CN110881981A - Alzheimer's disease auxiliary detection system based on virtual reality technology - Google Patents
Alzheimer's disease auxiliary detection system based on virtual reality technology Download PDFInfo
- Publication number
- CN110881981A CN110881981A CN201911123026.4A CN201911123026A CN110881981A CN 110881981 A CN110881981 A CN 110881981A CN 201911123026 A CN201911123026 A CN 201911123026A CN 110881981 A CN110881981 A CN 110881981A
- Authority
- CN
- China
- Prior art keywords
- alzheimer
- head
- disease
- information
- subsystem
- 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.)
- Pending
Links
- 208000024827 Alzheimer disease Diseases 0.000 title claims abstract description 69
- 238000001514 detection method Methods 0.000 title claims abstract description 53
- 238000005516 engineering process Methods 0.000 title claims abstract description 27
- 230000004886 head movement Effects 0.000 claims abstract description 56
- 230000000007 visual effect Effects 0.000 claims abstract description 52
- 210000001508 eye Anatomy 0.000 claims abstract description 43
- 230000004424 eye movement Effects 0.000 claims abstract description 23
- 238000011156 evaluation Methods 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims abstract description 12
- 238000013507 mapping Methods 0.000 claims abstract description 4
- 230000007246 mechanism Effects 0.000 claims description 32
- 210000003128 head Anatomy 0.000 claims description 21
- 238000004364 calculation method Methods 0.000 claims description 11
- 239000000463 material Substances 0.000 claims description 11
- 230000001133 acceleration Effects 0.000 claims description 9
- 230000000638 stimulation Effects 0.000 claims description 9
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000006073 displacement reaction Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 4
- 230000005484 gravity Effects 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 claims 1
- 238000007654 immersion Methods 0.000 abstract description 2
- 238000013473 artificial intelligence Methods 0.000 abstract 1
- 210000004556 brain Anatomy 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 206010039966 Senile dementia Diseases 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000000034 method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000750 progressive effect Effects 0.000 description 2
- 210000001747 pupil Anatomy 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 206010020675 Hypermetropia Diseases 0.000 description 1
- 206010034719 Personality change Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 210000005252 bulbus oculi Anatomy 0.000 description 1
- 210000003169 central nervous system Anatomy 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 208000010877 cognitive disease Diseases 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000004438 eyesight Effects 0.000 description 1
- 230000005669 field effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000004305 hyperopia Effects 0.000 description 1
- 201000006318 hyperopia Diseases 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 206010027175 memory impairment Diseases 0.000 description 1
- 230000004379 myopia Effects 0.000 description 1
- 208000001491 myopia Diseases 0.000 description 1
- 208000015122 neurodegenerative disease Diseases 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004434 saccadic eye movement Effects 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
- A61B5/4088—Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
- A61B2562/02—Details of sensors specially adapted for in-vivo measurements
- A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Surgery (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- Veterinary Medicine (AREA)
- Heart & Thoracic Surgery (AREA)
- General Health & Medical Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Neurology (AREA)
- Physics & Mathematics (AREA)
- Physiology (AREA)
- Psychology (AREA)
- Developmental Disabilities (AREA)
- Child & Adolescent Psychology (AREA)
- Neurosurgery (AREA)
- Hospice & Palliative Care (AREA)
- Psychiatry (AREA)
- Dentistry (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Eye Examination Apparatus (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
The invention provides an Alzheimer's disease auxiliary detection system based on a virtual reality technology, which comprises: the system comprises an eye movement tracking subsystem, a head movement tracking subsystem, a display subsystem and an analysis and evaluation subsystem; the eye movement tracking subsystem is used for detecting the fixation point information of the detected person; the head movement tracking subsystem is used for detecting head movement information of the detected person; the display subsystem includes: the display module is an embedded double-screen display with enough pixel density and refresh rate and is used for displaying a stereoscopic image with depth of field to a tested person; the lens module is positioned between the eyes of the testee and the display module and is used for magnifying and mapping the light rays projected by the display module into the eyes of the testee so that the stereoscopic image displayed by the display module occupies the whole visual field of the testee; and the analysis and evaluation subsystem is used for analyzing and detecting the data. According to the invention, the visual field frame is eliminated and the depth of field is enhanced, so that the sense of reality and the immersion of the virtual reality environment are improved, and the data is analyzed and processed by combining an artificial intelligence technology, so that more accurate auxiliary detection of the Alzheimer's disease is completed.
Description
Technical Field
The invention relates to an auxiliary detection system for Alzheimer's disease, in particular to an auxiliary detection system for Alzheimer's disease based on a virtual reality technology.
Background
Alzheimer's Disease (AD), also called senile dementia, is a degenerative disease of the central nervous system, with an implicit onset of the disease and a chronic progressive course of the disease, which is the most common type of senile dementia, mainly manifested as neuropsychiatric symptoms such as progressive memory impairment, cognitive dysfunction, personality change and language disturbance, which seriously affect social, occupational and life functions.
The diagnosis and treatment of alzheimer's disease follow the principle that the earlier the disease is, the better the earlier alzheimer's disease is detected and intervened in the patient, the worse the progression can be delayed. However, the existing detection of alzheimer's disease depends on experienced physicians and a large amount of biochemical and brain image detection, and a large amount of manpower, material resources and financial resources are required to be invested, which is a great challenge for family and social medical resources with alzheimer's disease. Therefore, in order to reduce the dependence on physicians, it is recommended to adopt a more objective quantitative index to assist in the detection of alzheimer's disease.
Research has shown that the visual attention mechanism and the head mode of the Alzheimer disease have the specificity, and the specificity can be used as two objective markers for the auxiliary detection of the Alzheimer disease. The visual stimulation material (such as pictures or videos) can be displayed for middle-aged and elderly people, the distribution of the fixation points on the stimulation material and corresponding head movement information are collected through the corresponding eye movement tracking sensor and the corresponding head movement tracking sensor, data are analyzed, and the visual attention mechanism and the head movement mode of the middle-aged and elderly people are obtained to assist in judging the Alzheimer's disease.
However, a significant proportion of patients with alzheimer's disease have difficulty listening to pictures or videos on the gaze plane display with continuous focus instruction and react quickly to changes, which can result in the distribution of their gaze points possibly exceeding the limits of the visual stimulus material and the data collected by the sensors being invalid. Therefore, the information of the gazing point of alzheimer's disease collected by the general planar visual stimulation material cannot exclude the environmental interference outside the display device.
Meanwhile, the common plane eye movement tracking sensor needs to calibrate the sight focus of a user, the head movement with a large range influences the matching accuracy of a calibration result and the fixation point position in the actual test process, and the two indexes influence each other to reduce the quality of the collected fixation point data.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an auxiliary detection system for Alzheimer's disease based on a virtual reality technology.
According to an aspect of the present invention, there is provided a virtual reality technology-based alzheimer's disease auxiliary detection system, including: the display system comprises a display subsystem, an eye movement tracking subsystem and a head-mounted display bracket, wherein the display subsystem and the eye movement tracking subsystem are both arranged in the head-mounted display bracket;
the eye tracking subsystem is used for detecting the fixation point information of the detected person;
the display subsystem is used for displaying a 360-degree stereoscopic image without a visual field boundary to a tested person, and comprises:
the display module is an embedded double-screen display with enough pixel density and refresh rate and is used for displaying a stereoscopic image with depth of field to a tested person;
the lens module is positioned between the eyes of the testee and the display module and used for magnifying and mapping the light rays projected by the display module into the eyes of the testee, so that the stereoscopic image displayed by the display module occupies the whole visual field of the testee.
Furthermore, the two screens of the display module simulate the observation angle of human eyes to real scenery, and respectively project images with the same scenery and different angles to the two eyes of the person to be measured.
Further, the lens modules are respectively provided with a lens at the left and right sides in the head-mounted display support, and each lens module is provided with a circular prism array.
Further, the system for assisting detection of alzheimer's disease based on virtual reality technology further includes:
the head-moving tracking subsystem is also arranged in the head-wearing display bracket and is used for detecting the head movement information of the tested person;
and the analysis and evaluation subsystem is used for collecting the fixation point information and the head movement information from the eye movement tracking subsystem and the head movement tracking subsystem, and calculating the visual attention mechanism and the head movement mode of the tested person according to the collected fixation point information and the collected head movement information so as to judge the detection result of the Alzheimer's disease.
Further, the head-motion tracking subsystem comprises: an accelerometer module for gravity monitoring to determine whether the head mounted display stand is upright; the accelerometer module is also used for detecting the acceleration of the head of the tested person on each axis; and the gyroscope module is used for tracking the rotation angular velocity and the angle change of the head of the measured person.
Further, the analysis and evaluation subsystem comprises:
the visual attention mechanism calculation module calculates the visual attention mechanism of the testee based on a visual attention mechanism algorithm according to the fixation point information;
the head movement mode calculation module is used for calculating the head movement mode of the measured person based on a head tracking algorithm according to the head movement information;
the Alzheimer's disease detection and evaluation module calculates and evaluates the specificity of the visual attention mechanism and the head movement mode of the testee based on an Alzheimer's disease detection and evaluation algorithm according to the visual attention mechanism and the head movement mode of the testee, and outputs the Alzheimer's disease detection result of the testee.
Furthermore, according to the collected information of the fixation point, a model is built by utilizing a deep neural network to simulate the visual attention mechanism of the tested person, and the prediction of the eye movement distribution of the Alzheimer disease of any visual stimulation material is obtained.
Furthermore, according to the collected head motion information, a model is built by utilizing a deep neural network to simulate the head motion mode of the tested person.
Further, the gazing point information includes: and the gazing position information and the gazing duration information of the tested person on the stereo image.
Further, the head motion information includes: head movement speed information, displacement information, and rotation direction information of the subject.
Compared with the prior art, the invention has at least one of the following beneficial effects:
1. according to the auxiliary detection system for the Alzheimer's disease based on the virtual reality technology, on one hand, the reality sense and the immersion property of a virtual reality environment are improved by eliminating a display frame through each module of a head-mounted three-dimensional imaging display subsystem, the influence of an external environment on the effectiveness of the information of the fixation point is favorably reduced, and the matching degree of the Alzheimer's disease is improved; on the other hand, the visual stimulation material in the virtual reality environment is more three-dimensional and has more depth of field than the planar stimulation material, the actual scene contacted by a human can be simulated, and the effectiveness of data acquisition is improved.
2. According to the auxiliary detection system for the Alzheimer's disease based on the virtual reality technology, the eye movement tracking subsystem is embedded in the head-mounted display support, and the eye movement tracking subsystem and the eyes of the tested person do not have relative displacement, so that the eye movement tracking subsystem can realize synchronous following of head movement, and the phenomenon that the eyes of the tested person are out of focus due to large-scale movement of the head is avoided.
3. The Alzheimer's disease auxiliary detection system based on the virtual reality technology is embedded in a head movement tracking subsystem of a head-mounted display support, and can follow head movement without relative displacement, so that the accuracy of head movement detection is improved.
4. According to the auxiliary detection system for the Alzheimer's disease based on the virtual reality technology, the detection result of the Alzheimer's disease of the detected person is objectively calculated according to two indexes of a visual attention mode and a head movement mode, the characteristics can be quantified, and the system is accurate and efficient.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a block diagram of an assisted alzheimer's disease detection system based on virtual reality technology according to the present invention;
FIG. 2 is a block diagram of a head-tracking subsystem;
FIG. 3 is a block diagram of the analysis and evaluation subsystem;
fig. 4 is a schematic structural diagram of a head-mounted display.
In the figure: the system comprises a display subsystem 1, a lens module 11, a display module 12, an eye tracking subsystem 2, a head tracking subsystem 3, an accelerometer module 31, a gyroscope module 32, an analysis evaluation subsystem 4, a head mode calculation module 41, a visual attention mechanism calculation module 42, an Alzheimer's disease detection evaluation module 43 and a head-mounted display support 5.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
The invention provides an embodiment of an assisted detection system for alzheimer's disease based on virtual reality technology, which is shown in fig. 1 and 4 and comprises: display subsystem 1, eye movement tracking subsystem 2 and head mounted display support 5, display subsystem 1 and eye movement tracking subsystem 2 are all built-in head mounted display support 5, and display subsystem 1 for show the stereoscopic image of no field of vision border to the measurand, it includes: the display module 12, the display module 12 is an embedded dual-screen display with sufficient pixel density and refresh rate, and is used for displaying a stereoscopic image with depth of field to the tested person; the lens module 11 is positioned between the eyes of the testee and the display module 12, and is used for magnifying and mapping the light rays projected by the display module 12 into the eyes of the testee, so that the stereoscopic image displayed by the display module 12 occupies the whole visual field of the testee; and the eye tracking subsystem 2 is used for detecting the fixation point information of the tested person.
Specifically, the head-mounted display bracket 5 of the invention is internally provided with a display subsystem 1 and an eye movement tracking subsystem 2, the pixel density of a display module 12 in the display subsystem 1 is required to be more than 400ppi, the refresh rate is at least 60Hz, the display module 12 is embedded in the front end of the head-mounted display bracket 5, and a double-screen display of the display module is opposite to the eyes of a tested person. As is known, when a person looks at a scene, because the positions and angles of two eyes are different, the images seen by the eyes are different, and the person cannot see the two scenes at the same time, because the brain analyzes the visual signals transmitted by the two eyes respectively and integrates the scenes at different angles, the person can know the depth of field of the scene in front of the eyes and feel that the scene is more stereoscopic, and the person can judge the distance of each object in the visual field and calculate the distance from the object to the person. On the other hand, if one eye is blocked, the brain receives the visual signal of only one eye, and the human feels that the scene in front of the eye tends to be flat because the image of the scene at another angle is not synthesized. The existing method for detecting the Alzheimer's disease has the defect that only one display is arranged in front of a tested person, the scenes seen by two eyes are the same, the reality of the simulated scenes can be influenced, the tested person is difficult to immerse, the feedback behavior of the tested person is not real enough, and the final judgment result of the Alzheimer's disease is influenced. The display module 12 of the invention respectively demonstrates the images of the same scene from different angles for two eyes of the tested person, simulates the real state of human eyes, and makes the tested person feel the depth of field effect of the images through the analysis and synthesis of the brain of the tested person, and the feeling and feedback behavior of the tested person are more real, therefore, the assistant detection system for Alzheimer's disease based on the virtual reality technology provided by the invention has more accurate judgment result on whether the tested person has Alzheimer's disease. In this embodiment, the display module 12 receives an image signal transmitted from an external computer and displays the image signal to a tested person, in other embodiments, the display module 12 may also adopt a mobile phone screen, and the mobile phone screen is divided into two screens as a dual-screen display to directly play a test image stored in the mobile phone;
the lens module 11 can amplify the light emitted by the display module 12 and then project the amplified light to the human eyes, so that the frame of the double-screen display of the display module 12 in the human eyes can be eliminated, the testee can be immersed in the environment created by the display subsystem 1, the feedback behavior of the testee to different images is more real, and the accuracy of the judgment result of the Alzheimer's disease is improved;
the eye tracking subsystem 2, which is a device capable of tracking and measuring the position and movement information of the eyeball, is embedded in the head-mounted display bracket 5. In this embodiment, the eye tracking subsystem 2 may generate an image seen by the pupil through near infrared and capture the generated image through a camera. In other embodiments, the eye tracking subsystem 2 may also perform eye tracking by recognizing eye features such as pupil profile, limbal iris, iris boundaries, corneal reflections from a near pointing light source. The eye movement tracking subsystem 2 is embedded in the head-mounted display support 5, and always moves synchronously with the head of the tested person, so that the problem that the focus of the eye movement tracking device on the eyes of the tested person is out of focus once the head of the tested person moves greatly due to the fixed position of the existing eye movement tracking device is solved.
Preferably, on the basis of any of the above embodiments, the two screens of the display module 12 simulate the observation angle of human eyes to real scenery, and project images with the same scene and different angles to the two eyes of the person to be measured respectively.
Preferably, on the basis of any of the above embodiments, referring to fig. 4, the lens modules 11 are respectively disposed in left and right of the head mounted display bracket 5, and each lens module 11 is provided with a circular prism array.
Specifically, the circular prism array enables the lens module 11 to have the same effect as a large curved lens, and the light from the display module 12 is scattered in human eyes, so that the visual stimulation material presented by the double-screen display occupies the whole visual field of the tested person. The position of the circular prism array can be finely adjusted according to the actual conditions of the user (such as myopia, hyperopia, the width of the eye distance and the like).
Preferably, on the basis of any of the above embodiments, referring to fig. 1 and 4, the virtual reality technology-based alzheimer's disease auxiliary detection system further includes: the head-moving tracking subsystem 3 is also arranged in the head-wearing display bracket 5 and is used for detecting the head movement information of the tested person; and the analysis and evaluation subsystem 4 is used for collecting the fixation point information and the head movement information from the eye movement tracking subsystem 2 and the head movement tracking subsystem 3, and calculating the visual attention mechanism and the head movement mode of the tested person according to the collected fixation point information and the collected head movement information so as to judge the detection result of the Alzheimer's disease.
Preferably, on the basis of any of the above embodiments, as shown in fig. 2, the head-tracking subsystem 3 includes: an accelerometer module 31, the accelerometer module 31 being used for gravity monitoring to determine whether the head mounted display stand 5 is upright; the accelerometer module 31 is also used for detecting the acceleration of the head of the tested person on each axis; the gyroscope module 32, the gyroscope module 32 is used for tracking the rotation angular velocity and the angle change of the head of the person to be measured.
Specifically, in the present embodiment, the accelerometer module 31 measures the acceleration direction and the velocity magnitude thereof in three axes, i.e., x, y, and z, by using the inertial force of the sensing device. In other embodiments, an x, y two-axis acceleration measuring sensor may be used, wherein the x-axis acceleration is 0g and the y-axis acceleration is 1 g.
The gyroscope module 32 tracks the rotation angular velocity or angular variation of the head-mounted display support 5 along the x, y, and z axes to provide more accurate object rotation information for the analysis and evaluation subsystem 4. The module can calculate the angular velocity by measuring the included angle between the vertical axis of the gyro rotor and the equipment in the three-dimensional coordinate system, and the motion state of the head of the measured person in the three-dimensional space is judged by the included angle and the angular velocity.
Preferably, on the basis of any of the above embodiments, as shown in fig. 3, the analysis and evaluation subsystem 4 includes: the visual attention mechanism calculating module 42, the visual attention mechanism calculating module 42 calculates the visual attention mechanism of the testee based on the visual attention mechanism algorithm according to the fixation point information; the head movement mode calculation module 41, the head movement mode calculation module 41 calculates the head movement mode of the measured person based on the head tracking algorithm according to the head movement information; the alzheimer's disease detection and evaluation module 43, the alzheimer's disease detection and evaluation module 43 calculates and evaluates the specificity of the visual attention mechanism and the head movement pattern of the testee based on the alzheimer's disease detection and evaluation algorithm according to the visual attention mechanism and the head movement pattern of the testee, and outputs the alzheimer's disease detection result of the testee.
Specifically, the head movement pattern calculating module 41 calculates the head movement pattern of the subject based on the head tracking algorithm by using the speed, position, and direction information of the head movement changing with time. In the present embodiment, the input information of the module is derived from the acceleration direction, the acceleration magnitude, and the rotation angular velocity and the angular change along the three axes x, y, and z of the head-mounted display measured by the accelerometer module 31 and the gyroscope module 32. In other embodiments, the signal input of the head-motion mode calculation module can also be derived from the displacement and rotation angle of the head-mounted display measured by an infrared detection component arranged in the environment where the Alzheimer's disease auxiliary detection system based on the visual attention mechanism and the head-motion mode in the virtual reality is located.
The visual attention mechanism calculating module 42 calculates the information of the attention point and the non-attention area of the tested person based on the visual attention mechanism algorithm by using the position and the time distribution of the fixation point and the saccade of the tested person obtained by the eye tracking subsystem 2, and extracts the visual attention mechanism.
The alzheimer's disease detection and evaluation module 43 utilizes the features and the corresponding relations of the visual attention mechanism and the head movement pattern obtained by the head movement pattern calculation module 41 and the visual attention mechanism calculation module 42 on the same time line, and the features and the relations are calculated and compared with the features of the alzheimer's disease individuals and the standard developmental individuals through the alzheimer's disease detection and evaluation algorithm to evaluate the specificity of the visual attention mechanism and the head movement pattern of the tested person, and output and display the detection result of the alzheimer's disease. The module may run on, but is not limited to, a personal computer or a server. The detection result can be but is not limited to a display screen of a personal computer or an additional LED display screen and other display devices.
Preferably, on the basis of any of the above embodiments, the visual attention mechanism of the subject is simulated by using the deep neural network modeling according to the collected gaze point information, so as to obtain the prediction of the eye movement distribution of the alzheimer's disease of any visual stimulation material.
Preferably, on the basis of any of the above embodiments, a model is built by using an algorithm such as a deep neural network to simulate the head movement pattern of the subject according to the collected head movement information.
Preferably, on the basis of any of the above embodiments, the gazing point information includes: and the gazing position information and the gazing duration information of the tested person on the stereo image.
Preferably, on the basis of any of the above embodiments, the header motion information includes: head movement speed information, displacement information, and rotation direction information of the subject.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (10)
1. An assistant detection system for Alzheimer's disease based on virtual reality technology comprises: the eye tracking system comprises a display subsystem, an eye movement tracking subsystem and a head-mounted display bracket, and is characterized in that the display subsystem and the eye movement tracking subsystem are both arranged in the head-mounted display bracket;
the eye tracking subsystem is used for detecting the fixation point information of the detected person;
the display subsystem is used for displaying a 360-degree stereoscopic image without a visual field boundary to a tested person, and comprises:
the display module is an embedded double-screen display with enough pixel density and refresh rate and is used for displaying a stereoscopic image with depth of field to a tested person;
the lens module is positioned between the eyes of the testee and the display module and used for magnifying and mapping the light rays projected by the display module into the eyes of the testee, so that the stereoscopic image displayed by the display module occupies the whole visual field of the testee.
2. The virtual reality technology-based auxiliary detection system for alzheimer's disease as claimed in claim 1, wherein the two screens of the display module simulate the viewing angle of human eyes to real scenery, and project images with the same scenery and different angles to the two eyes of the testee respectively.
3. The virtual reality technology-based auxiliary detection system for alzheimer's disease as claimed in claim 1, wherein the lens modules are provided one each at left and right in the head mounted display bracket, and each lens module is provided with a circular prism array.
4. The virtual reality technology-based alzheimer's disease auxiliary detection system according to claim 1, further comprising:
the head-moving tracking subsystem is also arranged in the head-wearing display bracket and is used for detecting the head movement information of the tested person;
and the analysis and evaluation subsystem is used for collecting the fixation point information and the head movement information from the eye movement tracking subsystem and the head movement tracking subsystem, and calculating the visual attention mechanism and the head movement mode of the tested person according to the collected fixation point information and the collected head movement information so as to judge the detection result of the Alzheimer's disease.
5. The virtual reality technology-based alzheimer's disease auxiliary detection system according to claim 4, wherein said head-motion tracking subsystem comprises:
an accelerometer module for gravity monitoring to determine whether the head mounted display stand is upright;
the accelerometer module is also used for detecting the acceleration of the head of the tested person on each axis;
and the gyroscope module is used for tracking the rotation angular velocity and the angle change of the head of the measured person.
6. The virtual reality technology-based aided detection system for Alzheimer's disease as claimed in claim 4, wherein said analysis and evaluation subsystem comprises:
the visual attention mechanism calculation module calculates the visual attention mechanism of the testee based on a visual attention mechanism algorithm according to the fixation point information;
the head movement mode calculation module is used for calculating the head movement mode of the measured person based on a head tracking algorithm according to the head movement information;
the Alzheimer's disease detection and evaluation module calculates and evaluates the specificity of the visual attention mechanism and the head movement mode of the testee based on an Alzheimer's disease detection and evaluation algorithm according to the visual attention mechanism and the head movement mode of the testee, and outputs the Alzheimer's disease detection result of the testee.
7. The virtual reality technology-based auxiliary detection system for Alzheimer's disease according to any one of claims 4-6, wherein a deep neural network is used to build a model to simulate the visual attention mechanism of the tested person according to the collected information of the fixation point, so as to obtain the prediction of the eye movement distribution of Alzheimer's disease of any visual stimulation material.
8. The virtual reality technology-based auxiliary detection system for Alzheimer's disease as claimed in any one of claims 4-6, wherein a deep learning technology such as deep neural network is used to model and simulate the head movement pattern of the tested person according to the collected head movement information.
9. The virtual reality technology-based aided detection system for Alzheimer's disease according to any one of claims 4-6, wherein the head motion information comprises: head movement speed information, displacement information, and rotation direction information of the subject.
10. The virtual reality technology-based alzheimer's disease auxiliary detection system according to any of claims 1-6, wherein said point-of-regard information comprises: the gazing position information, the gazing sequence information and the gazing duration information of the tested person on the stereo image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911123026.4A CN110881981A (en) | 2019-11-16 | 2019-11-16 | Alzheimer's disease auxiliary detection system based on virtual reality technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911123026.4A CN110881981A (en) | 2019-11-16 | 2019-11-16 | Alzheimer's disease auxiliary detection system based on virtual reality technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110881981A true CN110881981A (en) | 2020-03-17 |
Family
ID=69747687
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911123026.4A Pending CN110881981A (en) | 2019-11-16 | 2019-11-16 | Alzheimer's disease auxiliary detection system based on virtual reality technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110881981A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111739374A (en) * | 2020-07-12 | 2020-10-02 | 中国科学院宁波材料技术与工程研究所 | Alzheimer disease early screening training system based on virtual reality |
| CN113101493A (en) * | 2021-05-08 | 2021-07-13 | 宁波市康宁医院(宁波市精神疾病预防控制中心、宁波市微循环与莨菪类药研究所) | Adjustable-frequency Alzheimer disease improvement eyeshade |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107929007A (en) * | 2017-11-23 | 2018-04-20 | 北京萤视科技有限公司 | A kind of notice and visual capacity training system and method that tracking and intelligent evaluation technology are moved using eye |
| CN108170279A (en) * | 2015-06-03 | 2018-06-15 | 塔普翊海（上海）智能科技有限公司 | The eye of aobvious equipment is moved moves exchange method with head |
| CN108732746A (en) * | 2018-05-29 | 2018-11-02 | 北京理工大学 | A kind of wear-type visual sight control system |
| CN109276228A (en) * | 2017-07-21 | 2019-01-29 | 北京集思明智科技有限公司 | A kind of system and its apparatus detecting cerebral function |
| CN109645955A (en) * | 2019-01-31 | 2019-04-19 | 北京大学第三医院（北京大学第三临床医学院） | Based on VR and eye movement the Multifunctional visual sense function detection device tracked and method |
-
2019
- 2019-11-16 CN CN201911123026.4A patent/CN110881981A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108170279A (en) * | 2015-06-03 | 2018-06-15 | 塔普翊海（上海）智能科技有限公司 | The eye of aobvious equipment is moved moves exchange method with head |
| CN109276228A (en) * | 2017-07-21 | 2019-01-29 | 北京集思明智科技有限公司 | A kind of system and its apparatus detecting cerebral function |
| CN107929007A (en) * | 2017-11-23 | 2018-04-20 | 北京萤视科技有限公司 | A kind of notice and visual capacity training system and method that tracking and intelligent evaluation technology are moved using eye |
| CN108732746A (en) * | 2018-05-29 | 2018-11-02 | 北京理工大学 | A kind of wear-type visual sight control system |
| CN109645955A (en) * | 2019-01-31 | 2019-04-19 | 北京大学第三医院（北京大学第三临床医学院） | Based on VR and eye movement the Multifunctional visual sense function detection device tracked and method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111739374A (en) * | 2020-07-12 | 2020-10-02 | 中国科学院宁波材料技术与工程研究所 | Alzheimer disease early screening training system based on virtual reality |
| CN113101493A (en) * | 2021-05-08 | 2021-07-13 | 宁波市康宁医院(宁波市精神疾病预防控制中心、宁波市微循环与莨菪类药研究所) | Adjustable-frequency Alzheimer disease improvement eyeshade |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107224261B (en) | Visual impairment detection system using virtual reality | |
| US10231614B2 (en) | Systems and methods for using virtual reality, augmented reality, and/or a synthetic 3-dimensional information for the measurement of human ocular performance | |
| Durgin et al. | Palm boards are not action measures: An alternative to the two-systems theory of geographical slant perception | |
| US9538947B2 (en) | Method, system and device for assisting diagnosis of autism | |
| CN104866105B (en) | The eye of aobvious equipment is dynamic and head moves exchange method | |
| US8371693B2 (en) | Autism diagnosis support apparatus | |
| US8032842B2 (en) | System and method for three-dimensional interaction based on gaze and system and method for tracking three-dimensional gaze | |
| CN109645955B (en) | Multifunctional visual function detection device and method based on VR and eye movement tracking | |
| US20130171596A1 (en) | Augmented reality neurological evaluation method | |
| US20170263017A1 (en) | System and method for tracking gaze position | |
| JP5689850B2 (en) | Video analysis apparatus, video analysis method, and gaze point display system | |
| CN109634431B (en) | Medium-free floating projection visual tracking interaction system | |
| CN106214118A (en) | A kind of ocular movement based on virtual reality monitoring system | |
| CN110537895A (en) | Retina detecting system based on virtual reality technology | |
| CN111603134A (en) | Eyeball movement testing device and method | |
| CN109643152A (en) | Use face and the eyes tracking of the face sensor in head-mounted display and FA Facial Animation | |
| JP2022002761A (en) | Device for testing visual behavior of person, and method for determining at least one optical design parameter of ophthalmic lens using such device | |
| WO2019125700A1 (en) | System and method of obtaining fit and fabrication measurements for eyeglasses using simultaneous localization and mapping | |
| JP2018099174A (en) | Pupil detector and pupil detection method | |
| CN111134693B (en) | Virtual reality technology-based autism child auxiliary detection method, system and terminal | |
| CN110881981A (en) | Alzheimer's disease auxiliary detection system based on virtual reality technology | |
| JP6747172B2 (en) | Diagnosis support device, diagnosis support method, and computer program | |
| CN111627526B (en) | Eye movement and attention feature vector determination method for children ADHD screening and evaluation system | |
| KR20130014275A (en) | Method for controlling display screen and display apparatus thereof | |
| Lin | An eye behavior measuring device for VR system |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200317 |
|
| WD01 | Invention patent application deemed withdrawn after publication |