CN112315753A - Visual field expansion training device based on glaucoma late-stage disease and training method thereof - Google Patents
Visual field expansion training device based on glaucoma late-stage disease and training method thereof Download PDFInfo
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Abstract
The invention discloses visual field expansion training equipment based on glaucoma late-stage disease and a training method thereof, wherein the visual field expansion training equipment comprises: the device comprises an image acquisition unit, an image processing unit, an image display unit, an information input control unit and an eyeball movement monitoring unit, wherein the image acquisition unit is used for receiving the information of the image acquisition unit and carrying training method software; the training method is characterized in that various image parameters are set, motion stroboscopic light point training is superposed, brain visual cells are stimulated, damaged visual function cells are assisted to recover, periodic training is carried out, partial visual field of a patient with late-stage tubular visual field of glaucoma is effectively improved, surgery and drug treatment are not needed, image training equipment is used for training and optimizing the visual field with the edge being lost, and pain of the patient is effectively relieved actually.
Description
Technical Field
The invention relates to the field of medical instruments, in particular to visual field extension training equipment based on glaucoma late-stage symptoms and a training method thereof.
Background
Glaucoma is a common disease in ophthalmology, has serious harm to the vision of a patient, has poor effect even though treated by data and method operations, and still influences the visual field of the patient. In recent years, with the development of cataract recovery surgery, the incidence of glaucoma blindness has been relatively increased in the control of infectious corneal diseases. The development of medical level and computer technology contributes to the early diagnosis and treatment of glaucoma, acute angle-closure glaucoma is effectively controlled in time, but some symptoms still remain, such as the primary open angle with hidden and slow development, and chronic angle-closure glaucoma can be diagnosed by the patient according to the time when the function is seriously damaged due to missed diagnosis, misdiagnosis or patient negligence, so that great risk is brought to treatment. In advanced glaucoma, the patient gradually leaves only a portion of the visual field in the center of the visual field, often referred to as the tubular field, as the visual field collapses.
The tubular visual field means that retina is damaged, so that peripheral vision is lost, but only central vision is existed, visual perception of a patient is limited, the pathological change is mainly caused by apoptosis of retinal ganglion cells or primary retinitis pigmentosa caused by pathological high intraocular pressure, the tubular visual field reduces life quality of people, increases potential safety hazard and traffic accident incidence rate, and increases corresponding burden for families and society of the patient, therefore, the research on visual field enlarging vision correction technology of the tubular visual field has important significance. The present invention solves such problems.
Disclosure of Invention
In order to solve the defects of the prior art, the invention aims to provide visual field expansion training equipment based on late glaucoma diseases and a training method thereof, wherein the image training equipment trains and optimizes visual fields with missing edges without operations and medication, and the method of training by setting various image parameters and superposing moving stroboscopic light points stimulates visual cells of the brain, assists in recovering damaged visual function cells, and effectively helps patients with late glaucoma tubular visual fields to realize partial visual field improvement through periodic training.
In order to achieve the above object, the present invention adopts the following technical solutions:
a glaucoma late stage condition based visual field extension training device comprising: the device comprises an image acquisition unit, an image processing unit, an image display unit, an information input control unit and an eyeball movement monitoring unit, wherein the image processing unit is used for receiving the information of the image acquisition unit and carrying training method software, the image display unit is used for receiving the information of the image processing unit, the information input control unit is connected with the image processing unit, and the eyeball movement monitoring unit is connected with the image processing unit.
The visual field expansion training device based on glaucoma late-stage symptoms comprises an image acquisition unit and a control unit, wherein the control unit comprises: a camera or an external video input source.
In the field of vision extension training device based on the glaucoma late-stage disease, the image processing unit comprises a mobile phone processor or a computer processor.
The visual field expansion training device based on glaucoma late-stage disease comprises an image display unit and a control unit, wherein the image display unit comprises: cell-phone screen, display screen, intelligent glasses.
The visual field expansion training device based on the glaucoma late stage disease comprises: the device comprises a key controller, an input controller, a gesture controller and a voice controller.
The field of vision extension training equipment based on glaucoma late stage disease, eyeball motion monitoring unit includes: camera, infrared sensor, eye movement sensor.
A visual field extension training method based on glaucoma late-stage disease, comprising the following steps:
step one, training preparation work:
one eye is shielded, one eye is trained independently, the vision center of a patient is guided to be fixed at a screen fixation point of an image display unit, and picture contents are input through an image acquisition unit;
step two, setting a multi-stage processing mode and default parameters:
the image processing unit receives the picture of the image acquisition unit and sets a multi-stage processing mode, each stage corresponds to different contrast, brightness and saturation parameters, and the brightness contrast adjustment of the image belongs to the gray level linear transformation of the image; taking N levels as default original images, setting default parameters to be consistent with the external environment according to the parameters based on the images acquired by the image acquisition system;
step three, finding out edges of each level:
guiding a patient to draw the visual field edge by default image display parameters, feeding back data by an eyeball motion monitoring unit through a visual field drawing method under a planar visual field image, ensuring that the sight center of the patient falls on a screen fixation point of an image display unit, automatically drawing boundary images of all angles in the system, recording the boundary images as N-level edges, and repeatedly recording the edges of all levels in the system under all image processing modes;
step four, finding out the defect part:
the image processing unit automatically compares the multi-level edge contour maps, considers the vision field of the reduced part as a default vision field range map, places the default vision field range map on the upper layer, places the maximum vision field range map on the lower layer, and forms a defect part after the upper layer and the lower layer are overlapped;
step five, training a visual field:
after the parameters are compared with the outline, the method enters a training mode, the middle fixation point guide function is also kept in the training mode, the upper layer of the image is defaulted to operate with N-level parameters, namely a normal image effect, the lower layer of the image alternately operates with the parameters in the optimal visual field range at a fixed speed, the edge visual cells are strengthened and stimulated through local images in a normal environment, the edge visual field effect is strengthened, and meanwhile, the flicker light point stimulation is added on the periphery of the edge boundary of the maximum visual field.
Step six, training end period:
after the training period, if the eyeball motion monitoring unit does not feed back that the patient has visual field movement and the patient shows that the patient sees stroboscopic light spots, in order to improve the appearance effect, the training is continued until the patient can see the whole light spot motion track, at the moment, the training period is ended, the current optimal visual field state is measured again, and the second training is repeatedly carried out;
if the patient shows a stroboscopic point beyond the identified training period, the default training is terminated and the result is used as the best result for improvement of the tubular field.
In the second step, the image processing unit receives the picture of the image acquisition unit and sets a processing mode from 1 to 11 stages, wherein each stage corresponds to different contrast, brightness and saturation parameters, and the brightness contrast adjustment of the image belongs to the gray scale linear transformation of the image; the formula is as follows:
y＝[x-127.5*(1-α)]*β+127.5*(1+α)；
x is the pixel value before adjustment;
y is the adjusted pixel value;
alpha is the brightness setting level of the processing mode, the values are [ -100%, 100% ], 0 is the default state, and the interval of each level is set to 20%;
beta is the contrast setting level of the processing mode, the values are [ -100%, 100% ], 0 is the default state, and the interval of each level is set to 20%;
and taking 6 levels as default original images, setting default parameters to be consistent with the external environment according to the parameters based on the images acquired by the image acquisition system.
In the second step, the image processing unit receives the frame of the image capturing unit and sets a processing mode of 11 stages from 1 to 11, where each stage corresponds to different contrast, brightness and saturation parameters, and the brightness contrast adjustment of the image belongs to the formula of gray scale linear transformation of the image:
y＝[x-127.5*(1-α)]*β+127.5*(1+α)；
x is the pixel value before adjustment;
y is the adjusted pixel value;
alpha is the brightness setting level of the processing mode, the values are [ -100%, 100% ], 0 is the default state, and the interval of each level is set to 20%;
beta is the contrast setting level of the processing mode, the values are [ -100%, 100% ], 0 is the default state, and the interval of each level is set to 20%;
when α is 0: y ═ x-127.5 × β + 127.5; only the contrast is adjusted at this time;
when β is 1, y is x +255 α; only the brightness is adjusted at this time.
The saturation adjustment is related to the real-time brightness of the image, and the brightness value of each frame of image needs to be calculated in real time, and the method is as follows:
L＝0.5*[xmax(R,G,B)+xmin(R,G,B)]
s’＝s×γ
wherein:
xmax(R, G, B) is the maximum value of RGB of the image pixel point;
xmin(R, G, B) is the minimum value of RGB of the image pixel point;
l is a screen brightness value;
s represents the pre-adjustment image saturation;
s' represents the adjusted image saturation;
gamma is a saturation setting level of the processing mode, a value is [0,200% ], 100% is a default state, and each level interval is set to be 20%;
and taking 6 levels as default original images, setting default parameters to be consistent with the external environment according to the parameters based on the images acquired by the image acquisition system.
In the aforementioned method for training visual field extension based on glaucoma advanced disease, in step five, the visual field is trained:
after the parameters are compared with the outline, the method enters a training mode, the middle fixation point guide function is also kept in the training mode, the upper layer of the image is defaulted to operate with 6-level parameters, namely a normal image effect, the lower layer of the image alternately operates with the parameters in the optimal visual field range at a fixed speed, the edge visual cells are strengthened and stimulated through local images in a normal environment, the edge visual field effect is strengthened, meanwhile, the stimulation of flashing light spots is added to the periphery of the edge boundary of the maximum visual field, the flashing frequency is set to be 2 periods per second with one period of brightness and one period of darkness.
The invention has the advantages that:
the invention trains and optimizes the visual field with the missing edge through the electronic image training equipment, stimulates the visual cells of the brain by setting different image parameters and superposing the training method of the motion stroboscopic point, assists in recovering the damaged visual function cells, and effectively helps the patient with the tubular visual field in the late stage of glaucoma to realize the improvement of partial visual field through a certain period of training;
the method of the invention can avoid operation and medicine and reduce the pain of the patient.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a default parameter double profile overlay in the training method of the present invention;
FIG. 2 is a schematic view of one embodiment of defective visual field parameter activation in the training method of the present invention;
fig. 3 is a schematic diagram of an embodiment of a blinking light point stimulation in the training method of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the embodiments.
A glaucoma late stage condition based visual field extension training device comprising: the device comprises an image acquisition unit, an image processing unit, an image display unit, an information input control unit and an eyeball movement monitoring unit, wherein the image processing unit is used for receiving the information of the image acquisition unit and carrying training method software, the image display unit is used for receiving the information of the image processing unit, the information input control unit is connected with the image processing unit, and the eyeball movement monitoring unit is connected with the image processing unit.
The image acquisition unit includes: a camera or an external video input source; the image processing unit includes: a mobile phone processor or a computer processor; the image display unit includes: cell-phone screen, display screen, intelligent glasses. The eye movement monitoring unit comprises: the camera, the infrared sensor and the eye movement sensor; preferably, the eye movement sensor is EYELINK series product EYELINK II. The information input control unit includes: the system comprises a key controller, an input controller, a gesture controller and a voice controller, wherein as an embodiment, the input controller adopts a screen input method to input instructions, a Hanvon handwriting pad and the like can be selected, the gesture controller adopts a millet Ez More controller, and the voice controller can adopt a Tianmao eidolon or a millet sound box similar product. It should be noted that: the above are not exhaustive, as long as hardware capable of carrying software of the system can be used in the field of view extension training device, and the specific model is not limited, and is not illustrated here.
A visual field extension training method based on glaucoma late-stage disease, comprising the following steps:
step one, training preparation work:
one eye is shielded, one eye is trained independently, the vision center of a patient is guided to be fixed at a screen fixation point of an image display unit, and picture contents are input through an image acquisition unit;
in the process, one eye is trained independently each time, and the other eye is shielded by a shade or a shading lens, so that the condition that two eyes have the same vision is avoided; setting the distance between the patient and the display unit to be 1 m, preferentially acquiring the visual field range of the patient, ensuring that the picture content of the image display unit can cover the residual visual field of the whole patient, and guiding the vision center of the patient to be fixed at the screen fixation point of the image display unit; the picture content is input through the image acquisition unit, and the main function is to train the image to be guaranteed in the general environment where the patient is located.
Step two, setting a multi-stage processing mode and default parameters:
the image processing unit receives the picture of the image acquisition unit and sets a multi-stage processing mode, each stage corresponds to different contrast, brightness and saturation parameters, and the brightness contrast adjustment of the image belongs to the gray level linear transformation of the image; taking N levels as default original images, setting default parameters to be consistent with the external environment according to the parameters based on the images acquired by the image acquisition system;
preferably, the image processing system sets a processing mode of 11 stages from 1 to 11, and takes 6 stages as default original images; the particular number of stages is merely preferred and other stages may be operated within the scope of the invention.
The details are as follows:
the image processing unit receives the picture of the image acquisition unit, and sets a processing mode of 11 stages from 1 to 11, wherein each stage corresponds to different contrast, brightness and saturation parameters, and the brightness contrast adjustment of the image belongs to the formula of gray scale linear transformation of the image as follows:
y＝[x-127.5*(1-α)]*β+127.5*(1+α)；
x is the pixel value before adjustment;
y is the adjusted pixel value;
alpha is the brightness setting level of the processing mode, the values are [ -100%, 100% ], 0 is the default state, and the interval of each level is set to 20%;
beta is the contrast setting level of the processing mode, the values are [ -100%, 100% ], 0 is the default state, and the interval of each level is set to 20%;
when α is 0: y ═ x-127.5 × β + 127.5; only the contrast is adjusted at this time;
when β is 1, y is x +255 α; only the brightness is adjusted at this time.
The saturation adjustment is related to the real-time brightness of the image, and the brightness value of each frame of image needs to be calculated in real time, and the method is as follows:
L＝0.5*[xmax(R,G,B)+xmin(R,G,B)]
s’＝s×γ
wherein:
xmax(R, G, B) is the maximum value of RGB of the image pixel point;
xmin(R, G, B) is the minimum value of RGB of the image pixel point;
l is a screen brightness value;
s represents the pre-adjustment image saturation;
s' represents the adjusted image saturation;
gamma is a saturation setting level of the processing mode, a value is [0,200% ], 100% is a default state, and each level interval is set to be 20%;
and taking 6 levels as default original images, setting default parameters to be consistent with the external environment according to the parameters based on the images acquired by the image acquisition system.
Step three, finding out edges of each level:
guiding a patient to draw the visual field edge by default image display parameters, feeding back data by an eyeball motion monitoring unit through a visual field drawing method under a planar visual field image, ensuring that the sight center of the patient falls on a screen fixation point of an image display unit, automatically drawing boundary images of all angles in the system, recording the boundary images as N-level edges, and repeatedly recording the edges of all levels in the system under all image processing modes; preferably, N is 6, which is recorded as a 6-level edge.
Step four, finding out the defect part:
as shown in fig. 1 and 2, the image processing unit automatically compares the multi-level edge contour maps, regards the reduced part of the field of view as a default field of view range map, places the default field of view range map on the upper layer, places the maximum field of view range map on the lower layer, and forms a defect part after stacking the upper layer and the lower layer;
step five, training a visual field:
after the parameters are compared with the outline, the method enters a training mode, the middle fixation point guide function is also kept in the training mode, the upper layer of the image is defaulted to operate with N-level parameters, namely a normal image effect, the lower layer of the image alternately operates with the parameters in the optimal visual field range at a fixed speed, the edge visual cells are strengthened and stimulated through local images in a normal environment, the edge visual field effect is strengthened, and meanwhile, the flicker light point stimulation is added to the periphery of the edge boundary with the maximum visual field, as shown in fig. 3. As a preference, the specific method of the stimulation of the flash spot is as follows: the flicker frequency is set to 2 cycles per second with one period of light and one period of dark.
Step six, training end period:
after the training period, if the eyeball motion monitoring unit does not feed back that the patient has visual field movement and the patient shows that the patient sees stroboscopic light spots, in order to improve the appearance effect, the training is continued until the patient can see the whole light spot motion track, at the moment, the training period is ended, the current optimal visual field state is measured again, and the second training is repeatedly carried out;
if the patient shows a stroboscopic point beyond the identified training period, the default training is terminated and the result is used as the best result for improvement of the tubular field.
The following examples demonstrate that the present invention is indeed effective in assisting patients with advanced glaucomatous tubular fields to perform partial visual field enhancement:
a training method for an edge visual field based on invisible left-eye vision and 8-degree residual left visual field center range of a right-eye visual field is specifically implemented as follows:
and displaying the image at a position 1 m in front of the eyes of the patient, measuring the residual visual field profile of the patient in an initial state, wherein the position of the maximum visual field range is 10 degrees at the lower left corner, and the position of the minimum visual field range is 8 degrees at the upper left corner. After the test is started, preferentially increasing the processing mode to 11 levels step by step, gradually increasing the contrast, brightness and saturation, recording the visible visual field edge of the patient under each level state, wherein the optimal level of the visual field under strong parameters is 8 levels, the left lower corner of the maximum visual field range under the current level is 12 degrees, and the left upper corner of the minimum visual field range is 9 degrees; restoring the treatment mode to an initial state, maintaining for 5 minutes, then adjusting the treatment mode downwards to level 1, recording the visible visual field edge of the patient under weak parameters, recording the optimal visual field level 7, wherein the lower right corner of the maximum visual field range is 12 degrees and the upper left corner of the minimum visual field range is 9 degrees under the current level; and 7 levels are selected as an optimal processing mode for training, the processor automatically generates a motion track according to the visual field range, the motion speed is 5mm/s, the light spot flicker frequency is 2 times/s, the visual field center offset occurs midway, and the light spot motion is suspended. After 3 months of training treatment, the residual visual field range of the patient is tested, the maximum visual field point is increased to 12 degrees, and the minimum visual field point is increased to 10 degrees.
The invention optimizes the visual field with missing edges through the training of electronic image training equipment, stimulates the visual cells of the brain through the method of setting different image parameters and then superposing the training of the motion stroboscopic light points, assists in recovering the damaged visual function cells, and the embodiment also verifies that the invention can effectively help the patient with tubular visual field at the late stage of glaucoma to realize the improvement of partial visual field; the method of the invention can avoid operation and medicine and reduce the pain of the patient.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It should be understood by those skilled in the art that the above embodiments do not limit the present invention in any way, and all technical solutions obtained by using equivalent alternatives or equivalent variations fall within the scope of the present invention.
Claims (10)
1. A visual field extension training device based on advanced glaucoma conditions, comprising: the device comprises an image acquisition unit, an image processing unit, an image display unit, an information input control unit and an eyeball movement monitoring unit, wherein the image processing unit is used for receiving the information of the image acquisition unit and carrying training method software, the image display unit is used for receiving the information of the image processing unit, the information input control unit is connected with the image processing unit, and the eyeball movement monitoring unit is connected with the image processing unit.
2. A field of vision extension training device based on advanced glaucoma conditions according to claim 1, wherein the image acquisition unit comprises: a camera or an external video input source.
3. The field of vision extension training device based on glaucoma late stage disease of claim 1, wherein the image processing unit comprises a cell phone processor or a computer processor.
4. The visual field extension training device based on glaucoma late stage disease according to claim 1, wherein the image display unit includes: cell-phone screen, display screen, intelligent glasses.
5. The visual field extension training device based on glaucoma late stage disease according to claim 1, wherein the information input control unit includes: the device comprises a key controller, an input controller, a gesture controller and a voice controller.
6. A field of vision extension training device based on glaucoma late stage conditions according to claim 1, wherein the eye movement monitoring unit comprises: camera, infrared sensor, eye movement sensor.
7. A visual field extension training method based on glaucoma late stage disease, comprising the steps of:
step one, training preparation work:
one eye is shielded, one eye is trained independently, the vision center of a patient is guided to be fixed at a screen fixation point of an image display unit, and picture contents are input through an image acquisition unit;
step two, setting a multi-stage processing mode and default parameters:
the image processing unit receives the picture of the image acquisition unit and sets a multi-stage processing mode, each stage corresponds to different contrast, brightness and saturation parameters, and the brightness contrast adjustment of the image belongs to the gray level linear transformation of the image; taking N levels as default original images, setting default parameters to be consistent with the external environment according to the parameters based on the images acquired by the image acquisition system;
step three, finding out edges of each level:
guiding a patient to draw the visual field edge by default image display parameters, feeding back data by an eyeball motion monitoring unit through a visual field drawing method under a planar visual field image, ensuring that the sight center of the patient falls on a screen fixation point of an image display unit, automatically drawing boundary images of all angles in the system, recording the boundary images as N-level edges, and repeatedly recording the edges of all levels in the system under all image processing modes;
step four, finding out the defect part:
the image processing unit automatically compares the multi-level edge contour maps, considers the vision field of the reduced part as a default vision field range map, places the default vision field range map on the upper layer, places the maximum vision field range map on the lower layer, and forms a defect part after the upper layer and the lower layer are overlapped;
step five, training a visual field:
after the parameters are compared with the outline, the method enters a training mode, the middle fixation point guide function is also kept in the training mode, the upper layer of the image is defaulted to operate with N-level parameters, namely a normal image effect, the lower layer of the image alternately operates with the parameters in the optimal visual field range at a fixed speed, the edge visual cells are strengthened and stimulated through local images in a normal environment, the edge visual field effect is strengthened, and meanwhile, the flicker light point stimulation is added on the periphery of the edge boundary of the maximum visual field.
Step six, training end period:
after the training period, if the eyeball motion monitoring unit does not feed back that the patient has visual field movement and the patient shows that the patient sees stroboscopic light spots, in order to improve the appearance effect, the training is continued until the patient can see the whole light spot motion track, at the moment, the training period is ended, the current optimal visual field state is measured again, and the second training is repeatedly carried out;
if the patient shows a stroboscopic point beyond the identified training period, the default training is terminated and the result is used as the best result for improvement of the tubular field.
8. The visual field expanding training method based on the advanced glaucoma disease of claim 7, wherein in the second step, the image processing unit receives the picture of the image acquisition unit and sets a processing mode from 1 to 11 stages, each stage corresponds to different contrast, brightness and saturation parameters, wherein the brightness contrast adjustment of the image belongs to the gray scale linear transformation of the image; the formula is as follows:
y＝[x-127.5*(1-α)]*β+127.5*(1+α)；
x is the pixel value before adjustment;
y is the adjusted pixel value;
alpha is the brightness setting level of the processing mode, the values are [ -100%, 100% ], 0 is the default state, and the interval of each level is set to 20%;
beta is the contrast setting level of the processing mode, the values are [ -100%, 100% ], 0 is the default state, and the interval of each level is set to 20%;
and taking 6 levels as default original images, setting default parameters to be consistent with the external environment according to the parameters based on the images acquired by the image acquisition system.
9. The visual field extension training method based on glaucoma late stage disease of claim 7, wherein in the second step, the image processing unit receives the frame of the image capturing unit and sets a processing mode from 1 to 11 stages, each stage corresponds to different contrast, brightness and saturation parameters, wherein the brightness contrast adjustment of the image belongs to the gray scale linear transformation of the image with the following formula:
y＝[x-127.5*(1-α)]*β+127.5*(1+α)；
x is the pixel value before adjustment;
y is the adjusted pixel value;
alpha is the brightness setting level of the processing mode, the values are [ -100%, 100% ], 0 is the default state, and the interval of each level is set to 20%;
beta is the contrast setting level of the processing mode, the values are [ -100%, 100% ], 0 is the default state, and the interval of each level is set to 20%;
when α is 0: y ═ x-127.5 × β + 127.5; only the contrast is adjusted at this time;
when β is 1, y is x +255 α; only the brightness is adjusted at this time.
The saturation adjustment is related to the real-time brightness of the image, and the brightness value of each frame of image needs to be calculated in real time, and the method is as follows:
L＝0.5*[xmax(R,G,B)+xmin(R,G,B)]
s’＝s×γ
wherein:
xmax(R, G, B) is the maximum RGB of the image pixel pointA value;
xmin(R, G, B) is the minimum value of RGB of the image pixel point;
l is a screen brightness value;
s represents the pre-adjustment image saturation;
s' represents the adjusted image saturation;
gamma is a saturation setting level of the processing mode, a value is [0,200% ], 100% is a default state, and each level interval is set to be 20%;
and taking 6 levels as default original images, setting default parameters to be consistent with the external environment according to the parameters based on the images acquired by the image acquisition system.
10. A visual field extension training method based on advanced glaucoma disease according to claim 7, wherein in step five, the visual field is trained:
after the parameters are compared with the outline, the method enters a training mode, the middle fixation point guide function is also kept in the training mode, the upper layer of the image is defaulted to operate with 6-level parameters, namely a normal image effect, the lower layer of the image alternately operates with the parameters in the optimal visual field range at a fixed speed, the edge visual cells are strengthened and stimulated through local images in a normal environment, the edge visual field effect is strengthened, meanwhile, the stimulation of flashing light spots is added to the periphery of the edge boundary of the maximum visual field, the flashing frequency is set to be 2 periods per second with one period of brightness and one period of darkness.
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