Mental health monitoring device and method

Abstract

Disclosed are a mental health monitoring device and an operation method thereof. The operation method of the mental health monitoring device, according to one embodiment, may comprise the steps of: receiving passive data from a user device; extracting behavioral characteristics data of a patient from the passive data; predicting active data of the patient by using a subjective emotional state prediction model trained with the behavioral characteristics data and active data; and monitoring the mental health of the patient on the basis of the predicted active data.

Description

Devices and methods for mental health monitoring

[0001] The following embodiments relate to devices and methods for mental health monitoring. More specifically, the following embodiments relate to devices and methods for mental health monitoring that analyze digital phenotypes based on Ecological Momentary Assessment (EMA) and predict patient activity data using artificial intelligence models.

[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] Meanwhile, with the unprecedented COVID-19 pandemic leading to the introduction of telemedicine and prescriptions, questions are continually being raised about whether remote consultations actually deliver therapeutic benefits to patients and their cost-effectiveness. Consequently, discussions are ongoing within the industry and pharmaceutical communities regarding the acceptable scope and methods of remote consultations in various medical fields, including diabetes, chronic heart failure, and mental illness.

[0004] Until recently, the diagnosis and management of mental illness remained largely focused on face-to-face interactions, relying on self-reporting and professional consultations. In the field of psychiatry, where illness is diagnosed based on the presence or absence of symptoms, unlike other medical fields where new technologies like artificial intelligence and various testing techniques are being introduced, patient self-reporting and face-to-face consultations with professionals remain the most important testing and diagnostic methods.

[0005] However, when diagnosing mental illness, especially depression, in hospitals and other settings, there is a problem in that it takes a lot of time and money if it is done through expert consultation based on a patient scale test in the form of a questionnaire.

[0006] The problem that the present disclosure seeks to solve is to provide a device and method for mental health monitoring.

[0007] 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.

[0008] A method for mental health monitoring according to one embodiment may include the steps of receiving passive data from a user device, extracting patient behavioral characteristic data from the passive data, predicting the patient's active data using a subjective emotional state prediction model learned from the behavioral characteristic data and active data, and monitoring the patient's mental health based on the predicted active data.

[0009] The above passive data may include information about 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 being used, and usage time of the app received from the user device.

[0010] The above behavioral characteristic data may include information on at least one of the visited location, time spent at each visited location, number of steps, distance moved, moving speed, distance walked, walking speed, activity complexity, and sleep time.

[0011] The above subjective emotional state prediction model includes a boosting-based algorithm and can be learned using a feature subset determined based on RFE (Recursive Feature Elimination) among the above behavioral characteristic data.

[0012] The above predicted active data may be a predicted total score of the items answered by the patient in a mental health scale test.

[0013] The step of extracting patient behavioral characteristic data from the above passive data may include the step of extracting patient activity complexity data from the above passive data.

[0014] The step of extracting the activity complexity data of the patient from the passive data may include the steps of collecting the location data of the patient for all dates and all time zones during a certain period, the step of clustering the location data at a specific time for each date and selecting a first location that is located the most at the specific time, the step of generating a repetitive life pattern graph based on the first location for all time zones, the step of comparing the repetitive life pattern graph with the life pattern graph of each date and calculating the time during which the life pattern graph of each date does not match the pattern of the repetitive life pattern graph, and the step of calculating the average of the time during the certain period during which the life pattern graph does not match the pattern of the repetitive life pattern graph.

[0015] A computer program for mental health monitoring according to one embodiment may be a computer program stored in a computer-readable recording medium, coupled with a computer as hardware, to perform the above-described mental health monitoring method.

[0016] A device for mental health monitoring according to one embodiment includes a memory and at least one processor, wherein the processor is configured to receive passive data from a user device, extract patient behavioral characteristic data from the passive data, predict active data of the patient using a subjective emotional state prediction model learned from the behavioral characteristic data and active data, and monitor mental health of the patient based on the predicted active data.

[0017] A mental health monitoring system according to one embodiment may include a mental health monitoring device including a user device that collects passive data from a patient, and a processor that receives the passive data from the user device, extracts patient behavioral characteristic data from the passive data, predicts the patient's active data using a subjective emotional state prediction model learned from the behavioral characteristic data and active data, and monitors the patient's mental health based on the predicted active data.

[0018] According to the embodiments, by continuously collecting quantitative and objective data related to the patient's mental illness and utilizing it in real time, the patient can be accurately understood and evaluated.

[0019] According to embodiments, the patient's passive data, which can be easily collected without the patient's passive input, can be used to continuously predict the patient's active data without the problem of decreased compliance.

[0020] According to embodiments, by analyzing behavioral characteristic data highly related to mental health from passive data, more accurate patient behavioral characteristics can be obtained, particularly by extracting activity complexity from GPS coordinates.

[0021] 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.

[0022] FIG. 1 is a conceptual diagram illustrating a mental health monitoring method according to one comparative example and one embodiment of the present disclosure.

[0023] FIG. 2 is a block diagram of a mental health monitoring system according to one embodiment of the present disclosure.

[0024] FIG. 3 is a flowchart illustrating the operation of a mental health monitoring device according to one embodiment of the present disclosure.

[0025] FIG. 4 is a conceptual diagram of the operation of a subjective emotional state prediction model according to one embodiment of the present disclosure.

[0026] Figures 5 to 8 are graphs showing an example of a method for extracting behavioral characteristic data from passive data.

[0027] FIG. 9 is a graph showing output data of a subjective emotional state prediction model according to one embodiment of the present disclosure.

[0028] FIGS. 10 to 12 illustrate user interface screens providing a mental health monitoring service according to an embodiment of the present disclosure.

[0029] Specific structural or functional descriptions of embodiments according to the concept of the present invention disclosed in this specification are merely illustrative for the purpose of explaining embodiments according to the concept of the present invention, and embodiments according to the concept of the present invention may be implemented in various forms and are not limited to the embodiments described in this specification.

[0030] Embodiments according to the concept of the present invention 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 invention to specific disclosed forms, but rather includes modifications, equivalents, or alternatives that fall within the spirit and technical scope of the present invention.

[0031] 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 intended solely 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 invention.

[0032] 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.

[0033] The terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the present invention. The singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, it should be understood that the terms "comprises" or "has" are intended to specify 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.

[0034] 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 invention 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.

[0035] 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.

[0036] In other words, a processor may mean a functional and / or structural combination of hardware for performing the technical idea of ​​the present invention and / or software for driving the hardware.

[0037] 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.

[0038] FIG. 1 is a conceptual diagram illustrating a mental health monitoring method according to one comparative example and one embodiment of the present disclosure.

[0039] Referring to Fig. 1, the mental health monitoring method according to a comparative example of the present disclosure may be performed repeatedly, such that a patient undergoes a professional consultation examination (1) through outpatient treatment at a certain point in time, and then undergoes a professional consultation (3) through outpatient treatment at a certain point in time N months later. When the professional consultation examination (3) through outpatient treatment at a certain point in time N months later is performed, a questionnaire-based scale examination (2) may also be performed simultaneously. The patient's self-report used in the mental health monitoring method according to a comparative example of the present disclosure is inaccurate and does not reflect the patient's unconsciousness. Due to incorrect symptom identification, a doctor may make an inaccurate diagnosis of the patient's mental health.

[0040] According to a mental health monitoring method according to one embodiment of the present disclosure, digital data (4) regarding a patient'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 patient behavioral data that can be automatically generated and / or collected without the patient's intervention.

[0041] Additionally, a mental health monitoring method according to one embodiment of the present disclosure can automatically assess a patient's mental illness and monitor the patient's mental health by analyzing collected digital data (4). For example, the collected digital data can be analyzed to analyze the patient's lifestyle patterns, and the analyzed lifestyle patterns and the patient's mental illness assessment score can be trained on an artificial intelligence model.

[0042] Using the trained AI model, active data corresponding to the patient's actual questionnaire-style scale test (2) can be predicted. Active data can refer to response data from a self-reported questionnaire administered by the patient. Here, the self-reported questionnaire administered by the patient may include a "mental health scale test."

[0043] A mental health monitoring method according to one embodiment of the present disclosure is a method for more accurately understanding a patient and conducting a mental health-related assessment by collecting passive data and / or active data according to ecological momentary assessment (EMA), that is, a method of repeatedly sampling and recording the patient's current behavior and experience in real time in the patient's daily life environment.

[0044] In addition, according to a mental health monitoring method according to one embodiment of the present disclosure, based on active data and passive data for two weeks before the patient's compliance declines, the patient's active data can be continuously predicted without the problem of compliance decline using digital data (4) of the patient that can be easily collected without the patient's passive input after the third week.

[0045] FIG. 2 is a block diagram of a mental health monitoring system according to one embodiment of the present disclosure.

[0046] A mental health monitoring system may include a mental health monitoring device (100), a user device (102), and / or a database (104).

[0047] In one embodiment, a mental health monitoring device (100) in a mental health monitoring system may receive passive data from a patient. The mental health monitoring device (100) may analyze the patient's behavioral characteristic data from the received passive data. The mental health monitoring device (100) may predict the patient's active data using a subjective emotional state prediction model (12) learned from the behavioral characteristic data and the active data.

[0048] The mental health monitoring 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 device (100) may be implemented as an application processor.

[0049] Additionally, the mental health monitoring device (100) or user device (102) may be implemented in a personal computer (PC), server, or portable device.

[0050] 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.

[0051] In one embodiment, the mental health monitoring method may be operated through a user device (102) in the form of an application or program. The application for mental health monitoring may be an application running on a PC or a portable device. The application for mental health monitoring may display information processed by the mental health monitoring device (100) or the user device (102) through a display, or a user interface (UI) or a graphical user interface (GUI) according to information processed by the mental health monitoring device (100) or the user device (102) through a display. The information processed by the mental health monitoring device (100) may include information on passive data, active data, and behavioral characteristic data. The application may include an interface linked to an existing hospital operating system.

[0052] The database (104) may be a database connected to the mental health monitoring device (100) via wired or wireless connection. In one embodiment, the database (104) may be a hospital database including basic information and / or clinical information of a patient, 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 patients.

[0053] The mental health monitoring device (100) includes a processor (10) and / or a memory (20). Although not shown in the drawing, the mental health monitoring device (100) may further include a transmitter / receiver and / or an interface.

[0054] 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 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, and is not limited thereto. The processor (10) may predict patient-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.

[0055] 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).

[0056] 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.

[0057] 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).

[0058] The memory (20) can store passive data received from the patient. 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 application received from the patient's user device (102). The memory (20) can store all or part of the subjective emotional state prediction model (12), input values ​​or output values ​​of the subjective emotional state prediction model (12), and variables required for calculating the subjective emotional state prediction model (12).

[0059] 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.

[0060] The memory (20) can be implemented as a volatile memory device or a non-volatile memory device.

[0061] 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).

[0062] The nonvolatile memory device may 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.

[0063] FIG. 3 is a flowchart illustrating the operation of a mental health monitoring device according to one embodiment of the present disclosure.

[0064] Referring to FIG. 3, the mental health monitoring device (100) can receive passive data from a user device (S302).

[0065] 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.

[0066] The mental health monitoring device (100) can extract patient behavioral characteristic data from passive data (S304).

[0067] The behavioral characteristic data may include information on at least one of visited locations, time spent at each visited location, number of steps, distance traveled, movement speed, distance walked, walking speed, activity complexity, and sleep time.

[0068] In one embodiment, the mental health monitoring device (100) can extract patient activity complexity data from passive data. The activity complexity data may be referred to as pattern mismatch time. The mental health monitoring device (100) can collect patient location data for all days and all time periods during a certain period. The mental health monitoring device (100) can cluster location data at specific times on each date and select the first location data that is most frequently located at a specific time. The mental health monitoring device (100) can generate a repetitive life pattern graph based on the first location data at each time period. The mental health monitoring device can compare the repetitive life pattern graph with the life pattern graph and calculate the time when the life pattern graph is inconsistent with the pattern of the repetitive life pattern graph. The mental health monitoring device (100) can calculate the average time when the pattern is inconsistent with the pattern during a certain period.

[0069] The mental health monitoring device (100) can predict the patient's active data by using a subjective emotional state prediction model (12) learned from behavioral characteristic data and active data (S306).

[0070] The subjective emotional state prediction model (12) may include a boosting-based algorithm. The subjective emotional state prediction model (12) may be trained using a feature subset determined based on RFE (Recursive Feature Elimination) among the above-described behavioral characteristic data. RFE is a method of feature selection, which means a method of repeating the process of removing features with low feature importance until a desired number of features remains. The desired number of features may be the number of features included in the feature subset in the subjective emotional state prediction model (12) according to an embodiment of the present disclosure. Here, feature importance means the degree to which each feature contributes to the subjective emotional state prediction model (12). In addition, feature selection means a process of selecting appropriate features necessary for learning for the accuracy or speed of the model.

[0071] In one embodiment, the feature subset may consist of steps, 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, day of the week, and whether it is a weekday or weekend.

[0072] Predicted active data can be the predicted total score for items answered by a patient on a mental health scale. For example, this could be the predicted total score (e.g., 25) that a patient would receive on a four-item mental health scale, with responses ranging from 1 to 7.

[0073] The mental health monitoring device (100) can monitor the mental health of the patient based on the predicted active data (S308).

[0074] The mental health monitoring device (100) can diagnose a patient's mental illness in real time and predict the prognosis based on the total score of a mental health scale test for a certain period of time predicted from a subjective emotional state prediction model (12).

[0075] FIG. 4 is a conceptual diagram of the operation of a subjective emotional state prediction model according to one embodiment of the present disclosure.

[0076] 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.

[0077] Referring to Fig. 4, the subjective emotional state prediction model (43) can use the patient's behavioral characteristic data (42) analyzed from passive data (41) as input data.

[0078] Passive data (41) is digital data quantified and digitized by devices such as smartphones and wearable devices, and may be included in digital data automatically collected without patient intervention. Passive data (41) related to mental health monitoring according to an embodiment of the present disclosure may include one or more of GPS coordinates, number of steps, screen ON time, screen OFF time, call start time, call end time, type of app (APP) in use, and app usage time.

[0079] A mental health monitoring device (100) or processor (10) can analyze the patient's behavioral characteristic data (42) from passive data (41).

[0080] The behavioral characteristic data (42) may be data representing the behavioral characteristics of the patient's daily life, such as the patient's average activity pattern, activity regularity, time spent at each location, and sleep time. The behavioral characteristic data (42) may be data analyzed from passive data, a digital phenotype, or at least one of the passive data. In one embodiment, the behavioral characteristic data (42) may include locations stayed, activity complexity, movement speed, movement distance, locations visited, daily sleep time, and sleep irregularity. Specific details of analyzing the patient's behavioral characteristic data (42) from the passive data (41) are described below with reference to FIGS. 5 to 8.

[0081] 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 the Extreme Gradient Boosting (XGBoost) algorithm. The XGBoost algorithm may be implemented as a GBM (Gradient Boosting Machine) model capable of parallel learning.

[0082] According to one embodiment of the present disclosure, a subjective emotional state prediction model (43) can learn the relationship between passive data (41) and active data collected from a patient.

[0083] The active data collected from patients may be 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 four-item diagnostic questionnaire (e.g., a mental health scale test), the accuracy of the active data collected from patients can be improved. 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.

[0084] 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) can be trained using digital data (4) and active data collected over a certain period of time (e.g., 2 weeks), and then predict the total score of the survey over a certain period of time (e.g., 1 week).

[0085] 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.

[0086] Figures 5 to 8 are graphs illustrating an example of a method for extracting behavioral characteristic data from passive data. The following description is provided with reference to Figure 2.

[0087] A mental health monitoring device (100) or processor (10) can extract patient behavioral characteristic data (42) from passive data (41).

[0088] The mental health monitoring device (100) can extract the number of locations a patient moves to each day from GPS coordinates collected at 5-minute intervals. For example, the number of locations moved can be extracted using DB-SCAN (Density-based Spatial Clustering of Applications with Noise), which estimates significant locations moved using the density of GPS coordinates. In this process, insignificant GPS coordinates can be excluded. The mental health monitoring device (100) can calculate the average number of locations moved per patient based on the number of locations moved.

[0089] As another example, the mental health monitoring device (100) can extract patient-specific walking-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.

[0090] For example, data on ecologically impractical step counts collected at 5-minute intervals, and data on distance traveled at 5-minute intervals that cannot be walked, can be included. Passive data such as step counts and distance traveled collected at 5-minute intervals can be used to calculate distance walked, walking speed, or walking time, etc. by excluding the speed of transportation used and only using data that are determined to be walked, using the average walking speed of adults.

[0091] The mental health monitoring device (100) can calculate the movement speed from the number of steps collected at 5-minute intervals and extract characteristic data on the time spent at each location. The mental health monitoring device (100) can perform primary clustering based on the movement speed by date, excluding the time spent moving between locations. The mental health monitoring device (100) can extract major locations where the user stayed, which can be referred to as secondary clustering. The mental health monitoring device (100) can calculate the time spent at major locations by date and calculate the sum of the time spent at each major location. In addition, the same locations can be clustered for all dates over a certain period of time. This can be referred to as tertiary clustering. The mental health monitoring device (100) can quantify the time spent at each location after the tertiary clustering. The mental health monitoring device (100) according to one embodiment of the present disclosure can perform repeated clustering to calculate specific locations and the time spent at each location in detail. For example, according to a mental health monitoring method according to one embodiment of the present disclosure, each of a home and a coffee shop located directly across from the home can be extracted as different locations, and the time spent at each location can be calculated.

[0092] As another example, the mental health monitoring device (100) can extract patient-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.

[0093] Referring to FIGS. 5 to 8, a method for extracting activity complexity from behavioral characteristic data from GPS coordinates is described as follows.

[0094] The mental health monitoring device (100) can calculate the average time deviating from a patient's repetitive pattern based on collected GPS coordinates. In this disclosure, the average time deviating from a patient's repetitive pattern is defined as "activity complexity." "Time deviating from the pattern" refers to the time that is inconsistent with the pattern.

[0095] The mental health monitoring device (100) can cluster patient locations by date and time zone during a given period. For example, the GPS coordinates of patient 117's location at 3:00 PM on all dates over a two-week period can be collected, and only the location data at 3:00 PM on all dates can be clustered.

[0096] The mental health monitoring 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 patients at 15:00 on each day for two weeks can be represented as in the graph of FIG. 5. The mental health monitoring device (100) can select the location or cluster on the graph that the patient visited the most at 15:00 during the given two weeks. The selected location (51) is the location that was visited the most 13 times out of 14 times, and has an X coordinate of 35.573233 and a Y coordinate of 129.189264. In FIG. 5, the first location at 15:00 has an X coordinate of 35.573233 and a Y coordinate of 129.189264.

[0097] Referring to FIG. 6, the mental health monitoring device (100) can determine a location for each time zone based on the first location for each time zone.

[0098] For example, the first position at 15 o'clock selected in Fig. 5 has an X coordinate of 35.573233 and a Y coordinate of 129.189264. The mental health monitoring 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.

[0099] Referring to FIG. 7, the mental health monitoring 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.

[0100] Referring to FIG. 8, the mental health monitoring 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).

[0101] 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.

[0102] In one embodiment, it is assumed that the two weeks for extracting the patient's activity complexity data from the passive data (41) are from May 14, 2023 to May 27, 2023. The mental health monitoring 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, 2023 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 the time for which the pattern does not match may be 9.166667 hours on May 25, 2023.

[0103] The mental health monitoring device (100) can calculate the average of the times that are inconsistent with the above pattern over a certain period of time.

[0104] In one embodiment, the mental health monitoring device (100) may calculate the average of the number of times the patient is out of sync with the pattern over a two-week period. For example, the average of the number of times the patient is out of sync with the pattern from May 14, 2023, to May 27, 2023, may be calculated.

[0105] 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'.

[0106] A mental health monitoring method according to one embodiment of the present disclosure extracts activity patterns for each patient and then compares them with repetitive patterns within the activities of each patient, thereby extracting more accurate behavioral characteristic data by taking into account differences in activity patterns between individuals.

[0107] FIG. 9 is a graph showing output data of a subjective emotional state prediction model according to one embodiment of the present disclosure.

[0108] Referring to the graph of FIG. 9, the X-axis represents a date, and the Y-axis represents output data of a subjective emotional state prediction model (43) according to an embodiment of the present disclosure. The output data of the subjective emotional state prediction model (43) may be a predicted value of the total score of the items answered by the patient in the mental health scale test. The graph corresponding to the first label is a graph representing the total score calculated based on the responses to the mental health scale test actually performed by the patient every day from March 25, 2023 to April 21, 2023. The graph corresponding to the second label is a graph representing the output data of the subjective emotional state prediction model (43), i.e., the total score that the patient is expected to obtain in the mental health scale test by date.

[0109] FIGS. 10 to 12 illustrate user interface screens providing a mental health monitoring service according to an embodiment of the present disclosure.

[0110] Referring to FIGS. 10 to 12, examples of user interface screens providing mental health monitoring services are shown on a user device (102) running a mobile application.

[0111] Referring to FIG. 10, the mental health monitoring device (100) can provide a patient with a user interface through which passive data (41) and / or behavioral characteristic data (42) including the number of steps, sleeping time, and moving distance are displayed via a user device (102).

[0112] Referring to FIG. 11, behavioral characteristic data (42) 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 patient. For example, data related to behavioral characteristic data (42) highly related to mental health, such as daily time spent at home, average value over a certain period, weekday average value, and weekend average value, can be provided. Therefore, according to a mental health monitoring service according to one embodiment of the present disclosure, behavioral characteristic data (42) can be analyzed through the collection of passive data (41).

[0113] Referring to FIG. 12, the mental health monitoring device (100) can provide the patient with user interfaces for conducting a mental health scale test through the user device (102).

[0114] The user interfaces for conducting the mental health scale test may include one or more of 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 a user interface (126) for recording additional information about the patient's condition.

[0115] The Mental Health Scale test according to the embodiment of the present disclosure is based on a novel 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 services can provide the Mental Health Scale test to patients 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 and allows for patient diary recording, thereby compensating for the limited number of questions in the Mental Health Scale test while ensuring expandability.

[0116] A mental health monitoring 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 S308 of FIG. 3 described above.

[0117] 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 S308 of the above-described drawing 3 can be loaded into the memory.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

[0123] The device and method for mental health monitoring as described above can be applied to the medical field including diagnosis and management of mental illness.

Claims

1. In a method for monitoring user mental health on a server, A step of receiving, at the server, passive data automatically collected from a user device; In the server, a step of inputting the passive data into a subjective emotional state prediction model that learns the relationship between data automatically collected by the user device and data collected through user input for a questionnaire-style scale test, thereby predicting the total score expected to be obtained by the patient in the health scale test as active data; and In the above server, a step of monitoring the mental health of the patient based on the active data; How to monitor mental health.

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

3. In paragraph 2, The step of receiving the above passive data is: In the above server, a step of extracting patient behavioral characteristic data from the passive data is included, The above behavioral characteristic data is, Including information on at least one of the following: visited location, time spent at each visited location, number of steps, distance moved, moving speed, distance walked, walking speed, activity complexity, and sleep time. How to monitor mental health.

4. In paragraph 3, The above subjective emotional state prediction model is, Includes a boosting-based regression algorithm, Learned using a feature subset determined based on RFE (Recursive Feature Elimination) among the above behavioral characteristic data. How to monitor mental health.

5. In paragraph 4, The above predicted active data is, The predicted total score of the items answered by the above patient in the Mental Health Scale Test, How to monitor mental health.

6. In paragraph 5, The step of extracting patient behavioral characteristic data from the above passive data is: A step of extracting activity complexity data of the patient from the passive data; The step of extracting the patient's activity complexity data from the above passive data is: A step of collecting the patient's location data for all dates and all time zones over a certain period of time; A step of clustering location data at a specific time for each date and selecting the first location that is located the most at the specific time; and A step of generating a repetitive life pattern graph based on the first location for each time zone; comprising; How to monitor mental health.

7. In paragraph 6, The step of extracting the patient's activity complexity data from the above passive data is: A step of comparing the above repetitive life pattern graph with the life pattern graph of each date, and calculating the time at which the above life pattern graph of each date is inconsistent with the pattern of the above repetitive life pattern graph; and Comprising a step of calculating the average of the time that is inconsistent with the pattern of the repetitive life pattern graph during the above period of time; How to monitor mental health.

8. As a mental health monitoring device, memory; and comprising at least one processor; The above processor, A device configured to receive passive data automatically collected from a user device, input the passive data into a subjective emotional state prediction model that learns the relationship between the data automatically collected by the user device and data collected through user input for a questionnaire-style scale test, predict a total score expected to be obtained by the patient in a mental health scale test as active data, and monitor the mental health of the patient based on the predicted active data.

9. A computer program stored on a computer-readable recording medium, combined with a computer as hardware, to perform the mental health monitoring method of any one of claims 1 to 7.

10. As a mental health monitoring system, A user device that collects passive data from a patient; and A mental health monitoring device comprising a processor for receiving passive data automatically collected from a user device, inputting the passive data into a subjective emotional state prediction model that learns the relationship between the data automatically collected by the user device and data collected through user input for a questionnaire-style scale test, predicting a total score expected to be obtained by the patient in a mental health scale test as active data, and monitoring the mental health of the patient based on the predicted active data; System.

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