KR102282101B1 - Method and system for determining state of object using functional near-infrared spectroscopy - Google Patents

Abstract

According to an embodiment of the present invention, a method for determining a state of an object using functional near-infrared spectroscopy (fNIRS) and a device thereof are provided. According to an embodiment of the present invention, a target state determining method in an object state determining system by using fNIRS comprises the following steps of: obtaining fNIRS data of an object by performing brain stimulation on the object; calculating analysis data for a supplementary motor area (SMA) region that controls movement of the object by using the obtained fNIRS data; and determining a state of the object based on the calculated analysis data.

Description

METHOD AND SYSTEM FOR DETERMINING STATE OF OBJECT USING FUNCTIONAL NEAR-INFRARED SPECTROSCOPY

The present invention relates to a method and system for determining the condition of a subject using functional near-infrared spectroscopy.

In modern society, the population of the elderly is rapidly increasing according to the aging era, and the user's actions to diagnose, prevent or treat various degenerative brain diseases such as dementia, Alzheimer's disease, and Parkinson's disease related to the user's exercise ability, cognitive ability, and thinking ability. There is a need for a method to quickly and accurately check the user's state by analyzing the brain region related to the realization of the target. In particular, when the user is paralyzed due to an accident, there is a brain region for planning and controlling movement among the user's brain regions. It can be seen that damage appears in at least one area, and various treatment methods are being developed to treat the damaged brain area.

In addition, a method for treating damage to a brain region due to a disease is being developed. Parkinson's disease (Parkinson's disease) is a chronic degenerative brain disease of the nervous system caused by the loss of dopamine neurons.

Accordingly, various diagnostic or test methods are being developed to check the presence and progression of Parkinson's disease.

SUMMARY OF THE INVENTION An object of the present invention is to provide a method and apparatus for determining the state of a subject using functional near-infrared spectroscopy.

Specifically, an object of the present invention is to provide a method and apparatus for quickly and accurately diagnosing, preventing, or treating a brain disease by analyzing a brain region related to a brain disease and determining the user's condition.

The problems of 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 following description.

In order to solve the above problems, a method and an apparatus for determining the state of an object using functional near-infrared spectroscopy according to an embodiment of the present invention are provided. According to an embodiment of the present invention, a method for determining a state of an object using functional near-infrared spectroscopy (fNIRS) includes: acquiring fNIRS data of the object by performing brain stimulation on the object; calculating analysis data for a supplementary motor area (SMA) region that controls the movement of the object by using the acquired fNIRS data; and determining the state of the object based on the calculated analysis data.

A system for determining a state of an object using functional near-infrared spectroscopy according to an embodiment of the present invention includes: a stimulus generator configured to perform brain stimulation on the object; a data acquisition unit configured to acquire fNIRS data of the subject by the brain stimulation; an analysis data calculation unit configured to calculate analysis data for an SMA region that controls the movement of the object by using the acquired fNIRS data; and a state determining unit configured to determine the state of the object based on the calculated analysis data.

The details of other embodiments are included in the detailed description and drawings.

The present invention can quickly and accurately diagnose, prevent, or treat a brain disease by determining the state of a subject using fNIRS data of an SMA region that controls human movement.

In addition, by providing the determined state of the subject as monitoring or feedback data, the present invention can provide an appropriate stimulus signal for treatment to a subject who cannot move freely, such as a Parkinson's disease patient or a patient whose body part is paralyzed due to an accident. .

In addition, the present invention can characterize and develop a prophylactic treatment for a neurodegenerative disease based on the subject's status determined using the fNIRS data of the SMA region.

The effect according to the present invention is not limited by the contents exemplified above, and more various effects are included in the present specification.

1 is a schematic diagram illustrating a system for determining a state of an object using functional near-infrared spectroscopy according to an embodiment of the present invention.
2 is a block diagram of a stimulus generating device according to an embodiment of the present invention.
3 is a block diagram of an apparatus for acquiring data according to an embodiment of the present invention.
4 is a block diagram of an electronic device according to an embodiment of the present invention.
5 is a schematic flowchart illustrating a method for determining a state of an object using functional near-infrared spectroscopy in a system for determining a state of an object according to an embodiment of the present invention.
6 is an exemplary diagram illustrating analysis data in an SMA region calculated using a z-score analysis method according to an embodiment of the present invention.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and common knowledge in the art to which the present invention pertains It is provided to fully inform those who have the scope of the invention, and the present invention is only defined by the scope of the claims. In connection with the description of the drawings, like reference numerals may be used for like components.

In this document, expressions such as "has," "may have," "includes," or "may include" refer to the presence of a corresponding characteristic (eg, a numerical value, function, operation, or component such as a part). and does not exclude the presence of additional features.

In this document, expressions such as "A or B," "at least one of A and/and B," or "one or more of A or/and B" may include all possible combinations of the items listed together. . For example, "A or B," "at least one of A and B," or "at least one of A or B" means (1) includes at least one A, (2) includes at least one B; Or (3) it may refer to all cases including both at least one A and at least one B.

As used herein, expressions such as "first," "second," "first," or "second," may modify various elements, regardless of order and/or importance, and refer to one element. It is used only to distinguish it from other components, and does not limit the components. For example, the first user equipment and the second user equipment may represent different user equipment regardless of order or importance. For example, without departing from the scope of rights described in this document, the first component may be named as the second component, and similarly, the second component may also be renamed as the first component.

A component (eg, a first component) is "coupled with/to (operatively or communicatively)" to another component (eg, a second component) When referring to "connected to", it will be understood that the certain element may be directly connected to the other element or may be connected through another element (eg, a third element). On the other hand, when it is said that a component (eg, a first component) is "directly connected" or "directly connected" to another component (eg, a second component), the component and the It may be understood that other components (eg, a third component) do not exist between other components.

As used herein, the expression "configured to (or configured to)" depends on the context, for example, "suitable for," "having the capacity to ," "designed to," "adapted to," "made to," or "capable of." The term “configured (or configured to)” may not necessarily mean only “specifically designed to” in hardware. Instead, in some circumstances, the expression “a device configured to” may mean that the device is “capable of” with other devices or parts. For example, the phrase “a processor configured (or configured to perform) A, B, and C” refers to a dedicated processor (eg, an embedded processor) for performing the operations, or by executing one or more software programs stored in a memory device. , may mean a generic-purpose processor (eg, a CPU or an application processor) capable of performing corresponding operations.

Terms used in this document are only used to describe specific embodiments, and may not be intended to limit the scope of other embodiments. The singular expression may include the plural expression unless the context clearly dictates otherwise. Terms used herein, including technical or scientific terms, may have the same meanings as commonly understood by one of ordinary skill in the art described in this document. Among the terms used in this document, terms defined in a general dictionary may be interpreted with the same or similar meaning as the meaning in the context of the related art, and unless explicitly defined in this document, ideal or excessively formal meanings is not interpreted as In some cases, even terms defined in this document cannot be construed to exclude embodiments of this document.

Each feature of the various embodiments of the present invention may be partially or wholly combined or combined with each other, and technically various interlocking and driving are possible, as will be fully understood by those skilled in the art, and each embodiment may be independently implemented with respect to each other, It may be possible to implement together in a related relationship.

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

1 is a schematic diagram illustrating a system for determining a state of an object using functional near-infrared spectroscopy according to an embodiment of the present invention.

Referring to FIG. 1 , the system for determining the state of an object 100 is a system for determining the state of an object using functional near-infrared spectroscopy (fNIRS) data measured from the scalp of the object. a stimulus generating device 110 configured to provide stimulation, a data acquiring device 120 configured to acquire fNIRS data from a scalp of a subject, and an electronic device 130 configured to determine a state of the subject by analyzing the acquired fNIRS data .

First, the stimulation generating device 110 may generate a stimulation signal for giving stimulation to the brain of an object and output the generated stimulation signal. For example, the stimulus generating device 110 may be a head mounted display (HMD) device that provides a virtual reality (VR) image for brain stimulation, but is not limited thereto. In various embodiments, the stimulus generating device 110 may be a variety of devices for providing content for brain stimulation in relation to at least one of visual, auditory, tactile, and the like of an object.

When the stimulus generating device 110 is an HMD device, the HMD device is mounted on the subject's head to provide the subject with brain stimulation contents for virtual reality so that the subject can have a spatial and temporal experience similar to the real thing, and at the same time, the user's biometrics It may be a complex virtual experience device capable of detecting physical, cognitive, and emotional changes of a user who is undergoing a virtual experience by acquiring data. For example, brain stimulation content includes non-interactive images such as movies, animations, advertisements, or promotional images, and interactive (interactive) images with users such as games, electronic manuals, electronic encyclopedias or promotional images. interactive) video, but is not limited thereto. Here, the image may be a 3D image, and a stereoscopic image may be included.

Such an HMD device may be formed in a structure that can be worn on the user's head, and may be implemented in the form of displaying various brain stimulation contents for virtual reality through a display unit inside the HMD device.

Also, when the HMD device includes a display unit, one surface of the display unit may be disposed to face the face of the subject so that the subject can check brain stimulation contents when the subject wears the HMD device.

In various embodiments, the stimulus generating device 110 may further include special equipment, such as gloves and clothes, so that the object feels a sensory effect that is actually seen and touched, and immersed in a vivid virtual reality environment.

Next, the data acquisition device 120 is a device for acquiring fNIRS data generated by brain stimulation of an object, and includes a plurality of light sources used to acquire fNIRS data and a detector corresponding to each of the plurality of light sources. can

Specifically, the data acquisition apparatus 120 may include a plurality of light sources and a detector that detects near-infrared rays from each of the plurality of light sources, and is disposed as a pair with each light source. The data acquisition apparatus 120 may be implemented in the form of a hat in which the light source and the detector are arranged in the form of an n x m matrix (n and m are natural numbers), and to cover the entire scalp of the subject. However, the data acquisition device 120 is not limited to such a form, and may be implemented in various forms for detecting near-infrared rays from the scalp of the subject. A channel is formed in a region between the light source and the detector arranged as described above, and near-infrared radiation can be detected in the formed channel.

The data acquisition device 120 irradiates light to the scalp of the object through a plurality of light sources, and corresponds to a supplementary motor area (SMA) region, which is a brain region that controls the movement of the object among near-infrared rays emitted from the scalp by the irradiated light. fNIRS data of the SMA region may be obtained by detecting near-infrared rays that are emitted through a detector paired with each light source. The data acquisition device 120 may transmit the acquired fNIRS data to the electronic device 130 .

The electronic device 130 is a device for determining the state of an object using fNIRS data, and may include at least one of a tablet PC (Personal Computer), a notebook computer, and/or a PC.

Specifically, the electronic device 130 may calculate analysis data for the SMA region by using the fNIRS data received from the data acquisition device 120 , and determine the state of the object based on the calculated analysis data. Here, the SMA region is a brain region that plans and realizes or controls the movement of an object, and may be activated when, for example, a person's upper or lower extremities, eye movements, or hand movements are performed.

In order to calculate the analysis data, the electronic device 130 may perform power spectrum analysis and z-score analysis, but is not limited thereto, and various data analysis methods for determining the state of the object are used. can be

In order to determine the state of the object based on the analysis data calculated through the analysis method, the electronic device 130 may use at least one of a pattern mining method and a machine learning method.

When using the pattern analysis method, the electronic device 130 may generate the analysis pattern of the object by using the pattern generation model learned to generate the analysis pattern based on the analysis data. In this case, the pattern generation model may be a model based on a clustering algorithm.

The electronic device 130 may determine the state of the object by using the state prediction model learned to probabilistically predict the state of the user based on the analysis pattern generated as described above. Here, the state of the object may mean a behavioral pattern changed by the stimulus content provided when the stimulus content for treatment is provided when the object is a patient with a specific disease. For example, the electronic device 130 may determine a level of motor ability, a level of a sense of balance, whether it has learning efficacy, a sense of motion, planning a motion, and/or synesthesia. It is possible to determine the state of the object, such as whether or not there is, but is not limited thereto.

When using the machine learning method, the electronic device 130 may determine the state of the object by using a prediction model for predicting the state of the object based on the analysis data. For example, a predictive model can be a Deep Neural Network (DNN), Convolutional Neural Network (CNN), Deep Convolution Neural Network (DCNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), It may be a single shot detector (SSD) model or a prediction model based on U-net, but is not limited thereto.

In various embodiments, the electronic device 130 may monitor the determined state as described above or provide feedback data for the determined state. For example, the electronic device 130 may indicate what the object's motor ability is, what the sense of balance is, whether it has a learning effect, whether it has a sense of movement, whether it plans a movement, and/or whether it has synesthesia, and the like. Monitoring data or feedback data may be provided.

In various embodiments, the entire system may measure brain region fNIRS data for external stimuli in the movement state of the object to diagnose brain conditions such as brain activation and brain diseases.

As described above, the present invention determines the state of an object using fNIRS data of the SMA region that controls human movement, and provides it as monitoring or feedback data, so that patients with Parkinson's disease or patients whose body parts are paralyzed due to an accident and An appropriate stimulation signal for treatment may be given to a subject that cannot move freely, and whether the subject's brain is functioning properly or is normal may be checked in spatiotemporal or real time.

Hereinafter, with reference to FIGS. 2 to 4 , the stimulus generating device 110 , the data acquisition device 120 , and the electronic device 130 constituting the object state determination system 100 using functional near-infrared spectroscopy will be described in detail. do.

2 is a block diagram of a stimulus generating device according to an embodiment of the present invention.

Referring to FIG. 2 , the stimulus generating device 200 includes an image reproducing device 210 and an input device 230 . The image reproducing device 210 may be connected to the input device 230 through wired/wireless communication. In the presented embodiment, the stimulus generating device 200 means the stimulus generating device 110 of FIG. 1 , and the image reproducing device 210 is assumed to be an HMD device that reproduces a virtual reality image.

First, the image reproducing apparatus 210 includes a communication unit 212 , a sensing unit 214 , a display unit 216 , a storage unit 218 , and a control unit 220 .

The communication unit 212 connects the image reproducing apparatus 210 to enable communication with an external device. The communication unit 212 may be connected to the electronic device 130 using wired/wireless communication to transmit/receive various data. Here, the various data may be virtual reality content, and the virtual reality content may be first-person or third-person game content. For example, the third-person game content may include an avatar that matches the object, and the avatar may be implemented similarly to the appearance of the object.

The sensing unit 214 may include a motion sensor for detecting the user's head motion, and may detect the user's head motion through the motion sensor and output a detection signal. However, the present invention is not limited thereto, and various sensors for detecting a head movement may be used.

The display unit 216 may display various contents (eg, text, image, video, icon, banner or symbol) to the user. Specifically, the display unit 216 may display virtual reality content received through the communication unit 212 or stored in the storage unit 218 .

The storage unit 218 may store various data used to provide stimulation to the subject's brain. For example, the storage unit 218 may store virtual reality content.

In various embodiments, the storage unit 218 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.), Random Access Memory (RAM), Static Random Access Memory (SRAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Programmable Read-Only Memory (PROM) , a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The image reproducing apparatus 210 may operate in relation to a web storage that performs a storage function of the storage unit 218 on the Internet.

The controller 220 is operatively connected to the communication unit 212 , the sensing unit 214 , the display unit 216 , and the storage unit 218 , and is configured to provide virtual reality content used to stimulate the brain of an object. Various commands can be executed.

Specifically, the controller 220 may display the virtual reality content received through the communication unit 212 or stored in the storage unit 218 through the display unit 216 . For example, when the virtual reality content is third-person game content, the third-person game content may include an avatar corresponding to the object. In this case, the controller 220 may provide a user interface for generating an avatar.

In various embodiments, the controller 220 may control a user interface related to virtual reality content by manipulating the input device 230 of the object. For example, the controller 220 may generate an avatar having an appearance similar to that of the object by controlling the user interface for generating the avatar by manipulating the input device 230 of the object.

In various embodiments, the controller 220 may detect a movement of the user's head through the sensing unit 214 , and reproduce and display virtual reality content in response to the direction of the user's head.

The input device (eg, a controller, etc.) 230 is connected to the image reproducing apparatus 210 , and an object manipulates the input device 230 connected to the image reproducing apparatus 210 to operate the display unit 216 of the image reproducing apparatus 210 . ), you can control the user interface related to the virtual reality content displayed through.

3 is a block diagram of an apparatus for acquiring data according to an embodiment of the present invention.

Referring to FIG. 3 , the data acquisition device 300 includes a light source 302 , a detector 304 , and a data transmitter 306 . In the presented embodiment, the data acquisition device 300 may refer to the data acquisition device 120 of FIG. 1 . In various embodiments, the data acquisition device 300 may be implemented in the form of a hat so that it can be closely attached while covering the entire scalp of the subject.

The light source 302 may be provided in plurality and may be arranged in the form of an n x m matrix to irradiate the light to the scalp.

The detector 304 may be provided in a pair with the light source 302 to correspond to the light source 302 and may be arranged in the form of an n x m matrix.

A channel is formed between the light source 302 and the detector 304 arranged in this way, and the detector 304 detects near-infrared rays in the channel to acquire fNIRS data.

The data transmitter 306 may transmit the acquired fNIRS data to the electronic device 130 through wired or wireless communication.

4 is a block diagram of an electronic device according to an embodiment of the present invention.

Referring to FIG. 4 , the electronic device 400 includes a data receiving unit 402 , a preprocessing unit 404 , an analysis data calculating unit 406 , and a state determining unit 408 . In the presented embodiment, the electronic device 400 may refer to the electronic device 130 of FIG. 1 .

First, the data receiver 402 may receive fNIRS data from the data acquisition device 120 .

The preprocessor 404 may perform preprocessing to remove biological or technical artifacts of the fNIRS data. For example, the preprocessor 404 may perform preprocessing by applying a moving average low-pass filter, but is not limited thereto, and various filters for removing artifacts may be used. .

The analysis data calculating unit 406 collects preprocessed data corresponding to a channel related to the SMA region at a specific sampling time interval, and based on the collected data, oxy and deoxy-hemoglobin Δ[oxy-HB] data representing a change in concentration can be obtained.

The analysis data calculation unit 406 may generate analysis data by analyzing the Δ[oxy-HB] data. Specifically, the analysis data calculating unit 406 generates analysis data using two analysis methods, for example, the two analysis methods may include a power spectrum analysis method and a z-score analysis method.

First, the analysis data calculating unit 406 may calculate a power spectrum value by using a power spectrum analysis method to identify a brain region. For example, the analysis data calculating unit 406 converts time series data (ie, Δ[oxy-HB] data collected at a specific sampling time interval) into a frequency domain using a fast Fourier transform, Power can be calculated within a close frequency window with a specific frequency range.

Next, in order to calculate the analysis data using the z-score analysis method, the analysis data calculating unit 406 calculates the fNIRS data in which x(t) ≡ Δ[oxy-HB](t), z(t) = [ x(t)- M ₀ ]/SD ₀ may be used to convert the time series z-score value (z(t)). Here, M ₀ and SD ₀ may mean the average value and standard deviation of Δ[oxy-HB] data during the reference time range (t).

The analysis data calculating unit 406 may calculate the power value and the z-score value as analysis data for the SMA region by using such analysis methods.

The state determiner 408 may determine the state of the object based on the calculated analysis data. Specifically, the state determiner 408 may determine the state of the object by using at least one of a pattern analysis method and a machine learning method. Here, the state determiner 408 determines whether the object has a degree of motor ability, a sense of balance, has learning efficacy, has a sense of movement, plans a movement, and/or has synesthesia. Whether or not can be determined as the state of the subject.

First, when using the pattern analysis method, the state determiner 408 may generate the analysis data pattern of the SMA area using the pattern generation model learned to generate the analysis data pattern. In this case, the pattern generation model may be a model based on a clustering algorithm. For example, in order to generate an analysis data pattern, the pattern generation model repeatedly performs a clustering algorithm to calculate the average value of the silhouette for each analysis data of a plurality of SMA regions, and among the number of patterns for which the average value increases most rapidly The number of patterns having the smallest value may be determined as the optimal number of patterns. Furthermore, the pattern generation model may generate an analysis data pattern of one SMA area by grouping patterns for analysis data of a plurality of SMA areas based on the determined optimal number of patterns using a clustering algorithm.

According to various embodiments, the state determiner 408 is configured to perform clustering ( clustering) can be performed.

In various embodiments, the state determination unit 408 calculates a similarity and dissimilarity matrix for two selected analysis data among the analysis data of a plurality of SMA areas by using the pattern generation model, and based on the calculated similarity and dissimilarity A correlation between the two analyzed data may be determined. Furthermore, the state determination unit 404 may generate one analysis data pattern based on the correlation between the analysis data and the optimal number of patterns.

Next, the state determiner 408 may predict the state of the object using the state prediction model learned to probabilistically predict the state of the object based on the analysis data pattern of the SMA region. In this case, the state prediction model calculates the analysis data of the SMA region for a plurality of sample objects different from the object, generates a pattern for each of the analysis data of the plurality of SMA regions by using the state prediction model, and sets each generated pattern. It may be a predictive model trained by grouping, generating one analysis data pattern for each of a plurality of sample objects, and determining a pattern associated with the state of the object from among the generated analysis data.

The state determiner 408 may use the state prediction model to compare a pattern associated with a predetermined state and an analysis data pattern for the object to probabilistically determine the state of the object based on the comparison result. For example, the state determination unit 404 determines how much the object's motor ability is, what the sense of balance is, whether it has a learning effect, whether it has a sense of movement, whether it plans a movement, and/or whether it has synesthesia, etc. can

Next, when using the machine learning method, the state determiner 408 may use a pre-trained state prediction model to predict the state of the object based on the analysis data. For example, the state prediction model may be based on various learning models that are learned based on analysis data of other objects. For example, the prediction models used in various embodiments of the present invention may be a prediction model based on DNN, CNN, DCNN, RNN, RBM, DBN, SSD model, or U-net, but is not limited thereto.

In other words, the state determiner 408 receives the analysis data of the object as an input to determine the state of the object, that is, what level of exercise ability, how much sense of balance, whether it has learning efficacy, whether there is a sense of movement, and movement. The state of the object may be predicted by using a state prediction model that has been pre-trained to predict whether planning and/or synesthesia, and the like.

As described above, the present invention may determine whether the subject has a brain disease or provide an index for preventing a brain disease by determining the state of the subject using the analysis data for the SMA region of the subject.

Hereinafter, a method for determining the state of the object using the functional near-infrared spectroscopy in the object state determination system 100 will be described with reference to FIG. 5 .

5 is a schematic flowchart illustrating a method for determining a state of an object using functional near-infrared spectroscopy in a system for determining a state of an object according to an embodiment of the present invention.

1 and 5 , the object state determination system 100 obtains fNIRS data of an SMA region that controls the movement of the object by performing brain stimulation on the object ( S500 ). Specifically, the object state determination system 100 may perform brain stimulation of the object by the stimulus generating device 110 , and acquire fNIRS data for the SMA region of the object through the data acquisition apparatus 120 . Here, fNIRS data for the SMA region may be acquired in a channel related to the SMA region among channels formed between a plurality of pairs of the light source and the detector and transmitted to the electronic device 130 .

The object state determination system 100 calculates analysis data for the SMA region using the acquired fNIRS data ( S510 ). Specifically, the object state determination system 100 may calculate analysis data for the SMA region using power spectrum analysis and z-score analysis through the electronic device 130 . This calculation method may be performed as mentioned above.

The object state determination system 100 determines the state of the object based on the calculated analysis data ( S520 ). Specifically, the object state determination system 100 may determine the state of the object using at least one of a pattern analysis method and a machine learning method through the electronic device 130 . The determined state may be provided as feedback data for delaying disease treatment or disease progression of a subject who is a disease patient, or may be provided as monitoring data for disease prevention. In addition, based on the feedback data and the monitoring data, the user can check whether the brain of the subject is operating properly or is normal, spatiotemporal or real-time.

Hereinafter, a method for determining the state of an object based on analysis data in the SMA region calculated using the z-score analysis method will be described with reference to FIG. 6 .

6 is an exemplary diagram illustrating analysis data in an SMA region calculated using a z-score analysis method according to an embodiment of the present invention.

Referring to FIG. 6 , the analysis data may be represented as a time series z-score value in the SMA region. In general, since the SMA area is an area that plans and controls the movement of an object, when the object performs any movement, the activity of the SMA area increases, so that the time series z-score value of the SMA area exceeds the baseline (z-score=0). It has a value that gradually increases with the center.

When a specific event for the object occurs, the time series z-score values of the SMA region in the sections 600 , 602 , 604 , and 606 in which the specific event occurs gradually decrease to have a value smaller than the baseline. Here, the specific event refers to a case in which the object learns movement and becomes proficient. As the object quickly learns to move, the slope of the time series z-score values may decrease sharply.

Even when the movement of the object is restricted or the object makes a difficult movement, the activity of the SMA region of the object may increase, and thus brain activation may be increased. For example, when an object performs a new movement or exercise (eg, tennis, swimming, boxing, etc.), the activity in the SMA area that is responsible for the movement increases, and the plan to execute and learn the new movement or exercise in the SMA area increases. can be activated. Through this, the object may learn or execute a new movement or exercise. As the subject becomes proficient in these movements or movements, the activity in the SMA area gradually decreases, allowing the subject to perform the movements or movements with less active, more natural, less deliberate, and less effort. In addition, patients with Parkinson's disease Alternatively, in the case of patients whose body parts are paralyzed due to an accident, the stimulation generating device 110 is worn on the patients' heads, and various brain stimulation contents are reproduced through the stimulation generating device 110, thereby stimulating the patients' brain disease treatment. can give a signal. In this case, the electronic device 130 calculates analysis data for the SMA area of the patients based on the fNIRS data obtained from the data acquisition device 120 , and determines the patient's condition using the calculated analysis data, and then determines the determined patient condition Based on this, it is possible to provide brain stimulation contents for treatment. In patients with Parkinson's disease or paralyzed patients, the activity of the SMA area may be insignificant or the SMA area may be inactive. By providing it, it can help treat these patients' brain disease.

As such, the present invention determines whether the subject has a brain disease in which activation of the SMA region is poor by determining the state of the subject using fNIRS data in the SMA region related to the behavioral implementation such as the subject's motor ability or sense of balance. can

In addition, the present invention provides the determined state of the subject as monitoring data or feedback data, so that an appropriate stimulation signal for preventing or treating a brain disease such as Parkinson's disease may be generated using the data.

In addition, according to the present invention, it is possible to determine whether the brain region of the subject is operating properly or is normal, spatio-temporal or real-time, by using the analysis data in the SMA region.

The apparatus and method according to an embodiment of the present invention may be implemented in the form of program instructions that can be executed by various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination.

The program instructions recorded on the computer readable medium may be specially designed and configured for the present invention, or may be known and available to those skilled in the computer software field. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - Includes magneto-optical media and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

Although the embodiments of the present invention have been described in more detail with reference to the accompanying drawings, the present invention is not necessarily limited to these embodiments, and various modifications may be made within the scope without departing from the technical spirit of the present invention. . Therefore, the embodiments disclosed in the present invention are not intended to limit the technical spirit of the present invention, but to explain, and the scope of the technical spirit of the present invention is not limited by these embodiments. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The protection scope of the present invention should be construed by the following claims, and all technical ideas within the equivalent range should be construed as being included in the scope of the present invention.

100: object state determination system
110: stimulus generating device
120: data acquisition device
130: electronic device

Claims (16)

A method for determining the state of an object using functional near-infrared spectroscopy (fNIRS) performed by an electronic device included in a system for determining the state of an object, the method comprising:
obtaining fNIRS data of the subject by performing brain stimulation on the subject;
calculating analysis data for a supplementary motor area (SMA) region that controls the movement of the object by using the acquired fNIRS data; and
Comprising the step of determining the state of the object based on the calculated analysis data,
The determining step is
In a situation where a specific event occurs in the object,
As the analysis data value for the SMA region increases around the baseline, it is determined that the subject is in a state with limited movement, and as the analysis data value decreases around the baseline, the object is determined to be in a state of proficient movement. A method for determining subject status using fNIRS. According to claim 1, wherein the state of the object,
A method for determining a subject's status using fNIRS, wherein the method is indicative of at least one of motor ability and a sense of balance. The method of claim 1, wherein determining the state of the object comprises:
A method for determining a state of an object using fNIRS, which is performed based on at least one of a pattern mining method and a machine learning method. According to claim 1,
A method for determining a state of an object using fNIRS, wherein a head mounted display (HMD) device that is worn on the head of the object and provides a virtual reality image is used to perform brain stimulation on the object. The method of claim 1, wherein the acquiring of fNIRS data of the subject comprises:
irradiating light to the scalp of the subject through a plurality of light sources; and
and detecting the fNIRS data from the scalp irradiated with the light through a detector corresponding to each of the plurality of light sources. According to claim 5, wherein the fNIRS data,
A method for determining a state of an object using fNIRS, which is data representing a change in the intensity of near-infrared light transmitted through the scalp and from which artifacts are removed through a moving average low-pass filter. According to claim 1,
A method for determining a state of a subject using fNIRS, wherein a power spectrum analysis method and a z-score analysis method are used to calculate the analysis data. According to claim 1, wherein the determined state,
A method for determining a subject's status using fNIRS, which is provided as monitoring data for a disease test for the subject or as feedback data for treating a disease in the subject. a stimulus generating unit configured to perform brain stimulation on the object;
a data acquisition unit configured to acquire functional near-infrared spectroscopy (fNIRS) data of the object by the brain stimulation;
an analysis data calculation unit configured to calculate analysis data for a supplementary motor area (SMA) region that controls the movement of the object by using the acquired fNIRS data; and
and a state determination unit configured to determine the state of the object based on the calculated analysis data,
The state determination unit,
In a situation in which a specific event for the object occurs, it is determined that the object is in a state of restricted movement as the analysis data value increases around the baseline, and as the analysis data value decreases around the baseline, the object moves A system for determining a subject's status using fNIRS, which determines a condition proficient in The method of claim 9, wherein the state of the subject is,
A system for determining subject status using fNIRS, wherein the system exhibits at least one of motor ability and a sense of balance. The method of claim 9, wherein the state determining unit,
A system for determining a state of an object using fNIRS, which determines the state of the object based on at least one of a pattern mining method and a machine learning method. The method of claim 9, wherein the stimulus generating unit,
A system for determining a state of an object using fNIRS, wherein a head mounted display (HMD) device that is worn on the head of the object and provides a virtual reality image is used to perform brain stimulation on the object. The method of claim 9, wherein the data acquisition unit,
Irradiating light to the scalp of the subject through a plurality of light sources,
and detecting the fNIRS data from the scalp irradiated with the light through a detector corresponding to each of the plurality of light sources. The method of claim 13, wherein the fNIRS data,
A system for determining a state of an object using fNIRS, which is data representing a change in the intensity of near-infrared light transmitted through the scalp and from which artifacts are removed through a moving average low-pass filter. 10. The method of claim 9, wherein the analysis data calculation unit,
A system for determining subject status using fNIRS, wherein a power spectrum analysis method and a z-score analysis method are used to calculate the analysis data. 10. The method of claim 9, wherein the determined state,
A system for determining a subject's status using fNIRS, which is provided as monitoring data for a disease test for the subject or as feedback data for treating a disease in the subject.

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