CN111568368B - Eyeball movement abnormality detection method, device and equipment - Google Patents
Eyeball movement abnormality detection method, device and equipment Download PDFInfo
- Publication number
- CN111568368B CN111568368B CN202010448825.5A CN202010448825A CN111568368B CN 111568368 B CN111568368 B CN 111568368B CN 202010448825 A CN202010448825 A CN 202010448825A CN 111568368 B CN111568368 B CN 111568368B
- Authority
- CN
- China
- Prior art keywords
- eyeball
- reference point
- movement
- spectrogram
- signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
- A61B3/113—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for determining or recording eye movement
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/0016—Operational features thereof
- A61B3/0025—Operational features thereof characterised by electronic signal processing, e.g. eye models
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
- A61B3/14—Arrangements specially adapted for eye photography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
- A61B3/14—Arrangements specially adapted for eye photography
- A61B3/145—Arrangements specially adapted for eye photography by video means
Abstract
The application discloses an eyeball movement abnormality detection method, which comprises the following steps: determining an eyeball reference point and acquiring a motion trail signal of the eyeball reference point; performing Fourier transform on the motion track signal to obtain a corresponding spectrogram; when the component of the high frequency signal in the spectrogram exceeds a threshold value, the abnormal eyeball movement is determined. According to the method, whether the eyeball movement is abnormal or not is determined according to the components of the high-frequency signals in the spectrogram, when the components of the high-frequency signals in the spectrogram exceed a threshold value, the signals change too fast in a time domain, and then the eyeball movement is determined to be abnormal; the whole process does not need a complex detection program or professional equipment, and only needs to acquire the movement track signal of the eyeball reference point, so that the eyeball movement detection process is greatly simplified, and the detection of whether the brain concussion exists or not is realized conveniently and quickly. The application also provides a device for detecting the abnormal eyeball movement, electronic equipment and a readable storage medium, and the device has the beneficial effects.
Description
Technical Field
The present invention relates to the field of eye movement detection, and in particular, to a method and apparatus for detecting abnormal eye movement, an electronic device, and a readable storage medium.
Background
The phenomenon that the brain is damaged due to the vibration of the brain caused by the impact is called concussion. The general manifestations of concussions are headache, nausea, vomiting, but some patients do not have these obvious phenomena. The related data show that the brain of a brain concussion patient is damaged, the nervous system is disordered, and the movement of the eyeball is influenced by the nervous system to show abnormality: the eyeball may move or rotate involuntarily.
When detecting whether the brain is shocked or not, the traditional brain CT detection cannot detect the phenomenon that the brain is not obviously damaged, and the detection procedure is complex; the use of tracking eye movement detection algorithms requires specialized equipment and is expensive.
Therefore, how to realize convenient and quick eye movement detection is a technical problem that needs to be solved by those skilled in the art.
Disclosure of Invention
The purpose of the application is to provide a method, a device, electronic equipment and a readable storage medium for detecting abnormal eyeball movement, which are used for realizing convenient and quick eyeball movement detection.
In order to solve the above technical problems, the present application provides a method for detecting abnormal eye movement, which includes:
determining an eyeball reference point and acquiring a motion trail signal of the eyeball reference point;
performing Fourier transform on the motion trail signals to obtain corresponding spectrograms;
and when the component of the high-frequency signal in the spectrogram exceeds a threshold value, determining the abnormal eyeball movement.
Optionally, acquiring the motion trail signal of the eyeball reference point includes:
establishing a coordinate system by taking the position of the eyeball reference point as a coordinate origin and taking the horizontal direction as an x axis and the vertical direction as a y axis;
recording the position change condition of the eyeball reference point in the coordinate system within preset time, and generating the movement track signal according to the position change condition.
Optionally, fourier transforming the motion trace signal to obtain a corresponding spectrogram, including:
calculating displacement between the eyeball reference points at adjacent moments in the motion trail signal, and generating corresponding displacement-time images;
and carrying out Fourier transform on the displacement-time image to obtain the spectrogram.
Optionally, recording the position change condition of the eyeball reference point in the coordinate system in a preset time includes:
outputting a calibration picture so that a user looks at the calibration picture;
and recording the position change condition of the eyeball reference point in the coordinate system through an image recording device.
Optionally, the method further comprises:
receiving a modification command;
and executing the modification command to modify the threshold.
Optionally, the determining the eyeball reference point includes:
acquiring eye image data, and performing edge extraction on the eye image data to obtain orbital image data;
performing edge extraction on the orbit image data to obtain eyeball characteristic data;
and calculating the center point of the eyeball according to the eyeball characteristic data, and determining the center point of the eyeball as the eyeball reference point.
Optionally, the eye feature data includes iris image data and/or pupil image data.
The application also provides a device for detecting abnormal eyeball movement, which comprises:
the first determining module is used for determining an eyeball reference point and acquiring a movement track signal of the eyeball reference point;
the transformation module is used for carrying out Fourier transformation on the motion trail signals to obtain corresponding spectrograms;
and the second determining module is used for determining the abnormal eyeball movement when the component of the high-frequency signal in the spectrogram exceeds a threshold value.
The application also provides an electronic device, which includes:
a memory for storing a computer program;
a processor for performing the steps of the method of eye movement anomaly detection as claimed in any one of the preceding claims when the computer program is executed.
The present application also provides a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of eye movement anomaly detection as described in any one of the above.
The method for detecting the abnormal eyeball movement provided by the application comprises the following steps: determining an eyeball reference point and acquiring a motion trail signal of the eyeball reference point; performing Fourier transform on the motion track signal to obtain a corresponding spectrogram; when the component of the high frequency signal in the spectrogram exceeds a threshold value, the abnormal eyeball movement is determined.
According to the technical scheme, the motion track signal of the eyeball reference point is obtained, then Fourier transformation is carried out on the motion track signal to obtain a corresponding spectrogram, whether the eyeball motion is abnormal or not is determined according to the components of the high-frequency signal in the spectrogram, when the components of the high-frequency signal in the spectrogram exceed a threshold value, the signal is indicated to change too fast in a time domain, and then the eyeball motion is determined to be abnormal; the whole process does not need a complex detection program or professional equipment, and only needs to acquire the movement track signal of the eyeball reference point, so that the eyeball movement detection process is greatly simplified, and the detection of whether the brain concussion exists or not is realized conveniently and quickly. The application also provides a device for detecting abnormal eyeball movement, electronic equipment and a readable storage medium, which have the beneficial effects and are not repeated here.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.
Fig. 1 is a flowchart of a method for detecting abnormal eye movement according to an embodiment of the present application;
FIG. 2 is a flowchart showing an actual implementation of S101 in the method for detecting abnormal eye movement provided in FIG. 1;
FIG. 3 is a schematic diagram of a coordinate system establishment procedure according to an embodiment of the present application;
fig. 4 is a schematic diagram of a change situation of the position of an eyeball reference point in a coordinate system according to an embodiment of the present application;
FIG. 5 is a displacement-time image of a motion trajectory signal according to an embodiment of the present disclosure;
FIG. 6 is a flowchart showing an actual implementation of S101 in the method for detecting abnormal eye movement provided in FIG. 1;
fig. 7 is a block diagram of an apparatus for detecting abnormal eye movement according to an embodiment of the present application;
FIG. 8 is a block diagram of another apparatus for detecting abnormal eye movement according to an embodiment of the present application;
fig. 9 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The core of the application is to provide a method, a device, electronic equipment and a readable storage medium for detecting abnormal eyeball movement, which are used for realizing convenient and quick eyeball movement detection.
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
When detecting whether the brain is shocked or not, the traditional brain CT detection cannot detect the phenomenon that the brain is not obviously damaged, and the detection procedure is complex; the use of tracking eye movement detection algorithms requires specialized equipment and is expensive.
Meanwhile, after a cerebral concussion patient is subjected to rehabilitation, monitoring the condition recovery condition by using a professional instrument is difficult, firstly, the instrument is used for monitoring certain harm to the body, secondly, the detection cost is relatively expensive, thirdly, the hospital resources are limited, so that a simple and quick detection means is also lacking in the current condition tracking of the cerebral concussion patient;
the present application provides a method for detecting abnormal eye movement, which is used for solving the above technical problems.
Referring to fig. 1, fig. 1 is a flowchart of a method for detecting abnormal eye movement according to an embodiment of the present application.
The method specifically comprises the following steps:
s101: determining an eyeball reference point and acquiring a motion trail signal of the eyeball reference point;
in the step, the purpose of determining the eyeball reference point is to represent the movement track of the eyeball through the movement track of the eyeball reference point, so that the acquisition difficulty of the eyeball movement track is reduced;
optionally, in a specific embodiment, to increase the speed of acquiring the eyeball reference point, a point on the eyeball may be randomly selected as the eyeball reference point;
optionally, in a specific embodiment, when the number of users to be detected is large and one detection device cannot be satisfied, in order to improve the overall efficiency of detecting the abnormal eye movement, the method may also be implemented by separately performing the collection and analysis of the movement track signal, for example, by collecting the movement track signal through a special signal collecting device, and analyzing the movement track signal through a special signal analyzing device, that is, after determining the eye reference point, the movement track signal of the corresponding eye reference point may also be obtained by inputting data through a user.
Optionally, when the number of users to be detected is small, and a detection device of one person can be satisfied, the efficiency of detecting the abnormal eye movement can be improved by acquiring the track in real time, that is, the method for acquiring the movement track signal of the eye reference point, which is specifically implemented by executing the steps shown in fig. 2, referring to fig. 2, fig. 2 is a flowchart of an actual expression of S101 in the method for detecting abnormal eye movement provided in fig. 1, which specifically includes the following steps:
s201: establishing a coordinate system by taking the position of an eyeball reference point as a coordinate origin and taking the horizontal direction as an x axis and the vertical direction as a y axis;
s202: and recording the position change condition of the eyeball reference point in the coordinate system within preset time, and generating a movement track signal according to the position change condition.
For example, referring to fig. 3 and fig. 4, fig. 3 is a schematic diagram illustrating a coordinate system establishment process according to an embodiment of the present application; fig. 4 is a schematic diagram of a change situation of the position of an eyeball reference point in a coordinate system according to an embodiment of the present application;
as shown in fig. 3 and fig. 4, the present application establishes a coordinate system with the position of the eyeball reference point as the origin of coordinates and the horizontal direction as the x axis and the vertical direction as the y axis, then records the position change condition of the eyeball reference point in the coordinate system within a preset time, and generates a corresponding movement track signal according to the position change condition.
Optionally, here, the recording of the position change of the eyeball reference point in the coordinate system within the preset time may specifically be:
outputting a calibration picture so that a user looks at the calibration picture;
the position change condition of the eyeball reference point in the coordinate system is recorded by an image recording device.
The image recording device mentioned here may include a camera, and other independent image pickup devices, or may be a mobile phone with a camera, AR glasses, a smart helmet, and other devices, which are not particularly limited in this application; in a preferred embodiment, after the user wears the AR glasses, the AR glasses play the calibration picture, and after the eyes are in front view, the eyeballs are stationary for a certain period of time, and during the stationary period, the camera inside the AR glasses records the position change condition of the eyeball reference point in the coordinate system.
S102: performing Fourier transform on the motion track signal to obtain a corresponding spectrogram;
in this step, the purpose of obtaining the corresponding spectrogram by fourier transforming the motion trace signal is to determine whether the eyeball motion is abnormal according to the component of the high-frequency signal in the spectrogram, and when the component of the high-frequency signal in the spectrogram exceeds the threshold value, the signal is changed too fast in the time domain, and then the eyeball motion is determined to be abnormal.
Optionally, fourier transforming the motion trace signal mentioned herein to obtain a corresponding spectrogram may specifically be:
calculating displacement between eyeball reference points at adjacent moments in a motion trail signal, and generating a corresponding displacement-time image;
and carrying out Fourier transform on the displacement-time image to obtain a spectrogram.
For example, referring to fig. 5, fig. 5 is a displacement-time image of a motion trajectory signal provided in an embodiment of the present application, as shown in fig. 5, in the embodiment of the present application, displacement between eyeball reference points at adjacent moments in the motion trajectory signal is calculated, a corresponding displacement-time image is generated, and then fourier transformation is performed on the displacement-time image to obtain a spectrogram, so that the obtained spectrogram is more accurate.
S103: when the component of the high frequency signal in the spectrogram exceeds a threshold value, the abnormal eyeball movement is determined.
When the frequency of human eye movement is too fast, the human eye movement shows obvious high-frequency signals, and at the moment, the patterns after spectrum analysis have high-frequency signals and high-frequency components are more. When the components of the high-frequency signals in the spectrogram exceed the set threshold, the abnormal movement of human eyes can be judged;
optionally, since the threshold may need to be modified as technology advances, in a specific embodiment, the following steps may also be performed to implement the modification of the threshold:
receiving a modification command;
executing the modification command modifies the threshold.
Based on the technical scheme, the method for detecting the abnormal eyeball movement provided by the application comprises the steps of obtaining a movement track signal of an eyeball reference point, carrying out Fourier transform on the movement track signal to obtain a corresponding spectrogram, determining whether the eyeball movement is abnormal according to the components of a high-frequency signal in the spectrogram, and determining that the signal changes too fast in a time domain when the components of the high-frequency signal in the spectrogram exceed a threshold value, wherein the abnormal eyeball movement is determined at the moment; the whole process does not need a complex detection program or professional equipment coordination, and only needs to acquire a movement track signal of an eyeball reference point, so that the eyeball movement detection process is greatly simplified, and further, the detection of whether cerebral concussion exists or not is realized conveniently and rapidly; meanwhile, for patients suffering from cerebral concussion and other diseases, the rehabilitation device can continuously perform recheck without injury in the rehabilitation process, so that recheck cost and occupation of hospital resources are reduced.
With respect to step S101 of the previous embodiment, the determination of the eyeball reference point described therein may be implemented by performing the steps shown in fig. 6, and is described below in conjunction with fig. 6.
Referring to fig. 6, fig. 6 is a flowchart of an actual implementation of S101 in the method for detecting abnormal eye movement provided in fig. 1.
The method specifically comprises the following steps:
s601: acquiring eye image data, and performing edge extraction on the eye image data to obtain orbital image data;
s602: performing edge extraction on the orbital image data to obtain eyeball characteristic data;
the eye characteristic data referred to herein may include iris image data and/or pupil image data, as this application is not specifically limited.
S603: and calculating the center point of the eyeball according to the eyeball characteristic data, and determining the center point of the eyeball as an eyeball reference point.
Based on the above technical scheme, in the embodiment of the application, the eye image data is obtained, the edge extraction is performed on the eye image data to obtain the eye orbit image data, then the edge extraction is performed on the eye orbit image data to obtain the eyeball characteristic data, finally the center point of the eyeball is calculated according to the eyeball characteristic data, and the center point of the eyeball is determined to be the eyeball reference point, so that the position of the eyeball reference point is the center point of the eyeball, and the accuracy of the obtained movement track signal is further improved.
Referring to fig. 7, fig. 7 is a block diagram of an apparatus for detecting abnormal eye movement according to an embodiment of the present application.
The apparatus may include:
the first determining module 100 is configured to determine an eyeball reference point and acquire a motion trail signal of the eyeball reference point;
the transformation module 200 is used for carrying out Fourier transformation on the motion trail signal to obtain a corresponding spectrogram;
the second determining module 300 is configured to determine that the eyeball movement is abnormal when the component of the high-frequency signal in the spectrogram exceeds the threshold.
Referring to fig. 8, fig. 8 is a block diagram of another apparatus for detecting abnormal eye movement according to an embodiment of the present application.
The first determining module 100 may include:
the establishing submodule is used for establishing a coordinate system by taking the position of the eyeball reference point as a coordinate origin and taking the horizontal direction as an x axis and the vertical direction as a y axis;
the recording sub-module is used for recording the position change condition of the eyeball reference point in the coordinate system in the preset time and generating a movement track signal according to the position change condition.
The transformation module 200 may include:
the first calculation sub-module is used for calculating the displacement between eyeball reference points at adjacent moments in the motion trail signal and generating a corresponding displacement-time image;
and the transformation submodule is used for carrying out Fourier transformation on the displacement-time image to obtain a spectrogram.
The recording sub-module may include:
the output unit is used for outputting the calibration picture so as to enable a user to look at the calibration picture;
and the recording unit is used for recording the position change condition of the eyeball reference point in the coordinate system through the image recording device.
The apparatus may further include:
the receiving module is used for receiving the modification command;
and the modification module is used for executing the modification command to modify the threshold value.
The first determining module 100 may include:
the acquisition sub-module is used for acquiring eye image data and carrying out edge extraction on the eye image data to obtain orbital image data;
the edge extraction sub-module is used for carrying out edge extraction on the orbit image data to obtain eyeball characteristic data;
and the second calculation sub-module is used for calculating the center point of the eyeball according to the eyeball characteristic data and determining the center point of the eyeball as an eyeball reference point.
Optionally, the eye feature data comprises iris image data and/or pupil image data.
Since the embodiments of the apparatus portion and the embodiments of the method portion correspond to each other, the embodiments of the apparatus portion are referred to the description of the embodiments of the method portion, and are not repeated herein.
Referring to fig. 9, fig. 9 is a block diagram of an electronic device according to an embodiment of the present application.
The electronic device 900 may vary considerably in configuration or performance and may include one or more processors (central processing units, CPU) 922 (e.g., one or more processors) and memory 932, one or more storage media 930 (e.g., one or more mass storage devices) storing applications 942 or data 944. Wherein the memory 932 and the storage medium 930 may be transitory or persistent. The program stored on the storage medium 930 may include one or more modules (not shown), each of which may include a series of instruction operations in the apparatus. Still further, the processor 922 may be arranged to communicate with a storage medium 930 to execute a series of instruction operations in the storage medium 930 on the electronic device 900.
The electronic device 900 may also include one or more power supplies 929, one or more wired or wireless network interfaces 950, one or more input output interfaces 958, and/or one or more operating systems 941, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, and the like.
The steps in the method of eye movement abnormality detection described above with reference to fig. 1 to 6 are realized by the electronic device based on the structure shown in fig. 9.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, apparatuses and modules described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus, device, and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules is merely a logical function division, and there may be additional divisions of actual implementation, e.g., multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.
The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a function calling device, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The method, the device, the electronic equipment and the readable storage medium for detecting the abnormal eyeball motion are provided in the application. Specific examples are set forth herein to illustrate the principles and embodiments of the present application, and the description of the examples above is only intended to assist in understanding the methods of the present application and their core ideas. It should be noted that it would be obvious to those skilled in the art that various improvements and modifications can be made to the present application without departing from the principles of the present application, and such improvements and modifications fall within the scope of the claims of the present application.
It should also be noted that in this specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
Claims (8)
1. A method for detecting abnormal eye movement, comprising:
determining an eyeball reference point and acquiring a motion trail signal of the eyeball reference point;
performing Fourier transform on the motion trail signals to obtain corresponding spectrograms;
when the components of the high-frequency signals in the spectrogram exceed a threshold value, determining abnormal eyeball movement;
the acquiring the motion trail signal of the eyeball reference point comprises the following steps:
establishing a coordinate system by taking the position of the eyeball reference point as a coordinate origin and taking the horizontal direction as an x axis and the vertical direction as a y axis;
recording the position change condition of the eyeball reference point in the coordinate system within preset time, and generating the movement track signal according to the position change condition;
recording the position change condition of the eyeball reference point in the coordinate system in preset time, wherein the position change condition comprises the following steps:
outputting a calibration picture so that a user looks at the calibration picture;
and recording the position change condition of the eyeball reference point in the coordinate system through an image recording device.
2. The method of claim 1, wherein fourier transforming the motion profile signal to obtain a corresponding spectrogram comprises:
calculating displacement between the eyeball reference points at adjacent moments in the motion trail signal, and generating corresponding displacement-time images;
and carrying out Fourier transform on the displacement-time image to obtain the spectrogram.
3. The method as recited in claim 1, further comprising:
receiving a modification command;
and executing the modification command to modify the threshold.
4. A method according to any one of claims 1-3, wherein said determining an eyeball reference point comprises:
acquiring eye image data, and performing edge extraction on the eye image data to obtain orbital image data;
performing edge extraction on the orbit image data to obtain eyeball characteristic data;
and calculating the center point of the eyeball according to the eyeball characteristic data, and determining the center point of the eyeball as the eyeball reference point.
5. The method of claim 4, wherein the eye feature data comprises iris image data and/or pupil image data.
6. An apparatus for detecting abnormal eye movement, comprising:
the first determining module is used for determining an eyeball reference point and acquiring a movement track signal of the eyeball reference point;
the transformation module is used for carrying out Fourier transformation on the motion trail signals to obtain corresponding spectrograms;
the second determining module is used for determining abnormal eyeball movement when the component of the high-frequency signal in the spectrogram exceeds a threshold value;
wherein the first determining module includes:
the establishing submodule is used for establishing a coordinate system by taking the position of the eyeball reference point as a coordinate origin and taking the horizontal direction as an x axis and the vertical direction as a y axis;
the recording submodule is used for recording the position change condition of the eyeball reference point in the coordinate system within preset time and generating the movement track signal according to the position change condition;
wherein, the record submodule includes:
an output unit configured to output a calibration picture so that a user is looking at the calibration picture;
and the recording unit is used for recording the position change condition of the eyeball reference point in the coordinate system through an image recording device.
7. An electronic device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method of detecting an eye movement abnormality according to any one of claims 1 to 5 when executing the computer program.
8. A readable storage medium, wherein a computer program is stored on the readable storage medium, which when executed by a processor, implements the steps of the method of detecting an eye movement abnormality according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010448825.5A CN111568368B (en) | 2020-05-25 | 2020-05-25 | Eyeball movement abnormality detection method, device and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010448825.5A CN111568368B (en) | 2020-05-25 | 2020-05-25 | Eyeball movement abnormality detection method, device and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111568368A CN111568368A (en) | 2020-08-25 |
| CN111568368B true CN111568368B (en) | 2023-06-06 |
Family
ID=72125341
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010448825.5A Active CN111568368B (en) | 2020-05-25 | 2020-05-25 | Eyeball movement abnormality detection method, device and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111568368B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000037352A (en) * | 1998-07-24 | 2000-02-08 | Canon Inc | Ophthalmic measuring instrument |
| RU2193337C2 (en) * | 2001-02-13 | 2002-11-27 | Саратовский государственный университет им. Н.Г.Чернышевского | Method for studying eyeball movements |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3987053B2 (en) * | 2004-03-30 | 2007-10-03 | 株式会社東芝 | Sleep state determination device and sleep state determination method |
| JP4891938B2 (en) * | 2008-02-28 | 2012-03-07 | 日本放送協会 | Compression encoding preprocessing apparatus and compression encoding preprocessing program |
| JP6224908B2 (en) * | 2013-04-17 | 2017-11-01 | キヤノン株式会社 | Imaging device |
| JP6520099B2 (en) * | 2014-06-30 | 2019-05-29 | 株式会社ニデック | Optical coherence tomography apparatus and data processing program |
| JP6530239B2 (en) * | 2015-05-28 | 2019-06-12 | 浜松ホトニクス株式会社 | Binocular measurement apparatus, binocular measurement method, and binocular measurement program |
| CN108354584B (en) * | 2018-03-06 | 2020-12-29 | 京东方科技集团股份有限公司 | Eyeball tracking module, tracking method thereof and virtual reality equipment |
| CN108937844A (en) * | 2018-06-06 | 2018-12-07 | 苏州桑德欧声听觉技术有限公司 | For manufacturing method, the mobile terminal of nystagmus test mobile terminal |
| WO2020069712A1 (en) * | 2018-10-05 | 2020-04-09 | Ocumove Aps | Neurotransmitter imbalance detection system and method of detecting a neurotransmitter imbalance |
| CN110246158B (en) * | 2019-07-19 | 2021-10-22 | 上海交通大学医学院附属第九人民医院 | Eye disease detection device, method, electronic terminal, and storage medium |
-
2020
- 2020-05-25 CN CN202010448825.5A patent/CN111568368B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000037352A (en) * | 1998-07-24 | 2000-02-08 | Canon Inc | Ophthalmic measuring instrument |
| RU2193337C2 (en) * | 2001-02-13 | 2002-11-27 | Саратовский государственный университет им. Н.Г.Чернышевского | Method for studying eyeball movements |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111568368A (en) | 2020-08-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11132430B2 (en) | Login method and apparatus and electronic device | |
| Lenhardt et al. | An adaptive P300-based online brain–computer interface | |
| US10945864B2 (en) | System and method for noninvasive identification of cognitive and behavioral goals | |
| EP2698112B1 (en) | Real-time stress determination of an individual | |
| CN110866450B (en) | Parkinson disease monitoring method and device and storage medium | |
| JP7373555B2 (en) | Quantification of motor function using EEG signals | |
| Cecotti et al. | Optimization of single-trial detection of event-related potentials through artificial trials | |
| JP2015229040A (en) | Emotion analysis system, emotion analysis method, and emotion analysis program | |
| Ha et al. | Decoding two-class motor imagery EEG with capsule networks | |
| AU2014234955B2 (en) | Automatic detection of task transition | |
| Pander et al. | An automatic saccadic eye movement detection in an optokinetic nystagmus signal | |
| CN109276243A (en) | Brain electricity psychological test method and terminal device | |
| US8990054B1 (en) | System and method for determining and training a peak performance state | |
| CN111568368B (en) | Eyeball movement abnormality detection method, device and equipment | |
| US20180235464A1 (en) | Method and system for detecting time domain cardiac parameters by using pupillary response | |
| RU2751137C1 (en) | Method for determining sleep phase in long-term eeg recording | |
| Neubig et al. | Recognition of imagined speech using electroencephalogram signals | |
| CN108628445B (en) | Brain wave acquisition method and related product | |
| Rihana et al. | Efficient eye blink detection system using RBF classifier | |
| WO2023047519A1 (en) | Learning device, estimation device, learning method, estimation method, and program | |
| Touyama | A collaborative BCI system based on P300 signals as a new tool for life log indexing | |
| CN113842128B (en) | Non-contact heart rate detection device based on multiple filtering and mixed amplification | |
| KR20230153852A (en) | System and method for measuring concentration based on distance between user and screen being measured using single camera | |
| Selvi et al. | Classification of P300 based brain computer interface systems using longshort-term memory (LSTM) neural networks with feature fusion | |
| WO2020036019A1 (en) | Pupil feature amount extraction device, pupil feature amount extraction method, and program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |