CN114469091A - Auxiliary diagnosis method, system, equipment and medium for autism - Google Patents
Auxiliary diagnosis method, system, equipment and medium for autism Download PDFInfo
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- 206010003805 Autism Diseases 0.000 title claims abstract description 50
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- 230000006399 behavior Effects 0.000 claims abstract description 65
- 238000011156 evaluation Methods 0.000 claims abstract description 56
- 230000008451 emotion Effects 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims abstract description 16
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
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- 230000003542 behavioural effect Effects 0.000 abstract 2
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Abstract
The invention discloses an auxiliary diagnosis method, a system, equipment and a medium for autism, relating to the technical field of autism diagnosis, wherein the method comprises the following steps: acquiring video data of a target person; preprocessing the video data to obtain a characteristic data set of a target person, wherein the characteristic data set comprises behavior data and face data; inputting the behavior data and the face data into an auxiliary diagnosis analysis model to obtain a behavior evaluation index and an emotion evaluation index; and obtaining a comprehensive index according to the behavior evaluation index and the emotion evaluation index, evaluating the degree of the target user suffering from the autism according to the comprehensive index, and outputting a diagnosis suggestion. The invention takes the behavioral and facial expression abnormality of the autism patient into consideration, introduces the behavioral evaluation index, the emotional evaluation index and the comprehensive evaluation index, realizes the rapid and accurate diagnosis of the autism, and has wide application prospect.
Description
Technical Field
The invention relates to the technical field of autism diagnosis, in particular to an autism auxiliary diagnosis method, system, equipment and medium.
Background
Autism Spectrum Disorder (ASD), a disease classified as a neurodevelopmental disorder. Individuals with autism spectrum disorders often present with two types of symptoms: questions in social communications or social interactions, and restricted or repetitive patterns of behavior, interests, or activities. Autistic patients have long experienced difficulties including difficulties in creating and maintaining social interactions, as well as difficulties in maintaining work and performing routine tasks.
In the prior art, diagnosis and diagnosis of patients with autism often depend on professional medical staff for diagnosis and judgment, the method is time-consuming and labor-consuming, and the number of related professionals is small, so that certain difficulty is brought to early screening of autism, and therefore some patients with autism cannot be screened in the early stage, and intervention and treatment are performed as early as possible. Therefore, a method capable of assisting a doctor in quickly diagnosing autism is of great significance to society.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an autism auxiliary diagnosis method, system, equipment and medium.
An auxiliary diagnosis method for autism, comprising the following steps:
s1, acquiring video data of the target person;
s2, preprocessing the video data to obtain a characteristic data set of the target person, wherein the characteristic data set comprises behavior data and face data;
s3, inputting the behavior data and the face data into an auxiliary diagnosis analysis model to obtain a behavior evaluation index and an emotion evaluation index;
and S4, obtaining a comprehensive index according to the behavior evaluation index and the emotion evaluation index, evaluating the degree of the target user suffering from the autism according to the comprehensive index, and outputting a diagnosis suggestion.
Preferably, in S1, the video data of the target person is the video data of the target person in a specific scene.
Preferably, in S2, the method for preprocessing the video data to obtain the feature data set of the target person includes:
randomly sampling video data of a target person in a specific scene;
frame splitting is carried out on the video data after random sampling, and Gaussian fuzzy noise reduction processing is carried out on each frame of video data;
and inputting the video data subjected to noise reduction into the graph convolution neural network model to obtain a characteristic data set of the target person.
Preferably, in S3, the auxiliary diagnostic analysis model includes a first neural network model and a second neural network model; the method for inputting the behavior data into the auxiliary diagnosis analysis model to obtain the behavior evaluation index and the emotion evaluation index comprises the following steps:
inputting the behavior data into a first neural network model trained in advance to obtain a behavior evaluation index;
and inputting the face data into a pre-trained second neural network model to obtain the emotion assessment index.
Preferably, in S3, the training method of the first neural network model includes:
acquiring behavior data of a sample user, and marking the behavior data of the sample user;
and training the first neural network model by using the behavior data of the sample user and the label corresponding to the behavior data.
In a second aspect, an autism assistance system includes:
the data acquisition module is used for acquiring video data of a target person;
the data processing module is used for preprocessing the video data to obtain a characteristic data set of a target person, wherein the characteristic data set comprises behavior data and facial data;
the auxiliary analysis module is used for inputting the behavior data and the face data into an auxiliary diagnosis analysis model to obtain a behavior evaluation index and an emotion evaluation index;
and the diagnosis suggestion module is used for obtaining a comprehensive index according to the behavior evaluation index and the emotion evaluation index, evaluating the degree of the target user suffering from the autism according to the comprehensive index, and outputting a diagnosis suggestion.
In a third aspect, an electronic device includes:
a memory: for storing a computer program;
a processor: for implementing the steps of any of the above-described methods for assisted diagnosis of autism when executing said computer program.
In a fourth aspect, a computer readable storage medium, said computer program, when executed by a processor, performs the steps of any of the above-described methods for assisted diagnosis of autism.
The invention has the beneficial effects that:
according to the method, the video data of the tested user is processed through the graph neural network model to obtain a tested user characteristic data set, the action behaviors and the facial expressions are accurately identified through the first neural network model and the second data network model to obtain a behavior evaluation index and a facial evaluation index, the comprehensive evaluation index is accurately calculated according to the behavior evaluation index and the facial evaluation index, and the higher the comprehensive evaluation index is, the greater the degree of the autism is. The invention greatly improves the efficiency and the accuracy of the autism diagnosis and has wide application prospect.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention;
FIG. 3 is a schematic diagram of the computer device of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
Example 1
As shown in fig. 1, an embodiment of the present invention provides an auxiliary diagnostic method for autism, including the following steps:
s1, acquiring video data of the target person;
specifically, the video data of the target person is the video data of the target person in a specific scene;
in an embodiment of the present invention, the specific scene may be a scene with more interactive behaviors such as a classroom and a family gathering, or a scene in which a specific video capable of stimulating a target person is played. In consideration of the authenticity and reliability of data acquisition, the invisible camera is adopted to acquire the video data of the target person, in consideration of the need of analyzing the facial expression in the video data, and the high-definition camera port is adopted to acquire the video data of the target person.
S2, preprocessing the video data to obtain a characteristic data set of the target person, wherein the characteristic data set comprises behavior data and face data;
specifically, the method for preprocessing the video data to obtain the feature data set of the target person includes: randomly sampling video data of a target person in a specific scene; frame splitting is carried out on the video data after random sampling, and Gaussian fuzzy noise reduction processing is carried out on each frame of video data; and inputting the video data subjected to noise reduction into the graph convolution neural network model to obtain a characteristic data set of the target person.
In an embodiment of the present invention, videos of a certain batch and size of a target person in a specific scene are selected, the aspect ratio of the videos is modified to 112 × 112, then each video is randomly adopted, 16 consecutive frames of video data are randomly sampled each time, the randomly sampled video data are deframed, and the deframed data are subjected to gaussian fuzzy noise reduction. The unification of the video data format is ensured through the preprocessing of the video.
Inputting the processed video data into a graph convolution neural network model so as to obtain a feature data set of a target person, specifically, a convolution neural network recognition model recognition training set is from each autism medical institution or each parent authorization video, and training and calculating aiming at the lowest frame rate of 15FPS and the highest frame rate of 60FPS of an autism sample (training the features of the joints of the limbs 33 and 21 of the trunk 468 of the human face) so as to obtain an initial feature data set, wherein the initial feature data set comprises initial behavior data and initial face data;
and tracking and calculating the initial characteristic data set, specifically, quickly calculating the motion condition of each characteristic point in the initial data set by using the initial characteristic data set through loading and enabling a tracker based on GPU calculation, and connecting to generate a relative position frame.
S3, inputting the behavior data and the face data into an auxiliary diagnosis analysis model to obtain a behavior evaluation index and an emotion evaluation index;
specifically, the auxiliary diagnostic analysis model comprises a first neural network model and a second neural network model; the method for inputting the behavior data into the auxiliary diagnosis analysis model to obtain the behavior evaluation index and the emotion evaluation index comprises the following steps: inputting the behavior data into a first neural network model trained in advance to obtain a behavior evaluation index; and inputting the face data into a pre-trained second neural network model to obtain the emotion assessment index.
In an embodiment of the present invention, the training method of the first neural network model includes: acquiring behavior data of a sample user, marking the behavior data of the sample user, wherein the behavior data of the sample user comes from authorized videos of various autism medical institutions or various parents, analyzing and processing the authorized videos to obtain sample videos, marking behaviors and facial expressions in the sample videos to obtain a sample set, dividing the sample set into a training set and a testing set, training a first neural network model through the training set, and testing the first neural network model through the testing set; and training the first neural network model by using the behavior data of the sample user and the labels corresponding to the behavior data, wherein similarly, the training method of the second neural network model is consistent with the training method of the first neural network model.
Specifically, the feature data set is input into the first neural network model, the action tags and the number of the action tags are identified, weighted average calculation is performed on the action tags according to the types and the number of the action tags, and finally a behavior evaluation index is obtained.
Because patients with autism often have unusual interests and unusual game modes, the patients often concentrate on certain games or activities for a long time, such as getting involved in rotating a pot cover or turning around on the ground, and often have special charm on certain objects or activities, and hold charm objects for tens of days. People have stereotypes, repetitive behaviors, and special motion gestures and facial expressions, and thus, motion labels recognized by the present application include, but are not limited to pacing back and forth alone, spinning on themselves, rolling around, jumping repeatedly, playing fingers while chatty, waving or flapping both hands while exciting; shaking the body back and forth or left and right, and repeatedly going up and down stairs; facial expressions identified herein include, but are not limited to, smiling, laughing, surprise, fear, anger, aversion.
And S4, obtaining a comprehensive index according to the behavior evaluation index and the emotion evaluation index, evaluating the degree of the target user suffering from the autism according to the comprehensive index, and outputting a diagnosis suggestion.
And finally, carrying out weighted calculation on the behavior evaluation index and the emotion evaluation index to obtain a comprehensive index.
In summary, according to the autism auxiliary diagnosis method provided by the embodiment of the present invention, video data of a detected user is processed through a graph neural network model to obtain a detected user feature data set, a motion behavior and a facial expression are accurately identified through a first neural network model and a second data network model to obtain a behavior evaluation index and a facial evaluation index, a comprehensive evaluation index is accurately calculated according to the behavior evaluation index and the facial evaluation index, and the higher the comprehensive evaluation index is, the greater the autism degree is; and different intervention treatment schemes can be provided for the tested user according to different degrees of the autism.
Example 2
As shown in fig. 2, an autism auxiliary diagnosis system provided in an embodiment of the present invention includes:
the data acquisition module is used for acquiring video data of a target person;
the data processing module is used for preprocessing the video data to obtain a characteristic data set of a target person, wherein the characteristic data set comprises behavior data and facial data;
the auxiliary analysis module is used for inputting the behavior data and the face data into an auxiliary diagnosis analysis model to obtain a behavior evaluation index and an emotion evaluation index;
and the diagnosis suggestion module is used for obtaining a comprehensive index according to the behavior evaluation index and the emotion evaluation index, evaluating the degree of the target user suffering from the autism according to the comprehensive index, and outputting a diagnosis suggestion.
In the embodiment of the invention, the higher the comprehensive evaluation index is, the higher the degree of the autism is, the degree comprises normal, mild, moderate and severe, that is, the proposal in the diagnosis suggestion comprises at least three, and can be adjusted according to the actual situation of the autism patient, if the autism patient has better stimulation performance to music in the diagnosis process, a music auxiliary intervention means is added, and the system provided by the invention realizes the rapid diagnosis and the auxiliary treatment of the autism.
The auxiliary diagnosis system for autism provided in the embodiment of the present invention and the auxiliary diagnosis method for autism provided in the foregoing embodiment are based on the same inventive concept, and therefore, for more specific working processes of each module in this embodiment, reference may be made to corresponding contents disclosed in the foregoing embodiment, and details are not repeated herein.
Example 3
As shown in fig. 3, an electronic device provided in an embodiment of the present invention includes:
a memory: for storing a computer program;
a processor: for implementing the steps of the autism assistance method in embodiment 1 as described above when executing the computer program, the specific steps of the autism assistance diagnosis method can refer to the corresponding contents disclosed in the foregoing embodiments, and are not described herein again.
It should be understood that, in the embodiment of the present invention, the Processor may be a Central Processing Unit (CPU), and the Processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In embodiments of the invention, the memory may include both read-only memory and random access memory, and provides instructions and data to the processor. The portion of memory may also include non-volatile random access memory. For example, the memory may also store device type information.
Example 4
In the computer-readable storage medium provided in the embodiment of the present invention, when being executed by a processor, the computer program implements the steps of any one of the autism auxiliary diagnosis methods in embodiment 1, and for specific steps of the autism auxiliary diagnosis method, reference may be made to corresponding contents disclosed in the foregoing embodiments, which are not described herein again.
The computer readable storage medium may be a memory of the identification device provided by the foregoing embodiments, for example, a hard disk or a memory of the identification device. The computer readable storage medium may also be an external memory of the apparatus, such as a plug-in hard disk provided on the identification device, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the computer readable storage medium may also include both internal and external memory of the identification device. The computer readable storage medium is used for storing the computer program and other programs and data. The computer readable storage medium may also be used to temporarily store data that has been output or is to be output.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (8)
1. An auxiliary diagnostic method for autism, comprising the steps of:
s1, acquiring video data of the target person;
s2, preprocessing the video data to obtain a characteristic data set of the target person, wherein the characteristic data set comprises behavior data and face data;
s3, inputting the behavior data and the face data into an auxiliary diagnosis analysis model to obtain a behavior evaluation index and an emotion evaluation index;
and S4, obtaining a comprehensive index according to the behavior evaluation index and the emotion evaluation index, evaluating the degree of the target user suffering from the autism according to the comprehensive index, and outputting a diagnosis suggestion.
2. The method for assisting diagnosis of autism according to claim 1, wherein in S1, the video data of the target person is video data of the target person in a specific scene.
3. The method of claim 2, wherein the step of preprocessing the video data to obtain the feature data set of the target person in step S2 comprises:
randomly sampling video data of a target person in a specific scene;
frame splitting is carried out on the video data after random sampling, and Gaussian fuzzy noise reduction processing is carried out on each frame of video data;
and inputting the video data subjected to noise reduction into the graph convolution neural network model to obtain a characteristic data set of the target person.
4. The method according to claim 1, wherein in S3, the aided diagnosis analysis model comprises a first neural network model and a second neural network model; the method for inputting the behavior data into the auxiliary diagnosis analysis model to obtain the behavior evaluation index and the emotion evaluation index comprises the following steps:
inputting the behavior data into a first neural network model trained in advance to obtain a behavior evaluation index;
and inputting the face data into a pre-trained second neural network model to obtain the emotion assessment index.
5. The aided diagnosis method of claim 4, wherein in S3, the training method of the first neural network model comprises:
acquiring behavior data of a sample user, and marking the behavior data of the sample user;
and training the first neural network model by using the behavior data of the sample user and the label corresponding to the behavior data.
6. An autism assistance system, comprising:
the data acquisition module is used for acquiring video data of a target person;
the data processing module is used for preprocessing the video data to obtain a characteristic data set of a target person, wherein the characteristic data set comprises behavior data and facial data;
the auxiliary analysis module is used for inputting the behavior data and the face data into an auxiliary diagnosis analysis model to obtain a behavior evaluation index and an emotion evaluation index;
and the diagnosis suggestion module is used for obtaining a comprehensive index according to the behavior evaluation index and the emotion evaluation index, evaluating the degree of the target user suffering from the autism according to the comprehensive index, and outputting a diagnosis suggestion.
7. An electronic device, comprising:
a memory: for storing a computer program;
a processor: the steps for implementing the method for aided diagnosis of autism according to any one of claims 1 to 5 when executing said computer program.
8. A computer-readable storage medium, characterized in that the computer program realizes the steps of the method for assisted diagnosis of autism according to any of claims 1 to 5 when being executed by a processor.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US20140304200A1 (en) * | 2011-10-24 | 2014-10-09 | President And Fellows Of Harvard College | Enhancing diagnosis of disorder through artificial intelligence and mobile health technologies without compromising accuracy |
| CN110349674A (en) * | 2019-07-05 | 2019-10-18 | 昆山杜克大学 | Autism-spectrum obstacle based on improper activity observation and analysis assesses apparatus and system |
| CN111883252A (en) * | 2020-07-29 | 2020-11-03 | 济南浪潮高新科技投资发展有限公司 | Auxiliary diagnosis method, device, equipment and storage medium for infantile autism |
| US20210015416A1 (en) * | 2018-12-14 | 2021-01-21 | Shenzhen Institutes Of Advanced Technology | Method for evaluating multi-modal emotion cognition capability of patient with autism spectrum disorder |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140304200A1 (en) * | 2011-10-24 | 2014-10-09 | President And Fellows Of Harvard College | Enhancing diagnosis of disorder through artificial intelligence and mobile health technologies without compromising accuracy |
| US20210015416A1 (en) * | 2018-12-14 | 2021-01-21 | Shenzhen Institutes Of Advanced Technology | Method for evaluating multi-modal emotion cognition capability of patient with autism spectrum disorder |
| CN110349674A (en) * | 2019-07-05 | 2019-10-18 | 昆山杜克大学 | Autism-spectrum obstacle based on improper activity observation and analysis assesses apparatus and system |
| CN111883252A (en) * | 2020-07-29 | 2020-11-03 | 济南浪潮高新科技投资发展有限公司 | Auxiliary diagnosis method, device, equipment and storage medium for infantile autism |
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