatigue recovery level, which is used for realizing the method. The invention also provides a detection and quantification device for the eye fatigue recovery level, which is used for realizing the system.
According to the purpose of the invention, the invention provides a detection and quantification method for the recovery level of eyestrain, which comprises the following steps:
detecting, collecting and processing signals of eye physiological states of a user in a task state, a resting state and a sleeping state to generate polymorphic eye physiological state signals;
extracting the physiological state characteristics of eyes of the user in a task state and a resting state according to the polymorphic eye physiological state signals, and respectively generating the physiological state characteristics of eyes in the task state and the physiological state characteristics of eyes in the resting state;
extracting the eye physiological state characteristics of the eyes of the user in the rapid eye movement sleeping state, the non-rapid eye movement light sleeping state and the non-rapid eye movement deep sleeping state according to the polymorphic eye physiological state signals and the sleeping time phase state of the user, and generating sleeping state eye physiological characteristics;
extracting polymorphic eyestrain recovery level indexes according to the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics and the sleep state eyestrain physiological characteristics, and obtaining an eyestrain recovery comprehensive evaluation index through fitting sequence data;
and repeating the steps to finish tracking detection and quantitative analysis of the continuous time window of the user eye fatigue recovery level, obtain a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve, generate a user eye fatigue recovery level quantitative report according to the requirement, and establish a user personalized eye fatigue recovery level tracking database.
More preferably, the specific steps of detecting, collecting and processing the physiological states of eyes of the user in the task state, the resting state and the sleeping state and generating the polymorphic physiological state signals of eyes further comprise:
detecting and collecting eye physiological state signals in a task state, a resting state and a sleeping state to generate an original signal of the polymorphic eye physiological state;
and performing signal processing on the original signals of the physiological states of the polymorphic eyes to obtain signals of the physiological states of the polymorphic eyes.
More preferably, the task state refers to a state scenario when a user executes different eye-using tasks in an awake state, and at least comprises a plurality of cross combination states of a duration dimension and a task intensity dimension; wherein the duration dimension at least comprises short time, medium time and long time, and the task intensity dimension at least comprises low intensity, medium intensity and high intensity.
More preferably, the resting state refers to a state scenario when the user does not perform any eye-using task in a awake state, and at least includes an eye-closing resting state and an eye-opening resting state.
More preferably, the sleep state refers to a state scenario when the user does not perform any eye-using task in the sleep state, and at least includes a rapid eye movement sleep state, a non-rapid eye movement light sleep state, and a non-rapid eye movement deep sleep state.
More preferably, the eye physiological state signal includes at least an eye electrical signal, an eye pressure signal, an eye movement signal, and an periocular blood oxygen level dependent signal.
More preferably, the signal processing at least comprises a/D digital-to-analog conversion, resampling, re-referencing, noise reduction, artifact removal, power frequency notch, low-pass filtering, high-pass filtering, band-stop filtering, band-pass filtering, correction processing and dynamic interception; the correction processing specifically includes signal correction and prediction smoothing processing on signal data segments containing artifacts or distortion in the signal, and the dynamic interception refers to moving interception processing on a target signal according to a detection and quantization time period or frequency requirement with a preset time window and a preset time step.
More preferably, the specific step of extracting the physiological state characteristics of the eyes of the user in the task state and the resting state according to the polymorphic eye physiological state signals to generate the physiological state characteristics of the eyes of the task state and the resting state respectively further includes:
extracting the eye physiological state characteristics from task eye physiological state signals of the polymorphic eye physiological state signals to generate task eye physiological characteristics;
extracting the eye physiological state features from resting eye physiological state signals of the polymorphic eye physiological state signals, and generating the resting eye physiological features.
More preferably, the ocular physiological state features include at least any one of ocular pressure state features, ocular muscle state features, and ocular metabolic state features; wherein the state features include at least numerical features, physical features, time-frequency features, signal envelope features, and nonlinear features.
More preferably, the task eye physiological characteristics at least comprise cross-combined task eye physiological characteristics of different eye duration and different eye intensity.
More preferably, the resting state eye physiological characteristic comprises at least an open eye resting state eye physiological characteristic and a closed eye resting state eye physiological characteristic.
More preferably, the specific step of extracting the eye physiological state characteristics of the eyes of the user in the rapid eye movement sleep state, the non-rapid eye movement light sleep state and the non-rapid eye movement deep sleep state according to the polymorphic eye physiological state signals and the sleep time phase state of the user, and generating the sleep state eye physiological characteristics further comprises:
identifying sleep time phase state characteristics of a user from sleep state eye physiological state signals of the polymorphic eye physiological state signals, and acquiring sleep time phase stages;
extracting the physiological state characteristics of the eyes from the sleep state eye physiological state signals of the polymorphic eye physiological state signals, and generating the physiological state characteristics of the eyes in a sleep state by combining the sleep time phase stage.
More preferably, the sleep phase stage identification method comprises the following steps:
1) Performing learning training and data modeling on eye physiological state signals of a scale sleep user sample and corresponding sleep stage data thereof through a deep learning algorithm to obtain a sleep time phase automatic stage model;
2) And inputting the sleeping state eye physiological state signal of the current user into the sleeping time phase automatic stage model to obtain the corresponding sleeping time phase stage.
More preferably, the sleep state eye physiological characteristics include at least rapid eye movement sleep state eye physiological characteristics, non-rapid eye movement light sleep state eye physiological characteristics, and non-rapid eye movement deep sleep state eye physiological characteristics.
More preferably, the specific step of extracting the polymorphic eyestrain recovery level index according to the task state eyestrain physiological characteristic, the resting state eyestrain physiological characteristic and the sleep state eyestrain physiological characteristic, and obtaining the eyestrain recovery comprehensive evaluation index through fitting the sequence data further comprises:
performing eye fatigue recovery evaluation calculation on the task state eye physiological characteristics to obtain a task state eye fatigue recovery level index;
performing eye fatigue recovery evaluation calculation on the resting state eye physiological characteristics to obtain a resting state eye fatigue recovery level index;
Performing eye fatigue recovery evaluation calculation on the sleep state eye physiological characteristics to obtain a sleep state eye fatigue recovery level index;
the polymorphic asthenopia recovery level index is generated in a collecting mode according to the task state asthenopia recovery level index, the resting state asthenopia recovery level index and the sleep state asthenopia recovery level index;
and performing sequence data fitting on the index sequence in the polymorphic eyestrain recovery level index to obtain the eyestrain recovery comprehensive evaluation index.
More preferably, the specific method for evaluating and calculating the eye fatigue recovery is as follows:
1) Acquiring eye physiological state characteristics and eye behavior scenes of a user;
2) Acquiring user information, extracting an eye physiological state characteristic value corresponding to the current eye behavior scene of the user from the eye physiological scene characteristic base line database of the healthy group, and obtaining an eye physiological state characteristic base line comparison set;
3) Calculating the relative variation of the eye physiological state characteristics and the eye physiological state characteristic baseline comparison set, and generating an eye physiological state characteristic baseline variation set;
4) And carrying out normalization and reconciliation calculation according to the preset eye physiological state characteristic importance weight and the eye physiological state characteristic baseline change set, and extracting normalization and reconciliation characteristic values, namely the eye fatigue recovery level index.
More preferably, the user eye behavior scene includes at least the task state, the rest state, and the sleep state.
More preferably, the specific formula of the normalization harmonic calculation is as follows:
for numerical sequences
In the sense that it is possible to provide,
1) If the numerical sequence is
If the sequence is a zero sequence, the normalized harmonic characteristic value is 0;
2) If the numerical sequence is
For a non-all zero sequence, its normalized harmonic eigenvalue is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
respectively the numerical sequence>
Is the normalization of the harmonic characteristic value, the +.>
Personal value and its weight, ">
Is the length of the numerical sequence and is a positive integer, +.>
To take absolute value operator->
Is a correction index related to the age of the user and
。
more preferably, the task state eye fatigue level index at least comprises cross combined task state eye fatigue level indexes of different eye duration and different eye intensity; the resting state eye fatigue level index at least comprises an eye rest state eye fatigue level index of open eyes and an eye rest state eye fatigue level index of closed eyes; the sleep state eyestrain recovery level index at least comprises a rapid eye movement sleep state eyestrain recovery level index, a non-rapid eye movement light sleep state eyestrain recovery level index and a non-rapid eye movement deep sleep state eyestrain recovery level index.
More preferably, the specific calculation method of the comprehensive evaluation index for eye fatigue recovery comprises the following steps:
1) Reordering indexes of the polymorphic eyestrain recovery level indexes according to preset index ordering to obtain a polymorphic eyestrain recovery level index sequence;
2) And performing sequence data fitting on the polymorphic eyestrain recovery level index sequence, and extracting the coefficient of the maximum power of the independent variable in a fitting function to serve as the eyestrain recovery comprehensive evaluation index.
More preferably, the comprehensive evaluation index of asthenopia recovery is comprehensive evaluation of the physiological recovery capability of the personalized asthenopia of the user, is determined by the current physiological state and physiological ground state of the user, and is the structural distribution and change trend characteristics of the polymorphic asthenopia recovery level index in different stress scenes of the eye behaviors in sequence or reverse sequence.
More preferably, the preset index sequencing is specifically a one-dimensional time sequence obtained according to different sequencing rules according to analysis requirements; the preset index sequence is in a sequence form of a task state eye fatigue recovery level index from a high intensity value to a low intensity value, an open eye rest state eye fatigue level index, a closed eye rest state eye fatigue level index, a rapid eye movement sleep state eye fatigue recovery level index, a non-rapid eye movement light sleep state eye fatigue recovery level index and a non-rapid eye movement deep sleep state eye fatigue recovery level index.
More preferably, the sequence data fitting method at least comprises linear fitting, polynomial fitting and nonlinear curve fitting.
More preferably, the steps are repeated to complete the tracking detection and quantitative analysis of the continuous time window of the user's eye fatigue recovery level, obtain a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve, generate a user's eye fatigue recovery level quantitative report as required, and the specific steps for establishing the user's personalized eye fatigue recovery level tracking database further comprise:
completing tracking detection and quantitative analysis of the user eye fatigue recovery level of a continuous time window within a preset time period, and generating the polymorphic eye fatigue recovery level curve and the eye fatigue recovery comprehensive evaluation curve;
generating and outputting the user eye fatigue recovery level quantification report according to a preset report period;
and collecting user key data in detection and quantification processes of different task states, rest states and sleep states, and establishing and continuously updating the user personalized eyestrain recovery level tracking database.
More preferably, the user's eyestrain recovery level quantitative report at least includes a scene state description, the polymorphic eyestrain recovery level curve, the eyestrain recovery comprehensive evaluation curve, and a detection quantitative summary.
More preferably, the user personalized eyestrain recovery level tracking database at least comprises user information, the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics, the sleep state eyestrain recovery level curve, the comprehensive eyestrain recovery evaluation curve and the user eyestrain recovery level quantitative report; the user personalized eyestrain recovery level tracking database provides data support for long-term eyestrain tracking, eye health personalized evaluation and service of the user.
According to the purpose of the invention, the invention provides a detection and quantification system for the recovery level of eyestrain, which comprises the following modules:
the state detection processing module is used for detecting, collecting and processing the physiological states of eyes of the user in a task state, a resting state and a sleeping state to generate polymorphic eye physiological state signals;
the awake feature analysis module is used for extracting the physiological state features of the eyes of the user in the task state and the resting state according to the polymorphic eye physiological state signals and respectively generating the physiological state features of the eyes in the task state and the resting state;
the sleep characteristic analysis module is used for extracting the eye physiological state characteristics of the eyes of the user in the rapid eye movement sleep state, the non-rapid eye movement shallow sleep state and the non-rapid eye movement deep sleep state according to the polymorphic eye physiological state signals and the sleeping time phase state of the user, and generating sleeping state eye physiological characteristics;
The fatigue recovery evaluation module is used for extracting a polymorphic eyestrain recovery level index according to the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics and the sleep state eyestrain physiological characteristics, and obtaining an eyestrain recovery comprehensive evaluation index through sequence data fitting;
the circulation quantitative management module is used for completing tracking detection and quantitative analysis of a continuous time window of the user's eye fatigue recovery level, obtaining a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve, generating a user's eye fatigue recovery level quantitative report according to the need, and establishing a user personalized eye fatigue recovery level tracking database;
and the data management center module is used for visual display, unified storage and data operation management of all process data and/or result data in the system.
More preferably, the state detection processing module further comprises the following functional units:
the state detection acquisition unit is used for detecting and acquiring eye physiological state signals of a task state, a resting state and a sleeping state to generate an original signal of the polymorphic eye physiological state;
and the state signal processing unit is used for performing signal processing on the original signals of the physiological states of the polymorphic eyes to obtain the physiological state signals of the polymorphic eyes.
More preferably, the awake feature analysis module further comprises the following functional units:
a task feature extraction unit, configured to extract the eye physiological state feature from a task eye physiological state signal of the polymorphic eye physiological state signal, and generate the task eye physiological feature;
and the resting feature extraction unit is used for extracting the eye physiological state features from resting eye physiological state signals of the polymorphic eye physiological state signals and generating the resting eye physiological features.
More preferably, the sleep characteristic analysis module further comprises the following functional units:
the sleep phase stage unit is used for identifying sleep phase state characteristics of a user from the sleep state eye physiological state signals of the polymorphic eye physiological state signals to obtain sleep phase stages;
the sleep characteristic extraction unit is used for extracting the eye physiological state characteristics from the sleep state eye physiological state signals of the polymorphic eye physiological state signals, and generating the sleep state eye physiological characteristics by combining the sleep time phase stage.
More preferably, the fatigue recovery evaluation module further includes the following functional units:
the baseline characteristic management unit is used for establishing, updating and managing a baseline database of physiological scene characteristics of healthy group eyes;
The task fatigue evaluation unit is used for performing eye fatigue recovery evaluation calculation on the task state eye physiological characteristics to obtain a task state eye fatigue recovery level index;
the resting state eye fatigue recovery evaluation unit is used for carrying out eye fatigue recovery evaluation calculation on the resting state eye physiological characteristics to obtain a resting state eye fatigue recovery level index;
the sleep fatigue evaluation unit is used for performing eye fatigue recovery evaluation calculation on the sleep state eye physiological characteristics to obtain a sleep state eye fatigue recovery level index;
the fatigue recovery integration unit is used for generating the polymorphic eyestrain recovery level index in a collecting mode according to the task state eyestrain recovery level index, the resting state eyestrain recovery level index and the sleep state eyestrain recovery level index;
and the index comprehensive evaluation unit is used for performing sequence data fitting on the index sequence in the polymorphic eye fatigue recovery level index to obtain the eye fatigue recovery comprehensive evaluation index.
More preferably, the cyclic quantization management module further includes the following functional units:
the cyclic detection quantification section unit is used for completing tracking detection and quantification analysis of the eye fatigue recovery level of the user in a continuous time window within a preset time period and generating the polymorphic eye fatigue recovery level curve and the eye fatigue recovery comprehensive evaluation curve;
The quantitative report generation unit is used for generating the quantitative report of the eye fatigue recovery level of the user according to a preset report period;
the report output management unit is used for uniformly managing the format output and the display form of the quantitative report of the eye fatigue recovery level of the user;
and the personalized data service unit is used for collecting user key data in the detection and quantization processes of different task states, resting states and sleeping states, and establishing and continuously updating the personalized eyestrain recovery level tracking database of the user.
More preferably, the data management center module further comprises the following functional units:
a user information management unit for registering input, editing, inquiry, output and deletion of user basic information;
the data visual management unit is used for visual display management of all data in the system;
the data storage management unit is used for uniformly storing and managing all data in the system;
and the data operation management unit is used for backing up, migrating and exporting all data in the system.
According to the purpose of the invention, the invention provides a detection and quantification device for the recovery level of eyestrain, which comprises the following modules:
The state detection processing module is used for detecting, collecting and processing signals of the eye physiological states of the user in a task state, a resting state and a sleeping state to generate polymorphic eye physiological state signals;
the awake characteristic analysis module is used for extracting the physiological state characteristics of eyes of the user in a task state and a resting state according to the polymorphic eye physiological state signals and respectively generating the physiological state characteristics of eyes in the task state and the physiological state characteristics of eyes in the resting state;
the sleep characteristic analysis module is used for extracting the eye physiological state characteristics of the eyes of the user in the rapid eye movement sleep state, the non-rapid eye movement shallow sleep state and the non-rapid eye movement deep sleep state according to the polymorphic eye physiological state signals and the sleep time phase state of the user, and generating sleep state eye physiological characteristics;
the fatigue recovery evaluation module is used for extracting a polymorphic eyestrain recovery level index according to the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics and the sleep state eyestrain physiological characteristics, and obtaining an eyestrain recovery comprehensive evaluation index through sequence data fitting;
the circulation quantitative management module is used for completing tracking detection and quantitative analysis of a continuous time window of the user's eye fatigue recovery level, obtaining a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve, generating a user's eye fatigue recovery level quantitative report according to the need, and establishing a user personalized eye fatigue recovery level tracking database;
The data visualization module is used for carrying out unified visual display management on all process data and/or result data in the device;
and the data management center module is used for uniformly storing and managing data operation of all process data and/or result data in the device.
According to the detection and quantification method, system and device for the eye fatigue recovery level, provided by the invention, the task state, the resting state and the eye physiological state of the user in the sleep state of the continuous time window in the preset time period are detected, collected, processed and analyzed by the signal, the polymorphic eye fatigue recovery level index and the eye fatigue recovery comprehensive evaluation index are further calculated and extracted, a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve are generated, and finally, the on-demand generation of a user eye fatigue recovery level quantification report and the establishment and continuous update of a user personalized eye fatigue recovery level tracking database are completed, so that the accurate baseline evaluation and dynamic trend analysis of the individual eye fatigue degree or recovery level of the user are further realized.
The core of the invention for solving the technical problems is that based on the physiological characteristics of task state, resting state and sleeping state eyes, polymorphic eyestrain recovery level indexes are extracted, and the eyestrain recovery comprehensive evaluation indexes are obtained by fitting, so that the quantization process of the invention has persistence; the sleeping time phase state of the user is also considered in the eye physiological characteristic extraction of the sleeping state, so that the noise is effectively reduced, and the accuracy is improved.
In an actual use scene, the detection and quantization method, the detection and quantization system and the detection and quantization device for the asthenopia recovery level can enable or embed relevant products and service processes of the detection and quantization of the asthenopia, provide detection and quantization schemes for the asthenopia recovery level for different crowd scenes, and finish continuous tracking quantization and comprehensive evaluation of the individual asthenopia physiological recovery capability of a user.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate and do not limit the invention.
Fig. 1 is a schematic flow chart of a method for detecting and quantifying the level of asthenopia recovery according to an embodiment of the present invention;
FIG. 2 is a schematic diagram showing the components of a system for detecting and quantifying the level of eye fatigue recovery according to an embodiment of the present invention;
Fig. 3 is a schematic diagram of a module configuration of an apparatus for detecting and quantifying an eye fatigue recovery level according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the objects and technical solutions of the present invention, the present invention will be further described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the embodiments described below are only some, but not all, embodiments of the invention. Other embodiments, which are derived from the embodiments of the invention by a person skilled in the art without creative efforts, shall fall within the protection scope of the invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be arbitrarily combined with each other.
As shown in fig. 1, the method for detecting and quantifying the recovery level of eyestrain provided by the embodiment of the invention comprises the following steps:
p100: and detecting, collecting and processing the physiological states of the eyes of the user in the task state, the resting state and the sleeping state to generate polymorphic eye physiological state signals.
Firstly, detecting and collecting eye physiological state signals in a task state, a resting state and a sleeping state to generate an original signal of the polymorphic eye physiological state.
In this embodiment, the structural distribution and the change trend characteristics of the polymorphic eyestrain recovery level index in the stress scenes of different eye behaviors of the user need to be quantitatively analyzed, so that the eye physiological states of the user in various different eye behaviors need to be detected and collected.
In this embodiment, different eye behavior scenes are mainly divided into a task state, a rest state and a sleep state. The task state refers to a state scene when a user executes different eye-using tasks in an awake state, and at least comprises a plurality of cross combination states of a duration dimension and a task intensity dimension; the duration dimension at least comprises short time, medium time and long time, and the task intensity dimension at least comprises low intensity, medium intensity and high intensity. The resting state refers to a state scene when the user does not execute any eye-using task in an awake state, and at least comprises a closed eye resting state and an open eye resting state. The sleep state refers to a state scenario when the user does not perform any eye-using task in the sleep state, and at least includes a rapid eye movement sleep state, a non-rapid eye movement light sleep state and a non-rapid eye movement deep sleep state.
In this embodiment, the eye physiological state signal at least includes an eye electrical signal, an eye pressure signal, an eye movement signal, and a periocular blood oxygen level dependent signal.
In this embodiment, an eye physiological state signal is an eye electrical signal, an eye pressure signal, and a periocular blood oxygen level dependent signal. The electrooculogram acquisition sensors are symmetrically arranged at the positions of 6 (2 x 3) of the center of the upper eyelid, the center of the outer canthus and the center of the lower eyelid of the left eye and the right eye, the reference electrode is the mastoid of the right ear, the sampling rate is 1024Hz, and the electrooculogram signals of the user are acquired; symmetrically placing near infrared blood oxygen level dependent acquisition sensor probes at the positions 2 (2 x 1) below the outer canthus sides of the left eye and the right eye, and acquiring blood oxygen level dependent signals around eyes of a user at a sampling rate of 10 Hz; the biological patches of the pressure acquisition sensors are symmetrically placed at the 2 (2 x 1) positions of the upper orbit inner center of the left eye and the right eye, the sampling rate is 64Hz, and the intraocular pressure signals of the user are acquired.
And secondly, performing signal processing on the original signals of the physiological states of the polymorphic eyes to obtain signals of the physiological states of the polymorphic eyes.
In this embodiment, the signal processing at least includes a/D digital-to-analog conversion, resampling, re-referencing, noise reduction, artifact removal, power frequency notch, low-pass filtering, high-pass filtering, band-stop filtering, band-pass filtering, correction processing, and dynamic clipping; the correction processing specifically includes signal correction and prediction smoothing processing on signal data segments containing artifacts or distortion in the signal, and the dynamic interception refers to moving interception processing on a target signal according to a detection and quantization time period or frequency requirement with a preset time window and a preset time step.
In this embodiment, the signal processing of the electro-oculogram signal mainly includes wavelet noise reduction, artifact removal and zero phase
The digital filter completes 50Hz and frequency-doubled power frequency notch, 0.3-400Hz band-pass filtering, correction processing and dynamic interception; the signal processing of the ocular pressure signal mainly comprises the de-artifacting, the correcting processing, the processing of the ocular pressure signal by +.>
Window, zero phase ∈>
Digital filter is done 2->
Low-pass filtering; signal processing of the periocular blood oxygen level dependent signal mainly involves acquisition of light intensity and conversion to Optical Density (OD),removing bad channels, removing artifacts, correcting processes, wavelet noise reduction, using modified beer-lambert law to convert the change in optical density or absorbance to oxyhemoglobin + ->
Deoxyhemoglobin->
And total hemoglobin->
Through->
Window, zero phase ∈>
The digital filter is finished by 0.01-0.35->
Band-pass filtering of (a) extracting->
、
And->
Concentration change signal of (a).
P200: and extracting the physiological state characteristics of the eyes of the user in the task state and the resting state according to the polymorphic eye physiological state signals, and respectively generating the physiological state characteristics of the eyes in the task state and the resting state.
In this embodiment, the ocular physiological state features include at least any one of ocular pressure state features, ocular muscle state features, and ocular metabolic state features; the state characteristics at least comprise numerical characteristics, physical characteristics, time-frequency characteristics, signal envelope characteristics and nonlinear characteristics.
In this embodiment, the ocular physiological state features include ocular pressure state features, ocular muscle state features, and ocular metabolic state features. Wherein, the eye muscle state characteristics (from the eye electric signals) mainly comprise numerical characteristics (root mean square and variation coefficient), time-frequency characteristics (total power, characteristic frequency band power and characteristic frequency band power duty ratio), wherein, the characteristic frequency band is divided into five frequency bands of 0.3-3Hz, 3-30Hz, 30-80Hz, 80-150Hz and 150-400 Hz; the ocular pressure status characteristics (from ocular pressure signals) mainly include numerical characteristics (root mean square, coefficient of variation); the metabolic state characteristics of the eye (signals dependent on blood oxygen levels from the periocular region) mainly include oxyhemoglobin
Digital characteristics (root mean square, coefficient of variation).
The first step, extracting the physiological state characteristics of eyes from the task eye physiological state signals of the polymorphic eye physiological state signals to generate task eye physiological characteristics.
In this embodiment, the task eye physiological features are generated according to the intraocular pressure state features, the eye muscle state features and the eye metabolic state features, and at least include cross-combined task eye physiological features of different eye duration and different eye intensity.
And secondly, extracting the physiological state characteristics of the eyes from the resting eye physiological state signals of the polymorphic eye physiological state signals to generate resting eye physiological characteristics.
In this embodiment, the resting state eye physiological characteristic is generated according to the above-mentioned ocular pressure state characteristic, ocular muscle state characteristic and ocular metabolism state characteristic, and at least includes an open eye resting state eye physiological characteristic and a closed eye resting state eye physiological characteristic.
P300: and extracting the eye physiological state characteristics of the eyes of the user in the rapid eye movement sleeping state, the non-rapid eye movement light sleeping state and the non-rapid eye movement deep sleeping state according to the polymorphic eye physiological state signals and the sleeping time phase state of the user, and generating sleeping state eye physiological characteristics.
In this embodiment, sleep is an indispensable link, because sleep is the most important process for physiological recovery and repair of human beings, and is the most direct baseline process for recovery of eye fatigue. Compared with the resting state, the sleeping state can reflect the physiological influence of the current physiological state and the physiological ground state of the user on the recovery of the asthenopia, and the eye behavior state and the recovery level of the asthenopia are different in different sleeping time phase states.
The first step, identifying the sleep time phase state characteristics of the user from the sleep state eye physiological state signals of the polymorphic eye physiological state signals, and obtaining sleep time phase stage.
In this embodiment, the sleep phase stage identification method includes:
1) Performing learning training and data modeling on eye physiological state signals of a scale sleep user sample and corresponding sleep stage data thereof through a deep learning algorithm to obtain a sleep time phase automatic stage model;
2) And inputting the sleeping state eye physiological state signal of the current user into a sleeping time phase automatic stage model to obtain the corresponding sleeping time phase stage.
And secondly, extracting the physiological state characteristics of the eyes from the sleep state eye physiological state signals of the polymorphic eye physiological state signals, and generating the physiological characteristics of the sleep state eyes by combining sleep time phase stage.
In this embodiment, the sleep state eye physiological characteristics are generated according to the above intraocular pressure state characteristics, eye muscle state characteristics, and eye metabolic state characteristics, and at least include a rapid eye movement sleep state eye physiological characteristic, a non-rapid eye movement light sleep state eye physiological characteristic, and a non-rapid eye movement deep sleep state eye physiological characteristic.
P400: and extracting polymorphic eyestrain recovery level indexes according to the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics and the sleep state eyestrain physiological characteristics, and obtaining an eyestrain recovery comprehensive evaluation index through fitting sequence data.
In this embodiment, the task state eye fatigue recovery level index, the resting state eye fatigue recovery level index, and the sleep state eye fatigue recovery level index are all based on the above-mentioned ocular pressure state characteristics, eye muscle state characteristics, and eye metabolic state characteristics to generate sleep state eye physiological characteristics, and a unified eye fatigue recovery evaluation calculation method is adopted.
In this embodiment, a healthy population eye physiological scene feature baseline database needs to be established preferentially before performing the eye fatigue recovery evaluation calculation. In an actual use scene, eye physiological state characteristics are extracted by monitoring, collecting, processing and analyzing signals of different eye physiological states of healthy people of different age groups in eye behavior scenes of different users, and corresponding baseline characteristic values are obtained by averaging, median or other quantile processing according to dimensions such as age, scene, signal type and characteristic attribute, and the like, so that a healthy group eye physiological scene characteristic baseline database is established and continuously updated.
In this embodiment, the specific method for evaluating and calculating the eye fatigue recovery is as follows:
1) Acquiring eye physiological state characteristics and eye behavior scenes of a user;
2) User information is obtained, and an eye physiological state characteristic value corresponding to the current user eye behavior scene is extracted from a healthy group eye physiological scene characteristic base line database to obtain an eye physiological state characteristic base line comparison set;
3) Calculating the relative variation of the eye physiological state characteristics and the eye physiological state characteristic baseline comparison set, and generating an eye physiological state characteristic baseline variation set;
4) And (3) performing normalization and reconciliation calculation according to the preset eye physiological state feature importance weight and the eye physiological state feature baseline change set, and extracting normalization and reconciliation feature values, namely the eye fatigue recovery level index.
In this embodiment, the eye behavior scene of the user at least includes a task state, a rest state and a sleep state.
In this embodiment, the specific formula of normalization harmonic calculation is:
for numerical sequences
In the sense that it is possible to provide,
1) If the numerical sequence is
If the sequence is a zero sequence, the normalized harmonic characteristic value is 0;
2) If the numerical sequence is
Is a non-all zero sequence, then it is normalizedA harmonic feature value is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
respectively the numerical sequence>
Is the normalization of the harmonic characteristic value, the +.>
Personal value and its weight, ">
Is the length of the numerical sequence and is a positive integer, +.>
To take absolute value operator->
Is a correction index related to the age of the user and
。
the step P400 specifically includes:
and firstly, performing eye fatigue recovery evaluation calculation on the task state eye physiological characteristics to obtain a task state eye fatigue recovery level index.
In this embodiment, the task state eye fatigue level index at least includes cross-combined task state eye fatigue level indexes of different eye durations and different eye intensities.
And secondly, performing eye fatigue recovery evaluation calculation on the resting state eye physiological characteristics to obtain a resting state eye fatigue recovery level index.
In this embodiment, the resting state eye fatigue level index at least includes an open eye resting state eye fatigue level index and a closed eye resting state eye fatigue level index.
Thirdly, performing eye fatigue recovery evaluation calculation on the physiological characteristics of the sleeping state eyes to obtain a sleeping state eye fatigue recovery level index.
In this embodiment, the sleep state eyestrain recovery level index includes at least a rapid eye movement sleep state eyestrain recovery level index, a non-rapid eye movement light sleep state eyestrain recovery level index, and a non-rapid eye movement deep sleep state eyestrain recovery level index.
Fourth, according to the task state eye fatigue recovery level index, the resting state eye fatigue recovery level index and the sleep state eye fatigue recovery level index, the polymorphic eye fatigue recovery level indexes are generated in a collecting mode.
In this embodiment, the polymorphic asthenopia recovery level index includes a task state asthenopia recovery level index, a resting state asthenopia recovery level index and a sleep state asthenopia recovery level index.
And fifthly, performing sequence data fitting on the index sequence in the polymorphic eyestrain recovery level index to obtain an eyestrain recovery comprehensive evaluation index.
In this embodiment, the comprehensive evaluation index of asthenopia recovery is a comprehensive evaluation of the physiological recovery ability of the personalized asthenopia of the user, and is determined by the current physiological state and physiological ground state of the user, and is the structural distribution and variation trend characteristics of the polymorphic asthenopia recovery level index in stress scenes of different eye behaviors in sequence or reverse sequence.
In this embodiment, the specific calculation method of the comprehensive evaluation index for asthenopia recovery is as follows:
1) Reordering indexes of the polymorphic eyestrain recovery level indexes according to preset index ordering to obtain a polymorphic eyestrain recovery level index sequence;
2) And performing sequence data fitting on the polymorphic eye fatigue recovery level index sequence, and extracting the coefficient of the maximum power of the independent variable in the fitting function to be used as an eye fatigue recovery comprehensive evaluation index.
In this embodiment, the preset exponential ranking is specifically a one-dimensional time sequence obtained according to different ranking rules according to analysis requirements; one sequential form of the preset index sequence is a task state eye fatigue recovery level index from a high intensity value to a low intensity value, an open eye rest state eye fatigue level index, a closed eye rest state eye fatigue level index, a rapid eye movement sleep state eye fatigue recovery level index, a non-rapid eye movement light sleep state eye fatigue recovery level index, and a non-rapid eye movement deep sleep state eye fatigue recovery level index.
In this embodiment, the method for fitting sequence data at least includes linear fitting, polynomial fitting, and nonlinear curve fitting. A linear fit is used here.
P500: and repeating the steps to finish tracking detection and quantitative analysis of the continuous time window of the user eye fatigue recovery level, obtain a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve, generate a user eye fatigue recovery level quantitative report according to the requirement, and establish a user personalized eye fatigue recovery level tracking database.
And firstly, finishing tracking detection and quantitative analysis of the user eye fatigue recovery level of a continuous time window within a preset time period, and generating a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve.
In this embodiment, tracking detection and quantization analysis are required to be sequentially performed on the user's asthenopia recovery level in different task states, rest states and sleep states, each time the detection and quantization is performed for 10 minutes (within a preset time period) and each time the detection and quantization are performed for 10 seconds (continuous time window), each time the polymorphic asthenopia recovery level index and the comprehensive asthenopia recovery evaluation index are extracted, and finally, the polymorphic asthenopia recovery level curve and the comprehensive asthenopia recovery evaluation curve are generated after the preset time period.
And secondly, generating and outputting a user eyestrain recovery level quantification report according to a preset report period.
In this embodiment, the user's eyestrain recovery level quantification report includes at least a scene state description, a polymorphic eyestrain recovery level curve, an eyestrain recovery comprehensive evaluation curve, and a detection quantification summary.
In this embodiment, after the detection and quantization analysis of the eye behavior scene of the user is completed, a user eye fatigue recovery level quantization report is generated and output.
And thirdly, collecting user key data in the detection and quantization processes of different task states, rest states and sleep states, and establishing and continuously updating a user personalized eyestrain recovery level tracking database.
In this embodiment, the user personalized eye fatigue recovery level tracking database at least includes user information, task state eye physiological characteristics, resting state eye physiological characteristics, sleep state eye physiological characteristics, polymorphic eye fatigue recovery level curves, comprehensive eye fatigue recovery evaluation curves, and user eye fatigue recovery level quantitative reports; the user personalized eyestrain recovery level tracking database provides data support for long-term eyestrain tracking, eye health personalized evaluation and service of the user.
As shown in fig. 2, an embodiment of the present invention provides a detection and quantification system of an eye fatigue recovery level, which is configured to perform the above-described respective method steps. The system comprises the following modules:
the state detection processing module S100 is used for detecting, collecting and processing signals of eye physiological states of a user in a task state, a resting state and a sleeping state to generate polymorphic eye physiological state signals;
the awake feature analysis module S200 is configured to extract the physiological state features of the eyes of the user in the task state and the resting state according to the physiological state signals of the polymorphic eyes, and generate the physiological features of the eyes in the task state and the resting state respectively;
The sleep characteristic analysis module S300 is used for extracting the eye physiological state characteristics of the eyes of the user in the rapid eye movement sleep state, the non-rapid eye movement shallow sleep state and the non-rapid eye movement deep sleep state according to the polymorphic eye physiological state signals and the sleeping time phase state of the user, and generating sleeping state eye physiological characteristics;
the fatigue recovery evaluation module S400 is used for extracting polymorphic eyestrain recovery level indexes according to the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics and the sleep state eyestrain physiological characteristics, and obtaining an eyestrain recovery comprehensive evaluation index through sequence data fitting;
the circulation quantitative management module S500 is used for completing tracking detection and quantitative analysis of a continuous time window of the user 'S eye fatigue recovery level, obtaining a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve, generating a user' S eye fatigue recovery level quantitative report according to the need, and establishing a user personalized eye fatigue recovery level tracking database;
the data management center module S600 is configured to visually display, uniformly store and manage all process data and/or result data in the system.
In this embodiment, the state detection processing module S100 further includes the following functional units:
The state detection acquisition unit is used for detecting and acquiring eye physiological state signals of a task state, a resting state and a sleeping state to generate an original signal of the polymorphic eye physiological state;
the state signal processing unit is used for performing signal processing on the original signals of the physiological states of the polymorphic eyes to obtain signals of the physiological states of the polymorphic eyes.
In this embodiment, the awake feature analysis module S200 further includes the following functional units:
the task feature extraction unit is used for extracting the physiological state features of the eyes from task state eye physiological state signals of the polymorphic eye physiological state signals and generating task state eye physiological features;
the resting feature extraction unit is used for extracting the physiological state features of the eyes from resting eye physiological state signals of the polymorphic eye physiological state signals and generating resting eye physiological features.
In this embodiment, the sleep characteristic analysis module S300 further includes the following functional units:
the sleep phase stage unit is used for identifying the sleep phase state characteristics of the user from the sleep state eye physiological state signals of the polymorphic eye physiological state signals to obtain sleep phase stages;
the sleep characteristic extraction unit is used for extracting the physiological state characteristics of the eyes from the sleep state eye physiological state signals of the polymorphic eye physiological state signals, and generating the physiological characteristics of the eyes in a sleep state by combining sleep time phase stage.
In this embodiment, the fatigue recovery evaluation module S400 further includes the following functional units:
the baseline characteristic management unit is used for establishing, updating and managing a baseline database of physiological scene characteristics of healthy group eyes;
the task fatigue evaluation unit is used for performing eye fatigue recovery evaluation calculation on the task state eye physiological characteristics to obtain a task state eye fatigue recovery level index;
the resting fatigue evaluation unit is used for performing eye fatigue recovery evaluation calculation on resting eye physiological characteristics to obtain resting eye fatigue recovery level indexes;
the sleep fatigue evaluation unit is used for performing eye fatigue recovery evaluation calculation on the physiological characteristics of the sleep state eyes to obtain a sleep state eye fatigue recovery level index;
the fatigue recovery integration unit is used for generating polymorphic asthenopia recovery level indexes in a collecting mode according to the task state asthenopia recovery level index, the resting state asthenopia recovery level index and the sleep state asthenopia recovery level index;
and the index comprehensive evaluation unit is used for performing sequence data fitting on the index sequence in the polymorphic eye fatigue recovery level index to obtain an eye fatigue recovery comprehensive evaluation index.
In this embodiment, the cyclic quantification management module S500 further includes the following functional units:
The cyclic detection quantification section unit is used for completing tracking detection and quantification analysis of the eye fatigue recovery level of the user in a continuous time window within a preset time period and generating a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve;
the quantitative report generation unit is used for generating a quantitative report of the eye fatigue recovery level of the user according to a preset report period;
the report output management unit is used for uniformly managing the format output and the display form of the quantitative report of the eye fatigue recovery level of the user;
and the personalized data service unit is used for collecting user key data in the detection and quantization processes of different task states, resting states and sleeping states, and establishing and continuously updating a personalized eyestrain recovery level tracking database of the user.
In this embodiment, the data management center module S600 further includes the following functional units:
a user information management unit for registering input, editing, inquiry, output and deletion of user basic information;
the data visual management unit is used for visual display management of all data in the system;
the data storage management unit is used for uniformly storing and managing all data in the system;
and the data operation management unit is used for backing up, migrating and exporting all data in the system.
As shown in fig. 3, the detection and quantification device for recovering eye fatigue level provided by the embodiment of the invention comprises the following modules:
the state detection processing module M100 is used for detecting, collecting and processing signals of eye physiological states of a user in a task state, a resting state and a sleeping state to generate polymorphic eye physiological state signals;
the awake feature analysis module M200 is used for extracting the physiological state features of the eyes of the user in the task state and the resting state according to the physiological state signals of the polymorphic eyes and respectively generating the physiological features of the eyes in the task state and the resting state;
the sleep characteristic analysis module M300 is used for extracting the eye physiological state characteristics of the eyes of the user in the rapid eye movement sleep state, the non-rapid eye movement shallow sleep state and the non-rapid eye movement deep sleep state according to the polymorphic eye physiological state signals and the sleep time phase state of the user, and generating sleep state eye physiological characteristics;
the fatigue recovery evaluation module M400 is used for extracting polymorphic eyestrain recovery level indexes according to the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics and the sleep state eyestrain physiological characteristics, and obtaining an eyestrain recovery comprehensive evaluation index through sequence data fitting;
the circulation quantitative management module M500 is used for completing tracking detection and quantitative analysis of a continuous time window of the eye fatigue recovery level of a user to obtain a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve, generating a user eye fatigue recovery level quantitative report according to the requirement, and establishing a user personalized eye fatigue recovery level tracking database;
The data visualization module M600 is used for carrying out unified visual display management on all process data and/or result data in the device;
the data management center module M700 is used for unified storage and data operation management of all process data and/or result data in the device.
The apparatus is configured to correspondingly perform the steps of the method of fig. 1, and will not be described in detail herein.
The present invention also provides various types of programmable processors (FPGA, ASIC or other integrated circuit) for running a program, wherein the program when run performs the steps of the embodiments described above.
The invention also provides corresponding computer equipment, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the memory realizes the steps in the embodiment when the program is executed.
Although the embodiments of the present invention are described above, the embodiments are only used for facilitating understanding of the present invention, and are not intended to limit the present invention. Any person skilled in the art to which the present invention pertains may make any modifications, changes, equivalents, etc. in form and detail of the implementation without departing from the spirit and principles of the present invention disclosed herein, which are within the scope of the present invention. Accordingly, the scope of the invention should be determined from the following claims.
Claims (34)
1. The detection and quantification method for the eye fatigue recovery level is characterized by comprising the following steps of:
detecting, collecting and processing signals of eye physiological states of a user in a task state, a resting state and a sleeping state to generate polymorphic eye physiological state signals;
extracting the physiological state characteristics of eyes of the user in a task state and a resting state according to the polymorphic eye physiological state signals, and respectively generating the physiological state characteristics of eyes in the task state and the physiological state characteristics of eyes in the resting state;
extracting the eye physiological state characteristics of the eyes of the user in the rapid eye movement sleeping state, the non-rapid eye movement light sleeping state and the non-rapid eye movement deep sleeping state according to the polymorphic eye physiological state signals and the sleeping time phase state of the user, and generating sleeping state eye physiological characteristics;
extracting polymorphic eyestrain recovery level indexes according to the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics and the sleep state eyestrain physiological characteristics, and obtaining an eyestrain recovery comprehensive evaluation index through fitting sequence data;
and repeating the steps to finish tracking detection and quantitative analysis of the continuous time window of the user eye fatigue recovery level, obtain a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve, generate a user eye fatigue recovery level quantitative report according to the requirement, and establish a user personalized eye fatigue recovery level tracking database.
2. The method of claim 1, wherein the specific steps of detecting, acquiring and processing the physiological states of the eyes of the user in the task state, the rest state and the sleep state, and generating the polymorphic physiological state signals of the eyes further comprise:
detecting and collecting eye physiological state signals in a task state, a resting state and a sleeping state to generate an original signal of the polymorphic eye physiological state;
and performing signal processing on the original signals of the physiological states of the polymorphic eyes to obtain signals of the physiological states of the polymorphic eyes.
3. The method of claim 2, wherein: the task state refers to a state scene when a user executes different eye-using tasks in an awake state, and at least comprises a plurality of cross combination states of a duration dimension and a task intensity dimension; wherein the duration dimension at least comprises short time, medium time and long time, and the task intensity dimension at least comprises low intensity, medium intensity and high intensity.
4. The method of claim 2, wherein: the resting state refers to a state situation when the user does not execute any eye-using task in a waking state, and at least comprises a closed eye resting state and an open eye resting state.
5. The method of claim 2, wherein: the sleep state refers to a state scene when a user does not execute any eye-using task in the sleep state, and at least comprises a rapid eye movement sleep state, a non-rapid eye movement light sleep state and a non-rapid eye movement deep sleep state.
6. The method of any one of claims 2-5, wherein: the eye physiological state signal comprises at least one of an eye electrical signal, an eye pressure signal, an eye movement signal and an eye peripheral blood oxygen level dependent signal.
7. The method of any one of claims 2-5, wherein: the signal processing at least comprises A/D digital-to-analog conversion, resampling, re-referencing, noise reduction, artifact removal, power frequency notch, low-pass filtering, high-pass filtering, band-stop filtering, band-pass filtering, correction processing and dynamic interception; the correction processing specifically includes signal correction and prediction smoothing processing on signal data segments containing artifacts or distortion in the signal, and the dynamic interception refers to moving interception processing on a target signal according to a detection and quantization time period or frequency requirement with a preset time window and a preset time step.
8. The method according to claim 1 or 2, wherein the specific step of extracting the physiological state features of the eyes of the user in the task state and the resting state according to the physiological state signals of the polymorphic eyes, and generating the physiological state features of the eyes in the task state and the physiological state features of the eyes in the resting state respectively further comprises:
Extracting the eye physiological state characteristics from task eye physiological state signals of the polymorphic eye physiological state signals to generate task eye physiological characteristics;
extracting the eye physiological state features from resting eye physiological state signals of the polymorphic eye physiological state signals, and generating the resting eye physiological features.
9. The method as recited in claim 8, wherein: the eye physiological state characteristics at least comprise any one of ocular pressure state characteristics, ocular muscle state characteristics and ocular metabolism state characteristics; wherein the state features include at least one of numerical features, physical features, time-frequency features, signal envelope features, and nonlinear features.
10. The method of claim 9, wherein: the task state eye physiological characteristics at least comprise cross-combined task state eye physiological characteristics with different eye duration and different eye intensity.
11. The method of claim 10, wherein: the resting state eye physiological characteristics at least comprise resting state eye physiological characteristics of open eyes and resting state eye physiological characteristics of closed eyes.
12. A method according to claim 1 or 2, characterized in that: the specific steps of extracting the eye physiological state characteristics of the eyes of the user in the rapid eye movement sleeping state, the non-rapid eye movement light sleeping state and the non-rapid eye movement deep sleeping state according to the polymorphic eye physiological state signals and the sleeping time phase state of the user, and generating the sleeping state eye physiological characteristics further comprise:
Identifying sleep time phase state characteristics of a user from sleep state eye physiological state signals of the polymorphic eye physiological state signals, and acquiring sleep time phase stages;
extracting the physiological state characteristics of the eyes from the sleep state eye physiological state signals of the polymorphic eye physiological state signals, and generating the physiological state characteristics of the eyes in a sleep state by combining the sleep time phase stage.
13. The method of claim 12, wherein the sleep phase stage is identified by:
1) Performing learning training and data modeling on eye physiological state signals of a scale sleep user sample and corresponding sleep stage data thereof through a deep learning algorithm to obtain a sleep time phase automatic stage model;
2) And inputting the sleeping state eye physiological state signal of the current user into the sleeping time phase automatic stage model to obtain the corresponding sleeping time phase stage.
14. The method as recited in claim 12, wherein: the sleep state eye physiological characteristics at least comprise a rapid eye movement sleep state eye physiological characteristic, a non-rapid eye movement light sleep state eye physiological characteristic and a non-rapid eye movement deep sleep state eye physiological characteristic.
15. A method according to claim 1 or 2, characterized in that: the specific steps of extracting the polymorphic eyestrain recovery level index according to the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics and the sleep state eyestrain physiological characteristics and obtaining the eyestrain recovery comprehensive evaluation index through sequence data fitting further comprise:
Performing eye fatigue recovery evaluation calculation on the task state eye physiological characteristics to obtain a task state eye fatigue recovery level index;
performing eye fatigue recovery evaluation calculation on the resting state eye physiological characteristics to obtain a resting state eye fatigue recovery level index;
performing eye fatigue recovery evaluation calculation on the sleep state eye physiological characteristics to obtain a sleep state eye fatigue recovery level index;
the polymorphic asthenopia recovery level index is generated in a collecting mode according to the task state asthenopia recovery level index, the resting state asthenopia recovery level index and the sleep state asthenopia recovery level index;
and performing sequence data fitting on the index sequence in the polymorphic eyestrain recovery level index to obtain the eyestrain recovery comprehensive evaluation index.
16. The method according to claim 15, wherein the specific method of eye fatigue recovery evaluation calculation is:
1) Acquiring eye physiological state characteristics and eye behavior scenes of a user;
2) Acquiring user information, and extracting an eye physiological state characteristic value corresponding to the current eye behavior scene for the user from a healthy group eye physiological scene characteristic base line database to obtain an eye physiological state characteristic base line comparison set;
3) Calculating the relative variation of the eye physiological state characteristics and the eye physiological state characteristic baseline comparison set, and generating an eye physiological state characteristic baseline variation set;
4) And carrying out normalization and reconciliation calculation according to the preset eye physiological state characteristic importance weight and the eye physiological state characteristic baseline change set, and extracting normalization and reconciliation characteristic values, namely the eye fatigue recovery level index.
17. The method of claim 16, wherein the user eye behavior scene includes at least the task state, the rest state, and the sleep state.
18. The method of claim 16 or 17, wherein the normalization harmonic calculation is specifically formulated as:
for numerical sequences
In the sense that it is possible to provide,
1) If the numerical sequence is
If the sequence is a zero sequence, the normalized harmonic characteristic value is 0;
2) If the numerical sequence is
For a non-all zero sequence, its normalized harmonic eigenvalue is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
respectively the numerical sequence>
Is the normalization of the harmonic characteristic value, the +.>
Personal value and its weight, ">
Is the length of the numerical sequence and is a positive integer, +.>
To take absolute value operator->
Is a correction index related to the age of the user and +.>
。
19. The method of claim 15, wherein the task state eye fatigue level index comprises at least a cross-combined task state eye fatigue level index of different eye duration, different eye intensity; the resting state eye fatigue level index at least comprises an eye fatigue level index of an open eye resting state and an eye fatigue level index of a closed eye resting state; the sleep state eyestrain recovery level index at least comprises a rapid eye movement sleep state eyestrain recovery level index, a non-rapid eye movement light sleep state eyestrain recovery level index and a non-rapid eye movement deep sleep state eyestrain recovery level index.
20. The method according to claim 15, wherein the specific calculation method of the comprehensive evaluation index for asthenopia recovery is as follows:
1) Reordering indexes of the polymorphic eyestrain recovery level indexes according to preset index ordering to obtain a polymorphic eyestrain recovery level index sequence;
2) And performing sequence data fitting on the polymorphic eyestrain recovery level index sequence, and extracting the coefficient of the maximum power of the independent variable in a fitting function to serve as the eyestrain recovery comprehensive evaluation index.
21. The method of claim 20, wherein the comprehensive evaluation index of asthenopia recovery is a comprehensive evaluation of the physiological recovery ability of the user's personalized asthenopia, determined by the current physiological state and physiological ground state of the user, and is a structural distribution and a change trend characteristic of the polymorphic asthenopia recovery level index in different stress scenes of eye behavior intensity in sequence or reverse sequence.
22. The method of claim 20, wherein the predetermined exponential ordering is specifically a one-dimensional sequential order obtained according to different ordering rules according to analysis requirements; the preset indexes are ordered in a sequence form from a high-intensity value to a low-intensity value, namely, a task state eye fatigue recovery level index, an open eye rest state eye fatigue level index, a closed eye rest state eye fatigue level index, a rapid eye movement sleep state eye fatigue recovery level index, a non-rapid eye movement light sleep state eye fatigue recovery level index and a non-rapid eye movement deep sleep state eye fatigue recovery level index.
23. The method of claim 20, wherein the method of sequence data fitting comprises at least one of linear fitting, polynomial fitting, nonlinear curve fitting.
24. The method according to claim 1 or 2, wherein the steps are repeated to complete the tracking detection and quantitative analysis of the continuous time window of the user's eye fatigue recovery level, obtain a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve, generate a user's eye fatigue recovery level quantitative report as required, and the specific step of establishing the user's personalized eye fatigue recovery level tracking database further comprises:
completing tracking detection and quantitative analysis of the user eye fatigue recovery level of a continuous time window within a preset time period, and generating the polymorphic eye fatigue recovery level curve and the eye fatigue recovery comprehensive evaluation curve;
generating and outputting the user eye fatigue recovery level quantification report according to a preset report period;
and collecting user key data in detection and quantification processes of different task states, rest states and sleep states, and establishing and continuously updating the user personalized eyestrain recovery level tracking database.
25. The method of claim 24, wherein the user's eye fatigue recovery level quantitative report includes at least a scene status description, the polymorphic eye fatigue recovery level curve, the eye fatigue recovery comprehensive evaluation curve, a detection quantitative summary.
26. The method of claim 24, wherein the user personalized eyestrain recovery level tracking database includes at least user information, the task state eyestrain physiological features, the resting state eyestrain physiological features, the sleep state eyestrain recovery level curves, the comprehensive eye strain recovery assessment curves, the user eyestrain recovery level quantitative reports; the user personalized eyestrain recovery level tracking database provides data support for long-term eyestrain tracking, eye health personalized evaluation and service of the user.
27. A system for detecting and quantifying the level of eye fatigue recovery, comprising the following modules:
the state detection processing module is used for detecting, collecting and processing the physiological states of eyes of the user in a task state, a resting state and a sleeping state to generate polymorphic eye physiological state signals;
the awake feature analysis module is used for extracting the physiological state features of the eyes of the user in the task state and the resting state according to the polymorphic eye physiological state signals and respectively generating the physiological state features of the eyes in the task state and the resting state;
the sleep characteristic analysis module is used for extracting the eye physiological state characteristics of the eyes of the user in the rapid eye movement sleep state, the non-rapid eye movement shallow sleep state and the non-rapid eye movement deep sleep state according to the polymorphic eye physiological state signals and the sleeping time phase state of the user, and generating sleeping state eye physiological characteristics;
The fatigue recovery evaluation module is used for extracting a polymorphic eyestrain recovery level index according to the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics and the sleep state eyestrain physiological characteristics, and obtaining an eyestrain recovery comprehensive evaluation index through sequence data fitting;
the circulation quantitative management module is used for completing tracking detection and quantitative analysis of a continuous time window of the user's eye fatigue recovery level, obtaining a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve, generating a user's eye fatigue recovery level quantitative report according to the need, and establishing a user personalized eye fatigue recovery level tracking database;
and the data management center module is used for visual display, unified storage and data operation management of all process data and/or result data in the system.
28. The system of claim 27, wherein the status detection processing module further comprises the following functional units:
the state detection acquisition unit is used for detecting and acquiring eye physiological state signals of a task state, a resting state and a sleeping state to generate an original signal of the polymorphic eye physiological state;
and the state signal processing unit is used for performing signal processing on the original signals of the physiological states of the polymorphic eyes to obtain the physiological state signals of the polymorphic eyes.
29. The system of claim 27 or 28, wherein the awake feature analysis module further comprises the following functional units:
a task feature extraction unit, configured to extract the eye physiological state feature from a task eye physiological state signal of the polymorphic eye physiological state signal, and generate the task eye physiological feature;
and the resting feature extraction unit is used for extracting the eye physiological state features from resting eye physiological state signals of the polymorphic eye physiological state signals and generating the resting eye physiological features.
30. The system of claim 27 or 28, wherein the sleep profile module further comprises the following functional units:
the sleep phase stage unit is used for identifying sleep phase state characteristics of a user from the sleep state eye physiological state signals of the polymorphic eye physiological state signals to obtain sleep phase stages;
the sleep characteristic extraction unit is used for extracting the eye physiological state characteristics from the sleep state eye physiological state signals of the polymorphic eye physiological state signals, and generating the sleep state eye physiological characteristics by combining the sleep time phase stage.
31. The system of claim 27 or 28, wherein the fatigue recovery evaluation module further comprises the following functional units:
The baseline characteristic management unit is used for establishing, updating and managing a baseline database of physiological scene characteristics of healthy group eyes;
the task fatigue evaluation unit is used for performing eye fatigue recovery evaluation calculation on the task state eye physiological characteristics to obtain a task state eye fatigue recovery level index;
the resting state eye fatigue recovery evaluation unit is used for carrying out eye fatigue recovery evaluation calculation on the resting state eye physiological characteristics to obtain a resting state eye fatigue recovery level index;
the sleep fatigue evaluation unit is used for performing eye fatigue recovery evaluation calculation on the sleep state eye physiological characteristics to obtain a sleep state eye fatigue recovery level index;
the fatigue recovery integration unit is used for generating the polymorphic eyestrain recovery level index in a collecting mode according to the task state eyestrain recovery level index, the resting state eyestrain recovery level index and the sleep state eyestrain recovery level index;
and the index comprehensive evaluation unit is used for performing sequence data fitting on the index sequence in the polymorphic eye fatigue recovery level index to obtain the eye fatigue recovery comprehensive evaluation index.
32. The system of claim 31, wherein the round robin quantization management module further includes the following functional units:
The cyclic detection quantification section unit is used for completing tracking detection and quantification analysis of the eye fatigue recovery level of the user in a continuous time window within a preset time period and generating the polymorphic eye fatigue recovery level curve and the eye fatigue recovery comprehensive evaluation curve;
the quantitative report generation unit is used for generating the quantitative report of the eye fatigue recovery level of the user according to a preset report period;
the report output management unit is used for uniformly managing the format output and the display form of the quantitative report of the eye fatigue recovery level of the user;
and the personalized data service unit is used for collecting user key data in the detection and quantization processes of different task states, resting states and sleeping states, and establishing and continuously updating the personalized eyestrain recovery level tracking database of the user.
33. The system of claim 27 or 28, wherein the data management center module further comprises the following functional units:
a user information management unit for registering input, editing, inquiry, output and deletion of user basic information;
the data visual management unit is used for visual display management of all data in the system;
the data storage management unit is used for uniformly storing and managing all data in the system;
And the data operation management unit is used for backing up, migrating and exporting all data in the system.
34. The detection and quantification device for the eye fatigue recovery level is characterized by comprising the following modules:
the state detection processing module is used for detecting, collecting and processing signals of the eye physiological states of the user in a task state, a resting state and a sleeping state to generate polymorphic eye physiological state signals;
the awake characteristic analysis module is used for extracting the physiological state characteristics of eyes of the user in a task state and a resting state according to the polymorphic eye physiological state signals and respectively generating the physiological state characteristics of eyes in the task state and the physiological state characteristics of eyes in the resting state;
the sleep characteristic analysis module is used for extracting the eye physiological state characteristics of the eyes of the user in the rapid eye movement sleep state, the non-rapid eye movement shallow sleep state and the non-rapid eye movement deep sleep state according to the polymorphic eye physiological state signals and the sleep time phase state of the user, and generating sleep state eye physiological characteristics;
the fatigue recovery evaluation module is used for extracting a polymorphic eyestrain recovery level index according to the task state eyestrain physiological characteristics, the resting state eyestrain physiological characteristics and the sleep state eyestrain physiological characteristics, and obtaining an eyestrain recovery comprehensive evaluation index through sequence data fitting;
The circulation quantitative management module is used for completing tracking detection and quantitative analysis of a continuous time window of the user's eye fatigue recovery level, obtaining a polymorphic eye fatigue recovery level curve and an eye fatigue recovery comprehensive evaluation curve, generating a user's eye fatigue recovery level quantitative report according to the need, and establishing a user personalized eye fatigue recovery level tracking database;
the data visualization module is used for carrying out unified visual display management on all process data and/or result data in the device;
and the data management center module is used for uniformly storing and managing data operation of all process data and/or result data in the device.
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