KR20230023835A - Method to perform motor skill development diagnosis of autistic patients based on body balance change - Google Patents

Abstract

The present invention relates to a method for diagnosing the development of motor skills of an autistic patient based on a change in body balance included in a medical video, which comprises the steps of: acquiring a medical video; inputting the medical video into a pre-trained neural network model to detect an object of interest, which performs jumping for each frame of the medical video; calculating the jump height, a jump speed, and a change rate of the body balance according to jumping from the detected object of interest; and diagnosing the development of motor skills of an autistic patient, based on the calculated jump height, jump speed, and the change rate of the body balance to generate diagnostic results. Accordingly, the accuracy of diagnosing the development of the motor skills of an autistic patient can be enhanced.

Description

A method for diagnosing the development of motor skills in autistic patients based on body balance changes

The present invention relates to a method for diagnosing motor ability development in autistic patients based on changes in body balance, and more particularly, to a method for diagnosing motor ability development in autistic patients based on changes in body balance included in a medical image.

In general, the control of body balance is significant from a cognitive science point of view because it involves complex interactions between information processing, motor planning, and the timing and sequence of muscle movements.

Circuits in the brain that connect perceptual information and behavior are known to form the cornerstones of higher mental functions such as perception and emotion.

In particular, basic motor impairment associated with standing and walking can have a key impact on the emergence of cognitive and social impairments.

For example, in order to analyze children's behavior data, coarse and fine motor movements are usually observed and analyzed, and about 70% of children with autism may have problems with muscle development.

Currently, research on muscle movements of children with autism is being conducted, but only some studies on small muscle movements such as grasping movements of autistic children are known, and studies on gross motor movements such as walking and jumping are still unknown. there is no bar

In addition, in the past, in order to measure the degree of body balance and muscle movement ability, it is necessary to measure only at a specific location where various measuring devices are installed, or to measure the measurement by attaching the measuring device to the body of the measurement subject due to environmental limitations. There were problems that were not easy to measure.

Therefore, in the future, it is required to develop a method for diagnosing the development of motor ability in autistic patients that can accurately measure changes in body balance without environmental limitations such as space and equipment and increase the accuracy of the diagnosis of motor ability development in autistic patients based on this. there is.

Republic of Korea Patent Publication No. 10-2017-0062113 (2017. 06. 07)

One object of the present invention to solve the above problems is to detect an object of interest performing a jumping motion using a neural network model, and to develop motor skills of autism patients based on the jumping height, jumping speed, and body balance change rate. By performing the diagnosis, it is possible to accurately measure changes in body balance without environmental limitations such as space and equipment for diagnosing the development of motor skills in autistic patients, and based on this, change in body balance that can increase the accuracy of the diagnosis of motor skill development in autistic patients. It is to provide a method for diagnosing the development of motor skills in autistic patients.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

A method for diagnosing motor ability development in autistic patients based on body balance change according to an embodiment of the present invention for solving the above problems includes acquiring a medical image, inputting the medical image into a pre-learned neural network model, and Detecting an object of interest performing a jumping motion for each frame of the medical image, calculating a jump height, jumping speed, and body balance change rate according to the jumping motion from the detected object of interest, and calculating the calculated jump and diagnosing the motor ability development of the autistic patient based on the height, jumping speed, and body balance change rate, and generating a diagnosis result.

In an embodiment, the detecting of the object of interest may include detecting a first object of interest for each frame of the medical image in order to calculate a body balance change rate of the target performing the jumping action, and A second object of interest may be detected for each frame of the medical image in order to calculate a jump height and a jump speed.

In an embodiment, the calculating of the body balance change rate may include calculating a change rate of left and right balance of the body before and after the jump and a change rate of front and rear balance of the body by comparing and analyzing objects of interest detected for each frame of the medical image. to be characterized

In an embodiment, the calculating of the body balance change rate may include extracting joint positions from the object of interest detected for each frame of the medical image, correcting the extracted joint positions, and before and after jumping based on the corrected joint positions. It is characterized by calculating the balance change rate of the left and right sides of the body and the change rate of the front and rear balance of the body.

In an embodiment, the step of calculating the balance change rate between the left and right sides of the body may include calculating an angle between three-point joints adjacent to each other for each frame of the medical image, and based on the calculated angle change between the three-point joints. It is characterized in that the balance change rate of the left and right sides of the body is calculated.

In an embodiment, the calculating of the change rate of the anterior-posterior balance of the body may include calculating a distance between joints adjacent to each other for each frame of the medical image, and based on the calculated change in the distance between the joints, the anterior-posterior balance of the body. It is characterized in that the rate of change is calculated.

In an embodiment, after the step of diagnosing the motor ability development of the autism patient, the additional object of interest of the target performing the jumping motion is obtained from the medical image by inputting the medical image to a pretrained second neural network model. The method further comprises: detecting, calculating a pose change rate according to a jumping motion from the detected additional object of interest, and further diagnosing motor ability development of an autistic patient based on the calculated pose change rate. .

In an embodiment, the detecting of the additional object of interest may include detecting at least one of a hand and a foot of the object performing the jumping motion.

In an embodiment, the step of calculating the pose change rate according to the jumping motion may include comparing a hand pose before performing the jumping motion and a hand pose after performing the jumping motion if the detected additional object of interest is a hand. and calculating a hand pose change rate according to the jumping motion.

In an embodiment, the calculating of the pose change rate according to the jumping operation may include, when the detected additional object of interest is a foot, a foot pose change rate according to the jumping operation based on poses of the left foot and the right foot performing the jumping operation. characterized in that it produces

In an embodiment, the foot pose change rate may include time difference information for reaching the ground by the left and right feet performing the jumping motion, angle information between the sole and the ground when the foot lands on the ground, and distance between the tip of the heel and the ground It is characterized in that it includes at least one of the information.

A computer program stored on a computer readable storage medium according to an embodiment of the present invention, when the computer program is executed on one or more processors, to perform the following operations for diagnosing the development of motor skills of autistic patients based on body balance changes, , The operations include: acquiring a medical image, inputting the medical image to a pretrained neural network model, and detecting an interest object that performs a jump operation for each frame of the medical image; The operation of calculating the jump height, jump speed, and body balance change rate according to the jumping motion from the object of interest, and diagnosing the motor ability development of the autistic patient based on the calculated jump height, jump speed, and body balance change rate, and generating a diagnosis result It is characterized in that it includes the operation of.

A computing device for providing a method for diagnosing motor ability development in autistic patients based on body balance change according to an embodiment of the present invention, comprising a processor including one or more cores, and a memory, wherein the processor acquires a medical image. and inputs the medical image to a pretrained neural network model to detect an interest object performing a jump operation for each frame of the medical image, and from the detected object of interest, jump height and jump according to the jump operation. It is characterized by calculating speed and body balance change rate, and diagnosing motor ability development of autism patients based on the calculated jump height, jump speed, and body balance change rate, and generating a diagnosis result.

A user terminal for diagnosing motor ability development of autism patients based on body balance change according to an embodiment of the present invention, comprising a processor including one or more cores, a memory, and an output unit providing a user interface, the user interface comprising: , In response to a medical image input, result information on the medical image is displayed, and the result information on the medical image acquires the medical image and inputs the medical image to a pretrained neural network model to An object of interest performing a jumping motion is detected for each frame of the medical image, a jump height, a jump speed, and a rate of change in body balance according to the jumping motion are calculated from the detected object of interest, and the calculated jump height and jump It is characterized in that it is generated by diagnosing the development of motor skills of autism patients based on the rate of change of speed and body balance.

In an embodiment, the result information on the medical image is obtained by acquiring the medical image, and inputting the medical image to a pretrained first neural network model to detect an object of interest performing a jump operation for each frame of the medical image. and calculating a jump height, jump speed, and body balance change rate according to a jumping motion from the detected object of interest, and diagnosing motor ability development of an autism patient based on the calculated jump height, jump speed, and body balance change rate, Inputting the medical image to a pretrained second neural network model to detect an additional object of interest of a target performing the jumping operation from the medical image, and calculating a pose change rate according to the jumping operation from the detected additional object of interest; , Characterized in that it is generated by additionally diagnosing the development of motor skills of autistic patients based on the calculated pose change rate.

In an embodiment, the user interface displays result information on the medical data in response to a user input, and the result information on the medical data acquires a medical image and pre-learns the medical image. input to the neural network model to detect an interest object that performs a jump motion for each frame of the medical image, and calculates a jump height, jump speed, and body balance change rate according to the jump motion from the detected object of interest, , Based on the calculated jump height, jump speed, and body balance change rate, it is characterized in that it is generated by diagnosing the development of motor skills of autistic patients.

A computer program that provides a method for diagnosing motor ability development of autistic patients based on body balance changes according to another embodiment of the present invention for solving the above problems is combined with a computer that is hardware to perform any one of the above methods. stored on the medium for

In addition to this, another method for implementing the present invention, another system, and a computer readable recording medium recording a computer program for executing the method may be further provided.

As described above, according to the present invention, by detecting an object of interest performing a jumping motion using a neural network model and diagnosing the motor ability development of an autistic patient based on the jumping height, jumping speed, and body balance change rate, It is possible to accurately measure changes in body balance without environmental limitations such as space and equipment for diagnosing motor ability development, and based on this, the accuracy of diagnosing motor ability development in autism patients can be increased.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 is a block diagram of a computing device performing an operation for providing a method for diagnosing motor ability development of an autistic patient based on changes in body balance, according to an embodiment of the present invention.
2 is a schematic diagram showing a network function of a neural network model according to an embodiment of the present invention.
3 is a diagram for explaining a process of detecting an object of interest according to an embodiment of the present invention.
4 is a flow chart for explaining a method for diagnosing motor ability development of autistic patients based on changes in body balance, according to an embodiment of the present invention.

Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the detailed description of the following embodiments taken in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, only these embodiments are intended to complete the disclosure of the present invention, and are common in the art to which the present invention belongs. It is provided to fully inform the person skilled in the art of the scope of the invention, and the invention is only defined by the scope of the claims.

Terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Prior to the description, the meaning of the terms used in this specification will be briefly described. However, it should be noted that the description of terms is intended to help the understanding of the present specification, and is not used in the sense of limiting the technical spirit of the present invention unless explicitly described as limiting the present invention.

In this specification, neural networks, artificial neural networks, and network functions may often be used interchangeably.

The term "image" or "image data" as used throughout the description and claims of the present invention refers to multidimensional data composed of discrete image elements (e.g., pixels in the case of a two-dimensional image); in other words, ( A term used to refer to a visible object (e.g., displayed on a video screen) or a digital representation of that object (e.g., a file corresponding to the pixel output of a CT, MRI detector, etc.).

For example, "image" or "video" may mean computed tomography (CT), magnetic resonance imaging (MRI), body motion imaging, ultrasound, or any other medical procedure known in the art. It may be a medical image of a subject collected by the imaging system. The image does not necessarily have to be provided in a medical context, but may be provided in a non-medical context, such as X-ray imaging for security screening.

Throughout the detailed description and claims of the present invention, the 'DICOM (Digital Imaging and Communications in Medicine)' standard is a term that collectively refers to various standards used for digital image expression and communication in medical devices, The DICOM standard is published by a joint committee formed by the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA).

In addition, throughout the detailed description and claims of the present invention, 'Picture Archiving and Communication System (PACS)' is a term that refers to a system that stores, processes, and transmits according to the DICOM standard, and includes X-ray, CT, , Medical imaging images acquired using digital medical imaging equipment such as MRI are stored in DICOM format and can be transmitted to terminals inside and outside the hospital through a network, and reading results and medical records can be added to them.

Also, throughout this specification, a neural network, a neural network, and a network function may be used with the same meaning. A neural network may consist of a set of interconnected computational units, which may be generally referred to as “nodes”. These “nodes” may also be referred to as “neurons”. A neural network includes at least two or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more “links”.

1 is a block diagram of a computing device performing an operation for providing a method for diagnosing motor ability development of an autistic patient based on changes in body balance, according to an embodiment of the present invention.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present invention, the computing device 100 may include other components for performing a computing environment of the computing device 100, and only some of the disclosed components may constitute the computing device 100.

The computing device 100 may include a processor 110 , a memory 130 , and a network unit 150 .

In the present invention, the processor 110 relates to a method of generating result information by reading a jump motion included in a medical image, obtaining a medical image, and inputting the medical image to a pretrained neural network model to obtain a medical image. Detects an interest object that performs a jump motion for each frame of , calculates a jump height, jump speed, and body balance change rate according to the jump motion from the detected object of interest, and calculates the jump height, jump speed, and body balance change rate according to the jump motion. Based on the rate of change, it is possible to diagnose the development of motor skills in autistic patients and generate diagnostic results.

Here, the medical image may include a moving picture of a subject performing a jumping motion, which is only an example, but is not limited thereto.

Next, when detecting the object of interest, the processor 110 detects a first object of interest for each frame of the medical image in order to calculate a body balance change rate of the target performing the jumping action, and the object performing the jumping action jumps. A second object of interest may be detected for each frame of the medical image in order to calculate the height and jump speed.

For example, the first object of interest may include a joint of a body performing a jumping motion, and the second object of interest may include at least one of a foot, a knee, and a leg of a body performing a jumping motion. , This is only one embodiment, but is not limited thereto.

In addition, the pretrained neural network model may include a deep learning-based open pose model, which is only one embodiment, but is not limited thereto.

Here, the neural network model may be pre-learned to determine whether a person has autism or not by calculating a jump height, jump speed, and body balance change rate from the object of interest when an object of interest of a body performing a jumping motion detected from a medical image is input.

Next, when calculating the change rate of body balance, the processor 110 compares and analyzes the objects of interest detected for each frame of the medical image to calculate the change rate of balance between the left and right sides of the body before and after the jump and the rate of change in front-back balance of the body according to the jumping motion. .

For example, when calculating the rate of change in body balance, the processor 110 extracts joint positions from the object of interest detected for each frame of the medical image, corrects the extracted joint positions, and corrects the extracted joint positions before and after jumping based on the corrected joint positions. The rate of change in left-right balance of the body and the rate of change in anterior-posterior balance of the body can be calculated.

Further, when correcting the extracted joint position, the processor 110 checks the extracted joint position and corrects the missing joint position or the incorrect joint position if the joint is lost due to being covered in the medical image or there is an erroneous joint position. .

In addition, when calculating the rate of change in balance between the left and right sides of the body, the processor 110 calculates the angle between adjacent 3-point joints for each frame of the medical image, and based on the calculated angle change between the 3-point joints, The left-right balance change rate can be calculated.

In addition, when calculating the rate of change of anterior-posterior balance of the body, the processor 110 calculates a distance between joints adjacent to each other for each frame of the medical image, and calculates a rate of change of anterior-posterior balance of the body based on the calculated distance change between the joints. can be calculated

Then, when calculating the jumping height and jumping speed, the processor 110 may compare and analyze the objects of interest detected for each frame of the medical image to calculate the jumping height and jumping speed of the subject performing the jumping operation.

Next, the processor 110, after the step of diagnosing the motor ability development of the autism patient, inputs the medical image to the pretrained second neural network model to detect an additional object of interest of the subject performing the jumping motion from the medical image. and calculates a pose change rate according to the jumping motion from the detected additional object of interest, and based on the calculated pose change rate, it is possible to additionally diagnose the motor ability development of the autistic patient.

Here, the second neural network model may be pre-learned to additionally interpret the development of the motor ability of an autism patient by calculating a pose change rate from the additional object of interest when an additional object of interest of a body performing a jumping motion detected from a medical image is input. there is.

Also, when detecting the additional object of interest, the processor 110 may detect at least one of the hand and foot of the object performing the jumping motion.

Here, when detecting the hand of the target performing the jumping motion, the processor 110 detects the hand from a single frame of medical images before performing the jumping motion and a single frame of medical images after performing the jumping motion, respectively. can

Also, when detecting the foot of the subject performing the jumping motion, the processor 110 may detect the foot of the subject performing the jumping motion from a plurality of frames of the medical image performing the jumping motion.

Then, when calculating the pose change rate according to the jumping motion, if the detected additional object of interest is a hand, the processor 110 compares the hand pose before performing the jumping motion with the hand pose after performing the jumping motion, and jumps. A hand pose change rate according to motion may be calculated.

For example, the hand pose change rate may include hand position change information, which is only an example, but is not limited thereto.

In addition, when calculating the pose change rate according to the jumping motion, the processor 110 calculates the foot pose change rate according to the jumping motion based on the poses of the left foot and the right foot performing the jumping motion, if the detected additional object of interest is a foot. can

For example, the foot pose change rate is at least one of time difference information for reaching the ground by the left and right feet performing a jumping motion, angle information between the sole and the ground when the foot lands on the ground, and distance information between the tip of the heel and the ground. It may include, which is only one embodiment, but is not limited thereto.

Next, when generating a diagnosis result, the processor 110 may generate result information including quantification and evaluation of the body balance control motor ability of the subject for motor ability development diagnosis, which is only one embodiment, but is not limited thereto. don't

Also, the aforementioned neural network model may be a deep neural network. Throughout this specification, neural network, network function, and neural network may be used interchangeably. A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), and a deep belief network (DBN). , Q network, U network, Siamese network, and the like.

A convolutional neural network is a type of deep neural network and includes a neural network including a convolutional layer. A convolutional neural network is a type of multilayer perceptron designed to use minimal preprocessing. A CNN may be composed of one or several convolutional layers and artificial neural network layers combined therewith. CNNs can additionally utilize weights and pooling layers. Thanks to this structure, CNNs can fully utilize the two-dimensional structure of the input data. Convolutional neural networks can be used to recognize objects in images. A convolutional neural network can represent and process image data as a matrix having dimensions. For example, in the case of image data encoded in RGB (red-green-blue), each R, G, and B color may be represented as a two-dimensional (eg, two-dimensional image) matrix. That is, the color value of each pixel of the image data can be a component of a matrix, and the size of the matrix can be the same as the size of the image. Thus, image data can be represented by three two-dimensional matrices (a three-dimensional data array).

In a convolutional neural network, a convolutional process (input/output of a convolutional layer) can be performed by moving the convolutional filter and multiplying the convolutional filter with matrix components at each position of the image. A convolutional filter may be composed of an n*n matrix. A convolutional filter can consist of a fixed shape filter, which is usually smaller than the total number of pixels in an image. That is, when an m*m image is input to a convolutional layer (for example, a convolutional layer whose size of convolutional filter is n*n), a matrix representing n*n pixels including each pixel of the image It can be component multiplied with this convolutional filter (ie, multiplication of each component of the matrix). A component matching the convolutional filter can be extracted from the image by multiplication with the convolutional filter. For example, a 3*3 convolutional filter to extract top and bottom linear components from an image would be constructed as [[0,1,0], [0,1,0], [0,1,0]] can When a 3*3 convolutional filter for extracting up and down linear components from an image is applied to an input image, up and down linear components matching the convolutional filter may be extracted from the image and output. The convolutional layer may apply a convolutional filter to each matrix for each channel representing an image (ie, R, G, B colors in the case of an R, G, B coded image). The convolutional layer may extract features matching the convolutional filter from the input image by applying the convolutional filter to the input image. The filter value of the convolutional filter (that is, the value of each element of the matrix) may be updated by backpropagation during the learning process of the convolutional neural network.

A subsampling layer is connected to the output of the convolutional layer to simplify the output of the convolutional layer, thereby reducing memory usage and computational complexity. For example, if the output of the convolutional layer is input to a pooling layer with a 2*2 max pooling filter, the image is compressed by outputting the maximum value included in each patch for each 2*2 patch in each pixel of the image. can The aforementioned pooling may be a method of outputting a minimum value of a patch or an average value of a patch, and any pooling method may be included in the present invention.

A convolutional neural network may include one or more convolutional layers and subsampling layers. A convolutional neural network may extract features from an image by repeatedly performing a convolutional process and a subsampling process (eg, max pooling described above). Through an iterative convolutional process and subsampling process, a neural network can extract global features of an image.

An output of the convolutional layer or the subsampling layer may be input to a fully connected layer. A fully connected layer is a layer in which all neurons in one layer are connected to all neurons in neighboring layers. A fully connected layer may refer to a structure in which all nodes of each layer are connected to all nodes of other layers in a neural network.

According to an embodiment of the present invention, the processor 110 may be composed of one or more cores, a central processing unit (CPU) of a computing device, a general purpose graphics processing unit (GPGPU) ), a processor for data analysis and deep learning, such as a tensor processing unit (TPU). The processor 110 may read a computer program stored in the memory 130 and perform data processing for machine learning according to an embodiment of the present invention. According to an embodiment of the present invention, the processor 110 may perform an operation for learning a neural network. The processor 110 performs neural network learning, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating neural network weights using backpropagation. calculations can be performed for At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, the CPU and GPGPU can process learning of network functions and data classification using network functions. In addition, in one embodiment of the present invention, the learning of a network function and data classification using a network function may be processed by using processors of a plurality of computing devices together. In addition, a computer program executed in a computing device according to an embodiment of the present invention may be a CPU, GPGPU or TPU executable program.

According to one embodiment of the present invention, the memory 130 may store a computer program for performing a diagnosis of the development of motor ability of an autism patient and providing a diagnosis result of the development of motor ability of an autistic patient, and the stored computer program may be stored in the processor 120 ) and can be read and driven. The memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 .

According to an embodiment of the present invention, the memory 130 is a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (eg SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Memory) Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The computing device 100 may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The description of the above memory is only an example, and is not limited thereto.

The network unit 150 according to an embodiment of the present invention may transmit/receive information on a motor ability development diagnosis result of an autistic patient to another computing device, server, or the like. In addition, the network unit 150 enables communication between a plurality of computing devices so that operations for diagnosing the motor ability development of an autism patient or learning a model can be performed in a distributed manner in each of the plurality of computing devices. The network unit 150 may enable communication between a plurality of computing devices to perform distributed processing of calculations for autism diagnosis or model learning using a network function.

The network unit 150 according to an embodiment of the present invention may operate based on any type of wired or wireless communication technology currently used and implemented, such as short-distance (short-distance), long-distance, wired, and wireless, and other networks. can also be used in

The computing device 100 of the present invention may further include an output unit and an input unit.

The output unit according to an embodiment of the present invention may display a user interface (UI) for providing a diagnosis result of motor ability development of an autism patient. The output unit may output any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 .

In one embodiment of the present invention, the output unit is a liquid crystal display (liquid crystal display, LCD), thin film transistor liquid crystal display (thin film transistor-liquid crystal display, TFT LCD), organic light-emitting diode (organic light-emitting diode, OLED) , a flexible display, and a 3D display. Some of these display modules may be of a transparent type or a light transmissive type so that the outside can be seen through them. This may be referred to as a transparent display module, and a representative example of the transparent display module is TOLED (Transparent OLED) and the like.

The input unit according to an embodiment of the present invention may receive a user input. The input unit may include keys and/or buttons on a user interface for receiving user input, or physical keys and/or buttons. A computer program for controlling a display according to embodiments of the present invention may be executed according to a user input through an input unit.

The input unit according to embodiments of the present invention may detect a user's button operation or touch input and receive a signal, or may receive a user's voice or motion through a camera or microphone and convert it into an input signal. For this purpose, speech recognition technology or motion recognition technology may be used.

The input unit according to embodiments of the present invention may be implemented as an external input device connected to the computing device 100 . For example, the input device may be at least one of a touch pad, a touch pen, a keyboard, or a mouse for receiving a user input, but this is only an example and is not limited thereto.

The input unit according to an embodiment of the present invention may recognize a user touch input. The input unit according to an embodiment of the present invention may have the same configuration as the output unit. The input unit may include a touch screen implemented to receive a user's selection input. The touch screen may use any one of a contact capacitive method, an infrared light sensing method, a surface ultrasonic (SAW) method, a piezoelectric method, and a resistive film method. Detailed description of the touch screen described above is only an example according to an embodiment of the present invention, and various touch screen panels may be employed in the computing device 100 . The input unit configured as a touch screen may include a touch sensor. The touch sensor may be configured to convert a change in pressure applied to a specific portion of the input unit or capacitance generated at a specific portion of the input unit into an electrical input signal. The touch sensor may be configured to detect not only the touched position and area, but also the pressure upon touch. When there is a touch input to the touch sensor, the corresponding signal(s) is sent to the touch controller. The touch controller may process the signal(s) and then transmit corresponding data to processor 110 . Accordingly, the processor 110 can recognize which area of the input unit has been touched.

In one embodiment of the present invention, the server may include other configurations for performing the server environment of the server. The server may include any type of device. The server may be a digital device, such as a laptop computer, a notebook computer, a desktop computer, a web pad, or a mobile phone, equipped with a processor and having an arithmetic capability with a memory.

A server (not shown) performing an operation for providing a user interface displaying a motor ability development diagnosis result of an autism patient according to an embodiment of the present invention to a user terminal may include a network unit, a processor, and a memory. .

The server may generate a user interface according to embodiments of the present invention. The server may be a computing system that provides information to clients (eg, user terminals) over a network. The server may transmit the generated user interface to the user terminal. In this case, the user terminal may be any type of computing device 100 capable of accessing the server. The processor of the server may transmit the user interface to the user terminal through the network unit. A server according to embodiments of the present invention may be, for example, a cloud server. The server may be a web server that processes services. The types of servers described above are examples only and are not limited thereto.

Therefore, the present invention detects an object of interest performing a jumping motion using a neural network model and diagnoses the motor ability development of an autistic patient based on the jumping height, jumping speed, and body balance change rate, thereby improving the motor ability of an autistic patient. Even without environmental limitations such as space and equipment for developmental diagnosis, it is possible to accurately measure changes in body balance and increase the accuracy of the diagnosis of motor ability development in autistic patients based on this.

2 is a schematic diagram showing a network function of a neural network model according to an embodiment of the present invention.

Throughout this specification, the terms computational model, neural network, network function, and neural network may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more links.

In a neural network, one or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of data of the output node may be determined based on data input to the input node. Here, a link interconnecting an input node and an output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in the neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values of the links, the two neural networks may be recognized as different from each other.

A neural network may be composed of a set of one or more nodes. A subset of nodes constituting a neural network may constitute a layer. Some of the nodes constituting the neural network may form one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of a layer in a neural network may be defined in a method different from the above. For example, a layer of nodes may be defined by a distance from a final output node.

An initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the neural network. Also, the hidden node may refer to nodes constituting the neural network other than the first input node and the last output node.

In the neural network according to an embodiment of the present invention, the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as the number of nodes progresses from the input layer to the hidden layer. can In addition, the neural network according to another embodiment of the present invention may be a neural network in which the number of nodes of the input layer may be less than the number of nodes of the output layer, and the number of nodes decreases as the number of nodes increases from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present invention is a type of neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as the number of nodes progresses from the input layer to the hidden layer. can A neural network according to another embodiment of the present invention may be a neural network in the form of a combination of the aforementioned neural networks.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), and restricted boltzmann machines (RBMs). machine), a deep belief network (DBN), a Q network, a U network, a Siamese network, a Generative Adversarial Network (GAN), and the like. The description of the deep neural network described above is only an example and is not limited thereto.

In one embodiment of the present invention, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network for outputting output data similar to input data. An auto-encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between input and output layers. The number of nodes of each layer may be reduced from the number of nodes of the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically with the reduction from the bottleneck layer to the output layer (symmetrical to the input layer). Autoencoders can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to dimensions after preprocessing of input data. In the auto-encoder structure, the number of hidden layer nodes included in the encoder may decrease as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, a sufficient amount of information may not be conveyed, so more than a certain number (e.g., more than half of the input layer, etc.) ) may be maintained.

The neural network may be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Learning of the neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

A neural network can be trained in a way that minimizes output errors. In the learning of the neural network, the learning data is repeatedly input into the neural network, the output of the neural network for the training data and the error of the target are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. It is a process of updating the weight of each node of the neural network by backpropagating in the same direction. In the case of teacher learning, the learning data in which the correct answer is labeled is used for each learning data (ie, the labeled learning data), and in the case of comparative teacher learning, the correct answer may not be labeled in each learning data. That is, for example, learning data in the case of teacher learning regarding data classification may be data in which each learning data is labeled with a category. Labeled training data is input to a neural network, and an error may be calculated by comparing an output (category) of the neural network and a label of the training data. As another example, in the case of comparative history learning for data classification, an error may be calculated by comparing input learning data with a neural network output. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate is used in the early stage of neural network training to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and a low learning rate can be used in the late stage to increase accuracy.

In neural network learning, generally, training data can be a subset of real data (ie, data to be processed using the trained neural network), and therefore, errors for training data are reduced, but errors for real data are reduced. There may be incremental learning cycles. Overfitting is a phenomenon in which errors for actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that has learned a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing the error of machine learning algorithms. Various optimization methods can be used to prevent such overfitting. To prevent overfitting, methods such as increasing the training data, regularization, inactivating some nodes in the network during learning, and using a batch normalization layer should be applied. can

3 is a diagram for explaining a process of detecting an object of interest according to an embodiment of the present invention.

As shown in FIG. 3 , the present invention may detect an object of interest for each frame of a medical image in order to calculate a body balance change rate of a subject performing a jumping motion.

For example, the object of interest may include a body joint that performs a jumping motion.

Further, the present invention may compare and analyze objects of interest detected for each frame of the medical image to calculate a rate of change in balance between the right and left sides of the body before and after jumping according to a jumping motion and a rate of change in front/back balance of the body.

That is, the present invention extracts the joint position from the object of interest detected for each frame of the medical image, corrects the extracted joint position, and calculates the left-right balance change rate and the front-back balance change rate of the body before and after jumping based on the corrected joint position. can do.

Here, the present invention, when correcting the extracted joint position, checks the extracted joint position and corrects the missing joint position or the wrong joint position if the joint is hidden in the medical image and there is a missing joint position or an incorrect joint position.

In addition, in the present invention, when calculating the rate of change of balance between the left and right sides of the body, the angle between adjacent 3-point joints is calculated for each frame of the medical image, and the balance between the left and right sides of the body is based on the calculated angle change between the 3-point joints. rate of change can be calculated.

For example, in FIG. 3, the angle between joints 8 to 10, the angle between joints 1, 8, and 9, the angle between joints 1 to 3, and the angle between joints 2 to 4 The angle between joints 0 to 2, the angle between joints 11 to 13, the angle between joints 1, 11 and 12, and the angle between joints 1, 5 and 6 The balance change rate on the left and right sides of the body may be calculated based on a change in at least one or two or more of the angle and the angle between the joints #5 to #7.

In addition, the present invention, when calculating the change rate of the anterior-posterior balance of the body, calculates the distance between adjacent joints for each frame of the medical image, and calculates the change rate of the anterior-posterior balance of the body based on the calculated distance change between the joints. For example, the front/rear balance change rate of the body may be calculated based on at least one of a distance between joints #9 and #12 and a distance between joints #2 and #5 in FIG. 3 .

In addition, the body balance according to the present invention can be measured based on the center coordinates and left and right balance of joints 0, 1, 10, and 13 (Balance feature 1), and also between joints 9 and 12. It can be measured based on the calculation of dividing the distance by the distance between joints #2 and #5 (Balance feature 2).

Meanwhile, in the present invention, an object of interest may be detected for each frame of a medical image in order to calculate a jumping height and a jumping speed of a subject performing a jumping motion.

Here, the object of interest may include at least one of a foot, a knee, and a leg of a body performing a jumping motion.

In this case, when calculating the jumping height and jumping speed, the present invention may calculate the jumping height and jumping speed of a subject performing a jumping motion by comparing and analyzing objects of interest detected for each frame of the medical image.

4 is a flow chart for explaining a method for diagnosing motor ability development of autistic patients based on changes in body balance, according to an embodiment of the present invention.

As shown in FIG. 4 , in the present invention, a medical image may be obtained (S10).

Here, the medical image may include, but is not limited to, a moving picture of a subject performing a jumping motion.

Furthermore, according to the present invention, an object of interest performing a jump operation may be detected for each frame of the medical image by inputting the medical image to the pretrained neural network model (S20).

Here, the present invention detects a first object of interest for each frame of the medical image in order to calculate a body balance change rate of a subject performing a jumping motion, and calculates a jumping height and a jumping speed of a subject performing a jumping motion. A second object of interest may be detected for each frame of the medical image.

For example, the first object of interest may include a joint of a body performing a jumping motion, and the second object of interest may include at least one of a foot, a knee, and a leg of a body performing a jumping motion. .

Subsequently, the present invention may calculate a jump height, a jump speed, and a body balance change rate according to a jump motion from the detected object of interest (S30).

Here, the present invention extracts the joint position from the first object of interest detected for each frame of the medical image, corrects the extracted joint position, and based on the corrected joint position, the change rate of left and right balance of the body before and after jumping and the change rate of front and rear balance of the body can be calculated.

In addition, according to the present invention, the jumping height and jumping speed of a subject performing a jumping motion may be calculated by comparing and analyzing the second objects of interest detected for each frame of the medical image.

Next, the present invention, based on the calculated jump height, jump speed, and body balance change rate, can diagnose the development of motor skills of autism patients and generate diagnostic results (S40).

Here, the present invention may generate result information including quantification and evaluation of the body balance control motor ability of the subject for motor ability development diagnosis.

In addition, the present invention, after the step of diagnosing the motor ability development of autistic patients, inputs a medical image to a pre-learned second neural network model to detect an additional object of interest of a target performing the jumping motion from the medical image, , the pose change rate according to the jumping motion is calculated from the detected additional interest object, and the motor ability development of the autistic patient may be additionally diagnosed based on the calculated pose change rate.

Here, in the present invention, when detecting the additional object of interest, at least one of the hand and foot of the object performing the jumping motion may be detected.

For example, when detecting a hand of a target performing a jumping motion, the present invention may detect a hand from a single frame of a medical image before performing a jumping motion and a single frame of a medical image after performing a jumping motion, respectively. there is.

Also, when detecting the foot of a subject performing a jumping motion, the present invention may detect the foot of a subject performing a jumping motion from a plurality of frames of a medical image performing a jumping motion.

In the present invention, if the detected additional object of interest is a hand, a hand pose change rate according to the jumping motion may be calculated by comparing a hand pose before performing the jumping motion with a hand pose after performing the jumping motion.

Also, according to the present invention, if the detected additional object of interest is a foot, a foot pose change rate according to a jumping motion may be calculated based on poses of the left foot and the right foot performing the jumping motion.

For example, the foot pose change rate is at least one of time difference information for reaching the ground by the left and right feet performing a jumping motion, angle information between the sole and the ground when the foot lands on the ground, and distance information between the tip of the heel and the ground. can include

As described above, the present invention detects an object of interest performing a jumping motion using a neural network model and diagnoses the motor ability development of an autistic patient based on the jump height, jump speed, and body balance change rate, thereby Without environmental limitations such as space and equipment for diagnosing ability development, it is possible to accurately measure changes in body balance and increase the accuracy of diagnosing motor ability development in autistic patients based on this.

The method according to an embodiment of the present invention described above may be implemented as a program (or application) to be executed in combination with a server, which is hardware, and stored in a medium.

The aforementioned program is C, C++, JAVA, machine language, etc. It may include a code coded in a computer language of. Such codes may include functional codes related to functions defining necessary functions for executing the methods, and include control codes related to execution procedures necessary for the processor of the computer to execute the functions according to a predetermined procedure. can do. In addition, these codes may further include memory reference related code for determining where (address address) of the computer's internal or external memory the additional information or media required for the computer's processor to execute the functions should be referenced. there is. In addition, when the processor of the computer needs to communicate with any other remote computer or server in order to execute the functions, the code uses the communication module of the computer to determine how to communicate with any other remote computer or server. It may further include communication-related codes for whether to communicate, what kind of information or media to transmit/receive during communication, and the like.

The storage medium is not a medium that stores data for a short moment, such as a register, cache, or memory, but a medium that stores data semi-permanently and is readable by a device. Specifically, examples of the storage medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., but are not limited thereto. That is, the program may be stored in various recording media on various servers accessible by the computer or various recording media on the user's computer. In addition, the medium may be distributed to computer systems connected through a network, and computer readable codes may be stored in a distributed manner.

Steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented in a software module executed by hardware, or implemented by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which the present invention pertains.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features of the present invention. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

Claims (25)

In the method performed by the device,
obtaining a medical image;
detecting an interest object performing a jump operation for each frame of the medical image by inputting the medical image to a pretrained neural network model;
calculating a jump height, a jump speed, and a rate of change in body balance according to a jumping motion from the detected object of interest; and
Diagnosing the development of motor skills of autism patients based on the calculated jump height, jump speed and rate of change in body balance and generating a diagnosis result. According to claim 1,
The medical image,
A method for diagnosing motor ability development in autistic patients based on body balance change, comprising a video of a subject performing a jumping motion. According to claim 1,
The step of detecting the object of interest is,
A first object of interest is detected for each frame of the medical image in order to calculate a rate of change in body balance of the subject performing the jumping motion, and a frame of the medical image is calculated in order to calculate the jumping height and jumping speed of the subject performing the jumping motion. A method for diagnosing motor ability development in autistic patients based on body balance change, characterized in that for each second object of interest is detected. According to claim 3,
The first object of interest is
A method for diagnosing motor ability development in autistic patients based on body balance change, characterized in that it comprises a joint of the body performing the jumping motion. According to claim 3,
The second object of interest,
Body balance change-based motor ability development diagnosis method of autism patients, characterized in that it comprises at least one of the feet, knees and legs of the body performing the jumping motion. According to claim 1,
In the step of calculating the body balance change rate,
A method for diagnosing motor ability development in autism patients based on body balance change, characterized in that by comparing and analyzing the objects of interest detected for each frame of the medical image to calculate a rate of change in balance between the left and right sides of the body before and after jumping according to the jumping motion and a rate of change in front-back balance of the body . According to claim 6,
In the step of calculating the body balance change rate,
A joint position is extracted from the object of interest detected for each frame of the medical image, the extracted joint position is corrected, and a rate of change in left and right balance of the body before and after jumping and a rate of change in balance in front and rear of the body are calculated based on the corrected joint location. A method for diagnosing the development of motor skills in autistic patients based on changes in body balance. According to claim 7,
The step of correcting the extracted joint position,
Identifying the extracted joint position and diagnosing the movement ability development of autistic patients based on body balance change, characterized in that the missing joint position or incorrect joint position is corrected if the joint is obscured in the medical image and the missing joint position or incorrect joint position exists method. According to claim 7,
The step of calculating the balance change rate of the left and right sides of the body,
Calculating angles between 3-point joints adjacent to each other for each frame of the medical image, and calculating the balance change rate on the left and right sides of the body based on the calculated angular changes between the 3-point joints A method for diagnosing motor development in autistic patients. According to claim 7,
Calculating the change rate of the front and rear balance of the body,
Movement of autism patients based on body balance change, characterized in that the distance between joints adjacent to each other is calculated for each frame of the medical image, and the body front and rear balance change rate is calculated based on the calculated distance change between the joints. Methods for diagnosing competency development. According to claim 1,
Calculating the jump height and jump speed,
A method for diagnosing motor ability development in autism patients based on body balance change, characterized in that for comparing and analyzing the objects of interest detected for each frame of the medical image to calculate a jump height and a jump speed of the object performing the jumping motion. According to claim 1,
After diagnosing the motor development of the autistic patient,
inputting the medical image to a pretrained second neural network model and detecting an additional object of interest of a target performing the jumping operation from the medical image;
calculating a pose change rate according to a jumping motion from the detected additional object of interest; and
Further comprising the step of additionally diagnosing the motor ability development of autistic patients based on the calculated pose change rate. According to claim 12,
The step of detecting the additional object of interest,
A method for diagnosing motor ability development in autistic patients based on body balance change, characterized in that for detecting at least one of the hands and feet of the subject performing the jumping motion. According to claim 13,
The step of detecting the additional object of interest,
When detecting the hand of the target performing the jumping motion, a hand is detected from a single frame of medical images before performing the jumping motion and a single frame of medical images after performing the jumping motion, respectively. A method for diagnosing motor development in autistic patients based on balance changes. According to claim 13,
The step of detecting the additional object of interest,
Autistic patients based on body balance change, characterized in that when detecting the foot of the subject performing the jumping motion, the feet of the subject performing the jumping motion are detected from a plurality of frames of the medical image performing the jumping motion. A method for diagnosing the development of motor skills. According to claim 12,
Calculating the pose change rate according to the jump motion,
If the detected additional object of interest is a hand, a hand pose change rate according to the jumping motion is calculated by comparing a hand pose before performing the jumping motion with a hand pose after performing the jumping motion. A change-based method for diagnosing motor development in patients with autism. According to claim 16,
The hand pose change rate is,
A method for diagnosing motor ability development of autism patients based on body balance change, characterized in that it includes the hand position change information. According to claim 12,
Calculating the pose change rate according to the jump motion,
If the detected additional object of interest is a foot, a foot pose change rate according to the jumping motion is calculated based on poses of the left and right feet performing the jumping motion. . According to claim 18,
The rate of change of the foot pose is
Characterized in that it includes at least one of time difference information when the left and right feet performing the jumping motion reach the ground, angle information between the sole and the ground when the foot arrives on the ground, and distance information between the tip of the heel and the ground. A method for diagnosing the development of motor skills in autistic patients based on body balance changes. According to claim 1,
Generating the diagnosis result,
A method for diagnosing motor ability development in autistic patients based on body balance change, characterized in that for generating resultant information including quantification and evaluation of body balance control motor ability of a subject for motor ability development diagnosis. A computer program stored on a computer-readable storage medium, which, when executed in one or more processors, performs the following operations for diagnosing the development of motor skills of autistic patients based on changes in body balance, the operations comprising:
obtaining a medical image;
detecting an interest object that performs a jump operation for each frame of the medical image by inputting the medical image to a pretrained neural network model;
calculating a jump height, a jump speed, and a body balance change rate according to a jumping motion from the detected object of interest; and
A computer program stored in a computer readable storage medium comprising an operation of diagnosing the development of motor skills of an autistic patient based on the calculated jump height, jump speed and body balance change rate and generating a diagnosis result. A computing device for providing a method for diagnosing the development of motor skills in autistic patients based on body balance changes,
a processor comprising one or more cores; and
Memory;
including,
the processor,
acquire medical images;
Inputting the medical image to a pretrained neural network model to detect an interest object performing a jump operation for each frame of the medical image;
Calculate a jump height, jump speed, and body balance change rate according to a jumping motion from the detected object of interest, and
Computing device characterized in that for diagnosing the motor ability development of the autistic patient and generating a diagnosis result based on the calculated jump height, jump speed and body balance change rate. As a user terminal for diagnosing the motor ability development of autistic patients based on body balance change,
a processor comprising one or more cores;
Memory; and
Includes an output unit providing a user interface;
The user interface,
In response to a medical image input, displaying result information on the medical image, and
The result information on the medical image,
Obtaining a medical image, inputting the medical image to a pretrained neural network model to detect an object of interest performing a jump operation for each frame of the medical image, and performing a jump operation from the detected object of interest. A user terminal characterized in that it is created by calculating the jump height, jump speed, and body balance change rate, and diagnosing the development of motor ability of an autistic patient based on the calculated jump height, jump speed, and body balance change rate. According to claim 23,
The result information on the medical image,
A user terminal characterized in that the result information including the quantification and evaluation of the body balance control motor ability of the motor ability development diagnosis subject. According to claim 23,
The user interface,
In response to user input, display result information about the medical data, and
The resulting information on the medical data,
Obtaining a medical image, inputting the medical image to a pretrained neural network model to detect an object of interest performing a jump operation for each frame of the medical image, and performing a jump operation from the detected object of interest. A user terminal characterized in that it is created by calculating the jump height, jump speed, and body balance change rate, and diagnosing the development of motor ability of an autistic patient based on the calculated jump height, jump speed, and body balance change rate.

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