Apparatus and method for managing internet gaming disorder on basis of digital phenotyping

Abstract

The present invention relates to an apparatus and a method for managing internet gaming disorder, wherein the apparatus comprises: a data collection unit that monitors a digital learning process on a user terminal to collect multimodal data related to user behaviors; a data analysis unit that removes noise from the multimodal data and analyzes the multimodal data to detect at least one digital indicator associated with internet gaming disorder of the user; and an internet gaming disorder detection unit that determines, on the basis of the at least one digital indicator, the presence and level of internet gaming disorder of the user in the digital learning process.

Description

Device and method for managing Internet gaming disorders based on digital phenotyping

[0001] The present invention relates to a digital phenotyping service technology, and more specifically, to a digital phenotyping-based Internet gaming disorder management device and method capable of collecting various digital signals while a user uses a smart device and diagnosing, predicting, and managing the user's current emotional state or mental health state from a set of collected digital signals.

[0002]

[0003] Digital phenotyping can refer to the quantification of users' cognitive, emotional, behavioral, physiological, social, and environmental indicators in real time, without hospital visits, using personal digital devices such as smartphones. Digital phenotyping is attracting attention as a new mental health measurement tool due to its ability to provide multidimensional and large-scale objective information. It is expected to be used in the future as a tool for personalized medicine (preventive medicine, predictive medicine, participatory medicine, and precision medicine), which aims to improve the diagnostic accuracy of psychiatry by reflecting individual patient data and establishing a new dimensional diagnostic system for RDoC.

[0004] Recently, there have been attempts to collect, analyze, and utilize users' biometric indicators, mobile phone usage patterns, and behavioral characteristics through mobile devices and wearable devices. However, there are limitations in that the scope of utilization is very limited in the collection and analysis of massive amounts of data.

[0005] In particular, public education settings like schools are highly sensitive to personal information, and data collection can be significantly restricted due to parental opposition. Therefore, digital phenotyping technology is needed to non-invasively collect and utilize information beyond sensitive personal information like face, facial expressions, physical behavior, words or sentences expressed in writing or speech, and social media posts, including touch, strokes, and stylus usage.

[0006]

[0007] [Prior Art Literature]

[0008] [Patent Document]

[0009] Korean Publication No. 10-2021-0076462 (June 24, 2021)

[0010]

[0011] One embodiment of the present invention provides a device and method for early diagnosis of Internet gaming disorder based on digital phenotyping, which collects various digital signals while a user uses a smart device and predicts and manages the user's current emotional state or mental health state from a set of collected digital signals.

[0012]

[0013] Among the embodiments, the Internet game disorder management device based on digital phenotyping includes a data collection unit that monitors a digital learning process on a user terminal to collect multimodal data on user behavior; a data analysis unit that removes noise from the multimodal data and analyzes it to detect at least one digital indicator associated with the user's Internet game disorder; and an Internet game disorder detection unit that determines the degree and presence of the user's Internet game disorder in the digital learning process based on the at least one digital indicator.

[0014] The above data collection unit collects the multimodal data through an application executed on the user terminal during the digital learning process, and the multimodal data may include action indicators based on the user's actions on the display screen of the user terminal.

[0015] The above data collection unit can collect the user's keyboard-related actions and stroke actions collected by the on-screen keyboard sensor and touchscreen sensor of the user terminal during the problem-solving process of the digital learning process as the multimodal data.

[0016] The above data analysis unit can convert the multimodal data into a z-score and create a multiple regression model using bootstrapping.

[0017] The data analysis unit can determine at least one digital indicator from among stroke acceleration, stroke speed, vertical distance, number of blank spaces, and time interval.

[0018] The above Internet game failure detection unit learns the correlation between the at least one digital indicator and the number of correct answers in the problem-solving process to build a failure prediction model, and inputs multimodal data regarding the user's behavior into the failure prediction model to determine whether the user has an Internet game failure.

[0019] Among the embodiments, a method for managing Internet game disorder based on digital phenotyping includes: a step of collecting multimodal data on user behavior by monitoring a digital learning process on a user terminal through a data collection unit; a step of removing noise and analyzing the multimodal data through a data analysis unit to detect at least one digital indicator associated with the user's Internet game disorder; and a step of determining the degree and presence of the user's Internet game disorder in the digital learning process based on the at least one digital indicator through an Internet game disorder detection unit.

[0020]

[0021] The disclosed technology may have the following effects. However, this does not mean that a particular embodiment must include all or only the following effects, and therefore the scope of the disclosed technology should not be construed as being limited thereby.

[0022] An Internet game disorder management device and method based on digital phenotyping according to one embodiment of the present invention can collect various digital signals while a user uses a smart device, and diagnose, evaluate, predict, and manage the user's current emotional state or mental health state from a set of collected digital signals.

[0023] An Internet game disorder management device and method based on digital phenotyping according to one embodiment of the present invention can provide a daily life report service that reports students' study habits, academic achievements, mental health status, daily concentration / emotional status, activity level, etc. to parents and teachers through data generated in the process of students learning and engaging in activities with classmates in public / private education settings.

[0024]

[0025] Figure 1 is a drawing explaining an Internet game disorder management system according to the present invention.

[0026] Figure 2 is a drawing explaining the functional configuration of the user terminal of Figure 1.

[0027] Fig. 3 is a drawing explaining the system configuration of the failure management device of Fig. 1.

[0028] Fig. 4 is a drawing explaining the functional configuration of the failure management device of Fig. 1.

[0029] Figure 5 is a flowchart illustrating an Internet game disorder management method based on digital phenotyping according to the present invention.

[0030] Figure 6 is a diagram explaining the correlation and multiple regression results between IGDS and indicators according to the present invention.

[0031]

[0032] The description of the present invention is merely an example for structural and functional explanation, and therefore, the scope of the present invention should not be construed as being limited by the embodiments described in the text. That is, since the embodiments can be modified in various ways and can take various forms, the scope of the present invention should be understood to include equivalents that can realize the technical idea. In addition, the purposes or effects presented in the present invention do not mean that a specific embodiment must include all of them or only such effects, and therefore, the scope of the present invention should not be construed as being limited thereby.

[0033] Meanwhile, the meaning of the terms described in this application should be understood as follows.

[0034] Terms such as "first" and "second" are intended to distinguish one component from another, and the scope of the rights should not be limited by these terms. For example, the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component.

[0035] When a component is said to be "connected" to another component, it should be understood that while it may be directly connected to that other component, there may also be other components intervening. Conversely, when a component is said to be "directly connected" to another component, it should be understood that there are no other intervening components. Similarly, other expressions describing relationships between components, such as "between" and "directly between," or "adjacent to" and "directly adjacent to," should be interpreted similarly.

[0036] Singular expressions should be understood to include plural expressions unless the context clearly indicates otherwise, and terms such as "comprises" or "have" should be understood to specify the presence of a feature, number, step, operation, component, part or combination thereof, but not to exclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0037] For each step, the identifiers (e.g., a, b, c, etc.) are used for convenience of explanation and do not describe the order of the steps. The steps may occur in a different order than stated unless the context clearly dictates a specific order. That is, the steps may occur in the same order as stated, may be performed substantially simultaneously, or may be performed in the opposite order.

[0038] The present invention can be implemented as computer-readable code on a computer-readable recording medium. The computer-readable recording medium includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. Furthermore, the computer-readable recording medium can be distributed across network-connected computer systems, so that the computer-readable code can be stored and executed in a distributed manner.

[0039] Unless otherwise defined, all terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries should be interpreted to be consistent with their meaning within the context of the relevant technology, and should not be interpreted as having an idealized or overly formal meaning unless explicitly defined herein.

[0040]

[0041] Figure 1 is a drawing explaining an Internet game disorder management system according to the present invention.

[0042] Referring to FIG. 1, the Internet game failure management system (100) may include a user terminal (110), a failure management device (130), and a database (150).

[0043] A user terminal (110) may correspond to a terminal device operated by a user. In the embodiment of the present invention, a user may be understood as one or more users, and multiple users may be classified into one or more user groups. Each of the one or more users may correspond to one or more user terminals (110). That is, a first user may correspond to a first user terminal, a second user may correspond to a second user terminal, ..., an n-th user (where n is a natural number) may correspond to an n-th user terminal.

[0044] In addition, the user terminal (110) may correspond to a computing device that can participate in a digital learning process by linking with the disability management device (130) as a device that constitutes the Internet game disability management system (100). Here, digital learning may correspond to a learning process in which the user participates online in an educational process conducted in an Internet space, such as non-face-to-face education or online education. The user terminal (110) may be implemented as a smartphone, laptop, or computer that is connected to and operable with the disability management device (130), but is not necessarily limited thereto, and may also be implemented as various devices, including tablet PCs.

[0045] In particular, the user terminal (110) can install and execute a dedicated program or application for interfacing with the disability management device (130). For example, the user terminal (110) can participate in a dedicated online learning space provided by the disability management device (130) to perform digital learning, and can provide multimodal data based on the user's actions during the digital learning process to the disability management device (130). In addition, the digital learning process and data collection operations can be performed through an interface provided by the dedicated program or application.

[0046] Meanwhile, the user terminal (110) can be connected to the fault management device (130) through a network, and multiple user terminals (110) can be connected to the fault management device (130) simultaneously.

[0047] The fault management device (130) may be implemented as a computer or server corresponding to a program that performs the Internet game fault management method based on digital phenotyping according to the present invention. Furthermore, the fault management device (130) may be connected to a user terminal (110) via a wired network or a wireless network such as Bluetooth, WiFi, or LTE, and may transmit and receive data with the user terminal (110) via the network.

[0048] Additionally, the failure management device (130) may be implemented to operate in connection with an independent external system (not shown in FIG. 1) to perform the Internet game failure management method based on digital phenotyping according to the present invention. For example, the failure management device (130) may operate in conjunction with a learning system that provides online learning, a learning management system that manages learning history, an artificial intelligence system that builds models, and the like.

[0049] Meanwhile, for the convenience of explanation, the user terminal (110) and the fault management device (130) are expressed here as independent devices, but this is not necessarily limited to them, and it goes without saying that one device may be implemented by being included in the other device.

[0050] The database (150) may correspond to a storage device that stores various information required during the operation of the failure management device (130). For example, the database (150) may store learning materials and learning management information for digital learning, or may store multimodal data collected during the digital learning process. However, the database is not necessarily limited thereto, and may store information collected or processed in various forms during the process of the failure management device (130) performing the Internet game failure management method based on digital phenotyping according to the present invention.

[0051] In addition, in FIG. 1, the database (150) is depicted as a device independent of the fault management device (130), but it is not necessarily limited thereto, and it can be implemented as a logical storage device included in the fault management device (130).

[0052]

[0053] Figure 2 is a drawing explaining the functional configuration of the user terminal of Figure 1.

[0054] Referring to FIG. 2, the user terminal (110) can execute a dedicated program or application for digital learning and perform operations to collect various user behavioral data during the digital learning process. To this end, the user terminal (110) may be implemented with independent modules for monitoring user behavior and collecting data. Specifically, the user terminal (110) may include a multimodal data collection module (210), a foreground process monitoring module (230), and a web browser monitoring module (250).

[0055] The multimodal data collection module (210) can collect various passive sensor data, keyboard input data, and multimodal data. To this end, the multimodal data collection module (210) can operate in conjunction with various sensors included in the user terminal (110). For example, the multimodal data collection module (210) can periodically collect data information from passive sensors such as angular velocity, acceleration, and light sensors. The multimodal data collection module (210) can collect data such as keyboard values ​​and time when a user inputs a keyboard, and can collect data when an event such as a user using a stylus or touching with a finger occurs. The data collected by the multimodal data collection module (210) can be stored and preserved in the internal memory, and can be periodically transmitted to the fault management device (130) by the user terminal (110).

[0056] The foreground process monitoring module (230) can perform an operation to detect the execution of a process, and for this purpose, can include a command to check the package name of the foreground process. The foreground process monitoring module (230) can perform process monitoring as a periodic task, and can collect and record information about the foreground process using the display screen of the user terminal (110) at preset intervals.

[0057] The web browser monitoring module (250) can perform an operation to examine information of a web page that a user accesses through a web browser on a user terminal (110). The web browser monitoring module (250) can operate in an event-driven manner in which a processing function is executed whenever an event occurs, and can collect and record text information whenever a user interacts with a text input window (e.g., a search window, an address window, etc.) of a web browser.

[0058]

[0059] Fig. 3 is a drawing explaining the system configuration of the failure management device of Fig. 1.

[0060] Referring to FIG. 3, the fault management device (130) may include a processor (310), a memory (330), a user input / output unit (350), and a network input / output unit (370).

[0061] The processor (310) can execute an Internet game failure management procedure based on digital phenotyping according to an embodiment of the present invention, manage the memory (330) that is read or written in the process, and schedule a synchronization time between the volatile memory and the non-volatile memory in the memory (330). The processor (310) can control the overall operation of the failure management device (130), and is electrically connected to the memory (330), the user input / output unit (350), and the network input / output unit (370) to control the data flow therebetween. The processor (310) can be implemented as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit) of the failure management device (130).

[0062] The memory (330) may include an auxiliary memory device implemented as a non-volatile memory such as an SSD (Solid State Disk) or an HDD (Hard Disk Drive) and used to store all data required for the fault management device (130), and may include a main memory device implemented as a volatile memory such as a RAM (Random Access Memory). In addition, the memory (330) may store a set of commands that execute the Internet game fault management method based on digital phenotyping according to the present invention by being executed by an electrically connected processor (310).

[0063] The user input / output unit (350) includes an environment for receiving user input and an environment for outputting specific information to the user, and may include, for example, an input device including an adapter such as a touchpad, a touch screen, a virtual keyboard, or a pointing device, and an output device including an adapter such as a monitor or a touch screen. In one embodiment, the user input / output unit (350) may correspond to a computing device connected via a remote connection, and in such a case, the fault management device (130) may be performed as an independent server.

[0064] The network input / output unit (370) provides a communication environment for connecting to a user terminal (110) via a network, and may include, for example, an adapter for communication such as a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), and a Value Added Network (VAN). In addition, the network input / output unit (370) may be implemented to provide a short-range communication function such as WiFi or Bluetooth, or a wireless communication function of 4G or higher for wireless transmission of data.

[0065]

[0066] Fig. 4 is a drawing explaining the functional configuration of the failure management device of Fig. 1.

[0067] Referring to FIG. 4, the failure management device (130) may include a data collection unit (410), a data analysis unit (430), an Internet game failure detection unit (450), and a control unit (470).

[0068] The data collection unit (410) can monitor the digital learning process on the user terminal (110) to collect multimodal data on user behavior. To this end, the data collection unit (410) can operate in conjunction with the user terminal (110) and receive multimodal data collected through a dedicated program or application running on the user terminal (110).

[0069] In one embodiment, the data collection unit (410) may collect multimodal data through an application executed on the user terminal (110) during a digital learning process. Here, the multimodal data may include motion indicators generated by user actions on the display screen of the user terminal (110). For example, the multimodal data may include data collected from touch or stroke actions by the user on a touchscreen. In particular, the data collection unit (410) may collect various behavioral information generated while the user uses a dedicated application for a specific purpose as multimodal data.

[0070] In one embodiment, the data collection unit (410) may collect multimodal data of the user's keyboard-related actions and stroke actions collected by the on-screen keyboard sensor and touchscreen sensor of the user terminal (110) during the problem-solving process in a digital learning course. Here, the problem-solving process may correspond to a process in which a problem is provided through the display screen of the user terminal (110) and the user reads the problem and enters the correct answer. At this time, the user may input the correct answer by touching the display screen or using the keyboard displayed on the screen, and the data collection unit (410) may collect multimodal data collected through the display screen among the user actions occurring during the problem-solving process or the correct answer input process as a motion index.

[0071] The data analysis unit (430) can detect at least one digital indicator associated with the user's Internet gaming disorder by removing and analyzing noise from multimodal data. That is, the data analysis unit (430) can extract digital indicators highly related to Internet Gaming Disorder (IGD) from multimodal data collected during a digital learning process, and can perform preprocessing operations such as missing data processing and denoising during the process.

[0072] In one embodiment, the data analysis unit (430) can transform multimodal data into z-scores and create a multiple regression model using bootstrapping. First, the data analysis unit (430) can transform indicators including self-rating, frequency, duration (ms), length (in pixels), pressure, and ratio to have a mean of 0 and a standard deviation of 1 using z-scores. The data analysis unit (430) can compare indicators derived from different distributions through z-score transformation.

[0073] Next, the data analysis unit (430) can perform multiple regression using the bootstrap technique. Bootstrapping can be a method of estimating the sampling distribution by obtaining multiple samples with replacement from a single random sample. After constructing a multiple regression model through multiple regression, the data analysis unit (430) can remove multicollinearity indicators to generate the final regression results.

[0074] In one embodiment, the data analysis unit (430) may determine at least one digital indicator from among stroke acceleration, stroke speed, vertical distance, number of blank spaces, and time interval. Stroke acceleration may correspond to a rate of change in speed while making a stroke on the touchscreen, stroke speed may correspond to a number of pixels per second or a distance, and vertical distance may correspond to a distance from the top of the screen to a starting position during a stroke. In addition, the number of blank spaces may correspond to the number of times the spacebar is pressed between letters, words, and sentences, and time interval may correspond to the time (in milliseconds) between touching one key and touching another key.

[0075] For example, in the correlation between Internet Game Addiction (IGD) and digital indicators, the IGDS score, which is a measure of game addiction, may show a positive correlation with indicators such as stroke acceleration, stroke speed, and vertical distance, and a negative correlation with indicators such as number of blanks and time intervals. The data analysis unit (430) may determine digital indicators that have a high correlation with Internet game addiction in the digital learning process through data analysis.

[0076] The Internet gaming disorder detection unit (450) can determine the degree and presence of a user's Internet gaming disorder during a digital learning process based on at least one digital indicator. For example, the Internet gaming disorder detection unit (450) can predict the degree of a user's Internet gaming disorder (IGD) using the digital indicators derived by the data analysis unit (430), and can determine whether the user has IGD by considering the impact of IGD on the digital learning process. Specifically, a high IGDS score may be associated with an increase in stroke acceleration and a decrease in stroke velocity, strokes tend to start near the bottom of the screen, and may be associated with rapid typing and the omission of spaces between letters, words, or sentences.

[0077] In one embodiment, the Internet game disorder detection unit (450) may learn the correlation between at least one digital indicator and the number of correct answers in the problem-solving process to build a disorder prediction model, and input multimodal data on user behavior into the disorder prediction model to determine whether the user has an Internet game disorder. Specifically, the Internet game disorder detection unit (450) may learn learning data including digital indicators such as stroke acceleration, stroke speed, vertical distance, number of blanks, and time intervals collected in the digital learning process and the number of correct answers of the user through problem-solving to build a disorder prediction model. In other words, the disorder prediction model may correspond to an artificial intelligence model defined to receive digital indicators related to the user's Internet game disorder as input in the digital learning process and to generate an indication of whether the user has an Internet game disorder as output.

[0078] The control unit (470) controls the overall operation of the fault management device (130) and can manage the control flow or data flow between the data collection unit (410), the data analysis unit (430), and the Internet game fault detection unit (450).

[0079]

[0080] Figure 5 is a flowchart illustrating an Internet game disorder management method based on digital phenotyping according to the present invention.

[0081] Referring to FIG. 5, the failure management device (130) can collect multimodal data on user behavior by monitoring a digital learning process on a user terminal (110) through a data collection unit (410) (step S510). The failure management device (130) can detect at least one digital indicator associated with the user's Internet gaming failure by removing noise from and analyzing the multimodal data through a data analysis unit (430) (step S530).

[0082] In addition, the failure management device (130) can learn at least one digital indicator through the Internet game failure detection unit (450) to build a failure prediction model (step S550). The failure management device (130) can determine the degree and presence of a user's Internet game failure during the digital learning process based on at least one digital indicator through the Internet game failure detection unit (450) (step S570).

[0083]

[0084] Figure 6 is a diagram explaining the correlation and multiple regression results between IGDS and indicators according to the present invention.

[0085] Referring to FIG. 6, the failure management device (130) can determine digital indicators correlated with the IGDS to detect Internet game failures in a digital learning process. To this end, the failure management device (130) can extract meaningful indicators from multimodal data collected during the digital learning process. As shown in FIG. 6, multiple regression results can be presented for five digital indicators highly correlated with the IGDS. Specifically, F(5, 921) = 18.54, the significance level is p < 0.001, and the indicators of the IGDS can account for 10.0% of the adjusted coefficient of determination.

[0086] Accordingly, the disability management device (130) can collect digital indicators regarding the user's behavior during the digital learning process, and can effectively predict the user's level of Internet game disability from the digital indicators through a pre-learned disability prediction model.

[0087]

[0088] Although the present invention has been described above with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below.

[0089]

[0090] [Explanation of symbols]

[0091] 100: Internet Game Disability Management System

[0092] 110: User terminal 130: Fault management device

[0093] 150: Database

[0094] 210: Multimodal Data Collection Module

[0095] 230: Foreground Process Monitoring Module

[0096] 250: Web Browser Monitoring Module

[0097] 310: Processor 330: Memory

[0098] 350: User input / output section 370: Network input / output section

[0099] 410: Data Collection Department 430: Data Analysis Department

[0100] 450: Internet Game Disorder Detection Unit 470: Control Unit

Claims

1. A data collection unit that monitors the digital learning process on the user terminal and collects multimodal data on user behavior; A data analysis unit that removes noise from the multimodal data and analyzes it to detect at least one digital indicator associated with the user's Internet gaming disorder; and An Internet game disorder management device based on digital phenotyping, comprising an Internet game disorder detection unit that determines the degree and presence of the user's Internet game disorder in the digital learning process based on at least one digital indicator.

2. In paragraph 1, the data collection unit Collecting the multimodal data through the application running on the user terminal during the digital learning process; An Internet game disorder management device based on digital phenotyping, characterized in that the multimodal data includes action indicators based on the user's actions on the display screen of the user terminal.

3. In the second paragraph, the data collection unit An Internet game disorder management device based on digital phenotyping, characterized in that the user's keyboard-related actions and stroke actions, which are collected by the on-screen keyboard sensor and touchscreen sensor of the user terminal during the problem-solving process during the digital learning process, are collected as the multimodal data.

4. In paragraph 1, the data analysis unit An Internet gaming disorder management device based on digital phenotyping, characterized in that the above multimodal data is converted into a z-score and a multiple regression model is created using bootstrapping.

5. In paragraph 4, the data analysis unit An Internet game disorder management device based on digital phenotyping, characterized in that it determines at least one digital indicator from among stroke acceleration, stroke speed, vertical distance, number of blank spaces, and time interval.

6. In the third paragraph, the Internet game failure detection unit An Internet game disorder management device based on digital phenotyping, characterized in that it learns the correlation between the at least one digital indicator and the number of correct answers in the problem-solving process to build a disorder prediction model, and inputs multimodal data on the user's behavior into the disorder prediction model to determine whether the user has an Internet game disorder.

7. In the Internet game disorder management method performed in the Internet game disorder management device, A step of collecting multimodal data on user behavior by monitoring the digital learning process on the user terminal through a data collection unit; A step of removing noise from the multimodal data and analyzing the multimodal data through a data analysis unit to detect at least one digital indicator associated with the user's Internet gaming disorder; and A method for managing internet game disorder based on digital phenotyping, comprising: a step of determining the degree and presence of internet game disorder of the user in the digital learning process based on at least one digital indicator through an internet game disorder detection unit;

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