JP2022049564A - Information processor and program - Google Patents

Abstract

To provide an information processor and a program that can support the self-management of the activities of a user.SOLUTION: In an information processor 1, activity target information on a target of the activity amount of a user is received and is converted to activity amount information which represents the activity amount to be performed in a predetermined period of time in the common unit. After that, the type of an activity that satisfies an execution condition set for each type of activity is extracted, and specific recommended activity information for a user is generated on the basis of the activity amount of each specific unit amount related to the extracted type of activity and the activity amount information as the result of conversion of the activity target information of the user.SELECTED DRAWING: Figure 1

Description

The present invention relates to an information processing apparatus and a program.

In recent years, in the medical field, exercise therapy may be adopted for the purpose of improving muscle strength, maintaining vital capacity, and improving glucose metabolism and lipid metabolism. In exercise therapy, patients and others walk or go to the gym to exercise based on the guidance of a doctor.

Here, even in situations where it is not possible to increase so-called exercise / sports, such as being unable to go to the gym for various reasons, for daily activities such as housework, garden work, walking for commuting, and leisure time. The required amount of physical activity can be secured by increasing physical activity including hobbies and leisure activities. However, there is a limit to accurately grasping the patient's daily life and implementing appropriate lifestyle-related guidance and exercise therapy within the limited time of face-to-face contact. Specifically, doctors can provide general information such as presentation of target values such as energy consumption and METs, but cannot present exercise programs that patients can actually execute. is the current situation.

Japanese Unexamined Patent Publication No. 2010-21930

In other words, patients, etc. who have been provided with such general information can be presented with general target values such as energy consumption and METs by doctors, etc., but the activities themselves must be self-managed. .. However, it is difficult for patients and the like to know what kind of activity and how much the target value can be achieved.

Therefore, when a user who actually carries out an activity, such as a patient who has received guidance from a doctor, self-manages what kind of activity and how much should be performed in order to achieve the target amount of activity, the relevant person concerned. Technology to support self-management is required.

The present invention has been made in view of the above circumstances, and one of the objects thereof is to provide an information processing device and a program capable of supporting self-management of a user's activity.

In Patent Document 1, a device that detects walking by an acceleration sensor, receives input of consumed calories, converts the consumed calories into steps based on the user's walking speed, weight, and stride data, and displays the consumed calories. It has been disclosed.

One aspect of the present invention for solving the problems of the above-mentioned conventional example is an information processing device, which is a means for receiving activity target information which is a target of the amount of activity of the user, and a predetermined method for each type of activity. The amount of activity for each unit amount is held as a value of a predetermined common unit, and the information holding means for holding the implementation conditions for each type of the activity and the received activity target information are the amount of activity to be performed in a predetermined period. Is extracted into the means for converting the information into the activity amount information expressed in the common unit, and the type of the activity satisfying the implementation conditions is extracted from the types of the activity, and each predetermined unit amount related to the extracted activity type is extracted. Information generation means for generating predetermined recommended activity information for the user based on the activity amount of the above and the activity amount information which is the conversion result of the activity target information related to the user, and the generated recommended activity information. It is intended to include means for output.

According to the above aspect of the present invention, it is possible to support the self-management of the user's activities.

It is a block diagram which shows the structural example of the information processing apparatus which concerns on embodiment of this invention. It is explanatory drawing which shows the content example of the activity database used by the information processing apparatus which concerns on embodiment of this invention. It is a functional block diagram which shows the example of the information processing apparatus which concerns on embodiment of this invention.

An embodiment of the present invention will be described with reference to the drawings. The information processing device 1 according to the embodiment of the present invention is, for example, a mobile terminal or the like, and as illustrated in FIG. 1, the control unit 11, the storage unit 12, the operation unit 13, and the display unit 14 communicate with each other. It is configured to include a part 15. Further, the information processing apparatus 1 is connected to the server 2 via a network.

The control unit 11 is a program control device such as a CPU, and operates according to a program stored in the storage unit 12. In the example of this embodiment, the control unit 11 receives the activity target information that is the target of the activity amount of the user. Further, the control unit 11 accesses a database (hereinafter referred to as an activity database) that holds the activity amount for each predetermined unit amount as the value of a predetermined common unit for each type of activity. Then, the control unit 11 converts the received activity target information into information on the amount of activity to be performed in a predetermined period. The control unit 11 represents the converted information in a predetermined common unit.

Further, the control unit 11 extracts from the above activity database the types of activities that satisfy the implementation conditions among the types of activities. The control unit 11 provides predetermined recommended activity information for the user based on the activity amount for each predetermined unit amount related to the extracted activity type and the conversion result of the activity target information related to the user. Generate and output the generated recommended activity information. The detailed operation of the control unit 11 will be described later.

The storage unit 12 is a memory device or the like, and holds a program executed by the control unit 11. This program is provided stored in a computer-readable and non-temporary recording medium, and may be stored in the storage unit 12. The storage unit 12 also operates as a work memory of the control unit 11.

In an example of this embodiment, the storage unit 12 holds an activity database in which implementation conditions for each type of activity are recorded. This activity database may be set for each user, and the content thereof is as illustrated in FIG. 2, for the information specifying the type of activity (activity type name: A), the implementation condition (B) of the activity. And the information of the activity amount for each predetermined unit amount (activity amount: C) are recorded in association with each other. Further, in an example of the present embodiment, information on the characteristics of the activity (activity characteristic information: D) for each type of activity recorded in this activity database may be associated and recorded. Here, the activity characteristic information is, for example, information such as whether or not the amount of activity is fixed. Specifically, the amount of activity is fixed for activities such as commuting, but depending on the activity, the amount of activity can be adjusted according to the activity time. The activity characteristic information may include information indicating whether the amount of such activity is fixed or variable.

One of the characteristics of this embodiment is that the information on this amount of activity is converted into a predetermined common unit. Here, the common unit may be any unit of activity or momentum. Specifically, the common unit may be the number of steps, which is a unit of walking.

In addition, the conditions for implementing the activity here are the day of the week, weekdays, holidays, etc., the appropriate time zone, the appropriate weather conditions, the indoor / outdoor, and the time required to carry out the activity (completed after starting a series of activities). (Time to do), necessary equipment (Aerobike (registered trademark), etc.), contraindications (presence or absence of certain diseases, etc.), effects of factors that prevent going out (going out restrictions due to the spread of infectious diseases) Whether it is restricted or not) and other conditions, which can be set arbitrarily.

Further, in the storage unit 12, information that identifies the user (user name, etc.), gender, date of birth (or age), height, weight, contraindications (possession, etc.) are stored for each user of the information processing device 1. In addition to the user's physical information such as the type of activity restricted by the user), the user's work location, usual transportation information, home location information, commuting time, family structure ( Records the user's environmental information (environmental information) such as the presence or absence of a spouse, the number and age of children), the type of household affairs in charge at home, and the general schedule of the user's weekdays and holidays. It is assumed that the user database is stored. The information stored in this user database may be input from the user or the like in advance, or may be from the information registered by the user during the use of the information processing apparatus 1 of the present embodiment or from the user. It may be estimated based on the collected information. For example, the information processing device 1 may estimate the user's activity time or the like based on the aggregated result of the time when the user uses the information processing device 1 and store it in the user database.

The operation unit 13 is a touch panel, a keyboard, or the like, and receives instructions and information input from the user, and outputs the instructions and information to the control unit 11. The display unit 14 is a display or the like, and displays and outputs information according to an instruction input from the control unit 11.

The communication unit 15 is a network interface or the like, and sends and receives information to and from a server 2 or the like connected via a network according to an instruction input from the control unit 11. Further, the communication unit 15 outputs the information received from the server 2 or the like to the control unit 11.

In an example of this embodiment, the server 2 is a general server device (web server or the like), and generates information to be set in the activity database according to a request input from the information processing device 1 to generate the information. Information is sent to the information processing device 1.

As an example, the server 2 accepts a request from the information processing device 1 to generate information to be stored in the activity database together with information such as the user's age, gender, height, and weight. Then, the server 2 generates, for at least one type of activity, the execution condition of the activity and the information of the activity amount for each predetermined unit amount, and sends the information to the information processing apparatus 1.

Specifically, the server 2 is a type of activity.
・ In addition to exercises such as walking, running, and squats,
・ Basal metabolism ・ Commuting (walking)
・ Commuting (train ride)
-For information on various activities including housework such as work, shopping, going out, etc., the age and gender of the user who received the information on the amount of activity per certain time (for example, 1 hour) from the information processing device 1. Obtained based on information such as height and weight. Further, the server 2 obtains the information on the amount of activity as a predetermined common unit, for example, as a value converted into the number of steps which is a unit of walking exercise.

As an example, the basal metabolism per hour and per kg of body weight is calculated by the Harris-Benedict equation or the like having different coefficients for each gender, and the unit is expressed in calories (kcal). In the example of the present embodiment, it is assumed that the server 2 converts the number of steps (for example, the value of the number of steps such as "300 steps"), which is a unit of walking exercise, into the information processing apparatus 1 as a common unit.

Next, the operation of the control unit 11 will be described. In an example of the present embodiment, as illustrated in FIG. 3, the control unit 11 functionally includes an activity target setting unit 21, an activity amount holding unit 22, an information generation unit 23, and an output control unit 24. Consists of including.

The activity goal setting unit 21 receives activity goal information (target number of steps per day, etc.) that is a target of the user's activity amount from the user himself or a doctor or the like. Here, the activity amount target information may be set as an improvement item as a longer-term target information, for example, a change in a predetermined index according to a pathological condition in a predetermined period. Here, the index may directly represent, for example, calories burned, blood glucose control such as blood glucose level and HbA1c level, lipid metabolism such as HDL cholesterol level and LDL cholesterol level, non-HDL cholesterol, and triglyceride. It may be determined based on a value representing a pathological condition such as bone density, or specifically, the activity target information is the daily calorie consumption or the amount of change in HbA1c value (blood glucose) every 3 months. Also as the amount of change in the value representing lipid metabolism every month (improvement of lipid metabolism) (improvement of lipid metabolism), the amount of change in bone density every 3 months (improvement of bone density), etc. good.

The activity amount holding unit 22 holds the activity amount for each predetermined unit amount in the storage unit 12 as a value of a predetermined common unit for each type of activity, and holds the implementation conditions for each type of the activity.

In an example of this embodiment, the activity amount holding unit 22 requests the server 2 to generate information to be stored in the activity database together with information such as the user's age, gender, height, and weight set by the user. Send out. Further, the activity amount holding unit 22 receives the value of the activity amount for each predetermined unit amount from the server 2 for each type of activity. Here, the activity amount holding unit 22 receives from the server 2 the value of the activity amount calculated by the server 2 based on the physical information and the environmental information for each user.

The activity amount holding unit 22 records the activity amount information for each type of activity received from the server 2 in the activity database of the storage unit 12 in association with the implementation condition of the corresponding activity. The activity implementation conditions may be predetermined for each type of activity, and when the activity amount information is transmitted from the server 2, the corresponding implementation condition information may also be transmitted.

The information generation unit 23 converts the activity target information received by the activity target setting unit 21 into information on the activity amount of the common unit, which represents the activity amount to be performed within a predetermined period (for example, during one day). The method of converting the activity amount of this common unit into information is based on, for example, the improvement items (diabetes control, lipid metabolism, bone density, etc.) targeted by the activity target information, or the method predetermined for each index. It may be done.

For example, when the calorie consumption by daily exercise (calorie consumption excluding basal metabolism) is set to 300 kcal as activity target information, the information generation unit 23 sets this set value for a predetermined period (for example, during one day). Convert to the number of steps, which is information on the amount of activity of the common unit to be achieved. As an example, when the common unit is the number of steps, the relationship between the calories and the number of steps can be estimated by a known method using information such as the age, weight, and stride of the user, and the information generation unit 23 can use the method, for example, the above. The daily required activity amount in a common unit is calculated as "10000 steps".

In addition, when the amount of change in HbA1c value for improving diabetes is set as activity target information, the type and amount of activity during a certain period (value in the above common unit) performed by a large number of subjects in the past. Using the information of Using a machine learning model machine-learned to output a value, the required daily activity amount in the above common unit may be obtained based on the amount of change in the HbA1c value set as the activity target information.

It should be noted that this machine learning model is different for each index as well as the improvement items targeted by the activity goal information. For example, even when the purpose is to improve diabetes, when the index is blood glucose level, a machine learning model different from the case where HbA1c value is used as an index (insulin resistance effect model considering insulin resistance, etc.) is used. Use. And in this example, using the information of the type of activity and the amount of activity (value in the above common unit) performed by a large number of subjects in the past in a certain period, the predetermined period when the activity was performed. This machine learning model is machine-learned so as to output the value of the required activity amount for a predetermined period (for example, per day) in the above-mentioned common unit by inputting the change amount of the blood glucose level of the subject in.

Further, the machine learning model may be different not only for each index but also for each index, even if the same index belongs to different classifications of the user's physical information and environmental information. For example, if it is known that the correlation between the amount of activity and the HbA1c value differs depending on the age group of the user, the subjects who performed the activity in the past and measured the change in the HbA1c value are divided and divided according to the age group. Using information on the type and amount of activity (values in the above common unit) for each subject in a certain age group, the HbA1c value of the subject during the predetermined period when the activity was performed. A plurality of machine learning models machine-learned to output the value of the required activity amount for a predetermined period (for example, per day) in the common unit may be generated by using the change amount as an input. In this example, a machine learning model (after machine learning) corresponding to the age group of the user for which the activity target information is set is selected, and the selected machine learning model is used to specify the user. The daily required activity amount in the above common unit may be obtained based on the change amount of the HbA1c value.

The information generation unit 23 further refers to the information in the activity database, extracts the types of activities that satisfy the implementation conditions from the types of activities represented by the information held in the activity database, and the types of the extracted activities. Based on the activity amount for each predetermined unit amount related to the above and the conversion result of the activity target information related to the user, the recommended activity information for the user is generated.

The information generation unit 23 starts processing according to the instruction of the user, for example, and while referring to the physical information and the environmental information of the user, the information generating unit 23 identifies the type of activity that satisfies the implementation conditions at each time point of the day. Extract from the activity database.

For example, a user A is a man who is 40 years old and weighs 70 kg (physical information). After waking up, he commute to work by train and walk in the morning, mainly does desk work from morning to evening, and by train and walk at night. It is assumed that the environmental information that the person returns home, exercises lightly, and goes to bed (weekdays) is set.

First, the information generation unit 23 classifies weekdays and holidays, for example, and at least acquires a daily physical activity target other than basal metabolism from the activity database. As already described, this amount of activity is calculated by the server 2 from information such as the age and weight of the user, and here, for example, it is assumed that "10000 steps (about 300 kcal)" is set.

In addition, the information generation unit 23 sets the activity time zone.
・ Morning (after waking up)
・ Morning (9:00 to 12:00)
・ Afternoon (12:00 to 15:00)
・ Evening (15:00 to 18:00)
・ Night (18:00 to 21:00 (before bedtime))
Set separately as follows. Then, the information generation unit 23 provides information for specifying the type of activity that satisfies the implementation conditions in each time zone, for example, in each time zone of morning, afternoon, evening, and night (every 3 hours in the above example). With reference to the physical information and environmental information of the above, the activity of each time zone is sequentially determined by extracting from the activity database.

For example, the information generation unit 23 extracts from the activity database the activities for which the time zone on the morning of weekdays is determined to be an appropriate time zone. Specific examples of activities that set the morning time as an appropriate time zone include exercises such as running, as well as activities in daily life such as housework and commuting.

In the following example, the implementation conditions for "running" are defined as "temperature 25 degrees or less, outdoors, implementation time of 30 minutes or more, commuting not included in the morning hours, contraindications: heart disease ...". It shall be. In addition, it is assumed that conditions such as "the user's environmental information includes morning commuting" are set as the implementation conditions for "commuting".

Under this example, with respect to the above user A, the information generation unit 23 includes information that the user A commute to work during the morning hours on weekdays, so that the activity of "commuting" is performed. Extract, but do not extract "running" activities. Therefore, the information generation unit 23 defines "commuting" as an activity to be performed on such a user A on a weekday morning.

Hereinafter, in the same manner, the information generation unit 23 acquires information that specifies the activity (recommended activity) that the user should perform in each time zone of weekdays and holidays.

Further, the information generation unit 23 generates information on the recommended amount of activity for each of the recommended activities acquired for each time zone.

Next, the information generation unit 23 obtains the activity amount of the recommended activities acquired for each time zone, such as commuting, which is set as the activity amount is fixed in the activity database.

For example, in the case of the above user A, it is assumed that commuting by train in the morning and at night corresponds to an activity amount of 1000 steps each, and an activity amount of 2000 steps is obtained in desk work. In this case, the information generation unit 23 subtracts a total of 4000 steps from the target activity amount.
Get 10,000-4000 = 6000 steps. In the following, the value obtained here will be referred to as the residual activity amount for convenience.

Next, among the recommended activities acquired for each time zone, the information generation unit 23 describes the activities for which the activity amount is not obtained (here, the activities set as the activity amount is variable in the activity database). Find the amount of activity.

The method for obtaining the activity amount by the information generation unit 23 here may be, for example, dividing the residual activity amount by the number of activities for which the activity amount is not obtained to obtain the activity amount of each activity. The information generation unit 23 generates recommended activity information for one day for the user by the process exemplified above.

The output control unit 24 presents the recommended activity information generated by the information generation unit 23 to the corresponding user. This allows the user to know the type of activity and the target amount of activity that should be performed at each time of the day.

[motion]
The information processing apparatus 1 of the present embodiment performs the operations illustrated above, and operates as in the following example.

In the following example, 70 female users B who are receiving diabetes treatment from a doctor set the change amount α of the HbA1c value every 3 months as activity target information according to the instruction from the doctor. It will be done for 1. The information processing device 1 accepts this setting. It is assumed that this user B has set environmental information that he / she does housework in the morning, goes shopping in the afternoon, does housework in the evening, and goes to bed early at night.

The information processing device 1 converts the received activity target information into information on the amount of activity in a common unit (referred to as the number of steps). Here, the information processing apparatus 1 inputs the amount of change in the HbA1c value of the subject during the predetermined period during the activity, and inputs the value of the number of steps corresponding to the required amount of activity for the predetermined period (for example, per day). Using a machine learning model machine-learned to output, the daily required activity amount of this user B in the number of steps, which is a common unit, based on the change amount of the HbA1c value set as the activity target information. Ask. Here, it is assumed that the number of steps is calculated as 3000 steps.

The information processing apparatus 1 acquires information (hereinafter, referred to as an actual target activity amount) representing a daily physical activity amount target other than basal metabolism from the activity database. As already described, this amount of physical activity is calculated by the server 2 from information such as the age and weight of the user, and here, for example, it is assumed that "3000 steps" is set.

The information processing device 1 operates, for example, before the user's wake-up time (for example, a predetermined time such as 4:00 am), and the implementation conditions are set in each time zone of the morning, morning, afternoon, evening, and night of the day. Information that identifies a satisfying type of activity is extracted from the activity database with reference to the physical information and environmental information of the user B, and the activities in each of the above time zones are sequentially determined. In the following, a case where the process is executed on a weekday morning will be described as an example.

In this example, the information processing apparatus 1 extracts from the activity database the activities for which the time zone on the morning of weekdays is determined to be an appropriate time zone. As in the example described above, examples of activities that set the morning time zone as an appropriate time zone include exercises such as walking and running, as well as activities in daily life such as housework and commuting. In addition, here, the implementation conditions for "walking" and "running" are "temperature 25 degrees or less, no rain, outdoor, implementation time 30 minutes or more, commuting is not included in the morning time, contraindications: heart disease ..." etc. It shall be stipulated. In addition, it is assumed that conditions such as "the user's environmental information includes morning commuting" are set as the implementation conditions for "commuting".

As for the user B, the information processing apparatus 1 does not extract the activity of "commuting" because the environmental information does not include the information that the user commute to work during the morning time on weekdays. Therefore, the information processing apparatus 1 extracts "walking" or "running" as an activity to be performed on such a user B on a weekday morning.

Initially, the information processing apparatus 1 may randomly select one of the activities and present it to the user B as a recommended activity as an activity that should be performed in the morning. In addition, if user B inputs an activity that was recommended in the past but does not want to be performed, information to that effect is added to the activity database. For example, when the user B sets "running" as an unfavorable activity, the information processing apparatus 1 associates the "running" activity in the activity database with the information specifying the activity, for example, "user avoidance" in the activity characteristic information. I will add the information of.

Then, when the information processing apparatus 1 extracts the activity, the information processing apparatus 1 extracts the activity to which the information "user avoidance" is not added. In this example, the information processing apparatus 1 avoids "running" as an activity to be performed on such a user B on a weekday morning, and extracts "walking".

Similarly, the information processing apparatus 1 extracts the activity (recommended activity) that should be performed in each time zone of the morning, the afternoon, and the like from the activity database with reference to the physical information and the environmental information of the user B. Here, the information processing device 1 is morning: walking morning: cleaning, washing, ...
Afternoon: Shopping Evening: Cooking, tidying up, ...
Nighttime: Recommended activities such as indoor gymnastics shall be determined. Of the above activities,
・ For cleaning, washing, shopping, cooking, and tidying up, the amount of activity shall be fixed in advance, for example:
・ Cleaning: 300 steps ・ Washing: 100 steps ・ Shopping: 500 steps ・ Cooking: 200 steps ・ Cleaning: 100 steps, etc.

Further, the information processing apparatus 1 generates information on the recommended amount of activity for each of the recommended activities determined for each time zone. The information processing apparatus 1 subtracts the basal metabolic rate from the activity target information converted into the activity amount information of the common unit, and obtains the activity amount to be performed by the activity other than the basal metabolism.

Next, the information processing apparatus 1 obtains the total amount of activities such as commuting and housework for which the amount of activity is set to be fixed in the activity database among the recommended activities acquired for each time zone. In the case of this example of user B, various household chores correspond.

In the above example, the amount of activity is fixed for each activity of cleaning (300 steps), washing (100 steps), shopping (500 steps), cooking (200 steps), and tidying up (100 steps). Therefore, the information processing device 1 is
300 + 100 + 500 + 200 + 100 = 1200
To get. Then, the information processing apparatus 1 subtracts the sum of the obtained activity amounts from the actual target activity amount, and the residual activity amount:
3000-1200 = 1800
To get.

The information processing apparatus 1 divides the value (1800) obtained here by the number of recommended activities whose activity amount is variable to obtain the activity amount of each recommended activity. Therefore, in the above example, the information processing apparatus 1 determines the amount of activity for each recommended activity as follows.
Morning: Walking 1700 steps Morning: Cleaning, washing ... 400 steps Afternoon: Shopping 500 steps Evening: Cooking, tidying up ... 300 steps Night: Gymnastics (indoor) 100 steps And the information processing device 1 uses this information. Recommended activity information.

The information processing apparatus 1 displays the recommended activity information generated in this way and presents it to the user B.

[Consideration of weather, etc.]
In addition, the information processing device 1 acquires information on the weather in the area where the information processing device 1 is located (which can be detected by various well-known location information acquisition means) from a weather information service on the Internet, and performs recommended activities. It may be used in the determination.

For example, the information processing apparatus 1 acquires information on the temperature and weather of the area in each time zone at the start of the process of determining the recommended activity in each time zone.

Then, when the recommended activity is extracted from the activity database, the information processing apparatus 1 extracts the activity whose temperature and weather information matches the implementation conditions. As an example, when the information of the afternoon temperature acquired by the information processing device 1 is "35 degrees", the implementation conditions for "running" are "temperature 25 degrees or less, no rain, outdoors, implementation time 30 minutes or more, morning". When it is stipulated that commuting is not included in the time zone, contraindications: heart disease, etc., the information processing device 1 does not extract "running" as an activity to be performed in the afternoon (under the condition of "25 degrees or less"). Does not match).

Further, for example, in an area where restrictions on going out are required to prevent the spread of infectious diseases, the information processing apparatus 1 controls activities with "outdoor" as an implementation condition not to be extracted as recommended activities.

[Acquisition of achievements]
Further, the information processing device 1 may be provided with a step sensor, an acceleration sensor, or the like (not shown), and collects information on the amount of activity performed by the user from the information of the step sensor or the like, and sets recommended activities in each time zone. The following processing may be performed immediately before the start of (for example, a few minutes before).

In this process, the information processing apparatus 1 uses the recommended activity information already carried out on that day (within a predetermined period for setting the recommended activity information) as the actual target activity amount (out of the activity amount of the activity to be performed in one day). The remaining target activity amount is calculated by subtracting it from the activity amount excluding the activity amount corresponding to metabolism).

Further, the information processing apparatus 1 subtracts the fixed activity amount from the activity amount of the recommended activity set to be performed in the time zone after the time zone in which this processing is performed, and remains. Find the differential activity and divide the residual activity by the number of variable activities among the recommended activities that are set to be performed after the time of processing. , Determine the amount of activity that should be recommended for activities that are considered variable.

For example, in the above example of user B, when user B walks according to the recommended activity information in the morning, for example, if it is acquired that the number of steps is 1500, the information processing apparatus 1 will perform the morning time zone or later. The amount of activity of is determined as follows.

That is, the information processing apparatus 1 obtains the total of activities such as commuting and housework for which the amount of activity is fixedly set in the activity database among the recommended activities acquired for each time zone after the morning. As already mentioned, this sum is 1200 steps.

The information processing apparatus 1 subtracts the sum of the obtained activity amounts from the actual target activity amount (3000 steps in the example of the user B), and the residual activity amount:
3000-12000 = 1800
To get.

Immediately before the end of the morning time zone, the information processing apparatus 1 further subtracts the activity amount (1500) of the activity already carried out in the morning time zone from the residual activity amount (1800) obtained here. , Of the recommended activities acquired for each time zone after the morning, divide by the number of activities whose activity amount is set as variable in the activity database to obtain the activity amount of each activity after the afternoon. That is, at the end of each time zone, the information processing apparatus 1 subtracts the total activity amount of the activities performed so far from the actual target activity amount to obtain the residual activity amount, and the recommended activity in the subsequent time zone. The activity amount of the activity whose activity amount is fixed is subtracted from the residual activity amount, and the target activity amount after the subtraction is set as the activity amount of the recommended activities in the subsequent time zone is variable. Divide by the number of activities to determine the amount of activity for each activity set as variable.

Specifically, in this example, the information processing apparatus 1 determines the amount of activity of the recommended activity in the time zone after the morning as follows.
Morning: Cleaning, washing ... 400 steps Afternoon: Shopping 500 steps Evening: Cooking, tidying up ... 300 steps Night: Gymnastics (indoor) 300 steps And information processing device 1 uses this information as recommended activity information for users. Present.

Here, of the residual activity amount, after subtracting the activity amount of the activity whose activity amount is fixed among the recommended activities in the later time zone (the target activity amount after this subtraction is used for convenience). (Called the amount of activity to be distributed), divided by the number of activities for which the amount of activity is set to be variable among the recommended activities (for activities with variable amount of activity, the amount of activity is set to be equal). However, the present embodiment is not limited to this, and each activity has a variable activity amount by setting a weight for each activity and dividing the distribution target activity amount at a ratio according to the weight. You may set the amount of activity of.

Further, when the information processing device 1 acquires the information of the amount of activity performed by the user in a unit other than the common unit, the information processing device 1 converts the information into the common unit set for the activity target information in the common unit. Convert to the value of the activity amount of.

[Recommended alternatives]
Further, the information processing apparatus 1 may perform the next processing immediately before the start (for example, a few minutes before) of each time zone in which the recommended activity is determined.

At this timing, the information processing device 1 acquires information on the weather in the area where the information processing device 1 is located from the weather information service on the Internet, and the recommended activity determined for the time zone after the time of this processing. Judge whether or not the implementation conditions associated with the activity database are satisfied for the activity.

Specifically, it is assumed that the information processing apparatus 1 has decided "running" as a recommended activity in the night time zone. At this time, when the information of the nighttime temperature acquired in this process is "30 degrees", the information processing apparatus 1 has the implementation conditions for "running" of "temperature 25 degrees or less, no rain, outdoors, implementation time 30". If it is stipulated that commuting is not included in the morning hours for more than a minute, contraindications: heart disease, etc., the information processing device 1 determines that "running" is inappropriate as an activity to be performed at night. Therefore, activities to replace "running" (activities corresponding to the implementation conditions that match the acquired temperature information) will be extracted again and will be implemented at night.

In this example, activities such as "indoor gymnastics" in which the outside air temperature is not included in the implementation conditions are extracted and proposed as alternative activities.

[Use for telemedicine]
Further, in an example of this embodiment, the amount of activity presented to the user (the actual target amount of activity may be sufficient) and the actual result of the user's activity are presented to the doctor, for example, via a network. It may be possible. The doctor can refer to this information and consider resetting the target activity information. What is characteristic of this embodiment is that information that identifies recommended activities is not transmitted. In addition, since the actual target activity amount is also shown in a common unit, the doctor can know what kind of activity was presented to the user and what kind of activity the user actually performed. No, the privacy of the user is protected.

1 Information processing device, 2 Server, 11 Control unit, 12 Storage unit, 13 Operation unit, 14 Display unit, 15 Communication unit, 21 Activity target setting unit, 22 Activity amount holding unit, 23 Information generation unit, 24 Output control unit.

Claims (6)

A means of accepting the activity goal information of the user, which is the target of the activity amount,
An information holding means for holding the activity amount for each predetermined unit amount as a value of a predetermined common unit for each type of activity and holding the implementation conditions for each type of activity.
A means for converting the received activity target information into activity amount information in which the amount of activity to be performed in a predetermined period is expressed in the common unit.
Among the types of activities, the types of activities that satisfy the implementation conditions are extracted, the amount of activity for each predetermined unit amount related to the extracted type of activity, and the conversion of the activity target information related to the user. An information generation means that generates predetermined recommended activity information for the user based on the resulting activity amount information, and
An information processing device including a means for outputting the generated recommended activity information. In the information processing apparatus according to claim 1, further
A means of collecting information on the amount of activities that the user has already carried out within the prescribed period, and
A means for converting the collected information related to the amount of activity into information in a predetermined common unit, and
The means for displaying the converted information and
Information processing equipment including. In the information processing apparatus according to claim 2,
The conversion is an information processing device performed by a conversion method to a common unit set for activity target information. In the apparatus according to claim 2 or 3.
The information generation means subtracts the collected activity amount information from the activity amount represented by the activity target information of the user, and generates recommended activity information for the user based on the activity amount after the subtraction. Processing equipment. In the apparatus according to any one of claims 1 to 4.
The method for converting the activity amount into the predetermined unit is an information processing device that is predetermined according to any of blood glucose control, lipid metabolism improvement, and bone density improvement. Computer,
A means of accepting the activity goal information of the user, which is the target of the activity amount,
An information holding means for holding the activity amount for each predetermined unit amount as a value of a predetermined common unit for each type of activity and holding the implementation conditions for each type of activity.
A means for converting the received activity target information into activity amount information in which the amount of activity to be performed in a predetermined period is expressed in the common unit.
Among the types of activities, the types of activities that satisfy the implementation conditions are extracted, the amount of activity for each predetermined unit amount related to the extracted type of activity, and the conversion of the activity target information related to the user. An information generation means that generates predetermined recommended activity information for the user based on the resulting activity amount information, and
A means for outputting the generated recommended activity information and
A program that functions as.

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