KR20190023852A - Method of predicting mentality by associating response data and context data and device implementing thereof - Google Patents

Abstract

The present invention relates to a method for predicting a psychological state by associating response data with mobile context data and an apparatus for realizing the same. The method for predicting a psychological state by associating response data with mobile context data comprises the following steps. A mobile context collecting unit of a mobile device collects mobile context data including location information and realistic contact information generated from the mobile device. A control unit of the mobile device outputs an input guide message to an interface unit. And, the control unit calculates an association coefficient between inputted response data and context data in accordance with the input guide message.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for predicting a psychological state by associating response data with mobile context data and a device for implementing the method.

The present invention relates to a method for predicting a psychological state or mental health by associating response data with mobile context data and an apparatus for implementing the method.

A psychological state means a state by a set of various emotions. To predict or analyze a person's psychological state, analysis through language, analysis of brain activity, and behavior analysis are required.

However, the above-described analysis method should reflect the expression of various personality possessed by a person, and the accuracy is not high. Because of the diverse personality of each person, it is not possible to grasp the psychological state of the individual on the same basis. Therefore, a new method for optimally grasping and predicting the individual's psychological state is needed. It also needs to be predicted in the course of the user's daily activities.

In this specification, a method for collecting information through a personalized device such as a mobile device to predict and predict a psychological state of a user, and a method for predicting a psychological state of a user by appropriately selecting or converting the information to a user, .

The present invention can output an input guide message for predicting a user's psychological state based on various context data generated by the mobile device.

The present invention can calculate the association coefficient between the response data and the context data input by the user.

The problems to be solved by the present invention are not limited to the above-mentioned problems, and other problems not mentioned here can be understood by those skilled in the art from the following description.

A method of predicting a psychological state by associating response data and mobile context data according to an exemplary embodiment of the present invention is a method in which a context collector of a mobile device collects mobile context data including location information or actual contact information generated in a mobile device A step in which the control unit of the mobile device outputs an input guide message to the interface unit and a step of calculating an association coefficient between the response data and the mobile context data inputted in the input guide message output by the control unit.

A mobile device for predicting a psychological state by associating response data and mobile context data according to another embodiment of the present invention includes a context collector for collecting mobile context data including location information or actual contact information generated in a mobile device, An interface unit for outputting the guidance guide message and receiving response data therefrom, and a control unit for calculating an association coefficient between the response data and the mobile context data, which are input to the output guide guide message.

When implementing the present invention, the user's psychological state can be predicted based on the context data generated in the personal communication device of the user such as the mobile device.

In the case of implementing the present invention, it is possible to input predetermined information to a user in order to increase accuracy, using mobile context data calculated by a user's behavior, thereby associating mobile context data with information input by a user, .

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

1 is a block diagram of a mobile device according to an embodiment of the present invention.
2 is a diagram illustrating a process of collecting context data by a context collector according to an exemplary embodiment of the present invention.
FIG. 3 is a diagram illustrating a format in which an input guide message is output according to an exemplary embodiment of the present invention. Referring to FIG.
4 is a diagram illustrating a process of receiving characteristic information according to an exemplary embodiment of the present invention and estimating a user's state.
FIG. 5 is a diagram illustrating a process in which a mobile device predicts a psychological state by associating context data with response data according to an exemplary embodiment of the present invention.
FIG. 6 is a detailed view illustrating a process of calculating an association coefficient according to an embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention and the manner of achieving them will become apparent with reference to the embodiments described in detail below with reference to the accompanying drawings. It should be understood, however, that the invention is not limited to the disclosed embodiments, but is capable of many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, To fully disclose the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

In order to clearly illustrate the present invention, parts not related to the description are omitted, and the same or similar components are denoted by the same reference numerals throughout the specification. Further, some embodiments of the present invention will be described in detail with reference to exemplary drawings. In the drawings, like reference numerals are used to denote like elements throughout the drawings, even if they are shown on different drawings. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

In describing the components of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are intended to distinguish the components from other components, and the terms do not limit the nature, order, order, or number of the components. When a component is described as being "connected", "coupled", or "connected" to another component, the component may be directly connected or connected to the other component, Quot; intervening " or that each component may be " connected, " " coupled, " or " connected " through other components.

In the embodiment of the present invention, mobile communication devices such as mobile phones, tablets, wearable devices such as a notebook, a smart phone, and an iPhone are collectively referred to as mobile devices. The mobile device can generate mobile context information based on the user's activities in the user's daily activities. In addition, context data generated in a state where a plurality of mobile devices are connected can be collected and the psychological state of the user, for example, mental health state, can be predicted based on the collected context data.

1 is a block diagram of a mobile device according to an embodiment of the present invention. The components constituting the mobile device may be configured by hardware, software, or the like.

The communication unit 110 communicates with another mobile device disposed outside or communicates with an external server.

The context collector 170 may collect mobile context data generated by a user's activity or inactivity and store the collected mobile context data in the storage unit 120. [ In particular, the context collector 170 may collect mobile context data including location information or real contact information generated in the mobile device.

The interface unit 130 outputs predetermined information and receives a user's input. In particular, the control unit 150 may output a predetermined input guide message to the interface unit 130 to check how the mobile context data generated due to the user's activity / inactivity has a relation with the user's psychological state have. The interface unit 130 accumulates the association between the response data input by the user and the mobile context data in the storage unit 120 and predicts the individual psychological state of each user. Accordingly, the interface unit 130 may output the input guide message and receive the response data.

The context collecting unit 170 may collect various information. In one embodiment, the voice, external activity history, social network service (SNS) activity, input / search, confirmation text, call record, etc. of the smartphone can be collected as context data. In addition to the above-described information, the control unit 150 may generate the context data by estimating the context of the user. Context estimation refers to the technique of estimating the context of a person based on the place, time, and encounter encountered by a person. In one embodiment, the mobile device 100 can determine whether the user is alone at home or with someone else (e.g., friends) at home.

The sensing unit 190 includes a microphone, an optical sensor, a humidity or temperature sensor, an acceleration sensor, and the like, and can sense an external change.

The control unit 150 and the context collecting unit 170 collect data on the basis of the activity or inactivity of the user on a regular or irregular basis (event basis), normalize the data, It can be expressed as a score (1 to 10, 1 to 100, 0 to 20). Accordingly, the controller 150 can calculate the association coefficient between the response data and the mobile context data that are input to the output guide message.

FIG. 2 is a diagram illustrating a process of collecting context data by the context collecting unit 170 according to an embodiment of the present invention. The context collector 170 may use the type of the wireless signals sensed by the communication unit 110 of the mobile device 100, the identification information of the wireless signal, and the like. In addition, the information collected by the various sensing units 190 of the mobile device 100 and the communication unit 110 may be collected in the context collecting unit 170. Let's take a closer look.

The context collecting unit 170 may collect the location information in a context. The location information includes GPS information or information calculated from Wifi, BLE, Bluetooth, and the like. The location information may include information on whether the user is staying in the house, whether it took time to travel, distance traveled by the user, moving speed, and the like. Also, the position information can be grasped by using the entropy that can be calculated through the above-described signals such as Wifi / BLE / Bluetooth, or the normalized entropy and variation.

In addition, the context collecting unit 170 may collect movement information based on the user's bio-cycle or cluster information for each user's location based on the location information.

The location information collected by the context collecting unit 170 includes not only the user's current location information but also the location where the user is located and the context data associated with the location information.

In addition, the context collector 170 can collect the activity information, i.e., motion information, of the user using the positional information or the acceleration sensor of the sensing unit 190, and collect the context. The acceleration sensor can generate motion information based on motion, velocity, or a change in the average velocity and velocity.

The context collector 170 may collect the context information of the user based on the physical contact information. This means that the communication unit 110 acquires fingerprint information (e.g., address of Bluetooth, BLE, WiFi, NFC, etc.) of another mobile device and generates context data such as whether the user is with other people or alone .

Since the fingerprint information of the mobile device can be accumulated in the storage unit 120, the mobile device fingerprint information of people who repeatedly meet can be stored as the context data. By using the fingerprint information, Context data can be generated. In addition, context information such as whether the user is alone in a very large space, whether there are various people, conversation, etc., can be collected. In this process, the sound sensor of the sensing unit 190 of the mobile device acquires voice, conversation contents, surrounding background sounds, and the like, and the context collecting unit 170 can convert the context information.

Meanwhile, the context collecting unit 170 may collect the context information of the user based on the virtual contact information. When the user performs an activity such as sending or receiving a character using the SMS or the like, the user's context data can be collected. In addition, the context collecting unit 170 may collect information on a call with a specific person for a long time or on a frequent basis, as context data.

In addition, the context collecting unit 170 may collect the absence call information of the mobile device and the time information based on the point of time of the last call as context data. Also, the character transmission / reception information with the other person, the number of unread characters, the repetition of the unread action, etc. performed in the mobile device such as the SMS becomes the virtual contact information, and the context collector 170 can collect the context data.

An item for which the context collecting unit 170 collects context data, such as application use information which is usage information of a specific application, sound information which is obtained by analyzing an external sound by sensing the microphone, network signal information using WiFi SSID, to be. In addition, the context collecting unit 170 may collect, as context data, the sensing information that senses the humidity, the temperature, the intensity of the external light, and the sleep information on the sleep state of the user.

The types of context data presented in FIG. 2 are exemplary, and the mobile device herein may use some or all of them. In addition, various context data not shown in FIG. 2 may be collected depending on the functions provided by the mobile device.

Meanwhile, the control unit according to the embodiment of the present invention can output the input guide message at the interface unit.

The control unit 150 of the mobile device may generate and output an input inducement guidance message in a state in which the context data is collected. Alternatively, the control unit 150 of the mobile device may generate and output an input guide message independently of collecting the context data.

The input inducement message is intended to predict mental illness and may include a process and a method of collecting indicators in the form of a questionnaire. In one embodiment, the mobile device may output an input guidance message including a questionnaire corresponding to a specific mental disease, such as depression, to the user of the mobile device at the interface unit 130. The user creates an answer to the input induction guidance message and inputs response data corresponding to the input inducing guidance message.

The response data input to the input guiding announcement message can be performed periodically. For example, once a day, once every two days, once a week, and so on. The control unit 150 outputs the input guidance message to the interface unit 140 irregularly according to the context of the user calculated from the context data collected by the control unit 150 of the mobile device, And store it in the storage unit 120.

As a result of inputting in response to the input guidance message, for example, the response data, which is a result of the input guidance message output in the form of a questionnaire, can calculate quantitative data. In one embodiment, the response data, which is the result of the creation of each questionnaire, can be calculated as 1 to 10, 0 to 30, and so on.

And a cutoff criterion for distinguishing the various stages of minor depression or a specific mental illness (minor, moderate, severe) corresponding to each score can be preset.

FIG. 3 is a diagram illustrating a format in which an input guide message is output according to an exemplary embodiment of the present invention. Referring to FIG.

The controller 150 generates and outputs a first input guide message to the mobile device 100a. The first input inducement announcement message may include a question about the current state of mind of the user. In response to the response data input by the user, the controller 150 generates and outputs a second input guide message as shown at 100b. The output guidance guide message can be composed of various questionnaires. One or two or more of these questionnaires can be output on one page, and the questionnaire to be output can be selected according to the response data previously input by the user have. In yet another embodiment, predetermined input guiding announcement messages may be output independently of the response data entered by the user.

The response data inputted by the user is converged to indicate the step corresponding to the user's specific mental state according to the processing of the control unit 150. [ It is set to indicate a predetermined score or step set by the control unit 150 with respect to the context data shown in FIG. As a result, the controller 150 analyzes the relationship between the context index calculated by the context data and the psychometric index calculated by the response data. This can be calculated to have a value of -1 or more and 1 or less by calculating a correlation coefficient (for example, a linear correlation coefficient or other nonlinear correlation coefficient).

The control unit 150 can determine that the closer the linearity is to the calculation result 1, the larger the linear correlation is, and in the case of 1, the linear correlation is exactly the same. The control unit 150 can determine that the correlation is lower as the calculation result is closer to 0 and that the correlation is not related when the calculation result is 0. In addition, as the calculation result is closer to -1, the control unit 150 can determine that the correlation is inverse, that is, the inverse relationship is high.

In this calculation process, representative features can be extracted. The representative characteristic is a characteristic that is distinguished among various context information of FIG.

Table 1 shows a list of elements that are characteristics of context data according to an embodiment of the present invention. These are the components shown in FIG. 2 above.

For example, the control unit 150 can confirm the type of context data in which the correlation coefficient between the context index and the psychometric index is close to -1 or close to 1, that is, the correlation is high.

Meanwhile, the controller 150 may use a basic estimation algorithm (Inter-Person) or an individual estimation algorithm (Intra-Person) in the process of extracting the representative characteristic.

The basic estimation algorithm analyzes the association between various types of context data sources shown in FIG. 2 and the features in the feature list and the input induction message shown in FIG. 2 based on the data of various people, Among the various multivariate linear regression analysis methods, the most accurate method of analyzing the time series can be selected to estimate the mental illness score.

On the other hand, personalized estimation algorithms (based on only intra-person personal data) are used to select highly correlated attributes according to individual data, and to use various multivariate linear regression methods It can be estimated using the most accurate method. In one embodiment, Bayesian multivariate linear regression may be used.

An embodiment using real contact information among various context data will be described. The actual contact information can use a tag.

A tag has a unique identifier for identifying an object, a person, a place, and the like, which is referred to as a TUID (Tag Unique Identifier). A tag can be divided into an active tag (passive tag) and an active tag (passive tag). The active tag is a tag that constitutes a device having a function of wirelessly broadcasting transmission to notify its existence periodically. A passive tag is a tag that constitutes a device that notifies an external device of its existence by only responding to a request.

Tags can be divided into mobile tags, semi-mobile tags, and so on. An example of a mobile tag could be a mobile phone. Semi-mobile tags are tags that are installed temporarily for specific purposes. They include tags that are installed for specific purposes such as monitoring the temperature, humidity, and illumination of the home environment.

A device receiving data of a tag fixedly installed at a specific place can know its place identifier and a tag fixed to a specific object can notify the existence of an object. Therefore, when a person receives a tag worn by another person, it can know the encounter information with another person.

4 is a diagram illustrating a process of receiving characteristic information according to an exemplary embodiment of the present invention and estimating a user's state. We have previously seen that tags can be used as property information for constructing context data.

The communication unit receives the tag information (S11). In one embodiment, any person may possess a tag. The control unit 150 and the communication unit 110 can use a specific advertisement of the BLE in the mobile phone as a tag. When a person has a cell phone, the user can identify the specific location of the user based on the assumption that the tag is possessed. For example, when a user of the mobile device moves to a specific place, the communication unit 110 receives a tag (e.g., WiFi AP, iBeacon) fixedly present in the location (S11) ). The control unit may store the identified location identifier or the received tag information in the storage unit 120 (S12).

Also, the tag information received by the communication unit 110, such as S11, is stored in the storage unit 120 through a mobile phone owned by another person. The controller 150 may estimate the user's state using the stored tag information and the received tag information (S13). For example, the user of the mobile device can know information such as the frequency and duration of contact with a specific person, and can confirm whether the user has moved to a specific location.

In particular, the controller 150 can check the location information of the location and store the location information in the storage unit 120 by using tag information that is fixedly located in various places in the mobile device. Likewise, using the fixed tag information possessed by the specific other person's mobile phone and the SMS, the call history, and the content on the messenger (character content that can predict the meeting, such as " arrived & The frequency and period of contact between the user of the device and another person can be calculated. As a result, the control unit 150 can convert the current state of the user into the following exemplary characteristic information and generate the characteristic information.

The characteristics information that can be estimated through contact information between different people are as follows.

"I was alone at home." (Home WiFi tag information)

"I was alone in the dark room." (Incorporating additional context data from the light sensor)

"I was with my friends at home" (using tag information)

"I was alone in the cafe" (using the tag information of the cafe)

"I was at home with my mom." (Using tag information)

Based on the characteristic information, the user can estimate the mental state or the psychological state using the estimation algorithm described above by calculating the frequency, duration, etc. of the mobile device at home alone as characteristic information.

1 to 4, the mobile device according to the present invention may include a voice, an external activity history, an SNS, and the like based on the operation status of various apps, the information sensed by the sensing unit 190, Activities, texts, and call records can be used to predict mental illness. That is, the psychological state of the user can be estimated by collecting context information on the psychological abnormality of the user in the mobile device including the personal communication device.

In one embodiment of generating context information, information about a place or person to which an actual user contacts can be estimated. As shown in FIG. 4, the context of the user is estimated based on the place where the user is located, the time, and the encounter encountered by the other person, and a self-diagnosis recommending function based on the prediction result of the mental disease such as depression is provided. The self-diagnosis includes a method of downloading an externally prepared questionnaire, outputting an input guidance message, and inputting the input guidance message by the user. In addition, the contents of the questionnaire can improve the user convenience by providing the questionnaire with information that can be calculated from the context data accumulated through the user's mobile device in advance.

The mobile device 100 according to an embodiment of the present invention collects context data, generates an input guide message for confirming the user's psychological state, collects response data for the input guide message, and calculates the association between the context data and the response data can do.

FIG. 5 is a diagram illustrating a process in which a mobile device predicts a psychological state by associating context data with response data according to an exemplary embodiment of the present invention.

First, the context collector 170 of the mobile device 100 collects mobile context data including location information or actual contact information generated in the mobile device 100 (S21).

More specifically, in the case of location information, the mobile device 100 that has moved to a specific location is fixedly present in the location, and the communication unit 110 receives tag information (e.g., WiFi AP, iBeacon) transmitted by the wireless network , The controller 150 stores the tag information received by the communication unit 110 in the storage unit 120 as position information.

The location information for a specific space may include GPS information. Or the name of the store included in the WiFi. In addition, when the card payment information is received while the location information is maintained, the location information may include this information.

Meanwhile, in the case of real contact information, the communication unit 110 can receive tag information transmitted by one or more communication devices held by a plurality of external persons. The control unit 150 stores the tag information received by the communication unit 110 in the storage unit 120 as contact information.

The tag information may include tag information including packets used in BLE, Bluetooth, etc. transmitted by other communication devices. The controller 150 primarily uses the tag information as contact information, . And combines the accumulated information and another mobile context data generated by the mobile device to identify who the tag information is.

For example, in the process of exchanging a character or a message, the control unit 150 can confirm an actual meeting such as " meet ", " Can store the name or contact of the person who is meeting the contact information together.

Thereafter, the control unit of the mobile device outputs an input guide message at the interface unit (S22). This can be done periodically or irregularly.

An input guidance message including a long questionnaire for confirming the psychological state is output. Also, when the context data is generated, an input guidance message including a simple question may be output to confirm the corresponding psychological state of the user. For example, as shown in 100a of FIG. 3, a guidance message may be output to induce a user to input response data for the current state of mind.

The control unit 150 may calculate the association coefficient between the response data and the mobile context data that are input in response to the output guide message (S23).

For example, when the location information is collected, the controller 150 may calculate at least one of the visit time, the number of visits, and the residence time of the location information accumulated in the storage unit and the association coefficient of the inputted response data. If the response data inputted by the user in the state where the visit time is constant or the number of times of visit and the stay time are confirmed to correspond to the score corresponding to the " lonely " or negative psychological state inputted in the embodiment of Fig. 3, The control unit 150 may determine that the corresponding location information and the psychological state have a negative correlation and calculate the association coefficient. It is possible to increase or decrease the absolute value of the association coefficient or the value of the association coefficient by accumulating the association coefficient based on the correlation between the position information and the response data which is confirmed after the association coefficient is calculated first.

If the location information on which the user inputs the positive response data is accumulated, the control unit 150 may output an input guide message for guiding the user to visit the corresponding location.

Also, in a state where the contact information is collected, the control unit 150 calculates at least one of the contact time, the contact count, or the contact holding time of the contact information accumulated in the storage unit 120 and the association coefficient of the inputted response data can do.

For example, when the contact information is collected, the control unit 150 may calculate at least one of the contact time accumulated in the storage unit, the number of times of contact or the time of maintaining the contact, and the association coefficient of the inputted response data. In a state in which at least one of the contact time and the contact time and contact time is confirmed, the response data input by the user corresponds to the " cheerful " or positive psychological state inputted in the embodiment of FIG. 3 The controller 150 may determine that the person and the psychological state of the corresponding contact information have an affirmative association, and calculate the association coefficient. It is possible to increase or decrease the absolute value of the association coefficient or the value of the association coefficient by accumulating the association coefficient based on the correlation between the position information and the response data which is confirmed after the association coefficient is calculated first.

If the contact information to which the user inputs the positive response data is accumulated, the control unit 150 may output the input inducement guidance message for inducing the user to meet or contact the other person of the contact information.

FIG. 6 is a detailed view illustrating a process of calculating an association coefficient according to an embodiment of the present invention. The control unit 150 outputs to the interface unit an input guidance message for confirming the psychological state of the user corresponding to the generation period of the mobile context data or the generation of the mobile context data. For example, when specific location information is cumulatively stored or specific contact information is cumulatively stored, the control unit outputs an input guidance message to the interface unit for confirming the user's psychological state with a score. In one embodiment, an input guidance message of " Please enter your mood today from 0 to 5 " is displayed.

The interface unit 130 receives positive or negative response data corresponding thereto. The value entered by the user corresponding to the output of the message leading to the input of the score in advance is 0 to 5. Therefore, if it is 0 or close to 0 (1 or 2), negative, 5 or close to 5 (3 or 4), it becomes positive response data.

The control unit 150 increases or decreases the value of the mobile context data and the association coefficient in proportion to the size of the positive or negative response data (the size of a score such as 0 to 5) (S33).

For example, when the context data is confirmed in a state where the user has visited or visited the location information, the control unit 150 outputs an input guidance message message " Please enter the mood state from 0 to 5 points " If the user enters a score of " 5 ", the association coefficient may have a high value. For example, when the value that the association coefficient can have is -1 to 1, it can be calculated as the reference value (for example, 0.5) set to be close to the positive relation (1) when the association coefficient is calculated for the first time.

If the same position information is cumulatively collected and the response data is continuously generated as " 5 " or " 4 ", the controller 150 sets the association coefficient to a predetermined value It can be increased by size.

The case where the negative response data is calculated is also applied in the same manner. Also, the contact information due to a meeting with a specific person can also calculate the association coefficient according to the process of FIG.

Also, after S33, the accuracy of the association coefficient may be determined or the psychological state may be predicted, and the corresponding input guide message may be output from the mobile device 100. [ For example, when the generation of the mobile context data increases, the control unit 150 may control the interface unit 130 to output the input guidance message having a high association coefficient with the mobile context data.

For example, when a score is assigned to the current mood in response to specific location information or specific contact information, it is necessary to confirm whether the absolute value of the association coefficient is cumulatively increased or to correspond to the same input guidance message Or a related input guide message may be generated and output.

The following is summarized. The control unit can select the input guide message corresponding to the mobile context data such as the position information and the contact information while storing a plurality of input guide messages in the storage unit 120 in advance.

The input guidance message is a message that quantifies the current psychological state and allows the user to input the message. A message such as " Please input your mood state between 0 and 5 points " corresponds to this, and the score inputted here becomes quantified response data.

Further, the input inducement guidance message may be composed of a plurality of interrogating steps to constitute one response data including the input values. In this case, the values input by the user in response to the questions of many questions ("choice of predicate on current mood", "number of people who have recently met", "number of recent external visits", "number of exercises" One response data can be calculated.

Using the calculated response data and the mobile context data, the controller 150 can calculate the affirmative or negative association coefficient as shown in FIG. 5 and FIG.

Also, in this specification, the association coefficient may be calculated after accumulating and storing the response data and the mobile context data separately from the above-described method.

The control unit 150 outputs an input guidance message to the interface unit 130 in the form of a questionnaire for checking depression or mental health. The interface unit receives the response data corresponding to the input guide message. This process can be done on a daily, weekly, or bi-weekly basis. The response data is stored in the storage unit 120 in a cumulative manner. Likewise, the mobile context data is also accumulated and stored in the storage unit 120 when it is collected. In this process, the control unit 150 may map the mobile context data and the response data, and accumulate and store the mapped data in the storage unit 120.

The control unit 150 may calculate an association coefficient between the response data and the mobile context data when the storage unit 120 accumulates sufficient data. In other words, the response data refers to scores obtained by performing a questionnaire (eg, depression questionnaire such as CES-D, PHQ-9). The questionnaire process can be performed by the patient on a daily, weekly, or biweekly basis, and can separately store the score (response data) and the scores calculated from the mobile context data during that time. Then, when a large number of sample data are collected by using one sample data that is mapped and stored as two scores, a function of the correlation (for example, a linear equation in the case of linear regression analysis) is estimated in the period analysis, The equation can be calculated. In one embodiment, the equation includes an equation to cause the depression to match the positive response data in proportion to the number of visits to a particular location.

If new data comes in, then matching the value of the corresponding independent variable to the equation (for example, the score of the mobile context data) will result in a mental state corresponding to the actual mental illness without the response data of the questionnaire (input inducement message) The results of the questionnaire can be estimated as minor, moderate, severe, etc.).

That is, when the mobile context data is collected, the controller 150 generates the prediction data for predicting the state of the user based on the response data corresponding to the high association with the mobile context data. Then, when the mobile context data is collected, the state of the user is predicted and calculated based on the collected data, and the predicted data is generated. The generated prediction data may include a phrase or message suitable for the mental state or psychological state of the user, or a recommendation message for treatment.

The embodiments of the present invention calculate a function for deriving an association coefficient or association based on the association between mobile context data collected using a mobile device and response data input from a user, That is, it includes the provision of contents that can predict the mental health state and respond to the message or to be free from conditions such as depression.

For example, tag information such as where the user having the mobile device is located and who is meeting with the mobile context data can be collected. Then, the user inputs a questionnaire-type input guide message periodically or non-periodically and collects response data that can confirm the user's mental health status. If the collected response data includes the tag information and the mobile context data is accumulated more than a predetermined number of times during a certain period of time, the controller 150 can map the mobile context data and the response data. Also, in the mapping process, the association coefficient between the mobile context data and the response data is calculated to predict the psychological state and the mental health state of the user when specific mobile context data (in particular, tag information) is collected, A guidance message such as visiting a hospital or inducing contact with another person can be output.

When the present invention is applied, the psychological state or the mental state of the user can be predicted using the application installed in the control unit 150 or the mobile device 100. In particular, since the mobile device 100 can collect various kinds of situation information, the mobile device 100 can combine various information (mobile context data) collected from the mobile device 100 and response data reflecting the user's psychological or mental state The mobile device 100 can provide the content suitable for predicting the current state of the user with the personalized personalized service. As a result, it is possible to predict the user's mental illness (depression, manic depression, etc.) or to check the extent of the mental illness or the extent of the mental illness, so that the user can feel emotional or psychological improvement, It is possible to infer and classify based on context data.

It is to be understood that the present invention is not limited to such embodiments and that all the elements within the scope of the present invention are not limited to these embodiments, As shown in FIG. In addition, although all of the components may be implemented as one independent hardware, some or all of the components may be selectively combined to perform a part or all of the functions in one or a plurality of hardware. As shown in FIG. The codes and code segments constituting the computer program may be easily deduced by those skilled in the art. Such a computer program can be stored in a computer-readable storage medium, readable and executed by a computer, thereby realizing an embodiment of the present invention. The storage medium of the computer program includes a magnetic recording medium, an optical recording medium, and a storage medium including a semiconductor recording element. Also, a computer program embodying the present invention includes a program module that is transmitted in real time through an external device.

It is to be understood that the above-described embodiments are to be considered in all respects as illustrative and not restrictive, and the scope of the present invention will be indicated by the appended claims rather than by the foregoing detailed description. It is intended that all changes and modifications that come within the meaning and range of equivalency of the claims, as well as all equivalents thereof, be within the scope of the present invention.

100: mobile device 110: communication unit
120: storage unit 130: interface unit
150: control unit 170:
190: sensing unit

Claims (10)

Collecting mobile context data including location information or real contact information generated at the mobile device by a context collector of the mobile device;
The control unit of the mobile device outputs an input guide message to the interface unit; And
And calculating the association coefficient between the response data and the mobile context data, the response data being input to the output guide guidance message, wherein the response data and the mobile context data are associated with each other.
The method according to claim 1,
Receiving, by the communication unit of the mobile device, tag information to be transmitted in a wireless network;
Storing the tag information received by the communication unit as location information in a storage unit; And
Wherein the control unit includes a step of calculating at least one of a visit time, a visit count, or a residence time of the position information accumulated in the storage unit and an association coefficient of the input response data, and associating the response data with the mobile context data To predict psychological state.
The method according to claim 1,
Receiving, by the communication unit of the mobile device, tag information transmitted by one or more communication devices;
Storing the tag information received by the communication unit as contact information in a storage unit; And
Wherein the control unit includes a step of calculating at least one of a contact time of the contact information accumulated in the storage unit, a number of times of contact or a time of maintaining the contact, and an association coefficient of the inputted response data. How to predict psychological state by associating data.
The method according to claim 1,
The step of the control unit calculating the association coefficient
Outputting, to the interface unit, an input guiding announcement message that allows the control unit to confirm the psychological state of the user corresponding to the generation period of the mobile context data or the generation of the mobile context data;
Receiving an affirmative or negative response data from the interface unit;
And increasing or decreasing the value of the mobile context data and the association coefficient in proportion to the size of the affirmative or negative response data, by associating the response data with the mobile context data.
5. The method of claim 4,
After the step of increasing the value of the association coefficient
Wherein the control unit further comprises the step of controlling the interface unit to output an input guide message having a high association coefficient with the mobile context data when the generation of the mobile context data increases, associating the response data with the mobile context data How to predict psychological state.
The method according to claim 1,
The step of the control unit calculating the association coefficient
The control unit outputting an input guide message to the interface unit;
Receiving, by the interface unit, response data corresponding to the output guide message;
Accumulating and storing the mobile context data and the response data in a storage unit; And
Wherein the controller includes a step of calculating an association coefficient between the response data and the mobile context data to associate the response data with the mobile context data to predict the psychological state.
The method according to claim 6,
After the step of calculating the association coefficient
When the mobile context data is collected, the control unit generates the response data and the mobile context data, including the step of generating the predictive data for predicting the state of the user based on the response data corresponding to the high association with the mobile context data How to correlate and predict psychological state.
A context collector for collecting mobile context data including location information or real contact information generated in the mobile device;
An interface for outputting an input guide message and receiving response data; And
And a controller for calculating an association coefficient between the response data and the mobile context data input to the output guide message, and to predict the psychological state by associating the response data with the mobile context data.
9. The method of claim 8,
Further comprising a communication unit for receiving tag information transmitted from a wireless network,
Wherein the control unit stores the tag information received by the communication unit as location information in a storage unit, and associates at least one of a visit time, a visit frequency, and a residence time of the location information accumulated in the storage unit with an association A mobile device that calculates a coefficient, associates response data with mobile context data to predict a psychological state.
9. The method of claim 8,
Further comprising a communication unit for receiving tag information transmitted by one or more communication devices,
Wherein the controller stores the tag information received by the communication unit in the storage unit as contact information and stores the contact time, contact count, or contact time of the contact information accumulated in the storage unit, And associating the response data with the mobile context data to calculate the association coefficient of the data.

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