CN114847950A - Attention assessment and training system and method based on virtual reality and storage medium - Google Patents
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Abstract
The invention discloses an attention assessment and training system, method and storage medium based on virtual reality, wherein the system comprises a host, virtual reality equipment, electroencephalogram equipment, eye movement tracking equipment, electrocardio equipment, respiration sensing equipment and a cloud platform database; the main machine is provided with a test module, a data module, an evaluation module and a physiological feedback type intervention module, attention of a user is tested, after task performance data and neuro-physiological data in the test process are combined and analyzed and evaluated, an attention evaluation result of the user is automatically obtained, a difficulty matching training scheme is selected according to the attention evaluation result to be used for the user to conduct intervention training, and during the training process, neuro-physiological data of the user are fed back in real time to drive the user to adjust training response in a training scene in time so as to achieve a good intervention training effect. The invention organically combines attention assessment diagnosis and intervention training, is convenient and quick, and has no side effect of drugs.
Description
Technical Field
The invention relates to the technical field of attention VR rehabilitation, in particular to an attention assessment and training system and method based on virtual reality and a storage medium.
Background
Attention Deficit Hyperactivity Disorder (ADHD) is a disorder of neurodevelopmental dysfunction, also known as attention deficit disorder (first seen in the handbook of mental disorder diagnosis and statistics in 1968), attention deficit disorder (1980). ADHD usually occurs in childhood, but symptoms may persist into adolescence and possibly even into adulthood. Patients with hyperactivity have the characteristics of difficulty in maintaining attention, hyperactivity and impulsive behaviors and the like, and daily work, social behaviors and academic industry of the patients are affected.
According to the national education institution development statistics bulletin (2020), about 1700 thousands of patients with childhood hyperkinetic syndrome in the country have a diagnosis rate of less than 10%. On one hand, the evaluation and diagnosis of ADHD of children at present mainly depend on clinical scales and doctor diagnosis, the diagnosis process is slow and inconvenient, and the support of nerve physiological data such as electroencephalogram, eye movement and the like is lacked; on the other hand, the current ADHD treatment and rehabilitation mode for children mainly adopts drug treatment and assists few intervention training. However, there are significant side effects of drug therapy, such as affecting appetite, sleep and growth.
Therefore, the prior art has yet to be developed.
Disclosure of Invention
In view of the shortcomings of the prior art, the invention aims to provide a system, a method and a storage medium for attention assessment and training based on virtual reality, aiming at organically combining assessment diagnosis and intervention training, being convenient and quick and having no drug side effect.
In order to realize the purpose, the invention adopts the following technical scheme:
an attention assessment and training system based on virtual reality comprises a host, virtual reality equipment, electroencephalogram equipment, eye movement tracking equipment, electrocardio equipment, respiration sensing equipment and a cloud platform database, wherein the virtual reality equipment, the electroencephalogram equipment, the eye movement tracking equipment, the electrocardio equipment, the respiration sensing equipment and the cloud platform database are connected with the host;
the virtual reality device, the electroencephalogram device, the eye movement tracking device, the electrocardio device and the respiration sensing device are used for acquiring the neurophysiological data of a user, and the neurophysiological data comprises limb movement tracks, electroencephalogram signals, eye movement signals, heart rate and respiratory frequency;
the host computer is provided with test module, data module, evaluation module and physiological feedback formula intervention module, wherein:
the testing module outputs a virtual testing scene to the virtual reality equipment, acquires task performance data of a user in the virtual testing scene and simultaneously acquires neuro-physiological data of the user in the testing process in real time;
the data module uploads the task performance data and the neurophysiological data to a cloud platform database for storage, and the data module is matched with an evaluation module to call the stored data;
the evaluation module carries out attention evaluation according to the task performance data and the neurophysiological data in the cloud platform database to obtain an attention evaluation result of the user;
the physiological feedback type intervention module selects a physiological feedback type training scheme matched with difficulty according to the attention evaluation result for a user to perform intervention training, acquires the neuro-physiological data of the user in the intervention training process, feeds the neuro-physiological data back to a training scene in real time and judges whether the neuro-physiological data reaches the standard or not so as to prompt the user to adjust the training reaction in the training scene in time.
And the physiological feedback type intervention module generates a real-time training task according to the neurophysiological data in the training process and feeds the training task back to an intervention training scene.
The physiological feedback type intervention module also acquires task performance data of a user in an intervention training process, uploads the task performance data and the neurophysiological data in the training process to a cloud platform database through a data module for storage, and the evaluation module performs attention evaluation again according to the task performance data and the neurophysiological data in the training process and stores the attention evaluation again after the user intervention training is completed.
The physiological feedback type training scheme comprises a physiological feedback attention training module and a physiological feedback relaxation training module;
the physiological feedback attention training module comprises a brain wave attention training module, and the brain wave attention training module acquires an electroencephalogram signal in a training process, converts the electroencephalogram signal into a brain wave and feeds the brain wave back to a training scene;
the physiological feedback relaxation training module comprises a heart rate and respiration synchronization training module and a heart rate and belief training module, wherein the heart rate and respiration synchronization training module collects the respiratory rate and the heart rate of the training process and feeds back the respiratory rate and the heart rate to a training scene, and the heart rate and belief training module collects the heart rate of the training process and feeds back the heart rate to the training scene.
The brain wave attention training module performs training difficulty adaptive adjustment according to brain wave feedback of a user in a training process;
the brain wave attention training module converts the brain waves of the user in the training process into action control instructions in a training scene.
The test module comprises three test modes of selective attention test, continuous attention test and execution function test, and a test interference module is further arranged in the test module.
The embodiment of the invention also provides an attention assessment and physiological feedback training method based on virtual reality, wherein the system is adopted, and the method comprises the following steps:
s10, receiving a test request of a user, and outputting a virtual test scene to the user through virtual reality equipment;
s20, acquiring task performance data of a user in a virtual test scene and neurophysiological data in a test process and uploading the task performance data and the neurophysiological data to a cloud platform database;
s30, calling the task performance data and the neurophysiological data from the cloud platform database to perform evaluation analysis to obtain an attention evaluation result of the user;
s40, selecting a physiological feedback type training scheme with difficulty matching according to the attention assessment result;
s50, acquiring the neurophysiological data of the user in the interventional training process, and feeding back the neurophysiological data to a training scene in real time;
s60, judging whether the neurophysiological data reach the standard, if not, prompting the user to adjust the training response in time in the training scene and returning to the step S50; and finishing if the standard is reached.
The physiological feedback type training scheme comprises a physiological feedback attention training module, the physiological feedback attention training module comprises a brainwave attention training module, and the training process of the brainwave attention training module comprises the following steps:
s71, outputting an attention training scene to a user through virtual reality equipment or a display screen;
s72, acquiring electroencephalogram signals of the user in the training process in real time;
s73, converting the electroencephalogram signals into brain waves, feeding the brain waves back to a training scene for display, and converting the brain waves into action control instructions in the training scene;
s74, judging whether the control action of the user by brain waves in the training scene reaches the standard;
s75, if the standard is not met, prompting the user to concentrate attention for control in the training scene, and re-entering the step S72;
and S76, if the standard is reached, continuing training until the training task is completed.
The physiological feedback type training scheme comprises a physiological feedback relaxation training module, the physiological feedback relaxation training module comprises a heart rate and respiration synchronization training module and a heart rate and belief training module, and the training process of the heart rate and respiration synchronization training module comprises the following steps:
s81, outputting a relaxation training scene to the user through virtual reality equipment or a display screen;
s82, collecting the respiratory rate and the heart rate of the user in the training process in real time;
s83, judging whether the heart rate and the breath of the user in the training scene are synchronous or not;
s84, if the heart rate respiration is asynchronous, prompting the user to adjust the respiration and relax the mood in the training scene, and re-entering the step S82;
and S85, continuing training in the same way until the training task is completed.
The present invention also proposes a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, which computer program, when executed, implements the above-mentioned method.
According to the attention assessment and training system, method and storage medium based on the virtual reality, a host, a virtual reality device, an electroencephalogram device, an eye movement tracking device, an electrocardio device, a respiration sensing device and a cloud platform database are arranged, the host is provided with a testing module, a data module, an assessment module and a physiological feedback type intervention module, attention of a user is tested firstly, task performance data and neuro-physiological data in the testing process are combined for analysis and assessment, then an attention assessment result of the user is automatically obtained, a difficulty matching training scheme is selected according to the attention assessment result for the user to conduct intervention training, neuro-physiological data of the user are fed back in real time in the training process to drive the user to adjust training response in a training scene in time so as to achieve a good intervention training effect, and the whole assessment and training process is convenient and rapid.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a schematic diagram of a first embodiment of a virtual reality-based attention assessment and training system according to the present invention;
FIG. 2 is a schematic diagram of the components of the modules in the host of the present invention;
FIG. 3 is a schematic flow chart of an attention test performed by the test module according to the present invention;
FIG. 4 is a schematic flow chart of the attention assessment module according to the present invention;
FIG. 5 is a flowchart illustrating a first embodiment of a method for attention assessment and training based on virtual reality according to the present invention;
FIG. 6 is a schematic diagram of a brainwave attention training process in the method of the present invention;
FIG. 7 is a schematic diagram of a heart rate and respiration synchronization training process in the method of the present invention;
FIG. 8 is a schematic diagram of a heart rate memorial training process in the method of the present invention.
Description of reference numerals:
100-system, 1-host computer, 11-test module, 12-data module, 13-evaluation module, 14-physiological feedback intervention module, 15-physiological feedback training scheme, 151-physiological feedback attention training module, 1511-brain wave attention training module, 152-physiological feedback relaxation training module, 1521-heart rate and respiration synchronization training module, 1522-heart rate and belief training module, 2-virtual reality device, 3-electroencephalogram device, 4-eye movement tracking device, 5-electrocardio device, 6-respiration sensing device and 7-cloud platform database.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention proposes a virtual reality-based attention assessment and training system 100,
the device comprises a host 1, virtual reality equipment 2 connected with the host, electroencephalogram equipment 3, eye movement tracking equipment 4, electrocardio equipment 5, respiration sensing equipment 6 and a cloud platform database 7.
The virtual reality device 2, the electroencephalogram device 3, the eye movement tracking device 4, the electrocardio device 5 and the respiration sensing device 6 are used for acquiring the neurophysiological data of the user, and the neurophysiological data comprise limb movement tracks, electroencephalogram signals, eye movement signals, heart rate and respiration frequency. The user of the present invention refers to a child who is performing attention deficit hyperactivity disorder testing or interventional training. Specifically, the limb movement track is obtained through the virtual reality device 2, the electroencephalogram signal is obtained through the electroencephalogram device 3, the eye movement signal is obtained through the eye movement tracking device 4, the heart rate is obtained through the electrocardio device 5, and the respiratory frequency is obtained through the respiratory sensing device 6. The virtual reality device 2 comprises a VR helmet, a handle and other corollary devices. Eye movement signals are used to identify and analyze the visual attention patterns of an individual while performing a task, and may be used to implement a series of simulation, manipulation functions.
It is understood that the system 100 of the present invention may also be configured with more sensing devices to acquire other physiological data of the user, such as blood pressure, skin current, etc. The richer the neurophysiological data is, the more the indexes evaluated by the user are, and the accuracy of evaluation and diagnosis is improved.
The host 1 of the invention is provided with a test module 11, a data module 12, an evaluation module 13 and a physiological feedback type intervention module 14.
The testing module 11 outputs a virtual testing scene to the virtual reality device 2, obtains task performance data of the user in the virtual testing scene, and simultaneously obtains the neurophysiological data of the user in the testing process in real time. Task performance data of a user in a virtual test scene generally comprises task completion time, accuracy rate, error rate, reaction time and the like. The neurophysiological data in the test process comprises limb movement tracks, electroencephalogram signals, eye movement signals and the like. The limb movement track comprises a head rotation track, a hand movement track and the like.
Preferably, as shown in fig. 2, the test module 11 of the present invention comprises three test modes of a selective attention test 111, a continuous attention test 112 and a performance function test 113.
As an embodiment, the selective attention test 111, the continuous attention test 112, and the executive function test 113 of the present invention are each as follows:
selective attention test-Audio: audio testing is a hearing task. Confirmation criteria are given before each test and a user hearing a particular number while playing audio is required to press a "confirm" button. The numbers range from 0 to 9 and the criteria will change for each round. For example, the criterion is to hear the 3 after 7 to make a key press reaction, and the number sequence played in the audio is 27273293, the user should press the button while hearing the first 3. The outputs of the task results are the reaction time, the task completion time and the accuracy.
Sustained attention test-CPT: CPT is a visual task. In each round, a series of letters A through H, J, L and X will appear on the projection screen. When the letter X appears behind the letter a on the screen, the user should press the "ok" button on the controller; in other cases, the user should not press a button. The outputs of the task results are the reaction time, the task completion time and the accuracy.
Perform functional test-Stroop: under the Stroop module, a user can see some Chinese characters with colors, and if the color of the voice prompt is consistent with the color of the font seen by the user, the user should press a 'confirm' button on the controller; in other cases, the user should not press a button. The output of the task results are the reaction time, completion time, and accuracy.
Further, a test interference module 114 is disposed in the test module 11 of the present invention.
The test interference module 114 of the embodiment of the invention includes visual interference, auditory interference, olfactory interference and comprehensive interference.
The test interference module 114 is for acquiring performance of the user when performing cognitive function tests such as selective attention, continuous attention, and executive functions in various virtual environmental interferences. And providing the influence degree of the interference factors on the attention of the user for subsequent attention assessment.
As shown in fig. 3, the attention testing process based on virtual reality of the present invention is as follows:
1) the user wears the VR equipment and debugs the VR environment;
2) inputting user information by a user;
3) then selecting a test mode;
3) the VR outputs a corresponding test mode scene to a user for cognitive test;
4) adding interference stimulation in the testing process;
5) counting task performance data of a user in a testing process and collecting neuro-physiological data of the user;
6) and uploading to a cloud platform database for storage.
The virtual test scenario of the present invention may also be pre-stored in the virtual reality device 2.
The data module 12 of the present invention uploads the task performance data and the neurophysiological data to the cloud platform database 7 for storage, and calls the stored data in cooperation with the evaluation module 13. As shown in fig. 2, the data module 12 mainly performs data transmission and data extraction. The data acquired by the test module 11 are uploaded to the cloud platform database 7 through the data module 12 for storage, the data are extracted from the cloud platform database 7 when the data are needed for subsequent evaluation, and the data are stored in the cloud platform database 7, so that local data are prevented from being lost, and the data are large in data volume and more suitable for being stored in the cloud platform database 7. Meanwhile, data collected in the subsequent intervention training process are uploaded to the cloud platform database 7 through the data module 12 for storage.
The data module 12 of the invention adopts the internet of things remote service of SAAS platform, or uses WLAN, LTE/4G, Bluetooth, wired transmission and other modes to upload the test data of the user to the cloud platform database 7.
The evaluation module 13 of the present invention performs attention evaluation according to the task performance data and the neurophysiological data in the cloud platform database 7 to obtain an attention evaluation result of the user.
As shown in fig. 4, the evaluation module 13 of the embodiment of the present invention first performs feature extraction on the task performance data and the neurophysiological data, and then performs machine learning and statistical analysis to obtain the attention evaluation result of the user. The method comprises the following steps:
1) carrying out feature extraction on the user neuro-physiological sensing data;
2) performing machine learning by combining user task performance data;
3) and comprehensively obtaining the attention evaluation result of the user by utilizing a statistical analysis technology.
The feature extraction is to find an index capable of distinguishing ADHD children from normal children by analyzing in data as a feature. The machine learning learns the clinical test data and the biological index data such as the neuro-physiological sensing and the like to generate an evaluation model, and then the model is used for obtaining the attention evaluation result of the user after statistical analysis and evaluation. The invention can adopt deep learning technology for machine learning.
The physiological feedback type intervention module 14 selects a physiological feedback type training scheme 15 with matched difficulty according to the attention evaluation result for the user to perform intervention training, and the physiological feedback type intervention module 14 acquires the neuro-physiological data of the user in the intervention training process, feeds the neuro-physiological data back to a training scene in real time and judges whether the neuro-physiological data reaches the standard or not so as to prompt the user to adjust the training response in the training scene in time.
The physiological feedback training scheme 15 of the embodiment of the present invention generally uses vivid and interesting pictures to allow the user to perform attention training or relaxation training. In the training process, physiological data of the user, such as electroencephalogram, heart rate, respiration and the like, are collected, analyzed and converted into real-time pictures in a virtual environment for real-time feedback, so that the user is prompted to adjust training reaction in a training scene in time. After the user makes the adjustment of the training response, the physiological data of the user collected by the user can be changed immediately in the training scene, and the physiological signal feedback type training has real-time performance and good interactive feedback effect, so that the user can obtain better training effect.
Specifically, as shown in fig. 2, the physiological feedback training scheme 15 of the present invention includes a physiological feedback attention training module 151 and a physiological feedback relaxation training module 152. The physiological feedback attention training module 151 is used for improving the attention level of the user, and the physiological feedback relaxation training module 152 is used for relieving the stress of the user and improving the cognitive ability of the user.
The physiological feedback attention training module 151 comprises a brain wave attention training module 1511, and the brain wave attention training module 1511 collects electroencephalogram signals in a training process, converts the electroencephalogram signals into brain waves, and feeds the brain waves back to a training scene.
Specifically, the brainwave attention training module 1511 converts the brainwaves of the user in the training process into action control instructions in the training scene.
For example, the brain wave attention training module 1511 is a driving scene, and during the driving process, the system collects and analyzes data of each wave band related to attention in brain waves in the brain electrical signals of us, and converts the data into control of a real-time image in a virtual environment, such as control of a racing car. The attention wave is used to drive the car, the faster the car will run if the amplitude of the attention wave is high, and the car will not run if the amplitude of the attention wave is high. The state that the user sees the racing car is actually the current attention state of the user; through continuous training, the psychological mode of the racing car when running is repeatedly captured, the racing car can run more and more stably, the physiological feedback link in the training can help the user to continuously adjust the attention state, and the amplitude and the stability of the attention wave band are improved through repeated training, so that the training effect is well intervened.
Preferably, the brainwave attention training module 1511 of the embodiment of the present invention performs training difficulty adaptive adjustment according to brainwave feedback of a user in a training process. For example, if the user repeatedly controls the racing car for a plurality of times or if the user is unstable, the control difficulty of the racing car is adjusted in real time, which is beneficial for the user to grasp and adapt to the training process.
It is to be understood that the physiological feedback attention training module 151 of the present invention further comprises an eight-segment jin-imitation attention control training module.
The physiological feedback relaxation training module 152 comprises a heart rate and respiration synchronization training module 1521 and a heart rate and belief training module 1522, wherein the heart rate and respiration synchronization training module acquires the respiratory rate and the heart rate in the training process and feeds the respiratory rate and the heart rate back to the training scene, and the heart rate and belief training module acquires the heart rate in the training process and feeds the heart rate back to the training scene. Similarly, in the heart rate and respiration synchronization training module 1521 and the heart rate and belief training module 1522, the user can timely find and adjust problems in the training process by feeding back heart rate and respiration physiological signals to the training scene in real time.
Preferably, the physiological feedback type intervention module 14 of the present invention generates a real-time training task according to the neurophysiological data in the training process and feeds the training task back to the intervention training scene, so that the difficulty adaptive adjustment can be performed, so that the training scheme is more targeted and personalized, the training is more precise, and the effect is better.
Further, the physiological feedback type intervention module 14 of the embodiment of the present invention further obtains task performance data of the user during the intervention training process, uploads the task performance data and the neurophysiological data during the training process to the cloud platform database 7 through the data module 12 for storage, and the evaluation module 13 performs attention evaluation again and stores the attention evaluation again according to the task performance data and the neurophysiological data during the training process after the user intervention training is completed. The task performance data in the intervention training process also comprises task completion time, accuracy rate, error rate, reaction time and the like. The invention carries out attention evaluation again after the user intervention training is finished, firstly, the user can know the effect after the intervention training, secondly, the user does not need to enter the testing process when carrying out the next intervention training, and the training scheme of the corresponding grade can be obtained by directly using the attention evaluation result after the previous intervention training is finished for training, thereby saving the process and time.
The attention assessment and physiological feedback training system 100 based on virtual reality integrates core technologies such as virtual reality, artificial intelligence, wearable sensing and internet of things transmission, can accurately assess and assist in diagnosing children ADHD patients, meanwhile adopts a difficulty-adaptive physiological feedback intervention training module to improve the attention level of the patients, and utilizes a physiological feedback relaxation training system comprising a heart rate respiration synchronization (CRST) training module and a heart rate thoughts training module to relieve pressure and improve cognitive ability. The system 100 of the invention integrates attention assessment and intervention training, saves medical resources and is convenient and quick.
As shown in fig. 5, the present invention further provides a method for attention assessment and physiological feedback training based on virtual reality, which employs the system 100, and the method includes the following steps:
and S10, receiving the test request of the user, and outputting the virtual test scene to the user through the virtual reality equipment.
The user firstly wears the back virtual reality device 2 and the sensing device, inputs user information, selects a test mode and sends a test request after confirmation. The virtual test scene of the invention can be sent to the virtual reality device through the host 1 or directly pre-stored on the virtual reality device 2. After entering the virtual test scene, the user completes related tasks according to prompts in the scene, namely, the test process is started.
And S20, acquiring task performance data of the user in the virtual test scene and the neurophysiological data in the test process and uploading the data to a cloud platform database.
The virtual reality device 2 and the sensing devices such as the electroencephalogram device 3, the eye movement tracking device 4, the electrocardio device 5 and the respiration sensing device 6 collect performance data, limb movement tracks, electroencephalogram signals, eye movement signals and other neurophysiological data of a user when the user does a task in the test process, and upload the data to the cloud platform database 7 for storage.
And S30, calling the task performance data and the neurophysiological data from the cloud platform database to perform evaluation analysis to obtain the attention evaluation result of the user.
The evaluation analysis is to extract attention specific data by adopting purpose-made extraction, generate an attention evaluation module through machine learning, and automatically generate an attention evaluation result of the user through statistical analysis.
And S40, selecting a physiological feedback type training scheme with difficulty matching according to the attention assessment result.
According to the method, after the attention evaluation result of the user is obtained, the physiological feedback type training scheme with matched difficulty is automatically selected, so that the user can be subjected to difficulty self-adaptive intervention training.
And S50, acquiring the neurophysiological data of the user in the interventional training process, and feeding back the neurophysiological data to a training scene in real time.
The neuro-physiological data can be direct data, images or converted virtual objects fed back to the training scene in real time, so that a user can intuitively know the physiological data of the user in real time.
S60, judging whether the neurophysiological data reach the standard, if not, prompting the user to adjust the training response in time in the training scene and returning to the step S50; and finishing if the standard is reached.
If the neurophysiological data of the user does not reach the standard, if the attention is not concentrated, the heart rate and the respiration are asynchronous, the heart rate is fast, the respiration is fast and the like, the neurophysiological data are fed back to a training scene immediately to enable the user to carry out training reaction adjustment, if the user concentrates the attention, the respiration is slowed down, the mood is relaxed and the like. After the user makes the adjustment of the training response, the physiological data of the user can be changed in the training scene, and the real-time interactive feedback effect is achieved, so that the user can obtain a better training effect.
Preferably, as shown in fig. 6, the training process of the brainwave attention training module of the method of the present invention includes the following steps:
and S71, outputting the attention training scene to the user through a virtual reality device or a display screen.
The system 100 adopted by the method of the invention can be configured with a display screen, training scenes are trained in the display screen, and the training scenes can also be output to the virtual reality device 2 or directly pre-stored on the virtual reality device 2.
And S72, acquiring the electroencephalogram signals of the user in the training process in real time.
The electroencephalogram signals in the attention training process are adopted in real time through the electroencephalogram equipment 3.
And S73, converting the electroencephalogram signals into brain waves, feeding the brain waves back to the training scene for display, and converting the brain waves into action control instructions in the training scene.
In this example, the electroencephalogram signal is converted into a brain wave and fed back to the training scene for display in an image manner, and meanwhile, the attention wave signal in the brain wave is converted into an action control instruction in the training scene, for example, an action instruction such as starting and stopping of a racing car and speed control of the racing car in the game training scene is controlled by the attention wave in the brain wave.
And S74, judging whether the control action of the user by brain waves in the training scene reaches the standard or not.
And judging whether the action of the object controlled by the brain waves of the user reaches the standard, such as whether the racing car is started normally, whether the speed is within a preset range and the like.
And S75, if the training scene does not reach the standard, prompting the user to concentrate on the attention for control, and re-entering the step S72.
If the signals do not reach the standard, a prompt message is fed back in an attention training scene to enable the user to concentrate on attention for control, and the user performs adjustment, such as concentration operation of racing cars, and then electroencephalograms of the user are collected again and fed back and judged.
And S76, if the standard is reached, continuing training until the training task is completed.
And if the attention control of the user reaches the standard, the subsequent training is carried out until the whole training is finished.
The brainwave attention training of the present embodiment is used to raise the attention level of the user.
Preferably, as shown in fig. 7, the training process of the heart rate and respiration synchronization training module of the method of the present invention includes the following steps:
and S81, outputting the relaxation training scene to the user through the virtual reality device or the display screen.
And S82, acquiring the respiratory rate and the heart rate of the user in the training process in real time.
And S83, judging whether the heart rate and the breath of the user are synchronous in the training scene.
And S84, prompting the user to adjust respiration and relax mood in the training scene if the heart rate respiration is asynchronous, and re-entering the step S82.
And S85, continuing training in the same way until the training task is completed.
The heart rate and respiration synchronous training of the method enables a user to relax by adjusting respiration to enable respiration and heart rate to be synchronous.
Further, as shown in fig. 8, the training process of the heart rate memorial training module of the method of the present invention includes the following steps:
s91, outputting a relaxation training scene to the user through virtual reality equipment or a display screen;
s92, collecting the heart rate of the user in the training process in real time;
s93, judging whether the heart rate of the user in the training scene reaches the memorial heart rate;
s94, if not, prompting the user to relax the mood in the training scene, and re-entering the step S92;
and S95, if so, continuing training until the training task is completed.
The heart rate memorial training of the method enables the user to relieve stress by relaxing the mood and synchronizing the heart rate with the memorial heart rate.
The attention assessment and training system based on the virtual reality is provided with a host, a virtual reality device, an electroencephalogram device, an eye movement tracking device, an electrocardio device, a respiration sensing device and a cloud platform database, wherein the host is provided with a test module, a data module, an assessment module and a physiological feedback type intervention module. According to the attention assessment and training method based on the virtual reality, attention of a user is tested, after task performance data and neuro-physiological data in a testing process are combined and analyzed and evaluated, an attention assessment result of the user is automatically obtained, a difficulty matching training scheme is selected according to the attention assessment result and is used for the user to perform interventional training, during the training process, neuro-physiological data of the user are fed back in real time to drive the user to adjust a training response in a training scene in time so as to achieve a good interventional training effect, and the whole assessment and training process is convenient and rapid.
In a second round of clinical trials in a pediatric hospital affiliated to the university of Compound Dane, the virtual reality-based attention assessment and training system of the invention finds that in the test results of 41 healthy children and 54 ADHD children, 87 significant indexes exist in the detection of 186 biological indexes such as brain waves, eye movements, task performances, action tracks and the like. A child ADHD diagnosis/evaluation model is established through artificial intelligence and deep learning, and the diagnosis accuracy is up to 93%.
The invention also proposes a computer-readable storage medium, in which a computer program is stored, which, when executed, implements the above-mentioned evaluation and training method.
The module/unit integrating the attention estimation and training method based on virtual reality of the present invention, if implemented in the form of software functional unit and sold or used as a stand-alone product, can be stored in a computer readable storage medium. The specific implementation of the computer-readable storage medium of the present invention is substantially the same as the embodiments of the attention evaluating and training method based on virtual reality, and will not be described herein again.
It should be noted that the above-described embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An attention assessment and training system based on virtual reality is characterized by comprising a host, virtual reality equipment, electroencephalogram equipment, eye movement tracking equipment, electrocardio equipment, respiration sensing equipment and a cloud platform database, wherein the virtual reality equipment, the electroencephalogram equipment, the eye movement tracking equipment, the electrocardio equipment, the respiration sensing equipment and the cloud platform database are connected with the host;
the virtual reality device, the electroencephalogram device, the eye movement tracking device, the electrocardio device and the respiration sensing device are used for acquiring the neurophysiological data of a user, and the neurophysiological data comprises limb movement tracks, electroencephalogram signals, eye movement signals, heart rate and respiratory frequency;
the host computer is provided with test module, data module, evaluation module and physiological feedback formula intervention module, wherein:
the testing module outputs a virtual testing scene to the virtual reality equipment, acquires task performance data of a user in the virtual testing scene and simultaneously acquires neuro-physiological data of the user in the testing process in real time;
the data module uploads the task performance data and the neurophysiological data to a cloud platform database for storage, and the data module is matched with an evaluation module to call the stored data;
the evaluation module carries out attention evaluation according to the task performance data and the neurophysiological data in the cloud platform database to obtain an attention evaluation result of the user;
the physiological feedback type intervention module selects a physiological feedback type training scheme matched with difficulty according to the attention evaluation result for a user to perform intervention training, acquires the neuro-physiological data of the user in the intervention training process, feeds the neuro-physiological data back to a training scene in real time and judges whether the neuro-physiological data reaches the standard or not so as to prompt the user to adjust the training reaction in the training scene in time.
2. The system of claim 1, wherein the physiological feedback intervention module generates a real-time training task from the neurophysiologic data during the training process and feeds the training task back to an intervention training scenario.
3. The system according to claim 1, wherein the physiological feedback intervention module further obtains task performance data of the user during the intervention training process, uploads the task performance data and the neurophysiological data during the training process to the cloud platform database through the data module for storage, and the evaluation module performs attention evaluation again and stores the attention evaluation again according to the task performance data and the neurophysiological data during the training process after the user intervention training is completed.
4. The system of claim 1, wherein the physiological feedback training regimen comprises a physiological feedback attention training module and a physiological feedback relaxation training module;
the physiological feedback attention training module comprises a brain wave attention training module, and the brain wave attention training module acquires an electroencephalogram signal in a training process, converts the electroencephalogram signal into a brain wave and feeds the brain wave back to a training scene;
the physiological feedback relaxation training module comprises a heart rate and respiration synchronization training module and a heart rate and belief training module, wherein the heart rate and respiration synchronization training module collects the respiratory rate and the heart rate of the training process and feeds back the respiratory rate and the heart rate to a training scene, and the heart rate and belief training module collects the heart rate of the training process and feeds back the heart rate to the training scene.
5. The system according to claim 3, wherein the brain wave attention training module performs training difficulty adaptive adjustment according to brain wave feedback of a user in a training process;
the brain wave attention training module converts the brain waves of the user in the training process into action control instructions in a training scene.
6. The system of claim 1, wherein the test module comprises three test modes of selective attention test, continuous attention test and functional test execution, and a test interference module is further disposed in the test module.
7. A virtual reality based attention assessment and physiological feedback training method, using a system according to any of claims 1-6, the method comprising the steps of:
s10, receiving a test request of a user, and outputting a virtual test scene to the user through virtual reality equipment;
s20, acquiring task performance data of a user in a virtual test scene and neurophysiological data in a test process and uploading the task performance data and the neurophysiological data to a cloud platform database;
s30, calling the task performance data and the neurophysiological data from the cloud platform database to perform evaluation analysis to obtain an attention evaluation result of the user;
s40, selecting a physiological feedback type training scheme with difficulty matching according to the attention assessment result;
s50, acquiring the neurophysiological data of the user in the interventional training process, and feeding back the neurophysiological data to a training scene in real time;
s60, judging whether the neurophysiological data reach the standard, if not, prompting the user to adjust the training response in time in the training scene and returning to the step S50; and finishing if the standard is reached.
8. The method of claim 7, wherein the physiological feedback training regimen comprises a physiological feedback attention training module comprising a brainwave attention training module, wherein the training process of the brainwave attention training module comprises the steps of:
s71, outputting an attention training scene to a user through virtual reality equipment or a display screen;
s72, acquiring electroencephalogram signals of the user in the training process in real time;
s73, converting the electroencephalogram signals into brain waves, feeding the brain waves back to a training scene for display, and converting the brain waves into action control instructions in the training scene;
s74, judging whether the control action of the user by brain waves in the training scene reaches the standard;
s75, if the standard is not met, prompting the user to concentrate attention for control in the training scene, and re-entering the step S72;
and S76, if the standard is reached, continuing training until the training task is completed.
9. The method according to claim 7, wherein the physiological feedback type training scheme comprises a physiological feedback relaxation training module, the physiological feedback relaxation training module comprises a heart rate and respiration synchronization training module and a heart rate and belief training module, and the training process of the heart rate and respiration synchronization training module comprises the following steps:
s81, outputting a relaxation training scene to the user through virtual reality equipment or a display screen;
s82, collecting the respiratory rate and the heart rate of the user in the training process in real time;
s83, judging whether the heart rate and the breath of the user in the training scene are synchronous or not;
s84, if the heart rate respiration is asynchronous, prompting the user to adjust the respiration and relax the mood in the training scene, and re-entering the step S82;
and S85, continuing training in the same way until the training task is completed.
10. A computer-readable storage medium, in which a computer program is stored which, when executed, implements the method of any one of claims 7-9.
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