KR20200119690A - A mobile system and method that provides proactive suggestion of physical activities based on a user's context and preference - Google Patents

Abstract

According to an embodiment of the present invention, provided is a method for suggesting a physical activity performed by an intelligent intervention system which comprises the steps of: collecting physical activity details of a user through an electronic device; detecting an amount of a physical activity less than a predetermined reference from the amount of the physical activity obtained by accumulating the collected physical activity details; determining a time point of suggestion of the physical activity related to the detected amount of the physical activity less than the predetermined reference by using situation recognition information of the user; and suggesting the physical activity of the user through the electronic device at the time point of suggestion of the determined physical activity.

Description

{A MOBILE SYSTEM AND METHOD THAT PROVIDES PROACTIVE SUGGESTION OF PHYSICAL ACTIVITIES BASED ON A USER'S CONTEXT AND PREFERENCE}

The following description relates to a system and method for providing a customized preemptive service for improving physical activity.

Due to the development of mobile sensor technology, it is possible to measure the amount of physical activity of the user (for example, step count, heart rate, calories, etc.) through personal mobile devices. Based on this data, the development of intervention services for improving physical activity This became possible.

A typical mediation service for improving physical activity in the related art is a passive service that provides measured amount of physical activity and related information according to a user's direct interaction. In order for these services to be effective in promoting physical activity, it is essential that users continue to use the service actively, but it is very difficult to draw it out.

In recent years, instead of the conventional passive service, a preemptive service that proactively suggests physical activity using real-time processing of sensor data and push notification technology has been developed. As a representative example, Apple Watch, a smart watch from Apple, and a smartphone app from Fitbit, send a message suggesting to get up and move when the user's physical activity is less than a certain amount every hour through the smart watch and smartphone. It is being delivered preemptively.

However, such a preemptive service provision may provide a negative user experience according to the user state and context/context at the time the service is provided. For example, when a user is in a meeting with a supervisor or sleeping, it is inappropriate for the service to preemptively suggest physical activity and may lower the user satisfaction of the service. In addition, even if the service preemptively suggests physical activity in an appropriate situation, the proposal may be rejected according to the proposed physical activity, the content, tone of the message, and the user's individual disposition.

In order to allow the user to accept the preemptive physical activity proposal of the service, it is necessary to provide customized suggested content according to not only the user's situation but also the user's disposition. Through the provision of such personalized service, not only can the receptivity of the user's preemptive suggestion message be increased, but also the user satisfaction with the service can be increased.

An object of the present invention is to provide a mobile-based preemptive physical activity proposal service to a user in consideration of the user's situation and tendency.

A method for suggesting physical activity performed by an intelligent intervention system includes: collecting a user's physical activity details through an electronic device; Detecting an amount of physical activity less than a predetermined reference from the amount of physical activity obtained by accumulating the collected physical activity details; Determining a time point for suggesting a physical activity related to the detected amount of physical activity below a predetermined reference by using the user's context-aware information; And suggesting the user's physical activity through the electronic device at a time of suggesting the determined physical activity.

Proposing the user's physical activity through the electronic device at the determined suggestion time of the physical activity may include delivering a message suggesting a physical activity based on the amount of physical activity of the user at the determined suggestion of the physical activity. It may include steps.

Proposing the user's physical activity through the electronic device at the determined suggestion time of the physical activity may include, after the message is delivered, the amount of change in the user's physical activity collected from the electronic device is measured, and the measured It may include learning the preference of the message based on the amount of change in the user's physical activity.

Proposing the user's physical activity through the electronic device at the determined suggestion time of the physical activity may include, after the message is delivered, a request for evaluation of the suggestion time of the user's physical activity, and the suggestion of the physical activity It may include the step of learning viewpoint information related to the proposal time by using the evaluation information that has evaluated the viewpoint.

The collecting of the user's physical activity details may include the type of the user's repetitive physical activity, the number of repetitions of the user's physical activity, the duration of the user's physical activity, calories burned due to the physical activity, the It may include collecting at least one or more of the physiological states due to the user's physical activity as a user's physical activity details.

The step of determining a proposal time of a physical activity related to the amount of physical activity below the detected predetermined standard by using the user's context awareness information includes: 1) schedule information specified in the user's schedule, a user's meeting, and a class. Task situation information including physical activity information or physical activity history information, 2) Locational situation information including information on the place where you stay in the user's daily life, 3) Other users directly or indirectly related to the user's daily life It may include the step of utilizing context-aware information of at least one user among social interactions including interactions made.

The step of determining a proposal time of a physical activity related to the detected amount of physical activity less than or equal to a preset criterion using the user's context awareness information includes determining that the amount of physical activity less than or equal to the detected amount of physical activity is insufficient, and the It may include determining a time point for suggesting a physical activity in connection with a time point determined as insufficient amount of physical activity and schedule information of the user.

A computer program stored in a computer-readable storage medium to execute a method for suggesting a physical activity performed by an intelligent intervention system may include: collecting physical activity details of a user through an electronic device; Detecting an amount of physical activity less than a predetermined reference from the amount of physical activity obtained by accumulating the collected physical activity details; Determining a time point for suggesting a physical activity related to the detected amount of physical activity below a predetermined reference by using the user's context-aware information; And suggesting the user's physical activity through the electronic device at a time of suggesting the determined physical activity.

An intelligent mediation system for suggesting a physical activity may include: a collection unit for collecting physical activity details of a user through an electronic device; An activity amount detector configured to detect an amount of physical activity less than a predetermined reference from the amount of physical activity obtained by accumulating the collected physical activity details; A time point determination unit that determines a time point of suggestion of a physical activity related to the detected amount of physical activity below a predetermined reference by using the user's context recognition information; And an activity suggestion unit that proposes the physical activity of the user through the electronic device at the determined time of suggestion of the physical activity.

The activity suggestion unit may transmit a message proposing a physical activity based on the amount of physical activity of the user at a time of suggesting the determined physical activity.

After the message is transmitted, the activity suggestion unit may measure a change in physical activity of the user collected from the electronic device and learn a preference of the message based on the measured change in physical activity of the user.

After the message is delivered, the activity suggestion unit may request evaluation of the user's physical activity proposal time point, and learn viewpoint information related to the proposal time using evaluation information obtained by evaluating the physical activity proposal time point. .

The collection unit may include a type of the user's repetitive physical activity, the number of repetitions of the user's physical activity, a duration of the user's physical activity, calories burned due to the physical activity, and a physiological state due to the user's physical activity. At least one or more of them may be collected as a user's physical activity details.

The time point determination unit includes: 1) schedule information specified in the user's schedule, physical activity information including the user's meeting, class, or task situation information including physical activity history information, and 2) information on the place where the user stays in daily life. It is possible to use at least one user's context-aware information among social interactions including locational context information, 3) interactions with other users directly or indirectly related to the user's daily life.

The time point determination unit may determine an amount of physical activity equal to or less than the detected amount of physical activity as an insufficient amount of physical activity, and determine a time point for suggesting a physical activity in connection with the time point determined as the amount of physical activity and the schedule information of the user.

The present invention infers an appropriate time point to deliver a preemptive physical activity proposal to the user, and provides user-customized physical activity proposal content at that time, thereby enabling the user to actively accept the proposed physical activity.

1 is a block diagram illustrating the configuration of an intelligent mediation system according to an embodiment.
2 is a flowchart illustrating a method of proposing a physical activity in an intelligent intervention system according to an exemplary embodiment.
3 is a flowchart illustrating a method of learning a user's preference for a physical activity suggestion message in an intelligent intervention system according to an exemplary embodiment.
4 is a flowchart illustrating a method of learning the appropriateness of a time point for suggesting a physical activity in an intelligent intervention system according to an exemplary embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings.

In the embodiment, it is determined and learned whether the preemptive proposal timing is appropriate in consideration of the user's situation and feedback, and the user's preferred physical activity enhancement content is selected based on the user's response to the proactively proposed physical activity enhancement content. We will describe an intelligent intervention system that includes learning.

1 is a block diagram illustrating a configuration of an intelligent intervention system according to an exemplary embodiment, and FIG. 2 is a flowchart illustrating a method of proposing a physical activity in an intelligent intervention system according to an exemplary embodiment.

The intelligent intervention system 100 is for suggesting physical activities in consideration of the user's situation and disposition, and is implemented in the form of a program that operates independently, or is configured in an in-app form of a specific application. It may be implemented to be able to operate on an application, and in some cases, a service that suggests physical activity may be provided through interworking with a server. Alternatively, the intelligent mediation system may be a server, and may provide a service for suggesting physical activity to an electronic device through data transmission/reception between the server and the electronic device.

The processor of the intelligent mediation system 100 may include a collection unit 110, an activity amount detection unit 120, a viewpoint determination unit 130, and an activity suggestion unit 140. Components of such a processor may be expressions of different functions performed by the processor according to a control command provided by program code stored in the intelligent arbitration system. The processor and the components of the processor may control the intelligent intervention system to perform steps 210 to 230 included in the method for suggesting a physical activity of FIG. 2. In this case, the processor and the components of the processor may be implemented to execute an instruction according to the code of the operating system included in the memory and the code of at least one program.

The processor may load the program code stored in the program file for the physical activity suggestion method into the memory. For example, when a program is executed in the intelligent mediation system, the processor may control the intelligent mediation system to load the program code from the program file into the memory under the control of the operating system. At this time, each of the processor and the collection unit 110, the activity detection unit 120, the time determination unit 130, and the activity suggestion unit 140 included in the processor executes a command of a corresponding part of the program code loaded in the memory. It may be different functional expressions of the processor for executing the subsequent steps 210 to 240.

In step 210, the collection unit 110 may collect the user's physical activity details through the electronic device. For example, the collection unit 110 includes types of repetitive physical activities (for example, walking, running, swimming, etc.) that can be collected through the user's electronic device (eg, mobile terminal), and physical activities. At least one or more of the number of repetitions, duration of physical activity, calories burned due to physical activity, and physiological conditions (e.g., heart rate, heart rate variability, skin temperature, etc.) I can. For example, the collection unit 110 may activate a collection program for collecting physical activity details in the user's electronic device. In this case, the collection unit 110 may collect various types of physical activity details such as the number of steps, distance traveled, calories burned, and heart rate. In the embodiment, it is assumed that the number of steps is collected. In the collection program of the user's electronic device, the number of steps of the user and the time at which the steps are performed may be recorded in real time in an internal storage of the electronic device. Alternatively, the physical activity details recorded in the electronic device may be periodically or aperiodically transmitted to the intelligent intervention system.

In step 220, the activity amount detection unit 120 may detect an amount of physical activity less than a predetermined reference from the amount of physical activity obtained by accumulating the collected physical activity details. At this time, the preset criterion is based on the length of time that there was no movement above a given threshold from the time when the user recently finished the movement, the activity target value set by the user or a third party, and an exercise prescription set by the user or a third party. It may include at least one or more of physical activity target values (eg, physical activity type, activity amount, time, etc.). The preset criterion may be set differently according to the user's situation (eg, time, location, etc.). In other words, the activity amount detection unit 120 may detect an insufficient amount of physical activity. As an example, the activity amount detector 120 may periodically check the physical activity details and calculate the accumulated amount of physical activity for a predetermined time. When the level of the amount of physical activity is less than (less than) a certain value, the activity amount detection unit 120 may determine that the amount of physical activity is less than (less than) a certain value, and may determine the amount of normal physical activity when the level of the amount of physical activity is more than (exceed) a certain value. have. In an embodiment, when the activity amount detection unit 120 is less than 250 steps for 1 hour, it may be determined as "lack of physical activity amount". In addition, in addition to the quantified physical activity target value, the criteria related to the amount of physical activity are the length of time when there is no movement above a given threshold from the point when the user recently finished the movement, the activity target value set by the user or a third party, and the user or a third party set a It may include at least one or more of the physical activity target values (eg, type of physical activity, activity amount, time, etc.) according to the exercise prescription, and according to the user's situation (eg, time, location, etc.) It can be set differently.

In step 230, the viewpoint determination unit 130 may determine a time point for suggesting a physical activity related to an amount of physical activity less than a predetermined reference detected by using the user's context recognition information. The viewpoint determination unit 130 may determine the appropriateness of the preemptive proposal time by using at least one user's context-aware information from among the user's task context information, locational context information, and social context information. Specifically, the time point determination unit 130 includes 1) schedule information specified in the user's schedule, physical activity information including user's meeting, class, movement, etc., or task situation information including physical activity history information, 2) user Locational context information, including information on frequent and important places to stay in the daily life (for example, home, work/school, library, cafe, etc.), 3) Other users directly or indirectly related to the user's daily life At least one user's context-aware information may be utilized among social interactions including interactions made with and social meanings for the interactions. In this case, at least one model learned based on a rule set by a user or evaluation information (feedback) provided by a user may be included in order to determine the appropriateness of the preemptive proposal time point. The time point determination unit 130 may determine a physical activity amount equal to or less than the detected amount of physical activity as a lack of physical activity, and determine a time when the physical activity is suggested in connection with the time when the amount of physical activity is determined to be insufficient and schedule information of the user.

As an example, the time point determination unit 130 may determine the time point at which the time point at which the physical activity is to be proactively suggested at a time point determined as "lack of physical activity amount" in step 220 is linked with the user's schedule information to determine the time point for the proposal. When there is no schedule in the user's schedule when it is determined as "lack of physical activity", the timing determination unit 130 may determine the determined timing as a suggestion timing of the physical activity. In addition, when there is a schedule in the user's schedule at a time when it is determined as "lack of physical activity", the time point determination unit 130 may determine the time point at which the schedule ends as a time point for suggesting physical activity.

In step 240, the activity suggestion unit 140 may propose a physical activity through the electronic device at the determined suggestion time of the physical activity. The activity suggestion unit 140 may transmit a message (hereinafter, referred to as a “physical activity suggestion message”) proposing a physical activity based on the amount of physical activity of the user at the determined time of suggestion of the physical activity. The activity suggestion unit 140 may select a message having the highest user preference among predetermined physical activity suggestion messages, and transmit the selected physical activity suggestion message through the user's electronic device. For example, the physical activity suggestion message refers to a message for suggesting a physical activity to the user, and may suggest a physical activity related to a body part that is insufficient for the user or desired to be performed by the user. In this case, various physical activities may exist in relation to a body part that the user lacks or desires to perform, and among the various physical activities, a physical activity preferred by the user may exist. When two or more messages having the same preference exist, the activity suggestion unit 140 may select a physical activity suggestion message to be transmitted through random (random lottery) among the messages. In an embodiment, among the reinforcement learning methods, a physical activity suggestion message having the highest preference may be selected by using a Thomson sampling algorithm. The activity suggestion unit 140 may transmit the selected physical activity suggestion message through a notification system provided by the user's electronic device or a related API at the time of suggestion of the physical activity determined in step 230.

3 is a flowchart illustrating a method of learning a user's preference for a physical activity suggestion message in an intelligent intervention system according to an exemplary embodiment.

In step 310, the intelligent mediation system may select and transmit the user's physical activity suggestion message. In step 320, the intelligent intervention system may measure the amount of change in the user's physical activity after the physical activity suggestion message is transmitted. The intelligent intervention system can measure the amount of change in the user's physical activity over a certain period of time. The intelligent intervention system may measure the amount of change in physical activity based on the physical activity details collected through the user's electronic device. For example, the intelligent intervention system may measure the amount of change in physical activity by comparing the previously stored physical activity details (or the amount of physical activity) with the physical activity details (or the amount of physical activity) measured for a predetermined time. The intelligent intervention system may determine that the user's physical activity suggestion message suggested to the user is preferred when the measured change in physical activity of the user is more than a predetermined value. In other words, when the measured amount of change in physical activity of the user is greater than or equal to a predetermined value, the intelligent intervention system may predict that the transmitted physical activity suggestion message is effective in converting the user's physical activity. In addition, when the measured amount of change in physical activity of the user is less than a certain value, the intelligent intervention system may determine that the physical activity suggestion message proposed to the user is not preferred. In other words, the intelligent intervention system may predict that the transmitted physical activity suggestion message is inappropriate for converting the user's physical activity when the measured change in physical activity of the user is less than a certain value.

In step 330, the intelligent intervention system may learn the preference of the physical activity suggestion message based on the measured amount of change in the user's physical activity. As it is determined that the amount of change in the user's physical activity is greater than or equal to a predetermined value, the intelligent intervention system may assign a weight to the preference of the physical activity suggestion message delivered to the user. In this case, the intelligent mediation system may assign a weight to the physical activity proposal message delivered to the user by using the beta distribution. In the intelligent intervention system, statistical learning techniques (eg, Thompson sampling, UCB (Upper Confidence Bound), etc.) can be used to learn the preferences of the physical activity suggestion message.

4 is a flowchart illustrating a method of learning the appropriateness of a time point for suggesting a physical activity in an intelligent intervention system according to an exemplary embodiment.

In step 410, the intelligent mediation system may select and transmit the user's physical activity suggestion message. In step 420, after the physical activity suggestion message is transmitted, the intelligent intervention system may request evaluation of a suggestion time of the physical activity. For example, the intelligent intervention system may evaluate whether a time point of suggesting a physical activity or/and a time point at which a message suggesting a physical activity is transmitted is appropriate. A user self-evaluation may be performed as to whether the physical activity presented in the physical activity suggestion message can be performed in the current situation of the user. For example, a self-assessment can be answered on a 5-point scale, with 1 being very inappropriate (impossible), 3 being fair, and 5 being very appropriate (possible). Self-evaluation may generate evaluation information as the user answers according to a preset likert scale (eg 1 point: very inappropriate, 5 points: very appropriate). Since the intelligent mediation system requires the user to self-evaluate, evaluation can be selectively presented according to preset criteria in order to reduce the burden on the user. For example, the intelligent mediation system may selectively present an evaluation request according to a preset probability distribution (eg, 1 out of 10) in order to reduce the user's burden on self-evaluation.

In step 430, the intelligent intervention system may learn viewpoint information of the proposal time by using the evaluation information obtained by evaluating the proposal time of the physical activity. Intelligent mediation system delivers physical activity suggestion messages using machine learning methods (e.g., random forest, SVM, DNN, CNN, etc.) using context-aware information collected during a preset period and evaluation information obtained from users. You can learn the appropriateness of the viewpoint. Alternatively, the intelligent mediation system utilizes context-aware information collected during a preset period and evaluation information obtained from users, and re-uses machine learning methods (e.g., random forest, SVM, DNN, CNN, etc.) as necessary. Learning can be carried out. Alternatively, the intelligent mediation system may learn to determine the appropriateness of the proposal time using data collected by a user or data collected from a third party. In an embodiment, the intelligent mediation system may perform supervised learning using training data (eg, sensor data, user response pairs, etc.) previously collected using a random forest machine learning technique. In this case, for learning, various criteria defined by the intelligent mediation system (eg, a certain period, uncertainty of a model, importance of user feedback, etc.) may be considered.

The apparatus described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices and components described in the embodiments are, 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 and responding to instructions, such as one or more general purpose computers or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. Can be embodyed in The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of the program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

Although the embodiments have been described by the limited embodiments and drawings as described above, various modifications and variations can be made from the above description to those of ordinary skill in the art. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and claims and equivalents fall within the scope of the claims to be described later.

Claims (15)

In the method for suggesting physical activity performed by an intelligent intervention system,
Collecting a user's physical activity details through an electronic device;
Detecting an amount of physical activity less than a predetermined reference from the amount of physical activity obtained by accumulating the collected physical activity details;
Determining a time point for suggesting a physical activity related to the detected amount of physical activity below a predetermined reference by using the user's context-aware information; And
Proposing a physical activity of the user through the electronic device at a time of suggesting the determined physical activity
Physical activity suggestion method comprising a. The method of claim 1,
Proposing the user's physical activity through the electronic device at the determined suggestion time of the physical activity,
Delivering a message suggesting a physical activity based on the amount of physical activity of the user at the determined time point of suggesting the physical activity
Physical activity suggestion method comprising a. The method of claim 2,
Proposing the user's physical activity through the electronic device at the determined suggestion time of the physical activity,
After the message is delivered, measuring a user's physical activity change amount collected from the electronic device, and learning a preference of the message based on the measured user's physical activity change amount
Physical activity suggestion method comprising a. The method of claim 2,
Proposing the user's physical activity through the electronic device at the determined suggestion time of the physical activity,
After the message is delivered, requesting evaluation of the time point of suggesting the user's physical activity, and learning time point information related to the time point of proposal using the evaluation information obtained by evaluating the time point of the physical activity proposal
Physical activity suggestion method comprising a. The method of claim 1,
Collecting the user's physical activity details,
At least one or more of the type of the user's repetitive physical activity, the number of repetitions of the user's physical activity, the duration of the user's physical activity, the calories burned due to the physical activity, and a physiological state due to the user's physical activity Collecting data as a user's physical activity history
Physical activity suggestion method comprising a. The method of claim 1,
The step of determining a suggestion time of a physical activity related to the detected amount of physical activity below a preset reference by using the user's context awareness information,
1) Schedule information specified in the user's schedule, user's meeting, physical activity information including classes, or task situation information including physical activity history information, 2) Locational situation including information about the place where the user stays in his or her daily life Information, 3) using at least one user's context-aware information among social interactions, including interactions with other users directly or indirectly related to the user's daily life
Physical activity suggestion method comprising a. The method of claim 6,
The step of determining a suggestion time of a physical activity related to the detected amount of physical activity below a preset reference by using the user's context awareness information,
Determining an amount of physical activity less than the detected amount of physical activity as an insufficient amount of physical activity, and determining a time point for suggesting a physical activity in connection with the time when the amount of physical activity is determined to be insufficient and schedule information of the user
Physical activity suggestion method comprising a. In a computer program stored in a computer-readable storage medium to execute a method for suggesting physical activity performed by an intelligent intervention system,
Collecting a user's physical activity details through an electronic device;
Detecting an amount of physical activity less than a predetermined reference from the amount of physical activity obtained by accumulating the collected physical activity details;
Determining a time point for suggesting a physical activity related to the detected amount of physical activity below a predetermined reference by using the user's context-aware information; And
Proposing a physical activity of the user through the electronic device at a time of suggesting the determined physical activity
A computer program stored on a computer-readable storage medium comprising a. In the intelligent intervention system for suggesting physical activity,
A collection unit that collects a user's physical activity details through an electronic device;
An activity amount detector configured to detect an amount of physical activity less than a predetermined reference from the amount of physical activity obtained by accumulating the collected physical activity details;
A time point determination unit that determines a time point of suggestion of a physical activity related to the detected amount of physical activity below a predetermined reference by using the user's context recognition information; And
An activity suggestion unit that proposes the user's physical activity through the electronic device at the determined physical activity proposal time
Intelligent arbitration system comprising a. The method of claim 9,
The activity suggestion unit,
Delivering a message proposing a physical activity based on the amount of physical activity of the user at the determined time point of suggesting the physical activity
Intelligent arbitration system, characterized in that. The method of claim 10,
The activity suggestion unit,
After the message is delivered, measuring the amount of change in the user's physical activity collected from the electronic device, and learning the preference of the message based on the measured amount of change in the user's physical activity.
Intelligent arbitration system, characterized in that. The method of claim 10,
The activity suggestion unit,
After the message is delivered, requesting evaluation of the suggestion time of the user's physical activity, and learning viewpoint information related to the suggestion time using the evaluation information evaluating the suggestion of the physical activity
Intelligent arbitration system, characterized in that. The method of claim 9,
The collection unit,
At least one or more of the type of the user's repetitive physical activity, the number of repetitions of the user's physical activity, the duration of the user's physical activity, the calories burned due to the physical activity, and a physiological state due to the user's physical activity To collect the user's physical activity history
Intelligent arbitration system, characterized in that. The method of claim 9,
The viewpoint determination unit,
1) Schedule information specified in the user's schedule, user's meeting, physical activity information including classes, or task situation information including physical activity history information, 2) Locational situation including information about the place where the user stays in his or her daily life Information, 3) using at least one user's context-aware information among social interactions including interactions with other users directly or indirectly related to the user's daily life.
Intelligent arbitration system, characterized in that. The method of claim 14,
The viewpoint determination unit,
Determining the amount of physical activity less than the detected amount of physical activity as an insufficient amount of physical activity, and determining a time point for suggesting physical activity in connection with the time when the amount of physical activity is determined to be insufficient and schedule information of the user
Intelligent arbitration system, characterized in that.

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