Data processing method for mental health monitoring, and apparatus thereof

Abstract

Disclosed are a data processing method and an apparatus thereof, which are capable of monitoring the mental health of a user by extracting behavior characteristic data about the user on the basis of data collected from a user equipment. The data processing method for mental health monitoring may comprise the steps of: receiving passive data from a user equipment of the user; extracting basic patterns of user activities from the passive data; and using the basic patterns so as to estimate behavior characteristic data about the user from the passive data.

Description

Data processing method and device for mental health monitoring

[0001] The present disclosure relates to a data processing method and device for mental health monitoring. More specifically, the present disclosure relates to a data processing method and device capable of monitoring a user's mental health by extracting user behavioral characteristic data based on data collected from the user's device.

[0002] As the use of digital devices becomes more widespread, the potential for their application in the medical field is increasing. Digital phenotyping involves processing large amounts of lifestyle data to understand a patient's current condition. Digital phenotyping can have clinical implications for medical assessment and diagnosis by enabling measurement of previously difficult-to-measure areas. It can be used to derive clinical implications previously inaccessible in clinical settings or as a basis for new discoveries. Therefore, ongoing research is underway to consider and incorporate digital phenotyping.

[0003] The patent document related to this is Patent Publication No. 10-2021-0064797 (June 3, 2021).

[0004] The problem that the present disclosure seeks to solve is to provide a data processing method and device that enable monitoring a user's mental health by extracting the user's behavioral characteristic data based on data collected from the user's device.

[0005] The problems to be solved by the present disclosure are not limited to the problems described above, and problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present disclosure pertains from this specification and the attached drawings.

[0006] A data processing method for monitoring a user's mental health according to one embodiment of the present disclosure may include the steps of: receiving passive data from a user device of the user; extracting a basic pattern of user activity from the passive data; and estimating the user's behavioral characteristic data from the passive data using the basic pattern.

[0007] The above passive data may include information on at least one of GPS data received from the user device, acceleration sensor data, tilt sensor data, screen ON time, screen OFF time, call start time, call end time, type of app being used, and usage time of the app being used.

[0008] The above behavioral characteristic data may include characteristic information about at least one of the user's activity complexity, step data points, residence data points, sleep, and application usage.

[0009] The method may further include a step of estimating the residence of the user based on GPS data received from the user device.

[0010] A data processing device for monitoring a user's mental health according to one embodiment of the present disclosure may include a communication unit that receives passive data from a user device of the user; and a processing unit that extracts a basic pattern of user activity from the passive data and estimates the user's behavioral characteristic data from the passive data using the basic pattern.

[0011] According to one embodiment of the present disclosure, a non-transitory computer-readable recording medium may record a computer program executed by a hardware computer. The computer program may include the steps of: receiving passive data from a user device; extracting a basic pattern of user activity from the passive data; and using the basic pattern to estimate the user's behavioral characteristic data from the passive data.

[0012] According to embodiments, data collected from a user device can be processed in a form optimized for user mental health monitoring. This allows for the continuous collection and real-time utilization of quantitative and / or objective data necessary for user mental health monitoring.

[0013] According to embodiments, user activity complexity can be extracted based on GPS data, and user behavioral characteristics can be analyzed based on the extracted data. Furthermore, step data points and residence data points, which are closely related to user mental health, can be analyzed with high reliability.

[0014] The effects according to the present disclosure are not limited to the effects described above, and effects not mentioned can be clearly understood by a person having ordinary skill in the art to which the present disclosure pertains from this specification and the attached drawings.

[0015] FIG. 1 is a conceptual diagram comparing a mental health monitoring and treatment assistance method according to one embodiment of the present disclosure with a mental health monitoring method according to a comparative example.

[0016] FIG. 2 is a block diagram of a mental health monitoring and treatment assistance system according to one embodiment of the present disclosure.

[0017] FIG. 3 is a flowchart illustrating the operation of a mental health monitoring and treatment assistance device according to one embodiment of the present disclosure.

[0018] FIG. 4 is a conceptual diagram of the operation of a subjective emotional state prediction model according to one embodiment of the present disclosure.

[0019] Figure 5 is a conceptual diagram of the operation of a mental illness scale classification model according to one embodiment of the present disclosure.

[0020] Figures 6 to 9 are graphs showing an example of a method for extracting behavioral characteristic data from passive data.

[0021] FIG. 10 is a graph showing output data of a subjective emotional state prediction model according to one embodiment of the present disclosure.

[0022] FIGS. 11 to 14 illustrate user interface screens providing mental health monitoring and treatment assistance services according to one embodiment of the present disclosure.

[0023] FIG. 15 is a diagram for explaining the types of device log data and behavioral characteristic data according to an embodiment of the present disclosure.

[0024] FIG. 16 is a flowchart illustrating a method for calculating activity complexity among user behavior characteristic data according to an embodiment of the present disclosure.

[0025] FIG. 17 is a diagram illustrating a specific example of calculating activity complexity among user behavior characteristic data according to an embodiment of the present disclosure.

[0026] FIG. 18 is a drawing for explaining a specific example of setting a pattern radius to calculate activity complexity according to an embodiment of the present disclosure.

[0027] FIG. 19 is a flowchart illustrating a method for calculating step data points among user behavior characteristic data according to an embodiment of the present disclosure.

[0028] FIG. 20 is a flowchart illustrating a method for calculating residence data points among user behavioral characteristic data according to an embodiment of the present disclosure.

[0029] FIG. 21 is a diagram illustrating the configuration of a data processing device for mental health monitoring according to an embodiment of the present disclosure.

[0030] Specific structural or functional descriptions of embodiments according to the concept of the present disclosure disclosed in this specification are merely illustrative for the purpose of explaining embodiments according to the concept of the present disclosure, and embodiments according to the concept of the present disclosure may be implemented in various forms and are not limited to the embodiments described in this specification.

[0031] Embodiments according to the concept of the present disclosure may have various modifications and take various forms, and thus, embodiments are illustrated in the drawings and described in detail in this specification. However, this is not intended to limit embodiments according to the concept of the present disclosure to specific disclosed forms, and includes modifications, equivalents, or alternatives that fall within the spirit and technical scope of the present disclosure.

[0032] While terms such as "first" or "second" may be used to describe various components, these components should not be limited by these terms. These terms are only intended to distinguish one component from another. For example, a first component may be referred to as a "second component," and similarly, a second component may also be referred to as a "first component," without departing from the scope of the present disclosure.

[0033] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components in between. Conversely, when a component is referred to as being "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between. Expressions that describe relationships between components, such as "between," "immediately between," or "directly adjacent to," should be interpreted similarly.

[0034] The terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the present disclosure. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, it should be understood that the terms "comprises" or "has" indicate the presence of a described feature, number, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0035] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and will not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0036] The processor in this specification may mean hardware capable of performing functions and operations according to each name described in this specification, may mean computer program code capable of performing specific functions and operations, or may mean an electronic recording medium loaded with computer program code capable of performing specific functions and operations.

[0037] In other words, a processor may mean a functional and / or structural combination of hardware for performing the technical idea of ​​the present disclosure and / or software for driving the hardware.

[0038] Hereinafter, embodiments will be described in detail with reference to the attached drawings. However, the scope of the patent application is not limited or restricted by these embodiments. The same reference numerals in each drawing represent the same components.

[0039] FIG. 15 is a diagram for explaining the types of device log data and behavioral characteristic data according to an embodiment of the present disclosure.

[0040] A data processing device (hereinafter referred to as a "data processing device") for mental health monitoring according to an embodiment of the present disclosure can collect device log data (151) from a user device. The device log data (151) can include passive data (153) and active data (155). The data processing device can collect the device log data (151) by dividing it into passive data (153) and active data (155).

[0041] More specifically, passive data (153) according to an embodiment of the present disclosure refers to data that can be automatically collected from a user device. Passive data (153) may include GPS log data, acceleration sensor log data, call-related log data, and / or application-related log data.

[0042] Furthermore, active data (155) according to an embodiment of the present disclosure refers to data that can be collected through user input on a user device. Active data (155) may include the user's subjective mood score, appetite score, sleep score, sleep duration, residential address, and / or time spent at the residence.

[0043] A data processing device according to an embodiment of the present disclosure can process device log data (151) to generate or calculate user behavioral characteristic data (157). This is to convert data automatically collected from a user device into a form optimized for user mental health monitoring and to increase reliability.

[0044] The user's behavioral characteristic data (157) according to an embodiment of the present disclosure may include characteristic data regarding activity complexity (159), step data points (161), residence data points (163), sleep (165), and / or application usage (167).

[0045] More specifically, the data processing device can generate characteristic data (159) on activity complexity based on GPS log data among passive data (153).

[0046] The data processing device can collect GPS log data at preset time intervals, i.e., timestamps.

[0047] The data processing device can extract the user's main visit locations by clustering GPS log data by date and excluding data presumed to be part of the user's travel path. Once the user's main visit locations are extracted, the data processing device can link the GPS log data for the main visit locations by date to create a basic pattern of user activity. Alternatively, the data processing device can compare the user's travel paths across multiple dates and create a basic pattern of user activity by identifying the most frequently repeated patterns.

[0048] Once a basic pattern of user activity is generated, the data processing device can set various pattern radii. For example, the data processing device can set pattern radii to 10 meters, 50 meters, and 100 meters, and calculate and / or generate activity complexity (159) by counting the number of times the user's GPS coordinates exceed the set pattern radii.

[0049] Furthermore, the behavioral characteristic data (157) according to an embodiment of the present disclosure may include step data points (161).

[0050] Because this disclosure aims to monitor a user's mental health, data collected while the user is traveling by car will be processed very differently from data collected while the user is walking. This is because physical activity is positively correlated with mental health.

[0051] According to an embodiment of the present disclosure, acceleration sensor log data can be collected as passive data (153), and step data can generally be estimated using the acceleration sensor log data. However, acceleration sensor log data has a problem in that it is difficult to distinguish from vibrations caused by a moving vehicle, resulting in a large margin of error.

[0052] Therefore, a data processing device according to an embodiment of the present disclosure can generate a step data point (161) based on GPS log data among passive data (153).

[0053] More specifically, the data processing device can calculate the user's distance traveled per unit time, i.e., movement speed, based on GPS log data. Then, by comparing the average walking speed of adults with the movement speed calculated based on the GPS log data, the GPS log data corresponding to situations where the user uses a vehicle or other means of transportation and the GPS log data corresponding to situations where the user is stationary can be excluded, thereby extracting the GPS log data corresponding to situations where the user moves on foot as step data.

[0054] Once the step data is extracted, the data processing device can generate characteristic data about the user's steps, such as the user's walking time, walking distance, walking speed, and the average of each of these.

[0055] According to an embodiment of the present disclosure, the data processing device may perform a procedure for verifying step data points by checking the user's moving location and moving speed.

[0056] Furthermore, behavioral characteristic data (157) according to an embodiment of the present disclosure may include residence data points (163).

[0057] According to embodiments of the present disclosure, information regarding a user's address and / or time spent at their residence can be collected as active data (155). However, data collected through user input is unreliable, and addresses are difficult to collect because they are private information.

[0058] Therefore, a data processing device according to an embodiment of the present disclosure can generate a residence data point (163) based on GPS log data among passive data (153).

[0059] More specifically, the data processing device can identify the time and location of a user's stay in one place by clustering GPS log data based on movement speed and excluding data estimated to be a movement path.

[0060] The data processing device then clusters the GPS log data, excluding the movement path, based on GPS coordinates, thereby identifying the main places visited by the user.

[0061] Assuming that the location where a user spends the longest time is likely the user's residence, the data processing device can determine the location where the user spends the longest time by checking the time spent at each of the major visited locations, thereby inferring the location where the user spends the longest time as the user's residence. Furthermore, the time spent at the residence can be stored by date to create a residence data point (163).

[0062] Furthermore, the behavioral characteristic data (157) according to the embodiment of the present disclosure may include characteristic data (165) regarding sleep.

[0063] A data processing device according to an embodiment of the present disclosure can generate daily sleep time information based on the sleep start time and / or sleep end time among device log data (151). Furthermore, it can generate characteristic data (165) regarding the user's sleep, such as daily average sleep time and sleep irregularity using sleep time distribution.

[0064] Furthermore, behavioral characteristic data according to an embodiment of the present disclosure may include characteristic data (167) regarding application usage.

[0065] A data processing device according to an embodiment of the present disclosure can extract the name, category, operating time, screen playback time, etc. of an application used by a user based on app log data among passive data (153).

[0066] However, category information extracted from app log data suffers from a lack of reliability. Therefore, a data processing device according to an embodiment of the present disclosure can arbitrarily categorize social apps, messenger apps, communication apps, and dating apps, which are particularly important for mental health monitoring, and create a table based on the application's name. The applications recorded in the table can then process the app log data based on the application's name to generate characteristic data (167) regarding application usage.

[0067] FIG. 16 is a flowchart illustrating a method for calculating activity complexity among user behavior characteristic data according to an embodiment of the present disclosure.

[0068] A data processing device according to an embodiment of the present disclosure can collect GPS log data at preset time intervals, i.e., timestamps, cluster the GPS log data by date, and extract the user's main visited places by excluding data estimated to be a movement path (S169).

[0069] The data processing device can generate a basic pattern of user activity by linking GPS log data for major visit locations by date. Alternatively, the data processing device can generate a basic pattern of user activity by comparing the user's movement paths by date, and selecting the most frequently repeated pattern (S171).

[0070] The data processing device can set various pattern radii for the basic pattern to calculate activity complexity. For example, the pattern radii can be set to 10 meters, 50 meters, and 100 meters. Furthermore, the data processing device can detect the time when the user's GPS coordinates do not match the set pattern radii or count the number of such times (S173).

[0071] This allows the data processing device to record the activity complexity for the pattern radius in a database (S175).

[0072] FIG. 17 is a diagram illustrating a specific example of calculating activity complexity among user behavior characteristic data according to an embodiment of the present disclosure.

[0073] If we cluster the GPS log data collected from the user device for any date, as in Fig. 17 It can be clustered into 5 clusters (177, 179, 181, 183, 185). Clustering of GPS log data can be performed based on various criteria. For example, when clustering based on movement speed and / or residence time, 179, 181, and 183 are estimated to be GPS log data due to movement because they have high average movement speed and short residence time. In addition, 177 and 185 can be estimated to be GPS log data for major visited locations because they have low average movement speed and long residence time.

[0074] According to an embodiment of the present disclosure, the basic pattern of user activity is as shown in FIG. 17. Obtain the GPS log data of the user for all dates (Day 1, Day 2, Day 3, Day 4) as shown in Fig. 17. <c>The GPS log data of all users for all dates is summed up, as in Fig. 17. <d>It can be generated by extracting data on the most overlapping paths, such as this. This can be said to be data on the user's repetitive life patterns. After 17 <e>By processing data flattening like this, we can obtain the basic pattern of user activity.

[0075] As described above, the activity complexity (159) is as shown in Fig. 17. <d>Check the user's repetitive life pattern as shown in Fig. 17. <e>It can be generated by extracting only GPS log data for major visited places, such as .

[0076] FIG. 18 is a drawing for explaining a specific example of setting a pattern radius to calculate activity complexity according to an embodiment of the present disclosure.

[0077] According to an embodiment of the present disclosure, FIG. 18< / e> < / d> < / e> < / d> < / c> The pattern radius can be set to various radii, such as 189, 191, and 193. If the pattern radius is set wide, the pattern radius of Fig. 18 As such, the activity complexity is measured low. And, the narrower the pattern radius is set, the lower the activity complexity is in Fig. 18. <c>, and Fig. 18 <d>Activity complexity is measured as increasing.

[0078] FIG. 19 is a flowchart illustrating a method for calculating step data points among user behavior characteristic data according to an embodiment of the present disclosure.

[0079] A data processing device according to an embodiment of the present disclosure can calculate a user's moving distance per unit time, i.e., moving speed, based on GPS log data (S195).

[0080] Thereafter, the data processing device compares the average walking speed of an adult with the movement speed calculated based on the GPS log data, and by excluding the GPS log data corresponding to the situation where the user uses a means of transportation such as a car and excluding the GPS log data corresponding to the situation where the user is stationary, the GPS log data corresponding to the situation where the user moves on foot can be extracted as step data (S197).

[0081] After extracting GPS log data corresponding to steps, the data processing device can extract characteristic data regarding the user's steps, such as the user's walking time, walking distance, walking speed, and the average of each of these (S199).

[0082] A data processing device according to an embodiment of the present disclosure may also verify step data points by checking the user's moving location and moving speed (S201).

[0083] FIG. 20 is a flowchart illustrating a method for calculating residence data points among user behavioral characteristic data according to an embodiment of the present disclosure.

[0084] A data processing device according to an embodiment of the present disclosure can identify the time and location of a user's stay in one place by clustering GPS log data based on movement speed and excluding data estimated to be a movement path (S203).

[0085] Thereafter, the data processing device clusters the GPS log data excluding the movement path based on GPS coordinates, thereby identifying the main places visited by the user (S205).

[0086] The data processing device verifies and quantifies the time the user stayed at each major visited location (S207).

[0087] Thereafter, the data processing device estimates the user's residence as the main location visited by the user with the longest stay (S209). Furthermore, the residence data point can be created by storing the time spent at the residence by date.

[0088] FIG. 21 is a drawing for explaining the configuration of a data processing device according to an embodiment of the present disclosure.

[0089] Figure 21 is intended to provide a general and simplified description of a suitable computing environment in which embodiments of a data processing device may be implemented. Referring to Figure 5, a computing device is illustrated as an example of a data processing device.

[0090] The data processing device (500) may include at least a processing unit (503) and a system memory (501).

[0091] The data processing device (500) may include a plurality of processing units that cooperate when executing a program.

[0092] Depending on the exact configuration and type of the data processing device (500), the system memory (501) may be volatile (e.g., RAM), non-volatile (e.g., ROM, flash memory, etc.), or a combination thereof. The system memory (501) includes a suitable operating system (502) for controlling the operation of the platform, such as, for example, the WINDOWS operating system from Microsoft Corporation. The system memory (501) may also include one or more software applications, such as program modules, applications, etc.

[0093] The data processing device (500) may include additional data storage devices (504), such as magnetic disks, optical disks, or tapes. These additional storage devices (504) may be removable storage and / or non-removable storage. Computer-readable storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information, such as computer-readable instructions, data structures, program modules, or other data.

[0094] System memory (501) and storage device (504) are both merely examples of computer-readable storage media. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory devices, CD-ROMs, DVDs or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium capable of storing desired information and being accessed by the data processing device (500).

[0095] The input device (505) of the data processing device (500) may include, for example, a keyboard, a mouse, a pen, a voice input device, a touch input device, and a comparable input device.

[0096] The output device (506) of the data processing device (500) may include, for example, a display, a speaker, a printer, and other types of output devices. Since these devices are widely known in the art, a detailed description thereof will be omitted.

[0097] The data processing device (500) may also include a communication device (507) that allows the device to communicate with other devices via a network, such as a wired or wireless network, a satellite link, a cellular link, a local area network, and comparable mechanisms, for example in a distributed computing environment. The communication device (307) is one example of a communication medium, which may include computer-readable instructions, data structures, program modules, or other data therein. By way of example and not limitation, communication media include wired media, such as a wired network or direct connection, and wireless media, such as acoustic, RF, infrared, and other wireless media.

[0098] FIG. 1 is a conceptual diagram comparing a mental health monitoring method according to a comparative example of the present disclosure and a mental health monitoring and treatment assistance method according to an embodiment of the present disclosure.

[0099] Referring to Figure 1, in the mental health monitoring method according to a comparative example, at a certain point, the user may undergo a professional consultation test (1) and simultaneously undergo a questionnaire-based scale test (2) as a supplementary tool. The user's self-report used in the mental health monitoring method according to the comparative example of the present disclosure is inaccurate and fails to reflect the user's unconscious. Due to incorrect symptom identification, the expert may make an inaccurate judgment about the user's mental health.

[0100] According to a method for mental health monitoring and treatment assistance according to one embodiment of the present disclosure, digital data (4) regarding a user's daily life can be collected via a smartphone, wearable device, or the like. Here, the digital data (4) may be referred to as passive data or digital life log data. In the present disclosure, the digital data (4) refers to objective and quantitative user behavioral data that can be automatically generated and / or collected without user intervention.

[0101] In addition, according to a mental health monitoring and treatment assistance method according to one embodiment of the present disclosure, the user's mental health scale can be automatically predicted by analyzing the collected user's digital data (4), and the predicted mental health scale can be used as an assisting tool when a specialist diagnoses and / or treats the user's mental illness. The mental health monitoring and treatment assistance device can analyze the user's lifestyle patterns by analyzing the collected digital data, and teach the analyzed lifestyle patterns and the user's mental health assessment score to an artificial intelligence model.

[0102] According to the mental health monitoring and treatment assistance method, the collected digital data (4) of the user and the actual questionnaire-style scale test (2) already performed by the user for a certain period of time can be analyzed, and a regular status report (5) can be generated using a learned artificial intelligence model. In one embodiment, the learned artificial intelligence model can be used to predict active data corresponding to the user's actual questionnaire-style scale test (2). Here, the learned artificial intelligence model can be a subjective emotional state prediction model that uses behavioral characteristic data as input. The predicted active data can refer to response data of a self-reported questionnaire performed by the user, or a total sum score of the response data. The self-reported questionnaire performed by the user can include a 'mental health scale test'. Furthermore, in one embodiment, the learned artificial intelligence model can be used to predict a mental health (e.g., anxiety, depression) scale. For example, the learned artificial intelligence model can output 0 or 1 as a predicted value representing a depression scale. A depression scale of 0 can indicate a user within the normal range, and a depression scale of 1 can indicate a user at risk for depression. Here, the learned artificial intelligence model may be a mental illness scale classification model that takes behavioral characteristic data and predicted active data as input.

[0103] A mental health monitoring and treatment assistance method according to one embodiment of the present disclosure is a method for collecting passive data and / or active data by means of ecological momentary assessment (EMA), that is, a method of repeatedly sampling and recording the user's current behavior and experience in real time in the user's daily life environment, thereby enabling a more accurate understanding of the user and a mental health-related assessment and further a diagnosis of mental illness.

[0104] Therefore, according to a mental health monitoring and treatment assistance method according to one embodiment of the present disclosure, the user's active data can be continuously predicted without the problem of decreased compliance by using the user's digital data (4) that can be easily collected without the user's passive input after the present time based on the active data and passive data for the past 2 to 4 weeks before the user's compliance declines.

[0105] FIG. 2 is a block diagram of a mental health monitoring and treatment assistance system according to one embodiment of the present disclosure.

[0106] The mental health monitoring and treatment assistance system may include a mental health monitoring and treatment assistance device (100), a user device (102), and / or a database (104).

[0107] According to one embodiment, the mental health monitoring and treatment assistance device (100) can receive passive data and / or active data from the user. The mental health monitoring and treatment assistance device (100) can analyze the user's behavioral characteristic data from the received passive data. At this time, the mental health monitoring and treatment assistance device (100) can collect passive data for a certain period of time (e.g., 2 weeks, 4 weeks) or collect active data performed by the user every day for a certain period of time (e.g., 2 weeks, 4 weeks). The active data received from the user for a certain period of time may be referred to as 'first active data' herein.

[0108] The mental health monitoring and treatment assistance device (100) can predict a user's active data using a subjective emotional state prediction model (12) learned from behavioral characteristic data and active data. The user's active data, which is the output value of the learned subjective emotional state prediction model (12), may be referred to as "second active data" in this specification.

[0109] The mental health monitoring and treatment assistance device (100) may be implemented as a printed circuit board (PCB) such as a motherboard, an integrated circuit (IC), or a system on chip (SoC). For example, the mental health monitoring and treatment assistance device (100) may be implemented as an application processor.

[0110] Additionally, the mental health monitoring and treatment assistance device (100) and / or user device (102) may be implemented in a personal computer (PC), server, or portable device.

[0111] The portable device may be implemented as a laptop computer, a mobile phone, a smart phone, a tablet PC, a mobile internet device (MID), a personal digital assistant (PDA), an enterprise digital assistant (EDA), a digital still camera, a digital video camera, a portable multimedia player (PMP), a personal navigation device (PND), a handheld game console, an e-book, or a smart device. The smart device may be implemented as a smart watch, a smart band, or a smart ring.

[0112] In one embodiment, the mental health monitoring and treatment assistance method may be operated through a user device (102) in the form of an application or program. The application for mental health monitoring and treatment assistance may be an application running on a PC or a portable device. The application for mental health monitoring and treatment assistance may display a user interface (UI) or a graphical user interface (GUI) according to information processed in the mental health monitoring and treatment assistance device (100) and / or the user device (102) through a display, or information processed in the mental health monitoring and treatment assistance device (100) and / or the user device (102) through a display. The information processed in the mental health monitoring and treatment assistance device (100) may include information on passive data, active data, and behavioral characteristic data. The application may include an interface linked with an existing hospital operating system.

[0113] The database (104) may be a database connected to the mental health monitoring and treatment assistance device (100) via wired or wireless means. In one embodiment, the database (104) may be a hospital database including basic information and / or clinical information of the user, and / or a model database including learning data and preprocessing data. In one embodiment, the database (104) may be a cloud database storing passive data, active data, and / or data related to passive data or active data of a plurality of users.

[0114] The mental health monitoring and treatment assistance device (100) includes a processor (10) and / or a memory (20). Although not shown in the drawing, the mental health monitoring and treatment assistance device (100) may further include a transmitter / receiver and / or an interface.

[0115] The processor (10) can extract user behavioral characteristic data from passive data received from the user device. The processor (10) can use an artificial intelligence model to predict user-specific active data or user-specific mental illness scales from the user's behavioral characteristic data.

[0116] The processor (10) may include a subjective emotional state prediction model (12). The subjective emotional state prediction model (12) may include a boosting-based model. For example, the subjective emotional state prediction model (12) may include an XGBoost regression algorithm, but it will be understood by those skilled in the art that the subjective emotional state prediction model (12) may include any boosting-based model, but is not limited thereto. The processor (10) may predict user-specific active data using the subjective emotional state prediction model (12) including the XGBoost algorithm. Details regarding the subjective emotional state prediction model (12) will be described later with reference to FIG. 4.

[0117] The processor (10) may include a mental illness scale classification model (14). The mental illness scale classification model (14) may include a boosting-based model. For example, the mental illness scale classification model (14) may include an XGBoost classification algorithm, but it will be understood by those skilled in the art that the mental illness scale classification model (14) may include any boosting-based model, but is not limited thereto. The processor (10) may predict a mental illness scale for each user using the mental illness scale classification model (14) including the XGBoost algorithm. Details regarding the mental illness scale classification model (14) are described below with reference to FIG. 5.

[0118] The processor (10) can provide recommended treatment content or recommended treatment solution to the user based on at least one of the user's passive data, behavioral characteristic data, second active data, or a predicted value representing a mental illness scale.

[0119] Recommended treatment content may be content for mental health management or treatment of mental illness, as determined by the user's passive data, behavioral characteristic data, secondary active data, or predicted values ​​representing mental illness scales. For example, recommended treatment content may include classical music content, content for meditation programs (e.g., music content, books, video content, lecture material content), and / or content for electronic drug therapy.

[0120] A recommended treatment solution may be a solution for mental health management or treatment of mental illness appropriate for the user's condition, as determined by the user's passive data, behavioral characteristic data, secondary active data, or predicted values ​​representing mental illness scales. For example, in the case of a treatment solution for a user receiving home treatment, a treatment solution that includes a step-by-step intervention step, a step for early intervention depending on the situation, and a step for conducting non-face-to-face face-to-face consultation in emergency cases can be provided as a recommended treatment solution to the user to assist in the rehabilitation and recovery of the home treatment user.

[0121] Depending on the embodiment, recommended treatment content or recommended treatment solutions may be provided to users in the form of applications or programs. Specific examples of recommended treatment content or recommended treatment solutions in the form of applications or programs are described below in Figure 14.

[0122] In some embodiments, the mental health monitoring and treatment assistance device (100) may additionally include a recommendation module for providing recommended treatment content or recommended treatment solutions to the user. The recommendation module is controlled by the processor (10) and may output recommended treatment content or recommended treatment solutions most suitable for the user using an artificial intelligence model trained using the user's passive data, behavioral characteristic data, first active data, second active data, etc.

[0123] The processor (10) can process data stored in the memory (20). The processor (10) can execute computer-readable code (e.g., software) stored in the memory (20) and instructions generated by the processor (10).

[0124] A processor (10) may be a data processing device implemented as hardware having a circuit with a physical structure for executing desired operations. For example, the desired operations may include code or instructions included in a program.

[0125] For example, a data processing device implemented in hardware may include a microprocessor, a central processing unit, a processor core, a multi-core processor, a multiprocessor, an application-specific integrated circuit (ASIC), or a field programmable gate array (FPGA).

[0126] The memory (20) can store passive data and / or active data received from the user. For example, the memory (20) can store GPS coordinates, number of steps, sleep start time, sleep end time, call start time, call end time, type of application (APP) in use, and / or usage time of the APP received from the user device (102). The memory (20) can store all or part of the subjective emotional state prediction model (12), all or part of the mental illness scale classification model (14), input values ​​or output values ​​of the subjective emotional state prediction model (12), input values ​​or output values ​​of the mental illness scale classification model (14), and variables required for calculation of the subjective emotional state prediction model (12).

[0127] The memory (20) can store instructions (or programs) executable by the processor (10). For example, the instructions may include instructions for executing operations of the processor and / or operations of each component of the processor.

[0128] The memory (20) can be implemented as a volatile memory device or a non-volatile memory device.

[0129] Volatile memory devices can be implemented as dynamic random access memory (DRAM), static random access memory (SRAM), thyristor RAM (T-RAM), zero capacitor RAM (Z-RAM), or twin transistor RAM (TTRAM).

[0130] The nonvolatile memory device can be implemented as an Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory, Magnetic RAM (MRAM), Spin-Transfer Torque (STT)-MRAM, Conductive Bridging RAM (CBRAM), Ferroelectric RAM (FeRAM), Phase change RAM (PRAM), Resistive RAM (RRAM), Nanotube RRAM, Polymer RAM (PoRAM), Nano Floating Gate Memory (NFGM), holographic memory, Molecular Electronic Memory Device, or Insulator Resistance Change Memory.

[0131] FIG. 3 is a flowchart illustrating the operation of a mental health monitoring and treatment assistance device according to one embodiment of the present disclosure.

[0132] Referring to FIG. 3, the mental health monitoring and treatment assistance device (100) can receive passive data and first active data from a user device (S302).

[0133] Passive data may include information about one or more of GPS coordinates received from the user device, acceleration sensor data, tilt sensor data, screen ON time, screen OFF time, call start time, call end time, type of app being used, and usage time of the app.

[0134] The first active data may refer to response data from a self-reported questionnaire (e.g., a mental health scale test) administered by the user, or the total sum score of the response data. The self-reported questionnaire administered by the user may include a "mental health scale test." The mental health scale test may be a four-item test with responses ranging from 1 to 7.

[0135] Here, the passive data received from the user device (102) may be the user's passive data received through the user device over a certain period of time (e.g., 2 weeks, 4 weeks). The first active data received from the user device (102) may be the total sum score of self-reported questionnaire items performed by the user once a day. The first active data may be collected over a certain period of time (e.g., 2 weeks, 4 weeks) and the average score over the certain period may be checked to train a mental illness scale prediction model.

[0136] The mental health monitoring and treatment assistance device (100) can extract user behavioral characteristic data from passive data (S304).

[0137] The behavioral characteristic data may include information about at least one of the number of major visited locations, time spent at each major visited location, number of steps, distance traveled, movement speed, time walked, distance walked, walking speed, activity complexity, step data points, residence data points, sleep time, and application usage time.

[0138] In one embodiment, the mental health monitoring and treatment assistance device (100) can extract the user's activity complexity data from passive data. The activity complexity data may be referred to as pattern mismatch time. The mental health monitoring and treatment assistance device (100) can collect the user's location data for all days and all time periods during a certain period. The mental health monitoring and treatment assistance device (100) can cluster the location data at specific times on each day and select the first location data that is most frequently located at the specific time. The mental health monitoring and treatment assistance device (100) can generate a repetitive life pattern graph based on the first location data at each time. The mental health monitoring and treatment assistance device (100) can compare the repetitive life pattern graph with the life pattern graph to calculate the time when the life pattern graph is inconsistent with the pattern of the repetitive life pattern graph. The mental health monitoring and treatment assistance device (100) can calculate the average of the time when the pattern is inconsistent with the pattern during a certain period.

[0139] The mental health monitoring and treatment assistance device (100) can predict the user's second active data by using a subjective emotional state prediction model (12) that inputs behavioral characteristic data (S306).

[0140] The subjective emotional state prediction model (12) may include a boosting-based algorithm. The subjective emotional state prediction model (12) can be trained using a feature subset determined based on Recursive Feature Elimination with Cross Validation (RFECV) among the above behavioral characteristic data. RFECV is a feature selection method, and the process is as follows. First, features with low feature importance are eliminated, and model performance for each number of features is derived. Then, K-fold cross validation is utilized for each number of features to derive different performances. Next, the performances for each number of features derived in this way are averaged, and the number of features with the highest performance is selected. Finally, the features corresponding to that number are selected as the final features to be used. Here, feature importance refers to the degree to which each feature contributes to the subjective emotional state prediction model (12). In addition, feature selection refers to the process of selecting appropriate features necessary for learning to improve model accuracy or speed.

[0141] In one embodiment, the feature subset may consist of step count, distance traveled, places visited, and sleep time. In another embodiment, the feature subset may consist of distance traveled, speed, distance walked, and walking speed. In yet another embodiment, the feature subset may consist of pattern mismatch time (i.e., activity complexity), time spent at home, days of the week spent at home, and whether the days spent at home were weekdays or weekends.

[0142] Secondary active data may be a predicted total score for items answered by a user on a self-reported questionnaire (e.g., a mental health scale). For example, this could be the predicted total score (e.g., 25) for a user on a four-item mental health scale, with responses ranging from 1 to 7.

[0143] The mental health monitoring and treatment assistance device (100) can output a predicted value representing a mental illness scale by using a mental illness scale classification model (14) that inputs behavioral characteristic data and second active data (S308).

[0144] The mental illness scale classification model (14) may include a boosting-based classification algorithm. The mental illness scale classification model (14) may be trained using passive data or second active data augmented using the synthetic minority oversampling technique (SMOTE).

[0145] A mental health monitoring and treatment assistance device (100) can provide a user interface that displays predicted values ​​representing a mental illness scale to the user (S310).

[0146] The mental health monitoring and treatment assistance device (100) can predict the user's active data based on the total score of a mental health scale test for a certain period predicted from a subjective emotional state prediction model (12), and can diagnose the user's mental illness in real time and predict the prognosis based on the mental illness scale predicted using a mental illness scale classification model (14). Furthermore, the mental health monitoring and treatment assistance device (100) can provide the user with recommended treatment content or recommended treatment solution based on at least one of the user's passive data, behavioral characteristic data, second active data, or predicted value representing the mental illness scale.

[0147] FIG. 4 is a conceptual diagram of the operation of a subjective emotional state prediction model according to one embodiment of the present disclosure.

[0148] The subjective emotional state prediction model (43) of Fig. 4 can be applied to the subjective emotional state prediction model (12) of Fig. 2. Hereinafter, it will be described with reference to Fig. 2.

[0149] Referring to Fig. 4, the subjective emotional state prediction model (43) can use the user's behavioral characteristic data (42) analyzed from passive data (41) as input data.

[0150] Passive data (41) is digital data that is digitized and quantified in devices such as smartphones and wearable devices, and may be included in digital data that is automatically collected without user intervention. Passive data (41) related to mental health monitoring and treatment assistance according to an embodiment of the present disclosure may include one or more of GPS coordinates, acceleration sensor data, tilt sensor data, screen ON time, screen OFF time, call start time, call end time, type of app (APP) being used, and app usage time.

[0151] A mental health monitoring and treatment assistance device (100) or processor (10) can analyze the user's behavioral characteristic data (42) from passive data (41).

[0152] The behavioral characteristic data (42) may be data representing the user's daily life behavioral characteristics, such as the user's average activity pattern, activity regularity, time spent at each location, and sleep time, analyzed. The behavioral characteristic data (42) may be data analyzed from passive data (41), may be a digital phenotype, or may be at least one of the passive data (41). In one embodiment, the behavioral characteristic data (42) may include time spent at each major visited location, activity complexity, movement speed, movement distance, number of major visited locations, daily sleep time, and sleep irregularity. Specific details of analyzing the user's behavioral characteristic data (42) from the passive data (41) will be described later with reference to FIGS. 6 to 9.

[0153] A subjective emotional state prediction model (43) according to one embodiment of the present disclosure may include a boosting-based machine learning model, such as an XGBoost (Extreme Gradient Boosting) regression algorithm. The XGBoost regression algorithm may be implemented as a GBM (Gradient Boosting Machine) model capable of parallel learning.

[0154] According to one embodiment of the present disclosure, a subjective emotional state prediction model (43) can learn the relationship between passive data (41) automatically collected by a user device and active data collected from the user.

[0155] The active data collected from users may be data collected through a four-item questionnaire with responses ranging from 1 to 7. By applying item response theory, which reflects the forgetting curve, to a simplified diagnostic questionnaire (e.g., a mental health scale test) consisting of four questions, the accuracy of the active data collected from users can be increased. In one embodiment, a subjective emotional state prediction model (43) can be trained using digital data (4) and active data collected over a two-week period.

[0156] The subjective emotional state prediction model (43) can output active data (44). In one embodiment, the active data (44) output from the subjective emotional state prediction model (43) can be a predicted total score of a survey (e.g., a mental health scale test) consisting of four items that can be answered from 1 to 7 points. The subjective emotional state prediction model (43) is trained using digital data (4) and active data collected over a certain period of time (e.g., 2 weeks), and then can predict the total score of the survey over a certain period of time (e.g., 1 week).

[0157] As a result of the performance evaluation of the subjective emotional state prediction model (43) according to one embodiment of the present disclosure, the Mean RMSE (Root Mean Square Error) was measured as 3±0.2, and the Mean R-Squared was measured as -2.2.

[0158] FIG. 5 is a conceptual diagram of the operation of a mental illness scale classification model according to one embodiment of the present disclosure.

[0159] Referring to FIG. 2, the mental illness scale classification model (53) of FIG. 5 can be applied to the mental illness scale classification model (14) of FIG. 2, and the passive data (51), behavioral characteristic data (52), and active data (54) of FIG. 5 can correspond to the passive data (41), behavioral characteristic data (41), and active data (44) of FIG. 4, respectively. Hereinafter, this will be described with reference to FIG. 2 and FIG. 4 together.

[0160] Referring to FIG. 5, the mental illness scale classification model (53) can use the user's behavioral characteristic data (52) analyzed from the passive data (51) as input data. In addition, the mental illness scale classification model (53) can use active data (54) as input data. Here, the active data (54) can be active data (44) output from the subjective emotional state prediction model (43), i.e., second active data. The mental illness scale classification model (53) can use the behavioral characteristic data (52) and the active data (44) output from the subjective emotional state prediction model (43) as input data.

[0161] As another example, a mental illness scale classification model (53) may use user behavioral characteristic data (52) analyzed from passive data (51) and first active data as input data. The first active data may refer to response data from a self-reported questionnaire (e.g., a mental health scale test) actually performed by the user, or the total sum score of the response data.

[0162] Passive data (51) and behavioral characteristic data (52) are the same or similar concepts as the passive data (41) and behavioral characteristic data (52) of FIG. 4, so the description below is omitted within the overlapping scope.

[0163] A mental illness scale classification model (53) according to one embodiment of the present disclosure may include a boosting-based machine learning model, such as an XGBoost (Extreme Gradient Boosting) classification algorithm. The XGBoost classification algorithm may be implemented as a GBM (Gradient Boosting Machine) model capable of parallel learning.

[0164] According to one embodiment of the present disclosure, a mental illness scale classification model (53) can learn the relationship between passive data (41) automatically collected by a user device (102) and active data (54) predicted from a subjective emotional state prediction model (43).

[0165] The mental illness scale classification model (53) can output a mental illness scale prediction value (55).

[0166] In one embodiment, the predicted value (55) representing the mental illness scale output from the mental illness scale classification model (53) may be a predicted value representing a depression scale or a predicted value representing an anxiety scale. The predicted value (55) representing the mental illness scale may be 0 or 1 based on the cutoff score of the mental illness scale. For example, if the mental illness scale is PHQ-9, which is one of the depression scales, 0 may be output as the predicted value if the cutoff score is calculated to be 10 points or less, and 1 may be output as the predicted value if the score of PHQ-9 is calculated to be more than 10 points, which is the cutoff score. The depression scale may be PHQ-9 or CESDR, and the anxiety scale may be GAD-7, but the present invention is not limited thereto, and the mental illness scale classification model (53) may be applied to predict scales for various mental illnesses.

[0167] As a result of evaluating the performance of a mental illness scale classification model (53) that outputs an anxiety scale (GAD-7) using behavioral characteristic data (52) and the first active data as input data, the Mean Accuracy was measured as 0.87503, the Mean Precision as 0.846822, the Mean F1 Score as 0.878034, and the Mean ROC-AUC as 0.947422.

[0168] As a result of evaluating the performance of a mental illness scale classification model (53) that outputs a depression scale (PHQ-9) using behavioral characteristic data (52) and the first active data as input data, the Mean Accuracy was measured as 0.946156, the Mean Precision as 0.920763, the Mean F1 Score as 0.945297, and the Mean ROC-AUC as 0.98447.

[0169] As a result of evaluating the performance of a mental illness scale classification model (53) that outputs an anxiety scale (GAD-7) using behavioral characteristic data (52) and my active data as input data, the Mean Accuracy was measured as 0.886533, the Mean Precision as 0.872277, the Mean F1 Score as 0.88892, and the Mean ROC-AUC as 0.950104.

[0170] As a result of evaluating the performance of a mental illness scale classification model (53) that outputs a depression scale (PHQ-9) using behavioral characteristic data (52) and second active data as input data, the Mean Accuracy was measured as 0.932701, the Mean Precision as 0.89645, the Mean F1 Score as 0.932692, and the Mean ROC-AUC as 0.97625.

[0171] Figures 6 to 9 are graphs illustrating an example of a method for extracting behavioral characteristic data from passive data. The following description is provided with reference to Figures 2, 4, and 5.

[0172] A mental health monitoring and treatment assistance device (100) or processor (10) can extract user behavioral characteristic data (42) from passive data (41).

[0173] The mental health monitoring and treatment assistance device (100) can extract the number of locations visited by a user on a daily basis from GPS coordinates collected at 5-minute intervals. For example, the number of visited locations can be extracted using DB-SCAN (Density-based Spatial Clustering of Applications with Noise), which estimates significant visited locations using the density of GPS coordinates. In this process, insignificant GPS coordinates can be excluded. The mental health monitoring and treatment assistance device (100) can calculate the average number of visited locations per user based on the number of visited locations.

[0174] As another example, the mental health monitoring and treatment assistance device (100) can extract user-specific step-related characteristic data, such as average daily walking time, average walking speed, and total daily walking distance, from the number of steps collected at 5-minute intervals.

[0175] For example, data on ecologically impractical step counts collected at 5-minute intervals, and data on distance traveled at 5-minute intervals that are impractical to walk, may be included. For example, if the distance traveled in 5 minutes is 2,000 meters, the probability that the distance traveled was on foot is low. According to an embodiment of the present disclosure, a time range expected to be traveled on foot can be specified based on the average walking speed of an adult, and the distance traveled on foot can be estimated based on the total distance traveled within the time range.

[0176] Passive data such as the number of steps and distance traveled collected at 5-minute intervals can be used to calculate walking time, walking distance, and walking speed by using the average walking speed of adults, excluding data that are determined to indicate that the user used a means of transportation, and only using data that is determined to indicate that the user walked.

[0177] The mental health monitoring and treatment assistance device (100) can extract characteristic data on the time spent at each visited location. The mental health monitoring and treatment assistance device (100) can perform primary clustering based on the movement speed by date, excluding the time spent moving between locations. The mental health monitoring and treatment assistance device (100) can extract the major visited locations where the user frequently stayed, which can be referred to as secondary clustering. The mental health monitoring and treatment assistance device (100) can calculate the time spent at each major visited location by date and calculate the sum of the time spent at each major visited location. For all dates during a certain period, the same location can be clustered. This can be referred to as tertiary clustering. The mental health monitoring and treatment assistance device (100) can quantify the time spent at each major visited location after the tertiary clustering. A mental health monitoring and treatment assistance device (100) according to one embodiment of the present disclosure can perform repeated clustering to calculate specific locations and the time spent at those locations in detail. For example, according to a mental health monitoring and treatment assistance method according to one embodiment of the present disclosure, home and work can be identified as separate locations, and the time spent at each location can be calculated.

[0178] As another example, the mental health monitoring and treatment assistance device (100) can extract user-specific sleep-related characteristic data, such as daily sleep time and sleep time distribution (i.e., sleep irregularity), from sleep start time and sleep end time collected at 5-minute intervals.

[0179] Referring to FIGS. 6 to 9, a method for extracting characteristic data related to activity complexity from behavioral characteristic data from GPS coordinates is described as follows.

[0180] The mental health monitoring and treatment assistance device (100) can calculate the average time spent deviating from a user-specific repetitive pattern based on collected GPS coordinates. In this disclosure, the average time spent deviating from a user-specific repetitive pattern is defined as "activity complexity." "Time spent deviating from a pattern" refers to time that is inconsistent with the pattern.

[0181] The mental health monitoring and treatment assistance device (100) can cluster user locations by date and time zone during a given period. For example, the GPS coordinates of user 117's location at 3:00 PM on all dates over a two-week period can be collected, and only the location data for all dates at 3:00 PM can be clustered.

[0182] The mental health monitoring and treatment assistance device (100) can cluster location data at a specific time on each day during a given period, and select the first location that is located the most at the specific time. The location that is visited the most at the specific time can be referred to as the 'first location'. For example, the locations of users at 15:00 on each day for two weeks can be represented as in the graph of FIG. 6. The mental health monitoring and treatment assistance device (100) can select the location or cluster on the graph that users visited the most at 15:00 during the given two weeks. The selected location is X 35.573233 Y 129.189264, which is the most visited location, 13 times out of 14. In FIG. 5, the first location at 15:00 is X 35.573233 Y 129.189264.

[0183] Referring to FIG. 7, the mental health monitoring and treatment assistance device (100) can determine a location for each time zone based on the first location for each time zone.

[0184] For example, the first position at 15 o'clock selected in Fig. 6 is X 35.573233 Y 129.189264. The mental health monitoring and treatment assistance device (100) can determine the position for each time zone based on the first position at 0 o'clock to the first position at 24 o'clock.

[0185] Referring to FIG. 8, the mental health monitoring and treatment assistance device (100) can generate a graph of repetitive life patterns by removing irregularities in the first location data for each time zone using a data smoothing technique and performing repetitive pattern generalization. The first location data can include information about the first location, for example, information about the X-coordinate and Y-coordinate of the first location.

[0186] Referring to FIG. 9, the mental health monitoring and treatment assistance device (100) can compare a repetitive life pattern graph (82) for a certain period of time with a life pattern graph (81) on a specific date, and calculate the time at which the life pattern graph (81) on a specific date does not match the pattern of the repetitive life pattern graph (82).

[0187] A method of comparing a repetitive life pattern graph (82) for a certain period of time and a life pattern graph (81) for a specific date may include a method of setting the error range of the repetitive life pattern to 15% when the specific date is May 25, 2023 and comparing whether it matches the life pattern graph (81) for May 25, 2023. Assuming that the specific date is May 25, 2023 and the certain period of time is two weeks, the time (6) at which the life pattern graph (81) for May 25, 2023 matches the pattern of the repetitive life pattern graph (82) for two weeks and the time (7) at which the life pattern graph (81) for May 25, 2023 does not match the pattern of the repetitive life pattern graph (82) for two weeks can be calculated.

[0188] In one embodiment, it is assumed that the two weeks for extracting the user's activity complexity data from the passive data are from May 14, 2023 to May 27, 2023. The mental health monitoring and treatment assistance device (100) can compare the repetitive life pattern graph (82) and the life pattern graph (81) for each date corresponding to 14 days from May 14 to May 27, and calculate the time for which the life pattern graph (81) does not match the pattern of the repetitive life pattern graph (82). For example, the time for which the pattern does not match may be 17.416667 hours on May 18, 2023, and 9.166667 hours on May 25, 2023.

[0189] The mental health monitoring and treatment assistance device (100) can calculate the average of the times that are inconsistent with the above pattern over a certain period of time.

[0190] In one embodiment, the mental health monitoring and treatment assistance device (100) may calculate the average of the time inconsistent with the pattern over a two-week period. The average of the time inconsistent with the pattern from May 14, 2023 to May 27, 2023 may be calculated.

[0191] The average amount of time that is inconsistent with the pattern of the repetitive life pattern graph over a period of time can be defined as 'activity complexity'.

[0192] A mental health monitoring and treatment assistance method according to one embodiment of the present disclosure extracts activity patterns for each user and then compares them with repetitive patterns within the activities of each user, thereby extracting more accurate behavioral characteristic data by taking into account differences in activity patterns between individuals.

[0193] FIG. 10 is a graph showing output data of a subjective emotional state prediction model according to one embodiment of the present disclosure.

[0194] Referring to the graph of FIG. 10, the X-axis represents a date, and the Y-axis represents output data of a subjective emotional state prediction model according to an embodiment of the present disclosure. The output data of the subjective emotional state prediction model may be a predicted value of a total score of items answered by a user in a mental health scale test. The graph corresponding to the first label is a graph representing a total score calculated based on the responses to the mental health scale test actually performed by the user every day from March 25, 2023 to April 21, 2023. The graph corresponding to the second label is a graph representing output data of the subjective emotional state prediction model, i.e., a total score that the user is expected to obtain in the mental health scale test by date.

[0195] FIGS. 11 to 14 illustrate user interface screens providing mental health monitoring and treatment assistance services according to one embodiment of the present disclosure.

[0196] Referring to FIGS. 11 to 14, examples of user interface screens providing mental health monitoring and treatment assistance services are shown on a user device (102) running a mobile application.

[0197] Referring to FIG. 11, the mental health monitoring and treatment assistance device (100) can provide a user interface through which passive data and / or behavioral characteristic data including the number of steps, sleeping time, moving distance, etc. are displayed to the user through the user device (102).

[0198] Referring to FIG. 12, behavioral characteristic data highly related to mental health (e.g., time spent at home) can be analyzed and a user interface providing relevant data can be provided to the user. For example, data related to behavioral characteristic data highly related to mental health, such as daily time spent at home, average values ​​over a certain period, weekday average values, and weekend average values, can be provided. Therefore, according to a mental health monitoring and treatment assistance service according to one embodiment of the present disclosure, behavioral characteristic data can be analyzed through passive data collection.

[0199] Referring to FIG. 13, the mental health monitoring and treatment assistance device (100) can provide the user with user interfaces for conducting a mental health scale test through the user device (102).

[0200] User interfaces for conducting a mental health scale test may include a user interface (120) for starting the test, a user interface (122) for adding symptoms of interest, a user interface (124) for responding to questions included in the mental health scale test, and / or a user interface (126) for recording additional information about the user's status.

[0201] The mental health scale test according to the embodiment of the present disclosure is based on a new scale simplified to four items by applying the Ecological Momentary Assessment (EMA) concept. These four items may include questions about mood, appetite, sleep, and condition. Mental health monitoring and treatment assistance services may provide the mental health scale test to users so that they can be administered daily. In addition to the four main items, the mental health scale test allows for additional recording of symptoms of interest by selecting them and allowing for user diary recording, thereby compensating for the limited number of questions in the mental health scale test while ensuring expandability.

[0202] Referring to FIG. 14, the mental health monitoring and treatment assistance device (100) can provide user interfaces to the user for providing recommended treatment content or recommended treatment solutions through the user device (102).

[0203] Referring to Figure 14, user interfaces for providing a depression or anxiety treatment solution for perinatal women are illustrated.

[0204] User interfaces for providing a depression or anxiety treatment solution for perinatal women may include a user interface (140) for starting a scale test for perinatal women before treatment, a user interface (142) for providing various recommended treatment contents for objectively observing one's own state of mind, and a user interface (144) for starting a scale test for perinatal women after digital treatment with recommended treatment contents.

[0205] In a user interface (142) that provides various recommended treatment contents for objectively observing one's own state of mind, mindfulness contents for 1 to 4 sessions may be provided as recommended treatment contents. In an embodiment, when recommending recommended treatment contents or recommended treatment solutions using a learned artificial intelligence model, a pre-treatment scale test and a post-digital treatment scale test with recommended treatment contents may be used as learning data, and a content or solution predicted to have a large change value in a mental health scale or mental illness scale may be determined as a recommended treatment content or recommended treatment solution using a learned artificial intelligence model.

[0206] A mental health monitoring and treatment assistance device (100) according to one embodiment may be coupled with a computer or computing device as hardware and may include a computer program stored in a computer-readable recording medium to perform S302 to S310 of FIG. 3 described above.

[0207] It can be implemented as a computing device including at least one processor that executes instructions of programs loaded into a memory, and a program including instructions described to execute S302 to S310 of the above-described drawing 3 can be loaded into the memory.

[0208] The devices described above may be implemented as hardware components, software components, and / or a combination of hardware components and software components. For example, the devices and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. Furthermore, the processing device may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used singly; however, those skilled in the art will appreciate that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, the processing device may include multiple processors, or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0209] Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing device to perform a desired operation or may, independently or collectively, command the processing device. The software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave, for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over networked computer systems and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

[0210] The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program commands, data files, data structures, etc., alone or in combination. The program commands recorded on the medium may be those specially designed and configured for the embodiment or may be those known and available to those skilled in the art of computer software. 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, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program commands, such as ROMs, RAMs, and flash memories. Examples of the program commands include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.

[0211] Although the embodiments described above have been described by way of limited examples and drawings, those skilled in the art will appreciate that various modifications and variations can be made based on the above teachings. For example, appropriate results can still be achieved even if the described techniques are performed in a different order than described, and / or components of the described systems, structures, devices, circuits, etc. are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents.

[0212] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

[0213] The data processing method and device for mental health monitoring as described above can be applied to the medical field of managing mental health.< / d> < / c>

Claims

1. In a method for processing data for mental health monitoring by an electronic device, A step of receiving passive data from a user device of a user; A step of extracting basic patterns of user activity from the above passive data; and A step of estimating the user's behavioral characteristic data from the passive data using the above basic pattern, Data processing methods for mental health monitoring.

2. In paragraph 1, The above passive data includes information about at least one of GPS data, acceleration sensor data, tilt sensor data, screen ON time, screen OFF time, call start time, call end time, type of app being used, and usage time of app being used received from the user device. Data processing methods for mental health monitoring.

3. In paragraph 1, The above behavioral characteristic data includes characteristic information about at least one of the user's activity complexity, step data points, residence data points, sleep, and application usage. Data processing methods for mental health monitoring.

4. Further comprising a step of estimating the residence of the user based on GPS data received from the user device. Data processing methods for mental health monitoring.

5. In a device for processing data for mental health monitoring, A communication unit for receiving passive data from a user device; and A processing unit that extracts a basic pattern of user activity from the passive data and estimates the user's behavioral characteristic data from the passive data using the basic pattern, Data processing device for mental health monitoring.

6. In a non-transitory computer-readable recording medium on which a computer program executed by a hardware computer is recorded, The above computer program, A step of receiving passive data from a user device of a user; A step of extracting basic patterns of user activity from the above passive data; and A step of estimating the user's behavioral characteristic data from the passive data using the above basic pattern, Non-transitory computer-readable recording medium.

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