CN111325109A - Attention training method - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/18—Eye characteristics, e.g. of the iris
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Abstract
The invention provides an attention training method, S1, collecting human face image information of a subject, and positioning the center of the iris of a human eye according to the human face image, thereby obtaining the eye movement score of the subject; s2, evaluating the attention score of the subject; s3, comparing and analyzing the real-time eye movement score and attention score of the subject with corresponding normal data in a database, and intelligently pushing an optimal training scheme by utilizing a DQN network algorithm. The attention level data is acquired based on a CMA theoretical attention clinical model, the attention of ADHD children is digitally evaluated by utilizing the advantages of simple configuration requirements, low cost and intelligent operation of AI algorithm of the sight tracking technology, and the DQN NETWORK algorithm is utilized to realize intelligent push of related attention training guidance and scheme, so that the problem of family attention rehabilitation training under the condition of lack of scientific guidance in the current market is solved, and a more scientific and accurate family attention rehabilitation evaluation and training system is provided for the ADHD children.
Description
Technical Field
The invention relates to the technical field of internet, in particular to an attention training method.
Background
Attention Deficit Hyperactivity Disorder (ADHD), also known as Hyperactivity Disorder, is a common behavioral Disorder in childhood, and is clinically manifested mainly by features such as inattention, Hyperactivity, impulsion, etc., accompanied by deviations in cognition, emotion, behavior, etc., thereby seriously affecting learning and life of ADHD children, such as obvious learning difficulty, tension in relation with family and children of the same age, lack of self-esteem, dyskinesia, etc. The prevalence rate of ADHD of preschool children in China is 4.31% -5.83%, and recent data show that ADHD in childhood can be continued to adolescence or even adulthood, and obvious symptoms still exist in 30% -70% of ADHD in childhood to adulthood, which can influence daily life and work. Therefore, scientific and accurate early screening and evaluation of ADHD children attention disorders and timely intervention training are particularly important. At present, the traditional attention rehabilitation assessment mainly takes scale assessment and computer-aided assessment as main points, the limitations of individual subjectivity of an evaluator and diagnosis and treatment experience of a clinician exist in the scale assessment, the assessment needs to be carried out by a professional rehabilitation therapist or a doctor, and the assessment places mainly comprise hospitals, rehabilitation medical institutions and special education institutions.
Attention is a complex cognitive process that has been intensively studied by scholars from a theoretical and clinical point of view. Currently, more classical and widely used is the Clinical Model of Attention (CMA), in which attention is divided into five dimensions: 1) focus 2) continuous attention 3) alternative attention 4) alternative attention 5) attention. The CMA theoretical model is closely related to daily activities of individuals, and has proved to be practical in the aspect of evaluating attention of different pathological types, so that the CMA theoretical model is quite suitable for being used as a guiding theory of computer-aided attention rehabilitation.
The sight tracking method is an eye movement tracking technology which adopts a common camera as input equipment, does not need an infrared transmitting device, does not need a user to wear any device, and utilizes a designed algorithm to track the movement of eyeballs so as to obtain the gazing direction, gazing time, gazing point and other indexes of the eyeballs.
In the aspects of eye movement tracking technology and attention rehabilitation, the attention rehabilitation system combined with the sight tracking technology is not found at present through searching related research contents. Therefore, the application of the sight tracking technology and the clinically practical attention clinical model has great significance for the careful research of all the levels and all the dimensions of attention. The DQN network algorithm can provide technical support for building a large database, intelligently analyzing and comparing data and intelligently pushing a training scheme and guidance.
There are currently some computer-aided attention assessment or training systems:
patent CN109350907, "training method and system for attention deficit hyperactivity disorder of children based on virtual reality", proposes a detection method for ADHD, which utilizes virtual reality technology and audio-visual integration continuous testing technology to detect ADHD of a subject in a game manner, wherein the test execution process needs to continuously collect brain wave signals of the subject, and determines whether the subject is in a state of attention concentration according to the criteria of the brain wave signals. However, the virtual reality technology adopted by the system needs a larger space and a complex connecting line, the use is inconvenient, the electroencephalogram signal acquisition device can cause discomfort to users in the measurement process, and then patients can easily feel fatigue after long-time use of virtual reality training, and even nausea, dizziness and other adverse reactions occur.
Patent CN109646022 "child attention assessment system and method thereof" proposes an attention assessment model based on electroencephalogram, which is fixed on the forehead of a user by a single electrode to collect the forehead electroencephalogram of the child and acquire the original attention data, and process and analyze the same. The single electrode used by the system is worn on the forehead of the subject, so that the discomfort of the patient is increased, the experience is reduced, and the measurement error can be caused by the rotation of the head of the patient in the measurement process.
Patent CN108665976 "a children concentration force evaluation system and method" proposes a system that uses a computer system to set an input unit, a test unit, an evaluation unit, a recording unit, etc. to perform on-line and off-line tests of concentration force on a subject, and evaluate auditory perception and visual perception, and finally perform frequency analysis by using a descriptive statistical method, and generate an evaluation report. However, the system takes 30-40 minutes to complete all tests, and is difficult for a subject with attention deficit to maintain a long-term attention concentration state; the system relies on a computer, needs a readable storage medium containing a specific computer program, is inconvenient to carry, has a complex use process, and cannot provide family training for the subjects with attention deficiency.
The offline rehabilitation training mainly adopts the biofeedback therapy and the behavior correction therapy. The behavior correction therapy mainly adopts a one-to-one guiding training mode, needs the monitoring and guidance of a professional doctor or therapist, and can cause the shortage of the rehabilitation medical resources of children and the waste of the resources. The existing computer aided training system is mainly oriented to hospitals or special education institutions, can provide various types of aided training for therapists, but is expensive and popular, and training places are mostly limited to hospitals or institutions and cannot provide family training and guidance for ADHD children.
With the continuous development of science and technology, computer-aided training is the development trend of ADHD children rehabilitation, and methods mainly based on computer-aided therapy such as brain-computer interfaces, virtual reality technologies and the like are gradually applied in the field of ADHD treatment, so that the defects of the original treatment method are overcome.
The brain-computer interface takes signals from the brain, analyzes and converts them into commands, and converts them into signals from peripheral devices to provide the desired output. A preliminary assessment of ADHD children may be achieved by a specific evoked potential. The brain-computer interface is used as a signal acquisition and signal processing technology, the transmission efficiency of hardware is not high, and problems of information lag, overlong response time and the like can occur in practical application, so that the user experience is influenced. The feedback devices added to the feedback system will greatly increase the complexity of the system, affect the transmission rate of the system, and reduce the user experience due to complex operations.
At present, special attention rehabilitation systems in the market are relatively lacked, and the existing systems mostly use a cognitive or speech rehabilitation system as a carrier and serve as one of the modules in the systems. Taking the attention training evaluation system sold by Guangzhou Sankang medical equipment Co., Ltd as an example, the system is a built-in module of a COGNI speech cognition training system.
Disclosure of Invention
In view of the background, the present invention provides a child attention training method that combines a gaze tracking device, is simple, does not require an external device to be attached to a body part of a child, and can evaluate and train the child in a most natural state in real time, so that the result is more objective and accurate.
In order to achieve the purpose, the invention adopts the following scheme:
an attention training method comprising the steps of:
s1, acquiring the face image information of the subject, and positioning the iris center of the human eye according to the face image so as to obtain the eye movement score of the subject;
s2, evaluating the attention score of the subject;
s3, comparing and analyzing the real-time eye movement score and attention score of the subject with corresponding normal data in a database, and intelligently pushing an optimal training scheme by utilizing a DQN network algorithm.
Preferably, the S1 specifically includes:
step 1, collecting face image information;
step 2, detecting the position of a face frame;
step 3, calculating human face characteristic points by using a human face alignment algorithm to obtain an eye region image;
step 4, iris center detection is carried out on the obtained eye region image, gray level difference on an iris image ring is calculated through a calculus operator, then the maximum value is obtained from all difference results, and the center of the iris of the human eye is accurately positioned;
and 5: iris center coordinate positioning
and 6, acquiring eye movement data information including a fixation point, fixation time, fixation times and time from fixation to a stimulation source point for the first time, generating eye movement scores, and transmitting the eye movement scores to the attention evaluation module and the intelligent operation and scheme pushing module in real time.
Preferably, the method for calculating the eye movement score includes: the staying time of the eyes of the testee at the stimulation source point is counted as a fraction a1, the times of watching the stimulation source point by the testee before completing the task are counted as a fraction a2, the time of watching the stimulation source point by the testee for the first time is counted as a fraction a3, and the fractions a1, a2 and a3 are added to obtain the eye movement fraction A.
Preferably, in step 1, the face image information is acquired by a single camera without a light source.
Preferably, the S1 specifically includes: displaying one or more preset visual stimuli to the subject, outputting voice prompts of tasks needing to be completed according to the visual stimuli to the subject, completing the corresponding tasks according to the voice prompts by the subject, and generating corresponding attention scores according to the task completion degree and time.
Preferably, the attention score comprises six subtask scores:
concentration attention score: selecting specific numbers or characters or symbols from randomly arranged stimulation sources of the same type within a limited time;
sustained attention score: scratching out as many specific targets as possible in randomly arranged stimulation sources of the same type within a limited time;
selective attention score: selecting a specific target object from a plurality of different types of stimulation sources which are randomly arranged in a limited time;
alternative attention scores: selecting specific target objects alternately according to voice prompts in two types of stimulation sources which are randomly arranged in a limited time;
assigned attention score: selecting a specific target object from randomly arranged stimulation sources of the same type within a limited time, and hooking in a space if a specific syllable is heard in the process of a task;
conner parental symptoms questionnaire: the test is completed by one of father and mother, and the questionnaire scores of 6 factors of the index of the walking problem, the learning problem, the psychosomatic problem, the impulsive-hyperactivity, the anxiety and the hyperactivity of the completer are obtained according to the scores.
Preferably, the intelligent push optimal training scheme using the DQN network algorithm in S3 includes: continuously extracting data characteristics from the database for learning, and obtaining experience and knowledge through a large amount of data extraction and learning to realize selection and matching of training schemes; and automatically matching and adjusting the difficulty and grade of the next task according to the task completion condition of the subject, giving a corresponding voice prompt, and carrying out special training on the project pushing scheme with a low score.
Preferably, after S3, the method further includes:
and S4, receiving the eye movement scores, the attention scores and the intelligent training data, and uploading the data to the database.
Preferably, after S4, the method further includes:
s5, opening the history data of all users in the database to the operator and/or receiving the information of the specific user.
Preferably, the operation party is a doctor or therapist; the specific user information comprises user information selected according to preset sending standards and user score conditions.
In summary, the system of the present invention includes the following steps:
(1) after logging in the system, the user inputs basic information such as name, age, symptom duration and the like
(2) Tracking the coordinate information of the iris center of the subject in real time in the whole process, acquiring eye movement data information comprising fixation time, fixation times, the distance between a fixation point and a stimulus source and the like, and generating an eye movement score;
(3) giving the subject a visual stimulus, and obtaining a corresponding attention score according to the task completion condition of the subject and the Conner parental symptom questionnaire result;
(4) analyzing and comparing the evaluation data, automatically generating an optimal training scheme, and intelligently adjusting the next training scheme according to the eye movement information and the task completion condition in the training process;
(5) storing data and uploading the data to a database, expanding the capacity of the normal database, and directly calling historical data to continue a next training scheme without re-evaluation when the same user uses the same user again;
(6) and selectively sending user data information to a doctor port background according to the user score, and reminding a doctor or therapist to give suggestions and guidance within 2 working days.
Preferably, the specific user information comprises that the system selectively transmits the user information (such as user information with lower score, a + B + C + D + E + F + G < XX) to the doctor port background according to the set transmission standard and the user score condition, and reminds the doctor or therapist to give advice and guidance within 2 working days.
Compared with the prior art, the invention has the following advantages:
(1) the invention does not need to adopt a plurality of cameras or infrared light sources, does not need to calibrate the cameras in configuration, has simple system structure and can reduce the requirements on hardware or system configuration;
(2) the invention can collect the real-time eye movement data of the testee, and the attention level of the testee is evaluated by combining a clinical practical CMA theoretical attention clinical model, so that the accuracy is higher;
(3) the invention does not need any external equipment or invasive devices, has simple and safe operation, and is accompanied by parents of ADHD children to finish or independently finish attention rehabilitation training, thereby really realizing intelligent attention family rehabilitation training;
(4) based on different attention levels of different users, the data comparison is carried out by utilizing the database normals, and an intelligent pushing scheme is adopted, so that intelligent and personalized training is provided;
(5) according to the system, user data information is selectively sent to the doctor port background according to the sending standard and the user score set by the system, and a doctor or therapist gives suggestions and guidance opinions within a limited time, so that a more scientific training scheme can be provided for attention rehabilitation training;
in conclusion, the attention level data are acquired based on a CMA theoretical attention clinical model, attention of ADHD children is evaluated digitally by utilizing the advantages of simple requirements of sight tracking technology configuration, low cost and intelligent operation of AI algorithm, and the DQN NETWORK algorithm is used for realizing intelligent push of related attention training guidance and scheme, so that the problem of family attention rehabilitation training under the condition that scientific guidance is lacked in the current market is solved, and a more scientific and accurate family attention rehabilitation evaluation and training system is provided for the ADHD children.
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FIG. 1 is a flowchart illustrating an implementation process of an attention training method according to an embodiment of the present invention.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings.
The embodiment provides an ADHD children attention training method by using gaze tracking and DQN network algorithm, which comprises the following steps:
s1, acquiring the face image information of the subject, and positioning the iris center of the human eye according to the face image so as to obtain the eye movement score of the subject;
s2, evaluating the attention score of the subject;
s3, comparing and analyzing the real-time eye movement score and attention score of the subject with corresponding normal data in a database, and intelligently pushing an optimal training scheme by utilizing a DQN network algorithm.
The specific implementation steps of S1 are as follows:
step 1, acquiring face image information through a common single camera without a light source;
step 2, detecting the position of a face frame by using an Adaboost cascade algorithm;
step 3, calculating human face characteristic points by using a human face alignment algorithm (SDM) to obtain an eye region image; (requirement: the angle is less than 30 degrees when the head is basically kept still or slightly rotated)
Step 4, iris center detection is carried out on the obtained eye region image, gray level difference on an iris image ring is calculated through a calculus operator (Daugman algorithm), then the maximum value is obtained from all difference results, and the center of the iris of the human eye is accurately positioned; (requirement: good illumination condition, avoiding using template matching method to perform coarse positioning, and similar effect)
And 5: iris center coordinate positioning
The functions and methods of the gaze tracking module described above are derived from: the publication date is 2019.06 for Chinese image graphic newspaper, which is a sight tracking method using geometric features of human eyes.
The eye movement data information including the fixation point, the fixation time, the fixation times and the time from the first fixation to the stimulation source point is obtained through the steps, and the eye movement score is generated. The eye movement score comprises a fraction a1 of stay time of a stimulation source point of a subject, a fraction a2 of times of staring at the stimulation source point before the subject completes a task, a fraction a3 of time of first staring at the stimulation source point of the subject, and the fractions a1, a2 and a3 are added to obtain the eye movement score A (a1+ a2+ a3 ═ A).
The S2 is based on the CMA theory, which is a widely used and more classical clinical model of attention in clinical practice, and the assessment includes six subtasks, i.e., concentration, continuous attention, selective attention, alternative attention, distributed attention, and Conners' Parental Symptoms Questionnaire (PSQ).
One or more preset visual stimuli are displayed through a computer or a flat panel display screen, a voice prompt which is required to be completed according to the visual stimuli is given to the subject, and the subject is required to complete corresponding tasks according to the prompt. According to the task completion degree and time, the system automatically generates a corresponding attention score; the six subtask scores include:
① focused attention score B selecting specific numbers or words or symbols in randomly arranged same type of stimulus sources within a defined time;
② sustained attention score C, scratching out as many specific targets as possible in randomly arranged stimuli of the same type within a defined time;
③ selective attention score D selecting a specific target object among a plurality of different types of stimuli randomly arranged within a defined time;
④ alternating attention score E, selecting specific target object alternately according to voice prompt in two types of stimulation sources arranged randomly in a limited time;
⑤ assigned attention score F, selecting specific target object from randomly arranged same type of stimulus sources within a limited time, and hooking in the space if specific syllable is heard during the task;
⑥ wherein the Conner Parental Symptoms Questionnaire (PSQ) comprises 48 entries, completed by either the father or the mother of the child subject, and based on the parental scores, a questionnaire score G of 6 factors is derived for the performance questions, learning questions, psychophysical questions, impulsion-hyperactivity, anxiety and hyperactivity.
After the eye movement score and the attention score are obtained, the operation of S3 is specifically:
receiving real-time data of eye movement scores and attention scores, comparing and analyzing evaluation scores (A-G) of a subject with normative data in a database, and intelligently pushing an optimal training scheme by utilizing a DQN network algorithm; the DQN network algorithm continuously extracts data characteristics from the database for learning, and the module learns experience and knowledge through a large amount of data extraction and learning so as to realize selection and matching of a training scheme; according to the completion condition of a subject in a task, the difficulty and the grade of the next task are automatically matched and adjusted, corresponding voice prompt is given, and special training is carried out on a project pushing scheme with a low score, for example, the distance score a3 between a subject fixation point and a stimulation source point is lower than that of normal data.
After S3, an uploading step of S4 is further included: receiving data of sight tracking, attention assessment and intelligent training, uploading the data to a database, and expanding the capacity of the database, wherein the data can comprise basic data of attention levels of normal children and children with different degrees of ADHD; the historical data of each evaluation and training of the user will be stored in the profile of the subject, and the same user can directly call up the historical data.
After S4, the method may further include S5 steps of evaluating and feeding back: when logging in the system through a specific account (namely, a doctor end), an evaluation and feedback entry or interface can be displayed (the module is hidden when other common users log in, and the entry is not displayed), and a doctor or therapist can directly view historical data of all users in the data storage module through a doctor port; and selectively sending the information of the user (such as the information of the user with lower score, A + B + C + D + E + F + G < XX) to the doctor port background according to the set sending standard and the user score condition, and reminding the doctor or therapist to give advice and guidance within 2 working days.
In summary, the method of the present invention includes the following steps:
(1) after logging in the system, the user inputs basic information such as name, age, symptom duration and the like
(2) Tracking the coordinate information of the iris center of the subject in real time in the whole process, acquiring eye movement data information comprising fixation time, fixation times, the distance between a fixation point and a stimulus source and the like, and generating an eye movement score;
(3) giving the subject a visual stimulus, and obtaining a corresponding attention score according to the task completion condition of the subject and the Conner parental symptom questionnaire result;
(4) analyzing and comparing the evaluation data, automatically generating an optimal training scheme, and intelligently adjusting the next training scheme according to the eye movement information and the task completion condition in the training process;
(5) storing data and uploading the data to a database, expanding the capacity of the normal database, and directly calling historical data to continue a next training scheme without re-evaluation when the same user uses the same user again;
(6) and selectively sending user data information to a doctor port background according to the user score, and reminding a doctor or therapist to give suggestions and guidance within 2 working days.
The attention training method of the invention quantifies the evaluation and training indexes based on the attention clinical model and combining the existing sight tracking technology and the Deep Q-Network (DQN) algorithm, assigns the attention indexes by the sight tracking technology and the game scores actively participated by the user, can realize the objective quantification of the attention evaluation indexes, can specifically implement dynamic monitoring of the eyeball movement information of the children according to the characteristics of the children, can improve the accuracy and the scientificity of ADHD attention evaluation and training, can also provide scientific family training guidance for the ADHD children, and can relieve the tension of the rehabilitation medical resources of the children in China to a certain extent.
Claims (10)
1. An attention training method, characterized by:
s1, acquiring the face image information of the subject, and positioning the iris center of the human eye according to the face image so as to obtain the eye movement score of the subject;
s2, evaluating the attention score of the subject;
s3, comparing and analyzing the real-time eye movement score and attention score of the subject with corresponding normal data in a database, and intelligently pushing an optimal training scheme by utilizing a DQN network algorithm.
2. The method according to claim 1, wherein the S1 specifically includes:
step 1, collecting face image information;
step 2, detecting the position of a face frame;
step 3, calculating human face characteristic points by using a human face alignment algorithm to obtain an eye region image;
step 4, iris center detection is carried out on the obtained eye region image, gray level difference on an iris image ring is calculated through a calculus operator, then the maximum value is obtained from all difference results, and the center of the iris of the human eye is accurately positioned;
and 5: iris center coordinate positioning
and 6, acquiring eye movement data information including a fixation point, fixation time, fixation times and time from fixation to a stimulation source point for the first time, generating eye movement scores, and transmitting the eye movement scores to the attention evaluation module and the intelligent operation and scheme pushing module in real time.
3. The method of claim 2, wherein the eye movement score is calculated by: the staying time of the eyes of the testee at the stimulation source point is counted as a fraction a1, the times of watching the stimulation source point by the testee before completing the task are counted as a fraction a2, the time of watching the stimulation source point by the testee for the first time is counted as a fraction a3, and the fractions a1, a2 and a3 are added to obtain the eye movement fraction A.
4. The method according to claim 2, wherein in the step 1, the face image information is acquired by a single camera without a light source.
5. The method according to claim 1, wherein the S1 specifically includes: displaying one or more preset visual stimuli to the subject, outputting voice prompts of tasks needing to be completed according to the visual stimuli to the subject, completing the corresponding tasks according to the voice prompts by the subject, and generating corresponding attention scores according to the task completion degree and time.
6. The method of claim 5, wherein the attention score comprises six subtask scores:
concentration attention score: selecting specific numbers or characters or symbols from randomly arranged stimulation sources of the same type within a limited time;
sustained attention score: scratching out as many specific targets as possible in randomly arranged stimulation sources of the same type within a limited time;
selective attention score: selecting a specific target object from a plurality of different types of stimulation sources which are randomly arranged in a limited time;
alternative attention scores: selecting specific target objects alternately according to voice prompts in two types of stimulation sources which are randomly arranged in a limited time;
assigned attention score: selecting a specific target object from randomly arranged stimulation sources of the same type within a limited time, and hooking in a space if a specific syllable is heard in the process of a task;
conner parental symptoms questionnaire: the test is completed by one of father and mother, and the questionnaire scores of 6 factors of the index of the walking problem, the learning problem, the psychosomatic problem, the impulsive-hyperactivity, the anxiety and the hyperactivity of the completer are obtained according to the scores.
7. The method of claim 1, wherein said intelligently pushing an optimal training scheme using DQN network algorithm in S3 comprises: continuously extracting data characteristics from the database for learning, and obtaining experience and knowledge through a large amount of data extraction and learning to realize selection and matching of training schemes; and automatically matching and adjusting the difficulty and grade of the next task according to the task completion condition of the subject, giving a corresponding voice prompt, and carrying out special training on the project pushing scheme with a low score.
8. The method according to any one of claims 1 to 7, further comprising, after the S3:
and S4, receiving the eye movement scores, the attention scores and the intelligent training data, and uploading the data to the database.
9. The method according to claim 8, further comprising, after the S4:
s5, opening the history data of all users in the database to the operator and/or receiving the information of the specific user.
10. The method of claim 9, wherein the operator is a doctor or therapist; the specific user information comprises user information selected according to preset sending standards and user score conditions.
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| CN112168187A (en) * | 2020-09-29 | 2021-01-05 | 首都医科大学附属北京安定医院 | Diagnostic index, diagnostic model and diagnostic system for schizophrenia |
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| CN113476046A (en) * | 2021-08-20 | 2021-10-08 | 中国民航大学 | Psychological and kinetic ability evaluation method based on multi-target tracking paradigm |
| CN113663199A (en) * | 2021-08-17 | 2021-11-19 | 华南师范大学 | Human-computer interaction system based on eye movement information transmission and suitable for special children group |
| CN115097933A (en) * | 2022-06-13 | 2022-09-23 | 华能核能技术研究院有限公司 | Concentration determination method and device, computer equipment and storage medium |
| CN115920192A (en) * | 2023-02-06 | 2023-04-07 | 西南医科大学附属医院 | Special attention distribution capacity training system, operation method and storage medium |
| CN117253587A (en) * | 2023-11-17 | 2023-12-19 | 北京智精灵科技有限公司 | Attention training method and system based on air quality characteristics |
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