KR102518690B1 - Apparatus for purifying data for early screening of developmental disabilities based on learning model and method therefor - Google Patents

Abstract

An apparatus for purifying data includes a data processing unit that divides a streaming image including image and audio into predetermined time units to generate a plurality of unit images, and outputs the plurality of unit images according to a playback order of the streaming image; The image and audio of the unit video are analyzed using a learning model to determine the speaker of the unit video, the unit video is classified into a question part and a response part according to the identified speaker, and the unit video of the question part is analyzed. A data discriminating unit for classifying the type of question and classifying a category of a response performed with the response by analyzing a unit image of the response portion; a reproduction time of the question portion and the response portion in the streaming video; and a data cleaning unit for labeling and storing the type of question of the question part and the category of the response of the response part.

Description

Apparatus for purifying data for early screening of developmental disabilities based on learning model and method therefor}

The present invention relates to a technology for refining data, and more particularly, to an apparatus and method for refining data for early screening of developmental disabilities based on a learning model.

Developmental Disability is a set of brain and nervous system developmental disorders that begin in infancy and refers to severely delayed or unachieved development in terms of language, communication, cognition, and social skills. Developmental disabilities are defined as intellectual disabilities and autistic disabilities. In Korea, the total number of people with disabilities tends to decrease every year. It is estimated to be more.

Autism Spectrum Disorders (ASD) can be diagnosed around the age of 2, continue throughout life thereafter, and cause abnormalities in many of the most basic areas of development, resulting in a child's independent development, education, and quality of life in the family. It can be said to be a major obstacle. Early detection and early intervention are very important issues in both clinical and research aspects of ASD. Infancy and early childhood are a period of high plasticity of the brain, which not only provides an opportunity to change to a close to normal form, but also prevents secondary neurological damage and The gradual accumulation of secondary serious behavioral problems can be prevented in advance.

ASD diagnosis includes direct observation, information provided by caregivers and teachers, detailed history of the growth process, objective/quantitative evaluation of cognitive ability or other psychological functions, tests for differential diagnosis, neurological evaluation, and brain function tests. in need. Existing ASD screening tools take a very long training process for use, inconsistency in diagnosis exists depending on the experience and ability of individual experts, and it takes at least 6 to 7 hours of examination time and resources to diagnose one child. very voluminous Also, in the case of infants/children, depending on where and with whom the diagnostic test was performed, behaviors generally seen in daily life may be very different. Therefore, it is necessary to develop an ASD detection screening tool for infants/children for the convergence of artificial intelligence technology, and an AI-based solution that can efficiently objectively and quantitatively evaluate the content and cognitive ability or psychological change to which it is applied. . In other words, problem solving through the ASD early screening system, such as non-verbal communication recognition through automatic analysis and abnormal/homological target behavior recognition based on a new screening test tool, multi-sensory data collection of infants/children, and complex information-based psychological prediction need.

Korean Patent Publication No. 2020-0085766 published on July 15, 2020 (Name: Cognitive dysfunction diagnostic device and cognitive dysfunction diagnostic program)

An object of the present invention is to provide an apparatus and method for refining data for early screening of developmental disabilities based on a learning model.

An apparatus for purifying data according to a preferred embodiment of the present invention for achieving the above object is to divide a streaming video including video and audio into predetermined time units to generate a plurality of unit videos, and generate the streaming video. A data processing unit that outputs the plurality of unit images according to the playback order of the unit video and analyzes the image and audio of the unit video using a learning model to determine the speaker of the unit video and outputs the unit video according to the identified speaker. a data discriminating unit for classifying a question part and a response part, analyzing a unit image of the question part to classify a type of question, and classifying a category of a reaction performed with the response by analyzing a unit image of the response part; and a data refiner for labeling and storing reproduction times of the question part and the response part, the question type of the question part, and the response category of the response part with respect to the streaming video.

The learning model receives an image of the unit image and inputs an image discrimination network that calculates an image discrimination vector representing a probability that a speaker is a child and a probability that a speaker is an adult from the input image, and inputs audio corresponding to the image of the unit image. and a speech discrimination network that calculates a speech discrimination vector representing the probability that the speaker is a child and the probability that the speaker is an adult from the input speech, and the probability that the speaker is a child and the probability that the speaker is an adult by merging the image discrimination vector and the speech discrimination vector It includes a dialogue discrimination network that calculates a dialogue discrimination vector representing .

When the probability that the speaker of the conversation discrimination vector is a child is higher than the probability that the speaker is an adult, the learning model calculates a response discrimination vector representing a probability that a response expressed through the image and the voice belongs to each of a plurality of preset response categories. A response discriminating network that calculates a question discriminating vector indicating a probability that a question appearing in the speech belongs to each of a plurality of preset question types, when the probability that the speaker of the dialogue discriminating vector is an adult is higher than the probability that the speaker is a child It further includes a discriminant network.

A method for purifying data according to a preferred embodiment of the present invention for achieving the above object is a method in which a data processing unit divides a streaming video including image and audio by a predetermined time unit to generate a plurality of unit images, The step of outputting the plurality of unit images according to the playback order of the streaming video, and the data discriminating unit analyzes the image and audio of the unit video using a learning model to determine the speaker of the unit video, and determines the speaker of the unit video. Classifying the unit image into a question part and a response part, classifying the type of question by analyzing the unit image of the question part, and classifying the category of the response performed with the response by analyzing the unit image of the response part and labeling and storing, by a data purifying unit, reproduction times of the question part and the response part, the question type of the question part, and the response category of the response part in the streaming video.

The classifying step may include receiving an image of the unit image by the image discrimination network of the learning model and calculating an image discrimination vector representing a probability that the speaker is a child and a probability that the speaker is an adult from the input image; a step in which a voice discrimination network receives a voice corresponding to the image of the unit video and calculates a voice discrimination vector representing a probability that a speaker is a child and a probability that a speaker is an adult from the input voice; and merging the image discrimination vector and the voice discrimination vector to calculate a conversation discrimination vector indicating a probability that the speaker is a child and a probability that the speaker is an adult.

In the step of classifying, after the step of calculating the dialog discrimination vector, if the response discrimination network of the learning model has a higher probability that the speaker of the dialog discrimination vector is a child than a probability that the speaker is an adult, the reaction expressed through the image and the voice. Calculating a response discriminant vector representing a probability of belonging to each of the plurality of preset response categories, and when the question discriminant network of the learning model has a higher probability that the speaker of the dialogue discriminant vector is an adult than a child, the The method may further include calculating a question discriminant vector indicating a probability that a question appeared in the speech belongs to each of a plurality of preset question types.

According to the present invention, in a test for early screening of developmental disorders, the reproduction time of the part the examiner asks, the reproduction time of the part the subject responds to the question, the type of the examiner's question, and the category of the subject's response are calculated using a learning model. classified through video, and a corresponding label can be automatically assigned to the corresponding streaming video. In this way, it is possible to efficiently collect data necessary for the development of an ASD detection screening test tool and the research of an AI-based solution technique that can efficiently perform objective and quantitative evaluation of contents and cognitive abilities or psychological changes to which it is applied.

1 is a diagram for explaining the configuration of an apparatus for refining data for early screening of developmental disabilities based on a learning model according to an embodiment of the present invention.
2 is a diagram for explaining the detailed configuration of an apparatus for refining data for early screening of developmental disabilities based on a learning model according to an embodiment of the present invention.
3 is a diagram for explaining the configuration of a learning model for refining data for early screening of developmental disabilities based on a learning model according to an embodiment of the present invention.
4 is a flowchart illustrating a method of learning a learning model (ML) for refining data according to an embodiment of the present invention.
5 is a flowchart illustrating a method for refining data for early screening of developmental disabilities using a learning model (ML) according to an embodiment of the present invention.
6 is a screen example for explaining a method for refining data for early screening of developmental disabilities using a learning model (ML) according to an embodiment of the present invention.
7 is a diagram illustrating a computing device according to an embodiment of the present invention.

Prior to the detailed description of the present invention, the terms or words used in this specification and claims described below should not be construed as being limited to a common or dictionary meaning, and the inventors should use their own invention in the best way. It should be interpreted as a meaning and concept corresponding to the technical idea of the present invention based on the principle that it can be properly defined as a concept of a term for explanation. Therefore, the embodiments described in this specification and the configurations shown in the drawings are only the most preferred embodiments of the present invention, and do not represent all of the technical ideas of the present invention, so various equivalents that can replace them at the time of the present application. It should be understood that there may be water and variations.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. At this time, it should be noted that the same components in the accompanying drawings are indicated by the same reference numerals as much as possible. In addition, detailed descriptions of well-known functions and configurations that may obscure the gist of the present invention will be omitted. For the same reason, some components in the accompanying drawings are exaggerated, omitted, or schematically illustrated, and the size of each component does not entirely reflect the actual size.

First, an apparatus for refining data for early screening of developmental disabilities based on a learning model according to an embodiment of the present invention will be described. 1 is a diagram for explaining the configuration of an apparatus for refining data for early screening of developmental disabilities based on a learning model according to an embodiment of the present invention. 2 is a diagram for explaining the detailed configuration of an apparatus for refining data for early screening of developmental disabilities based on a learning model according to an embodiment of the present invention.

First, referring to FIG. 1 , the data purification device 10 according to an embodiment of the present invention includes a camera unit 11, a sensor unit 12, an input unit 14, a display unit 15, a storage unit 16 and A control unit 17 is included.

The camera unit 11 is for taking a color image including a plurality of continuous color frames and sound. The camera unit 11 includes a plurality of cameras. The plurality of cameras may generate color images by photographing the examiner and the examinee from different directions, respectively.

The sensor unit 12 includes a plurality of Kinect sensors. Each of the plurality of Kinect sensors corresponds to each of the plurality of cameras of the camera unit 11, and emits infrared rays to the examiner and the examinee in the same direction as each of the plurality of cameras of the camera unit 11, and from this, a plurality of consecutive A depth image including an IR depth frame is captured. Such a depth image is captured in synchronization with a color image.

The input unit 14 may receive a user's manipulation for controlling the data purification device 10, generate an input signal, and transmit it to the control unit 17. The input unit 14 includes various keys and buttons for controlling the data purification device 10.

The display unit 15 is for displaying a screen, and can visually provide the menu of the data purifier 10, input data, function setting information, and various other information to the user. The display unit 15 may be formed of a Liquid Crystal Display (LCD), Organic Light Emitting Diodes (OLED), Active Matrix Organic Light Emitting Diodes (AMOLED), or the like. Meanwhile, the display unit 15 may be implemented as a touch screen. In this case, the display unit 15 includes a touch sensor. The touch sensor detects a user's touch input. The touch sensor may be configured as a touch sensor such as a capacitive overlay, a pressure sensor, a resistive overlay, or an infrared beam, or a pressure sensor. . In addition to the above sensors, all types of sensor devices capable of detecting contact or pressure of an object may be used as the touch sensor of the present invention. The touch sensor may detect a user's touch input, generate a detection signal including an input coordinate representing a touched position, and transmit the generated detection signal to the controller 17 .

The storage unit 16 serves to store programs and data necessary for the operation of the data purifier 10. The storage unit 16 may store streaming images captured by a plurality of cameras of the camera unit 11, depth images captured by a plurality of kinetic sensors of the sensor unit 12, and voice collected by the audio processing unit 13. there is. Various types of data stored in the storage unit 16 may be deleted, changed, or added according to user manipulation.

The control unit 17 may control the overall operation of the data purifying device 10 and signal flow between internal blocks of the data purifying device 10, and may perform a data processing function of processing data. In addition, the control unit 17 basically plays a role of controlling various functions of the data purifying device 10. The controller 17 may include a central processing unit (CPU), a digital signal processor (DSP), and the like.

Referring to FIG. 2 , the control unit 17 includes a model generation unit 100, a data processing unit 200, a data determination unit 300, and a data purification unit 400 for labeling.

First, the model generator 100 is for learning a learning model (LM: Leaning Model). A learning model (LM: Leaning Model) may be a deep neural network. In particular, the learning model (LM) may be representative of a Convolution Neural Network (CNN). The model generating unit 100 provides the learned model LM to the data discriminating unit 300 . This learning model (LM) will be described in more detail below.

When the data processing unit 100 receives a color image including a plurality of continuous color frames and audio and a depth image including a plurality of continuous depth frames through the camera unit 11 and the sensor unit 12, a plurality of A multi-channel format streaming video including images and audio including continuous color frames and a plurality of continuous depth frames is generated. In addition, the data processing unit 100 may generate a plurality of unit images by dividing an image including a color frame and a depth frame and a streaming image including audio in units of a predetermined time. The data processing unit 100 outputs a plurality of unit images according to the playback order of the streaming images.

The data discrimination unit 200 analyzes the image and audio of the unit video using the learning model (LM) to determine the speaker of the unit video, and classifies the unit video into a question part and a response part according to the identified speaker. In addition, the data discrimination unit 200 analyzes the unit image of the question part using the learning model (LM) to classify the type of question, and analyzes the unit image of the response part to classify the category of the response performed as a response. can

The data purification unit 300 may label and store reproduction times of the question part and the response part, the question type of the question part, and the response category of the response part in the streaming video.

Next, the configuration of the learning model for refining data for early screening of developmental disabilities based on the learning model according to an embodiment of the present invention will be described. 3 is a diagram for explaining the configuration of a learning model for refining data for early screening of developmental disabilities based on a learning model according to an embodiment of the present invention.

Referring to Figure 3, the learning model (ML) is composed of a plurality of sub-network (sub-network). A plurality of sub-networks include a video discrimination network (VDN), a voice discrimination network (ADN), a dialogue discrimination network (CDN), a response discrimination network (RDN), and a question discrimination network ( QDN).

Each of the plurality of sub-networks includes an input layer (IL), at least one pair of alternately repeated convolution layers (CL) and a pooling layer (PL), and at least one complete It includes a fully-connected layer (FL) and an output layer (OL). Each of the plurality of sub-networks calculates an output value by performing an operation in which a plurality of inter-layer weights are applied to an input value. In particular, the convolution layer (CL) and the pooling layer (PL) are composed of at least one feature map (FM). The feature map (FM) is derived as a result of receiving values obtained by applying weights and thresholds to the calculation results of the previous layer and performing calculations on the input values. These weights are applied through a filter or kernel (W), which is a weight matrix of a predetermined size. In an embodiment of the present invention, the first filter W1 is used for the convolution operation of the convolution layer CL, and the second filter W2 is used for the pooling operation of the pooling layer PL.

When an input value (eg, image, audio, vector, etc.) is input to the input layer IL, the convolution layer CL performs convolution using the first filter W1 on the frame input to the input layer IL ( At least one first feature map FM1 is derived by performing a convolution operation and an operation using an activation function. Then, the pooling layer PL performs a pooling (or sub-sampling) operation using the second filter W2 on at least one first feature map FM1 of the convolution layer CL to obtain at least one feature map FM1. A second feature map FM2 is derived.

The complete connection layer FL is composed of a plurality of operation nodes F1 to Fm. The plurality of operation nodes F1 to Fm of the complete connection layer CL calculate a plurality of operation values through an operation using an activation function for at least one second feature map FM2 of the pooling layer PL.

The output layer OL includes a plurality of output nodes O1 to On. Each of the plurality of operation nodes F1 to Fm of the complete connection layer FL is connected to the output node O1 to On of the output layer OL as a channel having a weight (W). In other words, the plurality of calculation values of the plurality of calculation nodes F1 to Fm are input to each of the plurality of output nodes O1 to On after a weight W is applied. Accordingly, the plurality of output nodes O1 to On of the output layer OL calculates an output value through an operation by an activation function for a plurality of calculation values to which the weight W of the fully connected layer FL is applied.

Activation functions used in the aforementioned convolutional layer (CL), fully connected layer (FL), and output layer (OL) are sigmoid, hyperbolic tangent (tanh), exponential linear unit (ELU), and ReLU. (Rectified Linear Unit), Leakly ReLU, Maxout, Minout, Softmax, etc. can be exemplified. Any one of these activation functions may be selected and applied to the convolutional layer (CL), the fully connected layer (FL), and the output layer (OL).

In particular, each of the plurality of output nodes O1 to On of the output layer OL is a probability that the speaker is a child and a probability that the speaker is an adult, or a probability that a reaction expressed through an image and the voice belongs to each of a plurality of predetermined categories of reactions, , represents a probability that a question appearing in the voice belongs to each of a plurality of preset question types. For example, there are two output nodes O1 and O2, the first output node O1 may correspond to the probability that the speaker is a child, and the second output node O2 may correspond to the probability that the speaker is an adult. In this case, the output value of the first output node O1 is the probability that the speaker is a child, and the output value of the second output node O2 is the probability that the speaker is an adult. In this case, if the output values of the plurality of output nodes O1 and O2 are 0.256 and 0.744, since the output value of the first output node O1 is 0.026, the probability that the speaker is a child is 26%, and the second output node O2 Since the output value of is 0.744, there is a 74% probability that the speaker is an adult. The output value of each node may be a vector having the number of elements corresponding to the number of nodes. Accordingly, the data discrimination unit 300 may classify the input data according to the probability inherent in the vector output from the subnetwork. For example, as in the above example, if the output values of the plurality of output nodes O1 and O2 are 0.256 and 0.744, the vector is [0.256, 0.744], which means that the probability that the speaker is a child is 26% and the speaker is an adult. The probability represents 74%. Since the probability that the speaker is an adult is 74%, which is higher than the probability that the speaker of 26% is a child, the data discrimination unit 300 can classify the speaker of the input data as an adult.

As such, each of a plurality of sub-networks according to an embodiment of the present invention calculates a vector by performing an operation in which a plurality of inter-layer weights are applied to an input value. That is, the image discrimination network (VDN) receives an image of a unit image and calculates an image discrimination vector representing a probability that a speaker is a child and a probability that a speaker is an adult from the input image. The voice discrimination network (ADN) receives voice corresponding to the image of the unit video, and calculates a voice discrimination vector representing a probability that the speaker is a child and a probability that the speaker is an adult from the input voice. The dialogue discrimination network (CDN) merges the image discrimination vector and the audio discrimination vector to calculate a dialogue discrimination vector indicating a probability that the speaker is a child and a probability that the speaker is an adult. The response discrimination network (RDN) calculates a response discrimination vector representing the probability that a response expressed through video and voice belongs to each of a plurality of predetermined response categories, when the probability that the speaker of the conversation discrimination vector is a child is higher than the probability that the speaker is an adult. do. When the probability that the speaker of the dialog discrimination vector is an adult is higher than the probability that the speaker is a child, the question discrimination network (QDN) calculates a question discrimination vector representing a probability that a question appeared in the speech belongs to each of a plurality of preset question types.

Next, a method for refining data for early screening of developmental disabilities based on a learning model according to an embodiment of the present invention will be described. Prior to a detailed description of a method for refining data, a method for training a learning model (ML) will be described. 4 is a flowchart illustrating a method of learning a learning model (ML) for refining data according to an embodiment of the present invention.

Referring to FIG. 4 , the model generating unit 200 prepares learning data in step S110. Here, the learning data includes a plurality of unit images for learning in which streaming images including images and audio captured by the inspector and the examinee are divided into predetermined time units, and labels for the plurality of unit images for learning. In particular, labels include speaker labels, question labels, and response labels. The speaker label distinguishes whether the speaker of the learning unit image is a child or an adult. The question label distinguishes what type of question the question is from when the unit image is a question portion in which the examiner asks the examinee a question. Here, the types of questions may be ADOS (Autism Diagnostic Observation Schedule) format and BeDevel (Behavior Development Screening for Toddler) format. When the unit image is a response part representing the response of the examinee to the examiner's question, the response label distinguishes whether the response type of the examinee belongs to a certain category among a plurality of preset responses. Here, the category of response may include pointing at an object, customary gesture, distressed vocalization, biting, eye contact with an inspector, lifting and showing an object, looking at an object, and no response.

The model generating unit 200 individually trains the image discrimination network (VDN) and the voice discrimination network (ADN) using the training data in step S120. In step S120, the model generating unit 200 inputs an image from among unit images for learning to the VDN, and the VDN applies an unlearned weight between a plurality of layers to the input image. An image discrimination vector representing a probability that the speaker is a child and a probability that the speaker is an adult is calculated by performing an operation. Then, the model generating unit 200 calculates a loss representing the difference between the image discrimination vector and the speaker label, and performs optimization to modify the weight of the VDN so that the loss is minimized. In addition, in step S120, the model generating unit 200 inputs the audio corresponding to the image of the unit video for learning input to the video discrimination network (VDN) to the audio discrimination network (ADN), and the audio discrimination network (ADN) inputs the input A voice discrimination vector representing the probability that the speaker is a child and the probability that the speaker is an adult is calculated by performing an operation in which unlearned weights are applied between a plurality of layers for the generated voice. Then, the model generator 200 calculates a loss representing the difference between the speech discrimination vector and the speaker label, and performs optimization to modify the weight of the speech discrimination network (ADN) so that the loss is minimized.

Next, the model generating unit 200 learns the dialogue discrimination network (CDN) using the training data in step S130. In step S130, the model generating unit 200 inputs an image of the unit image for learning to the image discrimination network (VDN), and transmits a sound corresponding to the image of the unit image for learning input to the image discrimination network (VDN) to the voice discrimination network ( ADN). Then, the image discrimination network (VDN) calculates an image discrimination vector representing the probability that the speaker is a child and the probability that the speaker is an adult by performing an operation in which weights between a plurality of layers are applied to the input image, and the voice discrimination network (ADN) performs an operation in which a plurality of inter-layer weights are applied to the input voice, and calculates a voice discrimination vector indicating a probability that the speaker is a child and a probability that the speaker is an adult. The calculated image discrimination vector and voice discrimination vector are input to the dialogue discrimination network (CDN), and the dialogue discrimination network (CDN) performs an operation in which a plurality of inter-layer weights are applied to the image discrimination vector and the speech discrimination vector. A dialog discriminant vector representing the probability of being a child and the probability of being an adult is calculated. Then, the model generating unit 200 calculates a first loss representing the difference between the image discrimination vector and the speaker label, calculates a second loss representing the difference between the speech discrimination vector and the speaker label, and calculates the dialogue discrimination vector and the speaker label. A third loss representing the difference from the label is calculated. Next, the model generator 200 uses the VDN and ADN so that the third loss is smaller than the first and second losses and the first, second, and third losses are minimized. ) and the optimization of modifying the weights of the dialogue discrimination network (CDN).

Next, the model generator 200 trains the question discrimination network (QDN) in step S140. In step S140, the model generating unit 200 inputs images from among the unit images for learning to the image discrimination network (VDN), and transmits audio corresponding to the images of the unit images for learning input to the image discrimination network (VDN) to the voice discrimination network ( ADN). Then, the image discrimination network (VDN) calculates an image discrimination vector representing the probability that the speaker is a child and the probability that the speaker is an adult by performing an operation in which weights between a plurality of layers are applied to the input image, and the voice discrimination network (ADN) performs an operation in which a plurality of inter-layer weights are applied to the input voice, and calculates a voice discrimination vector indicating a probability that the speaker is a child and a probability that the speaker is an adult. The calculated image discrimination vector and voice discrimination vector are input to the dialogue discrimination network (CDN), and the dialogue discrimination network (CDN) performs an operation in which a plurality of inter-layer weights are applied to the image discrimination vector and the speech discrimination vector. A dialog discriminant vector representing the probability of being a child and the probability of being an adult is calculated. The calculated dialogue discrimination vector is input to the question discrimination network (QDN), and at the same time, the voice input to the speech discrimination network (ADN) through the shortcut SC is input to the question discrimination network (QDN). Accordingly, the question discrimination network (QDN) calculates a dialogue discrimination vector and a question discrimination vector representing a probability that a question appearing in the speech belongs to each of a plurality of preset question types. Here, the types of the plurality of preset questions may exemplify an ADOS format and a BeDevel format. In particular, when the probability of the speaker being a child is higher than the probability of being an adult in the dialogue discrimination vector, when this dialogue discrimination vector is input to the question discrimination network (QDN), the dialogue discrimination vector plays a role in canceling the input speech, If the probability that the speaker is an adult in the discriminant vector is higher than the probability that the speaker is a child, when the dialog discriminant vector is input to the question discrimination network (QDN), it plays a role in allowing the input voice to be input as it is without changing the input voice. Accordingly, the question discrimination network (QDN) may calculate a question discrimination vector representing a probability that a question expressed in speech belongs to each of a plurality of preset question types only when the probability that the speaker is an adult is higher than the probability that the speaker is a child. Accordingly, when the question discrimination vector is calculated, the model generating unit 200 calculates a loss representing the difference between the question discrimination vector and the question label, and the VDN and the voice discrimination network so that the calculated loss is minimized. Optimization is performed to modify the weights of the question discrimination network (QDN) while the weights of the question discrimination network (ADN) and the dialogue discrimination network (CDN) are fixed.

Next, the model generating unit 200 learns the response discrimination network (RDN) in step S150. In step S150, the model generating unit 200 inputs images from among the unit images for learning to the image discrimination network (VDN), and transmits audio corresponding to the images of the unit images for learning input to the image discrimination network (VDN) to the voice discrimination network ( ADN). Then, the image discrimination network (VDN) calculates an image discrimination vector representing the probability that the speaker is a child and the probability that the speaker is an adult by performing an operation in which weights between a plurality of layers are applied to the input image, and the voice discrimination network (ADN) performs an operation in which a plurality of inter-layer weights are applied to the input voice, and calculates a voice discrimination vector indicating a probability that the speaker is a child and a probability that the speaker is an adult. The calculated image discrimination vector and voice discrimination vector are input to the dialogue discrimination network (CDN), and the dialogue discrimination network (CDN) performs an operation in which a plurality of inter-layer weights are applied to the image discrimination vector and the speech discrimination vector. A dialog discriminant vector representing the probability of being a child and the probability of being an adult is calculated. The calculated dialog discrimination vector is input to the response discrimination network (RDN), and at the same time, the video input to the video discrimination network (VDN) through the shortcut (SC) and the voice input to the voice discrimination network (ADN) are input to the question discrimination network ( QDN). Accordingly, the response discrimination network (RDN) calculates a dialog discrimination vector, a response discrimination vector representing a probability that a response appearing in the video and audio belongs to each of a plurality of preset response categories. Here, the plurality of preset response categories may include pointing at an object, customary gesture, distressed vocalization, biting, eye contact with an inspector, lifting and showing an object, looking at an object, and no response. In particular, when the probability that the speaker is an adult in the dialogue discrimination vector is higher than the probability that the speaker is a child, when this dialogue discrimination vector is input to the response discrimination network (RDN), the dialogue discrimination vector serves to cancel the input image and voice. , If the probability that the speaker is a child in the dialog discrimination vector is higher than the probability that the speaker is an adult, when the dialog discrimination vector is input to the response discrimination network (RDN), it plays a role in inputting the corresponding voice as it is without changing the input image and voice. do. Therefore, the response discrimination network (RDN) can calculate a response discrimination vector representing a probability that a response shown in the video and audio belongs to each of a plurality of preset response categories only when the probability that the speaker is a child is higher than the probability that the speaker is an adult. . Accordingly, when the response discriminant vector is calculated, the model generator 200 calculates a loss representing the difference between the response discriminant vector and the response label, and the VDN and the voice discriminant network so that the calculated loss is minimized. Optimization is performed to modify the weights of the response discrimination network (RDN) while the weights of the ADN and the dialogue discrimination network (CDN) are fixed.

As described above, when the learning of the VDN, ADN, CDN, and QDN is completed, the learning of the learning model (ML) is completed.

Then, a method for refining data for early screening of developmental disabilities using the learning model (ML) on which learning is completed will be described. 5 is a flowchart illustrating a method for refining data for early screening of developmental disabilities using a learning model (ML) according to an embodiment of the present invention. 6 is a screen example for explaining a method for refining data for early screening of developmental disabilities using a learning model (ML) according to an embodiment of the present invention.

Referring to FIG. 5 , the data processing unit 100 receives a streaming image including images and audio captured by at least two people, for example, an examiner and a test subject, in step S210, and transmits the streaming image in units of a predetermined time in step S220. A plurality of unit images are generated by division, and the plurality of unit images are output according to the playback order of the streaming video.

The data discrimination unit 120 sequentially inputs a plurality of unit images to the learning model ML according to the playback order of the streaming images in step S230. The image of the unit video is input to the video discrimination network (VDN), and the audio corresponding to the video of the unit video input to the video discrimination network (VDN) is input to the audio discrimination network (ADN).

Then, in step S230, the image discrimination network (VDN) calculates an image discrimination vector representing the probability that the speaker is a child and the probability that the speaker is an adult by performing an operation in which weights between a plurality of layers are applied to the input image, and the voice discrimination network (ADN) calculates a voice discrimination vector representing a probability that a speaker is a child and a probability that a speaker is an adult by performing an operation in which weights between a plurality of layers are applied to the input voice. The calculated image discrimination vector and voice discrimination vector are input to the dialog discrimination network (CDN), and in step S240, the dialog discrimination network (CDN) performs an operation in which a plurality of inter-layer weights are applied to the image discrimination vector and the voice discrimination vector. Thus, a dialog discriminant vector representing the probability that the speaker is a child and the probability that the speaker is an adult is calculated.

The calculated conversation discrimination vector is input to each of the question discrimination network (QDN) and response discrimination network (RDN), and the video input to the video discrimination network (VDN) through the shortcut (SC) and the input to the voice discrimination network (ADN). The voice is input to the question discrimination network (QDN), and the voice input to the voice discrimination network (ADN) through the shortcut SC is input to the question discrimination network (QDN). Accordingly, in step S260, the response discrimination network (RDN) calculates a response discrimination vector indicating the probability that the responses appearing in the video and audio belong to each of a plurality of preset response categories, and the question discrimination network (QDN) calculates the probability that the responses appearing in the voice. A question discriminant vector representing a probability that a question belongs to each of a plurality of preset question types is calculated.

A more detailed description of this step S260 is as follows. If the probability that the speaker is an adult in the dialog discrimination vector is higher than the probability that the speaker is a child, when this dialog discrimination vector is input to the response discrimination network (RDN), the dialog discrimination vector plays a role in canceling the input image and voice, and If the probability that the speaker is a child is higher than the probability that the speaker is an adult in the discrimination vector, when the conversation discrimination vector is input to the response discrimination network (RDN), it plays a role of inputting the corresponding voice as it is without changing the input image and voice. Therefore, in step S260, the response discrimination network (RDN) calculates a response discrimination vector representing the probability that the response shown in the video and audio belongs to each of a plurality of preset response categories, only when the probability that the speaker is a child is higher than the probability that the speaker is an adult. do. In addition, if the probability that the speaker is a child in the dialogue discrimination vector is higher than the probability that the speaker is an adult, when this dialogue discrimination vector is input to the question discrimination network (QDN), the dialogue discrimination vector plays a role of canceling the input voice, If the probability that the speaker is an adult in the discriminant vector is higher than the probability that the speaker is a child, when the dialog discriminant vector is input to the question discrimination network (QDN), it plays a role in allowing the input voice to be input as it is without changing the input voice. Therefore, the question discrimination network (QDN) calculates a question discrimination vector representing a probability that a question presented in the voice belongs to each of a plurality of preset question types only when the probability that the speaker is an adult is higher than the probability that the speaker is a child in step S260.

As described above, when any one of the dialogue discrimination vector, response discrimination vector, and question discrimination vector is calculated, the data discrimination unit 300 determines the speaker of the unit video from the dialogue discrimination vector in step S270, and assigns the identified speaker to the identified speaker. According to this, the unit image is classified into a question part and a response part, and the category of response performed as a response is classified from the response discriminant vector, and the type of question is classified from the question discriminant vector.

That is, the dialog discrimination vector represents the probability that the speaker is a child and the probability that the speaker is an adult, and the data discrimination unit 300 can determine that the speaker is an examiner if the probability that the speaker is an adult is higher than the probability that the speaker is a child. When the probability that the speaker is a child is higher than the probability that the speaker is an adult, the data discrimination unit 300 may determine that the speaker is the test subject. In addition, the data discrimination unit 300 may classify the corresponding unit image as a question part when it is determined that the speaker is the inspector, and classify the corresponding unit image as a response part when it is determined that the speaker is the examinee.

In addition, the response discriminant vector represents a probability that a response displayed in a unit image belongs to each of a plurality of preset response categories. Accordingly, the data discrimination unit 300 classifies a response category with the highest probability among a plurality of response categories as a response performed by the test subject in the corresponding unit image.

Further, the question discrimination vector represents a probability that a question appearing in a unit image belongs to each of a plurality of preset question types. Accordingly, the data discrimination unit 300 classifies a question type having the highest probability among a plurality of types of questions as a question performed by an examiner in a corresponding unit image.

Next, as shown in FIG. 6 according to the classification of the data discrimination unit 300 in step S280, the data purification unit 400 determines the reproduction time of the question part and the response part of the streaming video, the type of question and You can label categories of responses to store them.

In the test for early screening of developmental disorders according to the method described above, the playback time of the part the examiner asks, the playback time of the part the subject responds to the question, the type of question the examiner asks, and the category of the subject's response It can be classified through a learning model, and a corresponding label can be automatically assigned to the corresponding streaming video. In this way, it is possible to efficiently collect data necessary for the development of an ASD detection screening test tool and the research of an AI-based solution technique that can efficiently perform objective and quantitative evaluation of contents and cognitive abilities or psychological changes to which it is applied.

7 is a diagram illustrating a computing device according to an embodiment of the present invention. The computing device TN100 of FIG. 7 may be a device described in this specification, for example, the data purification device 10 or the like.

In the embodiment of FIG. 7 , the computing device TN100 may include at least one processor TN110, a transceiver TN120, and a memory TN130. In addition, the computing device TN100 may further include a storage device TN140, an input interface device TN150, and an output interface device TN160. Elements included in the computing device TN100 may communicate with each other by being connected by a bus TN170.

The processor TN110 may execute program commands stored in at least one of the memory TN130 and the storage device TN140. The processor TN110 may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present invention are performed. Processor TN110 may be configured to implement procedures, functions, methods, and the like described in relation to embodiments of the present invention. The processor TN110 may control each component of the computing device TN100.

Each of the memory TN130 and the storage device TN140 may store various information related to the operation of the processor TN110. Each of the memory TN130 and the storage device TN140 may include at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory TN130 may include at least one of read only memory (ROM) and random access memory (RAM).

The transmitting/receiving device TN120 may transmit or receive a wired signal or a wireless signal. The transmitting/receiving device TN120 may perform communication by being connected to a network.

On the other hand, the method according to the embodiment of the present invention described above may be implemented in the form of a program readable by various computer means and recorded on a computer-readable recording medium. Here, the recording medium may include program commands, data files, data structures, etc. alone or in combination. Program instructions recorded on the recording medium may be those specially designed and configured for the present invention, or those known and usable to those skilled in computer software. For example, recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks ( It includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media and ROM, RAM, flash memory, etc. Examples of program commands may include high-level language wires that can be executed by a computer using an interpreter, as well as machine language wires such as those produced by a compiler. These hardware devices may be configured to act as one or more software modules to perform the operations of the present invention, and vice versa.

The present invention has been described above using several preferred examples, but these examples are illustrative and not limiting. As such, those skilled in the art to which the present invention belongs will understand that various changes and modifications can be made according to the doctrine of equivalents without departing from the spirit of the present invention and the scope of rights set forth in the appended claims.

10: data purification device 11: camera unit
12: sensor unit 13: voice collection unit
14: input unit 15: display unit
16: storage unit 17: control unit
100: data processing unit 200: model generation unit
300: data determination unit 400: data purification unit

Claims (6)

In the apparatus for purifying data,
a data processing unit generating a plurality of unit images by dividing a streaming image including video and audio into predetermined time units and outputting the plurality of unit images according to a playback order of the streaming image;
The image and audio of the unit video are analyzed using a learning model to determine the speaker of the unit video, the unit video is classified into a question part and a response part according to the identified speaker, and the unit video of the question part is analyzed. a data discriminating unit for classifying the type of question and classifying a category of response performed with the response by analyzing a unit image of the response part; and
a data purification unit for labeling and storing reproduction times of the question part and the response part, the question type of the question part, and the response category of the response part in the streaming video;
characterized in that it includes
A device for cleaning data. According to claim 1,
The learning model is
an image discrimination network that receives an image of the unit image and calculates an image discrimination vector representing a probability that the speaker is a child and a probability that the speaker is an adult from the input image;
a voice discrimination network that receives voice corresponding to the image of the unit video and calculates a voice discrimination vector indicating a probability that the speaker is a child and a probability that the speaker is an adult from the input voice;
a conversation discrimination network for merging the image discrimination vector and the speech discrimination vector to calculate a conversation discrimination vector indicating a probability that the speaker is a child and a probability that the speaker is an adult;
characterized in that it includes
A device for cleaning data. According to claim 2,
The learning model is
If the probability that the speaker of the conversation discrimination vector is a child is higher than the probability that the speaker is an adult, a response discrimination network for calculating a response discrimination vector indicating a probability that the response expressed through the video and the voice belongs to each of a plurality of preset response categories. ; and
a question discrimination network calculating a question discrimination vector indicating a probability that a question expressed in the speech belongs to each of a plurality of predetermined question types, when the probability that the speaker of the dialogue discrimination vector is an adult is higher than the probability that the speaker is a child;
characterized in that it further comprises
A device for cleaning data. In the method for cleaning the data,
generating a plurality of unit images by dividing a streaming image including image and audio by a data processing unit in units of a predetermined time, and outputting the plurality of unit images according to a playback order of the streaming image;
The data discrimination unit analyzes the image and audio of the unit video using a learning model to discriminate the speaker of the unit video, classifies the unit video into a question part and a response part according to the identified speaker, and unit of the question part Classifying the type of question by analyzing the image, and classifying the category of the response performed with the response by analyzing the unit image of the response part; and
Labeling and storing, by a data purification unit, playback times of the question part and the response part, the question type of the question part, and the category of the response of the response part in the streaming video;
characterized in that it includes
A method for cleaning data. According to claim 4,
The classification step is
receiving the image of the unit image by the image discrimination network of the learning model, and calculating an image discrimination vector indicating a probability that the speaker is a child and a probability that the speaker is an adult from the input image;
receiving a voice corresponding to the image of the unit image by the voice discrimination network of the learning model, and calculating a voice discrimination vector representing a probability that the speaker is a child and a probability that the speaker is an adult from the input voice; and
calculating, by the dialogue discrimination network of the learning model, a dialogue discrimination vector indicating a probability that the speaker is a child and a probability that the speaker is an adult by merging the image discrimination vector and the speech discrimination vector;
characterized in that it includes
A method for cleaning data. According to claim 5,
The classification step is
After calculating the dialog discrimination vector,
If the response discrimination network of the learning model has a higher probability that the speaker of the dialog discrimination vector is a child than an adult, a response indicating a probability that the response indicated through the video and the voice belongs to each of a plurality of preset response categories. Calculating a discriminant vector; and
When the probability that the speaker of the dialogue discrimination vector is an adult is higher than the probability that the speaker of the conversation discrimination vector is a child, the question discrimination network of the learning model calculates a question discrimination vector representing a probability that a question appearing in the speech belongs to each of a plurality of preset question types. doing;
characterized in that it further comprises
A method for cleaning data.

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