CN115770013B - Eye movement test method, device, equipment and medium for auxiliary weak population - Google Patents
Eye movement test method, device, equipment and medium for auxiliary weak population Download PDFInfo
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Abstract
The application discloses an eye movement testing method and device for assisting weak people, and relates to the field of intelligent medical treatment. The specific embodiment comprises the following steps: determining basic information of subjects belonging to the weak population, wherein the basic information at least comprises age information and disease information; calibrating VR test equipment corresponding to the subject, and collecting physical sign data of the subject through the VR test equipment, wherein the physical sign data at least comprises eye movement data and heart rate data; determining an early warning threshold corresponding to the subject according to the basic information; and if the physical sign data reach the early warning threshold value, carrying out early warning on the mental stress state of the subject. The eye movement data, heart rate data and the like of the subject are monitored, so that the physical health of the subject is effectively ensured. Once the data is abnormal, the neural pressure state of the subject in the eye movement test process is abnormal, early warning can be carried out, corresponding measures are executed, the health of the subject is guaranteed, and the probability of retesting is reduced.
Description
Technical Field
The application relates to the technical field of computers, in particular to the intelligent medical field, and especially relates to an eye movement test method, device, equipment and medium for assisting weak people.
Background
VR technology, i.e., virtual reality technology, is a computer simulation system that can create and experience a virtual world, using a computer to create a simulated environment into which a user is immersed. The virtual reality technology is to use data in real life, combine electronic signals generated by computer technology with various output devices to convert the electronic signals into phenomena which can be perceived by people, wherein the phenomena can be real and cut objects in reality or substances which can not be seen by naked eyes, and the phenomena are shown by a three-dimensional model.
Eye tracking techniques, in which near infrared light from an infrared emitter disposed around the eye is directed toward the center of the eye (pupil), cause detectable reflections in the pupil and cornea (the outermost optical elements of the eye). These reflections (expressed as vectors between cornea and pupil) are tracked by an infrared camera. This is an optical tracking of corneal reflection, known as Pupil Center Corneal Reflection (PCCR). Light from the visible spectrum may produce uncontrolled specular reflection, while infrared light simply "bounces" off the iris as it enters the pupil directly, thus allowing for accurate differentiation between the pupil and the iris. Furthermore, since infrared rays are not visible to humans, no distraction is caused to the participants in tracking the eyes. The eye tracker uses a near infrared light source to generate reflected images on the cornea and pupil of the user's eye, and then uses two image sensors to capture the eye and reflected images. The position of the eye in space and the position of the line of sight are accurately calculated through an image processing algorithm and a three-dimensional eyeball model, and meanwhile, real-time data of pupil size can be obtained.
The new technology combining both VR and eye movement can be used to measure cognitive levels in elderly people, with the technical advantages of immersive measurement experience and the ability to accurately measure quantitative data. The cognitive test is integrated into the VR scene, and the relevant physiological and pathological characteristics are correspondingly obtained by capturing and measuring the eye movement data, so that the cognitive test is taken as an efficient and convenient means, and provides considerable convenience for clinical tests in relevant medical fields, and the subjective cognitive error of the test caused by the traditional subjective eye movement observation test and the influence of the external environment of the test on the test result are reduced.
However, in the prior art, the current test time is relatively long, and most of the test objects are weak people such as the elderly, so that mental and eye fatigue easily occurs, especially in the process of performing the eye movement cognition test, the test needs to be recalibrated due to the fact that the test is separated from the instrument and the equipment to rest or the eyes are rubbed caused. The whole testing process is troublesome, and meanwhile, the potential safety hazard affecting the health is increased in the testing process of the weak population.
Disclosure of Invention
Aiming at the problem of difficult test of the weak population, the eye movement test method, the eye movement test device, the electronic equipment and the storage medium for assisting the weak population are provided.
According to a first aspect, there is provided an eye movement testing method for assisting a population with a weakness, comprising:
determining basic information of subjects belonging to the wealthy population, wherein the basic information at least comprises age information and disease information;
calibrating VR test equipment corresponding to the subject, and collecting physical sign data of the subject through the VR test equipment, wherein the physical sign data at least comprises eye movement data and heart rate data;
determining an early warning threshold corresponding to the subject according to the basic information;
and if the physical sign data reach the early warning threshold, carrying out early warning on the mental stress state of the subject.
According to a second aspect, there is provided an eye movement testing device for assisting a population of weakness, comprising:
a basic information determining unit for determining basic information of subjects belonging to the weak group, wherein the basic information at least comprises age information and disease information;
the data acquisition unit is used for calibrating VR test equipment corresponding to the subject, and acquiring physical sign data of the subject through the VR test equipment, wherein the physical sign data at least comprises eye movement data and heart rate data;
the early warning threshold determining unit is used for determining an early warning threshold corresponding to the subject according to the basic information;
and the early warning unit is used for carrying out early warning on the mental pressure state of the subject if the physical sign data reach the early warning threshold value.
According to a third aspect, there is provided an electronic device comprising: one or more processors; and a storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method such as any of the embodiments of the eye movement testing method for the assistive population.
According to a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs a method as in any of the embodiments of the eye movement testing method for an assistive population.
According to the scheme of the application, the eye movement data, the heart rate data and the like of the subject are monitored in the test process, so that the physical health of the subject can be effectively ensured. Once the data is abnormal, the abnormal neural pressure state of the subject in the eye movement test process is indicated, early warning can be carried out at the moment, corresponding measures are executed, the health of the subject is ensured, and the probability of retesting is reduced.
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Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
FIG. 1 is an exemplary system architecture diagram in which some embodiments of the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a method of eye movement testing for an assistive population according to the application;
FIG. 3 is a flow chart of a method of eye movement testing for an assisted weaknesses population according to one scenario of the present application;
FIG. 4 is a schematic structural view of one embodiment of an eye movement testing device for assisting a weaknesses according to the present application;
fig. 5 is a block diagram of an electronic device for implementing an eye movement testing method for an assistive population of an embodiment of this application.
Detailed Description
Exemplary embodiments of the present application are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the eye movement testing method of the present application or eye movement testing apparatus of the assistive population may be applied.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as video-type applications, live applications, instant messaging tools, mailbox clients, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices with display screens, including but not limited to smartphones, tablets, electronic book readers, laptop and desktop computers, and the like. When the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices. Which may be implemented as multiple software or software modules (e.g., multiple software or software modules for providing distributed services) or as a single software or software module. The present invention is not particularly limited herein.
The server 105 may be a server providing various services, such as a background server providing support for the terminal devices 101, 102, 103. The background server can analyze and the like the received physical sign data and the like, and feed back the processing result (such as an early warning threshold value) to the terminal equipment.
It should be noted that, the eye movement testing method for the auxiliary handicapped people provided in the embodiments of the present application may be executed by the server 105 or the terminal devices 101, 102, 103, and accordingly, the eye movement testing device for the auxiliary handicapped people may be disposed in the server 105 or the terminal devices 101, 102, 103.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2 and 3, a flow 200 of one embodiment of an eye movement testing method (in some scenarios, eye movement testing may also be referred to as eye movement cognition testing as shown in fig. 3) and a method flow in one scenario for an assisted weaknesses population according to the present application is shown. In this embodiment, the eye movement testing method for the handicapped person may be executed on an execution body (for example, a server or a terminal device shown in fig. 1). The eye movement testing method for the auxiliary weak population comprises the following steps:
The basic information can also comprise other information such as gender information, and the acquisition mode can be acquired based on questionnaire filling, other system importing modes and the like.
The people with weakness mainly take the elderly (for example, the aged 60 years, namely the aged), and of course, some people with partial diseases can be considered to belong to the people with weakness even if the age of the people does not meet the requirement. Wherein, the disease information mainly aims at diseases which are easy to get for the elderly, such as AD (Alzheimer disease), PD (Parkinson disease), hypertension, diabetes, coronary heart disease, and the like.
The existing fatigue degree judging method is mainly in the form of an inquiry or questionnaire, the inquiry test mode is greatly influenced by the main observation, the time is long, the compliance of the inquired person is low, and the required real-time, automatic and user-friendly fatigue reminding and test breaking effect cannot be achieved. Based on this, real-time automated judgment is made by physical sign data of the user.
The subject performs calibration before eye movement test after approaching VR test equipment, and eye movement data acquisition equipment is equipped with infrared eye movement sensor, infrared pupil sensor, infrared body temperature sensor, bluetooth receiver etc., and the supporting infrared body temperature measuring bracelet of wearable still, it is connected with VR test equipment through bluetooth protocol. The sensor signals are concentrated in the calculation unit of the VR test apparatus. The computing unit of the VR test device can record the sensor data of the multiple channels in real time at this time, and keep continuous detection.
Aiming at the weak crowds such as the elderly with various complications, the wearable wrist watch and other devices are combined during heart rate detection, and multi-device linkage is realized through calling interfaces, so that the detection of the weak crowds such as the elderly is facilitated.
Different physical states of the subjects are different, so that different early warning thresholds are set, the corresponding test results are more accurate, and the early warning is more reasonable.
And 204, if the physical sign data reach the early warning threshold, early warning is carried out on the mental stress state of the subject.
And judging the mental pressure state (also called as cognitive load state) of the subject according to the heart rate data and the eye movement data transmitted by the VR test equipment, so that after early warning, the test is stopped or terminated, and the health of the subject is ensured.
The infrared heart rate sensing technology with relatively convenient implementation degree is selected, heart rate data of a subject are collected in real time to assist fatigue monitoring work in the eye movement test process, and the eye movement test is not invasive and has no influence on the eye movement test. From this use the sensing data of binary channels, consider from technical principle, can let fatigue monitoring work reach real-time, high-efficient, safe work effect, promote eye movement test's security and humanization.
The method comprises the steps of integrating each sensor into an eye movement cognition VR test device, wherein the core function of the device is to provide an eye movement cognition test, the auxiliary function is to detect the mental load state of a subject in real time through each sensor, and the main purpose is to ensure the safety and reliability of the test by actively performing the intervention behavior of suspending or stopping the test under the condition that the cognitive load of the subject is large or the physiological and mental discomfort occurs in the test.
In the test process, the infrared cornea reflection sensor and the infrared heart rate sensor measure the abnormality of the physiological index of the subject, the test state of the subject is abnormal through data comparison at the moment, the test is paused or terminated at the moment, and an acoustic warning is given out, so that the test is stopped in time. The progress of the eye movement cognition test process is recorded, and the test progress is saved and then stored in a patient file in the system, so that the next test development is facilitated, and the test is continued.
In some optional implementations of this embodiment, the VR test device includes at least an eye movement data acquisition device including an infrared eye movement sensor, an infrared pupil sensor, an infrared body temperature sensor, and the like.
Eye movement data is acquired by an eye movement data acquisition device based on a pupil-cornea tracking technology (pupil center corneal reflections) and a bright pupil-dark pupil dual-channel mode.
The eye movement data comprise pupil position data and pupil size data, wherein the infrared light of the pupil brightening channel irradiates the eye movement data, so that the sensor receives infrared bright spots with the same size as the pupil, and the pupil size data are obtained through a vision algorithm.
Most of the prior art eye movement techniques fail to use a dual channel acquisition mode, and the acquisition mode of the bright pupil channel can acquire pupil size, but is not used in fatigue evaluation of users. Meanwhile, the only pupil diameter data cannot fully prove the fatigue degree, and additional parameter indexes are also needed to assist in judgment.
The eye movement acquisition technology uses a non-invasive infrared cornea reflection accurate positioning method, and no physical contact with eyes is needed. The pupil position is accurately positioned, the calculation parameters are adjusted according to different individuals, the color is adjusted to be bright, and the pupil position is suitable for testing comfortable environments of different crowds. Meanwhile, the pupil size can be measured by the eye movement acquisition mechanism after function expansion, and the pupil diameter data is time stamped to form a pupil diameter data set.
In some optional implementations of this embodiment, the VR test device includes at least a heart rate data acquisition device including a plurality of heart rate data acquisition devices disposed at different locations of the subject, respectively.
With heart rate sensing technology, it is possible in principle to place in a plurality of locations, such as hands, torso, face, etc. The reflective optical sensor with transistor output is usually used for biological measurement in the range of 700nm (red) to 1000nm (infrared), the light beam can pass through the narrow part of the body, the light beam returns to the sensor after reaching the reflection of blood vessels, then the signal is sent to an operational amplifier for 'enhancing and shaping', and finally a typical waveform heart rate chart can be obtained, so that the heart rate condition can be obtained by analyzing the wave crest, and heart rate data can be obtained.
In some optional implementations of this embodiment, the VR test device includes at least a temperature sensor that corresponds to the face of the subject. The temperature sensor is added on the surface of the sunshade at the forehead part, the face is attached to achieve the best measurement effect, and the temperature data is time stamped to form a temperature data set, so that the temperature data is used as part of data in the human body sign data, and the state of a subject is judged in an auxiliary mode.
In some optional implementations of this embodiment, the various VR test devices described above are integrated to obtain corresponding data and perform corresponding collection.
Specifically, the heart rate data includes a plurality of heart rate data corresponding to the plurality of heart rate data acquisition devices, for example, bluetooth wearable heart rate sensor data-heart rate B 1 (times/second) while head contact heart rate sensor data-heartbeat B 2 (times/second). Pupil size data includes pupil diameter, e.g., pupil size sensor data—pupil diameter R (millimeters).
Sampling interval time is set for the heartbeat data and the pupil size data, respectively. For example, the heart rate sensor samples at a rate f 1 The fixed value is 25Hz; pupil sampling rate f 2 The fixed value is 5Hz. And set the sampling time at intervals, the heart rate part is deltat 1 =1/f 1 Through Δt 1 Post heart rate dataIs B 1 ' and B 2 'A'; pupil portion is at 2 =1/f 2 Through Δt 2 The heart rate data after this is R'.
And determining corresponding change rate data according to the sampling interval time, wherein the change rate data comprises heart rate change rate and pupil change rate. At this time, the heart rate change rate k can be known according to the data flow of each sensor 1 =(B 1 ′-B 1 )/Δt 1 ;k 2 =|B 2 ′-B 2 |/Δt 1 The method comprises the steps of carrying out a first treatment on the surface of the Pupil change rate k 3 =|R′-R|/Δt 2 。
Further, according to the age information, determining thresholds corresponding to the heartbeat data and the pupil size data; and when the subject has various symptoms, weighting according to the symptom information to obtain thresholds corresponding to the heart rate change rate and the pupil change rate.
When the subject wears wearable equipment such as an intelligent bracelet or an intelligent watch, heart rate change data of the patient in a period of time in the past can be called, and judgment is assisted.
And if the heartbeat data is higher than the first threshold value, the pupil size data is lower than the second threshold value, the heart rate change rate is higher than the third threshold value, and the pupil change rate is higher than the fourth threshold value, triggering a judgment, and if the duration is higher than the preset time, suspending the test and early warning the mental pressure state of the subject. For example, a pause warning threshold is set, and any 1 threshold is triggered by three data to determine that the system is giving a warning. B (B) 1 ,B 2 More than or equal to 80; r is less than or equal to 4, and the change rate k 1 ,k 2 Not less than 10 (times/second) 2 ),k 3 0.2 mm/s; if this state continues for more than 10s, the system is determined to suspend testing, as shown in the following table.
If the heartbeat data is higher than the fifth threshold value, the pupil size data is lower than the sixth threshold value, the heart rate change rate is higher than the seventh threshold value, and the pupil change rate is higher thanAnd in the eighth threshold, triggering at least two determinations, stopping the test, and carrying out early warning on the mental stress state of the subject. The fifth threshold value, the seventh threshold value and the eighth threshold value are respectively higher than the first threshold value, the third threshold value and the fourth threshold value, and the sixth threshold value is lower than the second threshold value. For example, setting a termination threshold, three data triggers any 2 thresholds, and then determines that the system terminates the test. B (B) 1 ,B 2 More than or equal to 100; r is less than or equal to 3.5, and the change rate k 1 ,k 2 Not less than 20 (times/second) 2 ),k 3 0.5 mm/s, as shown in the following table.
In some alternative implementations of the present embodiment, feedback is provided to the subject and the tester after the test is paused or terminated, and the test breakpoint is recorded at the time of the pause. The heart rate data and pupil size data exceed pause or termination thresholds corresponding to the condition, and the system will simultaneously wake Shi Cezhe and the subject in the form of visual and audible feedback while pausing or terminating the ongoing cognitive eye movement test, recording the test breakpoint.
Before the next test, the system judges whether the test is in an interrupted state at the moment, if the test is in the interrupted state, the test needs to be pre-tested (also called a state judgment test) in advance before the test, and in the pre-test process, a relaxation video is played for the test, so that the test is fully relaxed, and meanwhile, the mental load state of the test is monitored through a multichannel sensor. If the warning threshold value is lower than the warning threshold value corresponding to the pause test within a certain time, the system can judge that the eye movement cognition test can be continued. If the test still accords with the early warning threshold corresponding to the pause test, the test is prompted to relax for two more minutes, if the test is higher than the early warning threshold corresponding to the stop test for more than 5s, or if the pause test is 3 times, the test is directly ended
Before restarting the test after breaking, judging whether the user can continue the test according to the data of various sensing devices so as to ensure that the physiological index and psychological state of the subject can continue the eye movement cognitive test.
With further reference to fig. 4, as an implementation of the method shown in the foregoing figures, the present application provides an embodiment of an eye movement testing device for assisting a population with a weakness, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the embodiment of the device may further include the same or corresponding features or effects as the embodiment of the method shown in fig. 2, except for the features described below. The device can be applied to various electronic equipment.
As shown in fig. 4, the eye movement testing device 400 for assisting the weak group of the present embodiment includes: a basic information determining unit 401, a data acquisition unit 402, an early warning threshold determining unit 403 and an early warning unit 404. Wherein the basic information determining unit 401 is configured to determine basic information of a subject belonging to a weak group, the basic information including at least age information, disorder information; a data acquisition unit 402 configured to calibrate a VR test device corresponding to the subject and acquire physical sign data of the subject through the VR test device, where the physical sign data includes at least eye movement data and heart rate data; an early warning threshold determining unit 403 configured to determine an early warning threshold corresponding to the subject according to the basic information; an early warning unit 404 configured to early warn the mental stress state of the subject if the physical sign data reaches the early warning threshold.
In this embodiment, the specific processes of the basic information determining unit 401, the data collecting unit 402, the early warning threshold determining unit 403 and the early warning unit 404 of the eye movement testing device 400 for assisting the weak crowd and the technical effects brought by the specific processes may refer to the related descriptions of the steps 201, 202, 203 and 204 in the corresponding embodiment of fig. 2, and are not repeated here.
According to embodiments of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 5, a block diagram of an electronic device that assists an eye movement testing method for a weak population according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the application described and/or claimed herein.
As shown in fig. 5, the electronic device includes: one or more processors 501, memory 502, and interfaces for connecting components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 501 is illustrated in fig. 5.
The memory 502 is used as a non-transitory computer readable storage medium, and may be used to store a non-transitory software program, a non-transitory computer executable program, and modules, such as program instructions/modules (e.g., the basic information determining unit 401, the data collecting unit 402, the early warning threshold determining unit 403, and the early warning unit 404 shown in fig. 4) corresponding to the eye movement testing method for the auxiliary weaknesses in the embodiments of the present application. The processor 501 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 502, i.e., implements the eye movement testing method for the auxiliary weaknesses in the method embodiments described above.
The electronic device for assisting the eye movement test method for the weak population can further comprise: an input device 503 and an output device 504. The processor 501, memory 502, input devices 503 and output devices 504 may be connected by a bus or otherwise, for example in fig. 5.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the eye movement testing apparatus for the handicapped population, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer stick, one or more mouse buttons, a track ball, a joystick, etc. The output devices 504 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASIC (application specific integrated circuit), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware. The described units may also be provided in a processor, for example, described as: the processor comprises a basic information determining unit, a data acquisition unit, an early warning threshold determining unit and an early warning unit. The names of these units do not constitute a limitation of the unit itself in some cases, and for example, the basic information determination unit may also be described as "a unit that determines basic information of a subject".
As another aspect, the present application also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: determining basic information of subjects belonging to the wealthy population, wherein the basic information at least comprises age information and disease information; calibrating VR test equipment corresponding to the subject, and collecting physical sign data of the subject through the VR test equipment, wherein the physical sign data at least comprises eye movement data and heart rate data; determining an early warning threshold corresponding to the subject according to the basic information; and if the physical sign data reach the early warning threshold, carrying out early warning on the mental stress state of the subject.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the invention referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or equivalents thereof is possible without departing from the spirit of the invention. Such as the above-described features and technical features having similar functions (but not limited to) disclosed in the present application are replaced with each other.
Claims (9)
1. A method of assisting an eye movement test for a population of weakness, the method comprising:
determining basic information of subjects belonging to the wealthy population, wherein the basic information at least comprises age information and disease information;
calibrating VR test equipment corresponding to the subject, and collecting physical sign data of the subject through the VR test equipment, wherein the physical sign data at least comprises eye movement data and heart rate data; the eye movement data comprises pupil position data and pupil size data; the heart rate data comprise a plurality of heart rate data corresponding to a plurality of heart rate data acquisition devices respectively; setting sampling interval time for the heartbeat data and the pupil size data respectively; determining corresponding change rate data according to the sampling interval time, wherein the change rate data comprises heart rate change rate and pupil change rate;
determining early warning thresholds corresponding to the heartbeat data and the pupil size data according to the age information; weighting according to the disease information to obtain early warning thresholds corresponding to the heart rate change rate and the pupil change rate;
if the heartbeat data is higher than a first threshold value, the pupil size data is lower than a second threshold value, the heart rate change rate is higher than a third threshold value, the pupil change rate is higher than a fourth threshold value, triggering a judgment, and if the duration is higher than a preset time, suspending the test and carrying out early warning on the mental pressure state of the subject;
and if the heartbeat data is higher than a fifth threshold, the pupil size data is lower than a sixth threshold, the heart rate change rate is higher than a seventh threshold and the pupil change rate is higher than an eighth threshold, triggering at least two determinations, stopping the test and carrying out early warning on the mental pressure state of the subject.
2. The method of claim 1, the VR testing device comprising at least an eye movement data acquisition device comprising an infrared eye movement sensor, an infrared pupil sensor, an infrared body temperature sensor;
the VR test device is used for collecting physical sign data of the subject, and specifically comprises the following steps:
and acquiring the eye movement data through the eye movement data acquisition equipment based on a pupil-cornea tracking technical method and a bright pupil-dark pupil dual-channel mode, wherein the sensor receives infrared bright spots with the same size as the pupil through infrared light irradiation of the bright pupil channel, so that the pupil size data is obtained through a visual algorithm.
3. The method of claim 1, the VR testing device comprising at least a heart rate data acquisition device comprising a plurality of heart rate data acquisition devices each disposed at a different location of the subject;
the VR test device is used for collecting physical sign data of the subject, and specifically comprises the following steps:
and transmitting corresponding infrared beams to the subject through the heart rate data acquisition equipment, and obtaining heart rate data corresponding to the subject through an operational amplifier according to the returned signals.
4. The method of claim 1, the VR testing device comprising at least a temperature sensor corresponding to a face of the subject;
the VR test device is used for collecting physical sign data of the subject, and specifically comprises the following steps:
and attaching the face of the subject through the temperature sensor so as to acquire temperature data of the subject, and taking the temperature data as part of the physical sign data.
5. The method of claim 1, the fifth, seventh, and eighth thresholds being higher than the first, third, and fourth thresholds, respectively, and the sixth threshold being lower than the second threshold.
6. The method of claim 5, the method further comprising:
after pausing or terminating the test, feeding back to the subject and the tester, and recording a test breakpoint upon pausing;
before the next test, if the test breakpoint corresponding to the test breakpoint exists, pre-testing is carried out on the test subject, in the pre-testing process, a relaxation video is played for the test subject, if the test subject still accords with an early warning threshold corresponding to the pause test, the test is paused again, and after the pause test reaches the preset times or accords with the early warning threshold corresponding to the termination test, the test is stopped.
7. An eye movement testing device for assisting a population of weakness, the device comprising:
a basic information determining unit for determining basic information of subjects belonging to the weak group, wherein the basic information at least comprises age information and disease information;
the data acquisition unit is used for calibrating VR test equipment corresponding to the subject and acquiring physical sign data of the subject through the VR test equipment, wherein the physical sign data at least comprises eye movement data and heart rate data; the eye movement data comprises pupil position data and pupil size data; the heart rate data comprise a plurality of heart rate data corresponding to a plurality of heart rate data acquisition devices respectively; setting sampling interval time for the heartbeat data and the pupil size data respectively; determining corresponding change rate data according to the sampling interval time, wherein the change rate data comprises heart rate change rate and pupil change rate;
the early warning threshold determining unit is used for determining early warning thresholds corresponding to the heartbeat data and the pupil size data according to the age information; weighting according to the disease information to obtain early warning thresholds corresponding to the heart rate change rate and the pupil change rate;
the early warning unit is used for triggering a judgment and suspending the test and early warning the mental pressure state of the subject if the heartbeat data is higher than a first threshold value, the pupil size data is lower than a second threshold value, the heart rate change rate is higher than a third threshold value and the pupil change rate is higher than a fourth threshold value and the duration time is higher than a preset time;
and if the heartbeat data is higher than a fifth threshold, the pupil size data is lower than a sixth threshold, the heart rate change rate is higher than a seventh threshold and the pupil change rate is higher than an eighth threshold, triggering at least two determinations, stopping the test and carrying out early warning on the mental pressure state of the subject.
8. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-6.
9. A computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-6.
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Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103886307A (en) * | 2014-04-15 | 2014-06-25 | 王东强 | Sight tracking and fatigue early warning method |
US9357966B1 (en) * | 2014-12-18 | 2016-06-07 | Karen Elise Cohen | Drug screening device for monitoring pupil reactivity and voluntary and involuntary eye muscle function |
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Publication number | Priority date | Publication date | Assignee | Title |
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CN103886307A (en) * | 2014-04-15 | 2014-06-25 | 王东强 | Sight tracking and fatigue early warning method |
US9357966B1 (en) * | 2014-12-18 | 2016-06-07 | Karen Elise Cohen | Drug screening device for monitoring pupil reactivity and voluntary and involuntary eye muscle function |
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