JP7807965B2 - Migratory Degree Estimation Device, Migratory Degree Estimation System, and Program - Google Patents

Description

The present invention relates to a mobility estimation device, a mobility estimation system, and a program.

In recent years, there have been many TV programs broadcast that introduce local spots, such as travel programs, gourmet programs, and city walking programs (so-called "machibura" programs), and as a result, there has been a growing desire to rediscover unexpected sights and aspects of familiar and distant spots.

The following technologies could be applied to meet this demand:

Patent Document 1 discloses a technology that acquires feature information characterizing features related to a location determined by a user based on the location information of the location, and transmits the acquired feature information to a network system that records third-party information transmitted by a third party other than the user.

With this technology, when related information related to the transmitted feature feature information is extracted from the recorded third-party information and transmitted, the transmitted related information is presented to the user. Therefore, with this technology, information about facilities that the user is completely unaware of can be recognized by associating it with a location determined by the user.

In cities, tourist destinations, and other areas, it is extremely important to attract not only local residents but also people from far away to visit in large numbers in order to revitalize the area.

In contrast, the technology described in Patent Document 1 can present unexpected information to users, but does not necessarily effectively encourage browsing.

As a technology that can be applied to solve this problem, Patent Document 2, filed by the present applicant, discloses a migration promotion support device designed to effectively promote migration. This migration promotion support device includes an acquisition unit that acquires physical quantities indicating the subject's level of interest in a target location based on the subject's actual behavior in that location, and a derivation unit that uses the physical quantities acquired by the acquisition unit to derive the subject's evaluation value for the target location. The migration promotion support device also includes a registration unit that registers the evaluation value derived by the derivation unit in a storage unit in a state that allows others to refer to it.

JP 2015-11550 A Japanese Patent Application Laid-Open No. 2020-109600

However, the technology disclosed in Patent Document 2 derives an evaluation value for a target location using physical quantities based on the subject's actual behavior in that location, more specifically, physical quantities such as the subject's time spent in the target location and the distance from a specified starting point to the target location.

As such, the technology disclosed in Patent Document 2 derives evaluation values using only indirect physical quantities, which poses the problem of not always being able to estimate evaluation values with high accuracy. This problem is particularly pronounced with evaluation values that indicate the degree to which a subject felt they had wandered around a target location (hereinafter referred to as "locomotor activity level"). Note that the term "locomotor activity level" (the degree to which a subject felt they had wandered around) refers to the degree to which a subject walked around a target location, such as a city or tourist spot, to see and hear about things that interest them, rather than walking in a straight line toward a destination.

The present invention was made in consideration of the above circumstances, and aims to provide a mobility estimation device, mobility estimation system, and program that can estimate mobility with higher accuracy.

The mobility estimation device of the present invention described in claim 1 comprises an estimation unit that estimates the mobility by inputting actual physical quantities of a subject in a target area of mobility into a mobility estimation model in which input information is physical quantities related to the walking of a person in a target area of mobility, and output information is mobility information indicating the level of the subject's subjective mobility when walking corresponding to the physical quantities, and a presentation unit that presents mobility-related information related to the mobility estimated by the estimation unit , wherein the presentation unit further presents to the subject meaningful information that is meaningful to the subject, derived using the mobility estimated by the estimation unit, and when the mobility is equal to or less than a predetermined threshold, the meaningful information is information introducing spots that have an overall mobility degree that is higher than the mobility degree, and that indicates an overall value of the mobility of all subjects .

According to the mobility estimation device of the present invention as set forth in claim 1, physical quantities relating to the walking of people in a target area of movement are used as input information, and mobility information indicating the level of the person's subjective mobility when walking corresponding to said physical quantities is used as output information. By inputting actual physical quantities of a subject in a target area of movement into a mobility estimation model, the mobility can be estimated by estimating the mobility, thereby making it possible to reflect the subjective mobility of a person in the estimation of the mobility, and as a result, it is possible to estimate the mobility with higher accuracy.
Furthermore, according to the mobility estimation device of the present invention described in claim 1, meaningful information that is meaningful to the subject and that is derived using the estimated mobility can be further presented to the subject, thereby making the subject aware of the significance of mobility, and thereby increasing their motivation to migrate.

The mobility estimation device according to the present invention, as set forth in claim 2, is the mobility estimation device according to claim 1, wherein the physical quantity includes at least one of the number of steps, walking distance, and walking speed.

According to the mobility estimation device of the present invention described in claim 2, by using at least one of the number of steps, walking distance, and walking speed as the physical quantity, mobility can be estimated more easily compared to when a qualitative physical quantity is used as the physical quantity.

A mobility estimation device according to the present invention as set forth in claim 3 is the mobility estimation device as set forth in claim 1 or claim 2 , wherein the meaningful information includes information related to health.

According to the mobility estimation device of the present invention as set forth in claim 3 , by including health-related information in the meaningful information, it is possible to further increase motivation to migrate.

The mobility estimation device according to the present invention as set forth in claim 4 is the mobility estimation device as set forth in claim 3 , wherein the health-related information includes at least one of information on mental health and information on physical health.

According to the mobility estimation device of the present invention as set forth in claim 4 , by including at least one of information on mental health and information on physical health in the health information, it is possible to allow the subject to understand the included health information.

The mobility estimation device of the present invention described in claim 5 is a mobility estimation device described in any one of claims 1 to 4 , wherein the estimation unit estimates the mobility for each attribute of the subject.

According to the mobility estimation device of the present invention as set forth in claim 5 , the mobility degree can be estimated with higher accuracy by estimating the mobility degree for each attribute of the subject.

The mobility estimation device according to the present invention as set forth in claim 6 is the mobility estimation device as set forth in any one of claims 1 to 5 , further comprising an acquisition unit that acquires learning physical quantities that are the physical quantities for learning and learning mobility information that is the mobility information for learning, and a learning unit that performs machine learning of the mobility estimation model using the learning physical quantities acquired by the acquisition unit as input information and the learning mobility information as output information.

According to the mobility estimation device of the present invention described in claim 6 , by obtaining the mobility estimation model through machine learning, it is possible to estimate the mobility with higher accuracy compared to when the mobility estimation model is obtained through statistical methods.

The mobility estimation system according to the present invention as described in claim 7 includes a mobility estimation device as described in any one of claims 1 to 6 , and a terminal including a transmission unit that transmits the physical quantity to the estimation unit of the mobility estimation device, and a display control unit that controls the display of mobility-related information presented by the presentation unit of the mobility estimation device on a display unit.

According to the mobility estimation system of the present invention as set forth in claim 7 , physical quantities relating to the walking of people in a target area of movement are used as input information, and mobility information indicating the level of the person's subjective degree of mobility when walking corresponding to said physical quantities is used as output information for the mobility estimation model.By estimating the mobility by inputting actual physical quantities of the subject in the target area of movement, it is possible to reflect the subjective degree of mobility in the estimation of the mobility, and as a result, it is possible to estimate the mobility with higher accuracy.
Furthermore, according to the mobility estimation system of the present invention described in claim 7, meaningful information that is meaningful to the subject and that is derived using the estimated mobility can be further presented to the subject, thereby making the subject aware of the significance of mobility, and thereby increasing their motivation to migrate.

The program of the present invention described in claim 8 is a process in which physical quantities related to people's walking in a target area for wandering are used as input information, and wandering degree information indicating the person's subjective level of wandering degree when walking corresponding to the physical quantities is used as output information to estimate the wandering degree by inputting the actual physical quantities of a subject in a target area for wandering into the model, and presenting wandering degree-related information related to the estimated wandering degree; the program causes a computer to execute the process by further presenting meaningful information that is meaningful to the subject, derived using the estimated wandering degree, and when the wandering degree is equal to or less than a predetermined threshold, the meaningful information is information introducing spots that have an overall wandering degree that is higher than the estimated wandering degree and indicates the overall value of the wandering degree of all subjects .

According to the program of the present invention as set forth in claim 8 , physical quantities relating to people's walking in a target area of movement are used as input information, and mobility estimation model information indicating the level of the person's subjective degree of mobility when walking corresponding to said physical quantities is used as output information. By inputting actual physical quantities of the subject person in the target area of movement to estimate the mobility, it is possible to reflect the person's subjective degree of mobility in the estimation of the mobility, and as a result, it is possible to estimate the mobility with higher accuracy.
Furthermore, according to the program of the present invention described in claim 8, meaningful information that is meaningful to the subject and that is derived using the estimated degree of movement can be further presented to the subject, thereby making the subject aware of the significance of movement, and thereby increasing their motivation to move around.

As explained above, the present invention makes it possible to estimate the degree of movement with greater accuracy.

1 is a block diagram showing an example of a hardware configuration of a mobility estimation system according to an embodiment. FIG. 2 is a block diagram illustrating an example of a functional configuration of the mobility estimation device according to the embodiment when learning a mobility estimation model. FIG. 2 is a block diagram illustrating an example of a functional configuration of the mobility estimation device according to the embodiment when a mobility estimation model is in operation. FIG. 2 is a schematic diagram illustrating an example of a configuration of a target region information database according to the embodiment. FIG. 2 is a schematic diagram illustrating an example of a configuration of a subject information database according to the embodiment. 10 is a flowchart illustrating an example of a learning process according to the embodiment. 10 is a flowchart illustrating an example of a terminal process according to the embodiment. 10 is a flowchart illustrating an example of a migration degree estimation process according to the embodiment. FIG. 2 is a front view showing an example of the configuration of an initial screen according to the embodiment. FIG. 10 is a front view showing an example of a configuration of a mobility degree display screen according to the embodiment. FIG. 10 is a front view showing an example of the configuration of a meaningful information display screen according to the embodiment.

Hereinafter, an example embodiment of the present invention will be described in detail with reference to the drawings. In this embodiment, the present invention will be described as being applied to a mobility estimation system that includes a mobility estimation device configured using a server computer or the like, and multiple subject terminals that are terminals used individually by each subject.

First, the configuration of a mobility estimation system 90 according to this embodiment will be described with reference to Figure 1. As shown in Figure 1, the mobility estimation system 90 according to this embodiment includes a mobility estimation device 10 and multiple target user terminals 30, each of which is capable of accessing a network 80. Examples of the mobility estimation device 10 include information processing devices such as personal computers and server computers. Examples of the target user terminals 30 include portable devices such as smartphones, tablet devices, and PDAs (Personal Digital Assistants, or handheld information terminals).

The subject terminal 30 in this embodiment is a terminal carried by each of multiple subjects who are intended to use the mobility estimation system 90. The subject terminal 30 includes a CPU (Central Processing Unit) 31, memory 32 as a temporary storage area, a non-volatile memory unit 33, an input unit 34 such as a touch panel, a display unit 35 such as an LCD display, and a media read/write device (R/W) 36. The subject terminal 30 also includes a camera 38, a microphone 39, a GPS (Global Positioning Systems) 40, an acceleration sensor 41, and a wireless communication unit 42. The CPU 31, memory 32, memory unit 33, input unit 34, display unit 35, media read/write device 36, camera 38, microphone 39, GPS 40, acceleration sensor 41, and wireless communication unit 42 are connected to each other via bus B1. The media read/write device 36 reads information written to and writes information to a recording medium 37.

The storage unit 33 is realized by a hard disk drive (HDD), a solid state drive (SSD), flash memory, etc. The storage unit 33 serves as a storage medium and stores a terminal processing program 33A. The terminal processing program 33A is stored (installed) in the storage unit 33 when a recording medium 37 on which the terminal processing program 33A is written is set in the medium reading and writing device 36 and the medium reading and writing device 36 reads the terminal processing program 33A from the recording medium 37. The CPU 31 reads the terminal processing program 33A from the storage unit 33, expands it into the memory 32, and sequentially executes the processes contained in the terminal processing program 33A.

On the other hand, the mobility estimation device 10 is a device that comprehensively stores and uses the various information handled by the mobility estimation system 90. The mobility estimation device 10 comprises a CPU 11, memory 12 as a temporary storage area, a non-volatile memory unit 13, an input unit 14 such as a keyboard and mouse, a display unit 15 such as an LCD display, a medium read/write device 16, and a communication interface (I/F) unit 18. The CPU 11, memory 12, memory unit 13, input unit 14, display unit 15, medium read/write device 16, and communication I/F unit 18 are connected to each other via bus B2. The medium read/write device 16 reads information written on a recording medium 17 and writes information to the recording medium 17.

The memory unit 13 is realized by an HDD, SSD, flash memory, etc. The memory unit 13 serves as a storage medium and stores a learning program 13A and a mobility estimation program 13B. The learning program 13A and the mobility estimation program 13B are stored (installed) in the memory unit 13 when a recording medium 17 on which these programs are written is set in the medium reading/writing device 16 and the medium reading/writing device 16 reads the programs from the recording medium 17. The CPU 11 reads the learning program 13A and the mobility estimation program 13B from the memory unit 13, expands them in the memory 12, and sequentially executes the processes that the learning program 13A and the mobility estimation program 13B each have.

The memory unit 13 also stores a target area information database 13C and a target person information database 13D. The target area information database 13C and the target person information database 13D will be described in detail below.

The memory unit 13 also stores a mobility estimation model 13E. The mobility estimation model 13E according to this embodiment receives input information from physical quantities (hereinafter simply referred to as "physical quantities") related to people's walking in a target area for mobility, and outputs output information from the model that indicates the subjective level of mobility of the person while walking, corresponding to the physical quantities.

In the mobility estimation system 90 according to this embodiment, three types of physical quantities are applied as physical quantities: the number of steps taken per predetermined period (10 minutes in this embodiment), walking distance, and walking speed, but this is not limitative. For example, any one or a combination of any two of these three types of physical quantities may be applied as the physical quantities, or physical quantities related to other types of walking may be applied in addition to these three types as the physical quantities.

The mobility estimation model 13E according to this embodiment is an AI (Artificial Intelligence) model using an MLP (Multilayer Perceptron), but is not limited to this. It is also possible to apply a machine learning model such as an AI other than MLP, such as an AI model using an RNN (Recurrent Neural Network), as the mobility estimation model 13E.

Next, with reference to Figure 2, the functional configuration of the mobility estimation device 10 according to this embodiment when learning the mobility estimation model 13E will be described. Figure 2 is a block diagram showing an example of the functional configuration of the mobility estimation device 10 according to this embodiment when learning the mobility estimation model 13E.

As shown in FIG. 2, the mobility estimation device 10 during learning of the mobility estimation model 13E includes an acquisition unit 11A and a learning unit 11B. The CPU 11 of the mobility estimation device 10 executes the learning program 13A, thereby functioning as the acquisition unit 11A and the learning unit 11B.

The acquisition unit 11A according to this embodiment acquires learning physical quantities, which are physical quantities for learning, and learning mobility information, which is mobility information for learning. In this embodiment, the learning physical quantities and learning mobility information are acquired by reading the physical quantities and mobility information registered by the mobility estimation process described below from the subject information database 13D (see also FIG. 5). However, this is not a limited form, and the physical quantities and mobility information for learning may be sequentially stored in the subject terminal 30 used by the subject, and the learning physical quantities and learning mobility information may be acquired by acquiring them from each subject terminal 30.

The learning unit 11B according to this embodiment performs machine learning of the mobility estimation model 13E using the learning physical quantities acquired by the acquisition unit 11A as input information and the learning mobility information as output information.

Next, with reference to Figure 3, the functional configuration of the mobility estimation device 10 according to this embodiment when the mobility estimation model 13E is in operation will be described. Figure 3 is a block diagram showing an example of the functional configuration of the mobility estimation device 10 according to this embodiment when the mobility estimation model 13E is in operation.

As shown in FIG. 3, when the mobility estimation model 13E is in operation, the mobility estimation device 10 includes an estimation unit 11C and a presentation unit 11D. The CPU 11 of the mobility estimation device 10 executes the mobility estimation program 13B, thereby functioning as the estimation unit 11C and the presentation unit 11D.

The estimation unit 11C according to this embodiment estimates the degree of mobility by inputting actual physical quantities of the subject at the target location of the mobility to the mobility estimation model 13E. The presentation unit 11D according to this embodiment then presents mobility-related information related to the mobility estimated by the estimation unit 11C.

In this embodiment, the mobility-related information used is the mobility itself estimated by the estimation unit 11C, and an image simulating a facial expression depending on the magnitude of the mobility (hereinafter referred to as a "face image"), but this is not limited to this. For example, only one of the mobility or the face image may be used as the mobility-related information. Also, in this embodiment, presentation by the presentation unit 11D is performed by display on the display unit, but this is not limited to this. For example, presentation by the presentation unit 11D may be performed by audio using an audio playback device such as a speaker, or by printing using an image forming device such as a printer.

Furthermore, the presentation unit 11D according to this embodiment uses the degree of mobility estimated by the estimation unit 11C to further present meaningful information that is meaningful to the subject (hereinafter simply referred to as "meaningful information") to the subject. Here, the presentation unit 11D according to this embodiment applies health-related information as meaningful information, and in particular, the presentation unit 11D according to this embodiment applies both mental health-related information and physical health-related information as the health-related information. However, this is not limited to this form. For example, if the degree of mobility estimated by the estimation unit 11C is equal to or less than a predetermined threshold, information introducing to the user spots that have a higher degree of mobility and are considered to have an overall mobility related degree (described below) may be applied as meaningful information. Furthermore, only either mental health-related information or physical health-related information may be applied as health-related information.

The estimation unit 11C according to this embodiment estimates the degree of movement for each attribute of the subject. In this embodiment, the three attributes applied are gender, age, and height, but this is not limitative. For example, one or a combination of two of these three attributes may be applied as the attribute, or other attributes such as weight may be applied in addition to these three attributes.

Furthermore, the CPU 31 of the subject terminal 30 executes the terminal processing program 33A, thereby functioning as the transmission unit and display control unit of the present invention.

Next, the target area information database 13C according to this embodiment will be described with reference to Figure 4. Figure 4 is a schematic diagram showing an example of the configuration of the target area information database 13C according to this embodiment.

As shown in FIG. 4, the target area information database 13C according to this embodiment stores information such as the target area name, target area location, social heat map image, travel route, spot name, spot location, and overall travel degree.

The target area name is information indicating the name of the target area (hereinafter simply referred to as the "target area") that is being handled by the mobility estimation system 90, and the target area position is information indicating the location where the corresponding target area is located. In this embodiment, the target area is an area surrounded by a circle in a planar view, and the target area position is defined as the coordinate position in a two-dimensional coordinate system of a pair of diagonal corners of a circumscribing rectangle of the circular area in a planar view. However, this is not limited to this form, and for example, other shapes such as an ellipse or rectangle may be used instead of the circle, or latitude and longitude may be used instead of the coordinate position in the two-dimensional coordinate system.

The social heat map image is an image showing a map that highlights locations with a lot of information that matches the subject's category by displaying areas with different densities or colors overlaid on the normally displayed map image of the corresponding target area. That is, in this embodiment, each subject is asked to answer multiple questions in advance, and the responses are analyzed and classified to determine each subject's category in advance. The social heat map image of this embodiment is then displayed overlaid on the map image so that the density increases in locations with a lot of information that matches the subject's category (in this embodiment, information posted on SNS (Social Networking Service)). However, this is not limited to changing the density, and the colors may also be changed in order from high density to low density, such as red → yellow → green.

In this embodiment, the mobility estimation device 10 is connected via a network 80 or the like to a server that provides the latest social heat map images for each target area, and the latest social heat map images are obtained from this server to sequentially update the target area information database 13C. However, this is not limiting, and the mobility estimation device 10 itself may also sequentially update the social heat map images corresponding to each target person.

The travel route is information indicating a recommended route to travel in the corresponding target area, the spot name is information indicating the name of a spot included in the corresponding target area, and the spot position is information indicating the location of the corresponding spot. In this embodiment, the spot is an area surrounded by a circle in a planar view, and the spot position is defined as the coordinate position in a two-dimensional coordinate system of a pair of diagonal corners of a circumscribing rectangle of the circular area in a planar view. However, this is not limited to this form, and for example, the circle may be replaced by another shape such as an ellipse or rectangle, or latitude and longitude may be used instead of the coordinate position in the two-dimensional coordinate system.

In this embodiment, the above-mentioned travel routes and spots are extracted from information posted on SNS using AI technology, but this is not limited to this. For example, travel routes and spots may be extracted from the homepages of television programs that introduce local spots, such as travel programs, gourmet programs, and city walking programs (so-called "town strolling programs").

In this way, in this embodiment, information indicating recommended routes to travel in the target area is provided, and this information is presented to the subject. However, it goes without saying that the subject does not necessarily have to travel exactly along the presented route, and can simply stroll around to see and hear about objects of interest.

Furthermore, the above-mentioned overall mobility degree is information indicating the overall value of the above-mentioned mobility degree for the corresponding spot. Note that in this embodiment, the simple average value of the mobility degree of all subjects at the corresponding spot is applied as the overall mobility degree, but this is not limited to this. For example, a weighted average value of the mobility degree of all subjects at the corresponding spot, in which the weight of people with important attributes is relatively increased, may be applied as the overall mobility degree. Hereinafter, the information stored in the target area information database 13C will be collectively referred to as "target area information."

Next, the subject information database 13D according to this embodiment will be described with reference to Figure 5. Figure 5 is a schematic diagram showing an example of the configuration of the subject information database 13D according to this embodiment.

As shown in FIG. 5, the subject information database 13D according to this embodiment stores information such as the subject terminal ID (Identification), subject name, attributes, visited spots, visit date and time, physical quantity, and degree of movement.

The subject device ID is information assigned to identify the subject device 30 owned by each subject who uses the mobility estimation system 90, and the subject name is information indicating the name of the corresponding subject.

The attributes are information indicating the attributes of the corresponding subject, the visited spots are information indicating the names of spots visited by the corresponding subject, and the visit dates and times are information indicating the dates and times when the corresponding subject visited the corresponding spots. The physical quantities are information indicating the number of steps, walking distance, and walking speed measured when the corresponding subject visited the corresponding spots, and the degree of wandering is information indicating the degree of wandering around the corresponding spots when the corresponding subject visited them. Note that, hereinafter, the information stored in subject information database 13D is collectively referred to as "subject information."

Next, the operation of the mobility estimation system 90 according to this embodiment will be explained with reference to Figures 6 to 11.

First, the operation of the mobility estimation device 10 when executing the learning process according to this embodiment will be described with reference to Figure 6. Figure 6 is a flowchart showing an example of the learning process according to this embodiment.

The learning process shown in FIG. 6 is performed by the CPU 11 of the mobility estimation device 10 executing the learning program 13A. The learning process shown in FIG. 6 is performed when the operator of the mobility estimation device 10 inputs an instruction to start execution of the learning program 13A via the input unit 14. Note that, in order to avoid confusion, the following description will be given of a case in which the number of pieces of physical quantity and mobility information required to learn the mobility estimation model 13E are registered in the subject information database 13D by the mobility estimation process described below (see also FIG. 8).

In step 100 of FIG. 6, the CPU 11 reads the attributes of one of the subjects (hereinafter referred to as the "processing subject") from the subject information database 13D, and in step 102, the CPU 11 reads each set of physical quantity and mobility information corresponding to the processing subject (corresponding to the learning physical quantity and learning mobility information described above) from the subject information database 13D.

In step 104, the CPU 11 uses the read-out attribute and learning physical quantity information as input information and the read-out learning mobility information as output information (correct answer information) to machine-learn the mobility estimation model 13E.

In step 106, the CPU 11 determines whether the machine learning in step 104 has been completed for all physical quantities and mobility levels corresponding to the processing target person stored in the target person information database 13D. If the determination is negative, the process returns to step 102, whereas if the determination is positive, the process proceeds to step 108. Note that when repeatedly executing the processes of steps 102 to 104, the CPU 11 processes physical quantities and mobility levels that have not been considered up to that point.

In step 108, CPU 11 determines whether machine learning in step 104 has been completed for all subjects stored in subject information database 13D. If the determination is negative, the process returns to step 100, whereas if the determination is positive, the learning process ends. Note that when repeatedly executing the processes of steps 100 to 106, CPU 11 will include subjects that have not previously been considered as subjects as processing subjects.

Through the above learning process, the mobility estimation model 13E is trained.

Next, the operation of the subject terminal 30 will be described with reference to Figure 7. The CPU 31 of one of the subject terminals 30 executes the terminal processing program 33A, thereby executing the terminal processing shown in Figure 7. The terminal processing shown in Figure 7 is executed, for example, when an instruction to execute the terminal processing is input from one of the subjects (hereinafter referred to as the "subject") via the input unit 34 of their own subject terminal 30.

In step 200 of FIG. 7, the CPU 31 acquires information indicating the location (hereinafter referred to as "location information") from the GPS 40, and in the next step 202, the CPU 31 transmits the acquired location information to the mobility estimation device 10 via the wireless communication unit 42.

In the mobility estimation process (see also Figure 8) described below, the mobility estimation device 10 reads target area information (hereinafter referred to as "implementation target area information") related to the target area (hereinafter referred to as "implementation target area") that includes the location indicated by the location information received from the subject terminal 30 from the target area information database 13C. Furthermore, the mobility estimation device 10 creates information indicating an initial screen (hereinafter referred to as "initial screen information") described below based on the implementation target area information. The mobility estimation device 10 then transmits the implementation target area information and the initial screen information to the subject terminal 30 (hereinafter referred to as "implementation target terminal") that transmitted the location information.

Therefore, in the next step 204, the CPU 31 waits to receive initial screen information and implementation area information from the mobility estimation device 10, and in the next step 206, the CPU 31 controls the display unit 35 to display the initial screen indicated by the received initial screen information.

Figure 9 shows an example of an initial screen according to this embodiment. As shown in Figure 9, the initial screen according to this embodiment displays the names and travel routes of each spot included in the target area superimposed at the corresponding positions on a social heat map image of the target area. The target person then begins traveling along the travel route displayed on the initial screen. Note that to avoid confusion, this embodiment describes a case where there is only one candidate target area, but this is not limited to this. For example, if there are multiple candidate target areas, the target area with the highest total value of the overall travel rate of the included spots or the target area most recently registered in the target area information database 13C may be selectively applied as the target area. Also, as shown in Figure 9, the circumscribing circles of each spot included in the target area are displayed with dashed lines on the initial screen according to this embodiment, but it goes without saying that this is not limited to this.

As mentioned above, the subject does not necessarily have to travel exactly along the displayed route, but can simply walk around to see and hear anything that interests them.

In the next step 208, the CPU 31 acquires location information from the GPS 40. In the next step 210, the CPU 31 waits until the location indicated by the acquired location information reaches the area of one of the spots included in the received implementation target area information, thereby waiting until the implementation target person arrives in the area of one of the spots in the implementation target area.

In the next step 212, the CPU 31 starts counting the number of steps taken by the subject using the output signal from the acceleration sensor 41 and also starts timing using a built-in timing unit (not shown). In the next step 214, the CPU 31 acquires location information from the GPS 40. In the next step 216, the CPU 31 waits until the location indicated by the acquired location information becomes the location where the subject leaves the spot determined to have been reached in the processing of step 210 (hereinafter referred to as the "leaving spot").

In the next step 218, the CPU 31 stops counting the number of steps and timing that began with the processing of step 212. By processing steps 212 to 218, the CPU 31 can obtain the number of steps PH taken by the subject at the exit spot and the time spent PV by the subject at the exit spot.

In the next step 220, the CPU 31 calculates the walking distance SD and walking speed SS of the subject at the exit spot using the acquired number of steps PH and staying time PV. Note that in this embodiment, the walking distance SD is calculated by multiplying the number of steps PH by the distance per step UD of the subject, and the walking speed SS is calculated by dividing the walking distance SD by the staying time PV, but this is not limited to this. For example, the walking distance SD may be the length of the subject's movement route, which is sequentially obtained by the GPS 40.

Then, in step 220, the CPU 31 controls the transmission of the spot name, step count PH, walking distance SD, and walking speed SS corresponding to the exit spot (hereinafter, the spot name, step count PH, walking distance SD, and walking speed SS are referred to as "basic information") to the mobility degree estimation device 10 via the wireless communication unit 42. At this time, the CPU 31 controls the transmission of the time when timing started by the processing of step 212 (hereinafter referred to as the "start time") and the time when timing stopped by the processing of step 218 (hereinafter referred to as the "end time") together with the basic information to the mobility degree estimation device 10. Upon receiving the basic information from the target participant terminal, the mobility degree estimation device 10 uses the received basic information in the mobility degree estimation process described below to derive a mobility degree for the target participant and transmits it to the target participant terminal.

Therefore, in the next step 222, the CPU 31 waits to receive the mobility degree from the mobility degree estimation device 10, and in the next step 224, the CPU 31 controls the display unit 35 to display a mobility degree display screen that primarily displays the received mobility degree.

Figure 10 shows an example of a mobility display screen according to this embodiment. As shown in Figure 10, the mobility display screen according to this embodiment displays the name of the exit spot and the name of the participant, and also displays an adjustable mobility level for the participant. As shown in Figure 10, the mobility display screen according to this embodiment also displays a face image 35D that simulates a facial expression according to the level of the mobility level.

When the mobility display screen shown in FIG. 10 is displayed on the display unit 35, the subject inputs an instruction to adjust the mobility if the displayed mobility does not match the mobility for the exit spot that the subject subjectively felt. In this embodiment, the instruction to adjust the mobility is input by selecting the increase button 35C if the subject wishes to increase the mobility, or by selecting the decrease button 35B if the subject wishes to decrease the mobility, but this is not limited to this. For example, the adjusted value may be directly entered in the mobility display area. Then, the subject selects the end button 35A to end the display of the mobility display screen.

Therefore, in the next step 226, the CPU 31 waits until the End button 35A is selected. In the next step 228, the CPU 31 transmits information indicating that the End button 35A has been selected (hereinafter referred to as "end information"), together with the adjusted mobility degree (hereinafter referred to as "adjusted mobility degree") if the mobility degree has been adjusted on the mobility degree display screen, to the mobility degree estimation device 10 via the wireless communication unit 42. Note that if the End button 35A is selected without adjusting the mobility degree on the mobility degree display screen, the CPU 31 transmits only the end information to the mobility degree estimation device 10.

When the mobility estimation device 10 receives completion information from the participant terminal, it derives meaningful information, which is information that is meaningful to the participant, and transmits it to the participant terminal in the mobility estimation process described below.

Therefore, in the next step 230, the CPU 31 waits to receive meaningful information from the mobility estimation device 10, and in the next step 232, the CPU 31 controls the display unit 35 to display a meaningful information display screen that primarily displays the received meaningful information.

Figure 11 shows an example of a meaningful information display screen according to this embodiment. As shown in Figure 11, the meaningful information display screen according to this embodiment displays the name of the exit spot and the name of the person to be involved in the activity, as well as meaningful information.

In this embodiment, meaningful information includes information indicating the level of mental health (hereinafter referred to as "mental health level") and information indicating the level of physical health (hereinafter referred to as "physical health level"). Also, in this embodiment, meaningful information includes information indicating the level of leisure and enjoyment at the departure spot for the participant. However, it goes without saying that meaningful information is not limited to these types of information.

When the meaningful information display screen shown in Figure 11 is displayed on the display unit 35, the subject understands the content of the displayed information and then presses the end button 35A.

Therefore, in the next step 234, the CPU 31 waits until the end button 35A is selected. In the next step 236, the CPU 31 determines whether the above processing has been completed for all spots in the target area, and if the determination is negative, the process returns to step 206, but if the determination is positive, the device processing ends.

Next, with reference to Figure 8, the operation of the mobility estimation device 10 when the mobility estimation model 13E is in operation will be described. The mobility estimation process shown in Figure 8 is executed by the CPU 11 of the mobility estimation device 10 executing the mobility estimation program 13B. The mobility estimation process shown in Figure 8 is executed, for example, when an instruction to execute the mobility estimation process is input by the operator of the mobility estimation device 10 via the input unit 14. Note that, in order to avoid confusion, the case where the subject ID, subject name, and attribute information are pre-registered in the subject information database 13D will be described.

In step 300 of FIG. 8, the CPU 11 waits until location information is received from one of the target device 30, and in the next step 302, the CPU 11 reads target area information (= implementation target area information) relating to the target area that includes the location indicated by the received location information from the target area information database 13C.

In the next step 304, the CPU 11 uses the read-out implementation target area information to create the above-mentioned initial screen information. In the next step 306, the CPU 11 controls the transmission of the created initial screen information and the read-out implementation target area information to the implementation target person terminal via the communication I/F unit 18 and the network 80. Upon receiving the initial screen information and implementation target area information, the implementation target person terminal uses the above-mentioned terminal processing to transmit information on the basic information, start time, and end time to the mobility degree estimation device 10 when the person leaves each spot in the visited target area.

In the next step 308, the CPU 11 waits until the basic information, start time, and end time are received from the subject terminal. In the next step 310, the CPU 11 reads the attributes of the subject corresponding to the subject terminal from the subject information database 13D. The CPU 11 then inputs the read attributes and the physical quantities of the number of steps PH, walking distance SD, and walking speed SS in the received basic information into the mobility estimation model 13E. At this time, the CPU 11 converts the physical quantities of the number of steps PH, walking distance SD, and walking speed SS into values per the above-mentioned predetermined period (10 minutes in this embodiment) and inputs them. When the above physical quantities and attributes are input, the mobility estimation model 13E outputs a mobility corresponding to the input information, and the CPU 11 acquires the mobility output from the mobility estimation model 13E.

In the next step 312, the CPU 11 controls the transmission of the acquired mobility degree to the target user terminal. Upon receiving the mobility degree, the target user terminal displays a mobility degree display screen through terminal processing, as described above, and is able to accept adjustments to the mobility degree on the mobility degree display screen. If the adjustment to the mobility degree is accepted, the adjusted mobility degree and end information described above are transmitted to the mobility degree estimation device 10; if the adjustment to the mobility degree is not accepted, only the end information is transmitted.

Therefore, in the next step 314, the CPU 11 waits until it receives completion information from the target user's terminal. In the next step 316, the CPU 11 determines whether the completion information has been received along with the adjusted mobility degree, thereby determining whether the mobility degree has been adjusted by the target user, and if the determination is affirmative, proceeds to step 318.

In step 318, the CPU 11 stores (registers) the received start and end times, the spot name and each physical quantity included in the received basic information, and the adjusted mobility in the corresponding storage areas in the subject information database 13D, and then proceeds to step 322.

On the other hand, if the determination in step 316 is negative, i.e., if the adjusted mobility level has not been received from the target user terminal, proceed to step 320.

In step 320, the CPU 11 stores (registers) the received start time and end time, the spot name and each physical quantity included in the received basic information, and the degree of movement obtained by the processing of step 310 in the corresponding storage areas of the subject information database 13D, and then proceeds to step 322. Note that when registering the physical quantities in the subject information database 13D in steps 318 and 320, the CPU 11 converts each physical quantity, the number of steps PH, walking distance SD, and walking speed SS, into values per the above-mentioned predetermined period (10 minutes in this embodiment) and registers them.

In step 322, the CPU 11 reads all of the mobility indices registered up to that point in the target person information database 13D for the spots corresponding to the spot names included in the received basic information, calculates the overall mobility indices using the read mobility indices as described above, and stores the calculated overall mobility in the corresponding area in the target area information database 13C, thereby updating the overall mobility indices for the corresponding spots.

In step 324, the CPU 11 derives the meaningful information described above. Note that in this embodiment, the mental health level and physical health level included in the meaningful information are derived as values indicating which level out of a predetermined number of levels (10 levels in this embodiment). In this embodiment, the mental health level is derived as the relative magnitude of the mobility level of the subject to the overall mobility level corresponding to the corresponding spot (or the adjusted mobility level, if an adjusted mobility level has been received) when the overall mobility level is set to the median (here, 5). Also, in this embodiment, the physical health level is derived as the relative magnitude of any one type of physical quantity (in this embodiment, the number of steps per specified period) of the subject to the average value of the same type of physical quantity for all subjects corresponding to the corresponding spot when the average value is set to the median (here, 5). However, it goes without saying that the mental health level and physical health level are not limited to these examples.

In step 326, the CPU 11 transmits the derived meaningful information to the subject terminal.

In the next step, step 328, the CPU 11 determines whether processing from step 308 onwards has been completed for all spots in the target area. If the determination is negative, the process returns to step 308, but if the determination is positive, the process proceeds to step 330.

In step 330, the CPU 11 determines whether a predetermined end timing has arrived. If the determination is negative, the process returns to step 300, whereas if the determination is positive, the process ends the mobility estimation process. Note that in this embodiment, the end timing is the timing when the operator of the mobility estimation device 10 inputs an instruction to end the mobility estimation process via the input unit 14, but this is not limited to this.

The mobility for each subject and the overall mobility obtained by the mobility estimation device 10 according to this embodiment can also be effectively utilized in urban planning and regional planning (so-called town development).

For example, by using the overall mobility index, it is possible to understand that the higher the overall mobility index, the easier it is to "wander around," meaning that the target area is attractive, safe, and ultimately has a high level of economic activity, and it can be used as an index to judge the attractiveness of a target area.

As explained above, according to this embodiment, physical quantities related to people's walking in a target area of movement are used as input information, and mobility estimation model output information indicates the level of a person's subjective level of mobility when walking corresponding to the physical quantities. The mobility estimation model estimates the actual physical quantities of a person walking in a target area of movement, thereby estimating the mobility. Therefore, it is possible to reflect the person's subjective level of mobility in the estimation of mobility, resulting in a more accurate estimation of mobility.

Furthermore, according to this embodiment, the number of steps, walking distance, and walking speed are used as physical quantities. Therefore, the degree of movement can be estimated more easily compared to when qualitative physical quantities are used as physical quantities.

Furthermore, according to this embodiment, the estimated degree of movement is used to further present the subject with meaningful information that is meaningful to the subject. This makes it possible to make the subject feel the significance of their movement, thereby increasing their motivation to move around.

Furthermore, according to this embodiment, meaningful information includes health-related information, which can further increase motivation to wander around.

Furthermore, according to this embodiment, health-related information includes both mental health-related information and physical health-related information. Therefore, it is possible for the subject to be able to understand this health-related information.

Furthermore, according to this embodiment, the degree of movement is estimated for each attribute of the subject. Therefore, the degree of movement can be estimated with higher accuracy.

Furthermore, according to this embodiment, the mobility estimation model is obtained through machine learning. Therefore, mobility can be estimated with higher accuracy compared to when the mobility estimation model is obtained through statistical methods.

In the above embodiment, the case where the mobility display screen is displayed as a screen different from the initial screen has been described, but this is not limiting. For example, the mobility display screen may be displayed as a pop-up screen on the initial screen. In this case, an example of a form in which the mobility display screen is displayed in the form of a so-called speech bubble from the position of the exit spot on the initial screen is possible.

In addition, in the above embodiment, a case has been described in which an area including the location where the subject's terminal is located is automatically applied as the target area, but this is not limited to this. For example, the target area may be specified by the subject specifying a remote location away from the location where the subject's terminal is located on an electronic map or by inputting the name of the remote location. Alternatively, information indicating the subject's preference trends may be acquired in advance by the mobility estimation device 10, and information on the target area according to the subject's preferences may be provided to the subject as needed.

In addition, in the above embodiment, a case has been described in which the mobility of a target person for each spot is derived and displayed at the time when the target person leaves the spot, but this is not limited to this. For example, the mobility may be derived and displayed at the time when the target person leaves the target area.

In addition, in the above embodiment, the mobility estimation process is performed by the mobility estimation device 10, but this is not limited to this. For example, the mobility estimation process may be performed by each subject terminal 30. In this case, the mobility estimation device of the present invention would be included in the subject terminal 30.

In addition, in the above embodiment, a case was described in which an image showing a map that highlights locations with a lot of information matching the target user's category was used as the social heat map image, but this is not limited to this. For example, an image in which the higher the overall visitor frequency of a spot, the deeper the red color may be used as the social heat map image.

Furthermore, in the above embodiment, a case has been described in which the mobility estimation model 13E is a single model in which the subject's attributes and physical quantities are used as input information, but this is not limited to this. For example, a mobility estimation model 13E may be prepared in advance for each type of subject's attribute, and the mobility estimation model 13E corresponding to the subject's attribute may be selectively applied, and only the physical quantities may be input to the model for use.

Furthermore, in the above embodiment, a case has been described in which the mobility estimation model 13E is constructed as a single model that covers all spots, but this is not limited to this. For example, a mobility estimation model 13E may be constructed and applied for each spot.

In addition, in the above embodiment, for example, the hardware structure of the processing unit that executes the processes of the acquisition unit 11A, learning unit 11B, estimation unit 11C, and presentation unit 11D can use the various processors listed below. As mentioned above, the various processors include a CPU, which is a general-purpose processor that executes software (programs) and functions as a processing unit, as well as programmable logic devices (PLDs), such as field-programmable gate arrays (FPGAs), whose circuit configuration can be changed after manufacture, and dedicated electrical circuits, such as application-specific integrated circuits (ASICs), which are processors with a circuit configuration designed specifically to execute specific processes.

The processing unit may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The processing unit may also be configured with a single processor.

Examples of a processing unit being configured with a single processor include, first, a form in which a single processor is configured with a combination of one or more CPUs and software, as typified by client and server computers, and this processor functions as the processing unit. Second, a form in which a processor is used to realize the functions of the entire system, including the processing unit, on a single IC (Integrated Circuit) chip, as typified by systems on chips (SoCs). In this way, the processing unit is configured as a hardware structure using one or more of the various processors listed above.

Furthermore, the hardware structure of these various processors can be, more specifically, an electrical circuit that combines circuit elements such as semiconductor elements.

10: Mobility estimation device 11: CPU
11A Acquisition unit 11B Learning unit 11C Estimation unit 11D Presentation unit 12 Memory 13 Storage unit 13A Learning program 13B Mobility estimation program 13C Target area information database 13D Target person information database 13E Mobility estimation model 14 Input unit 15 Display unit 16 Medium reading and writing device 17 Recording medium 18 Communication I/F unit 30 Target person terminal 31 CPU
32 Memory 33 Storage unit 33A Terminal processing program 34 Input unit 35 Display unit 36 Medium reading and writing device 37 Recording medium 38 Camera 39 Microphone 40 GPS
41 Acceleration sensor 42 Wireless communication unit 80 Network 90 Mobility estimation system

Claims (8)

an estimation unit that estimates the degree of movement by inputting actual physical quantities of a subject person in a target place of movement to a movement degree estimation model in which physical quantities related to the person's walking in a target place of movement are used as input information and movement degree information indicating the degree of movement degree according to the subject person's own subjective opinion when walking corresponding to the physical quantities is used as output information; and
a presentation unit that presents information related to the mobility degree estimated by the estimation unit; and
Equipped with
the presentation unit further presents to the subject meaningful information that is meaningful to the subject and that is derived using the degree of movement estimated by the estimation unit;
When the degree of mobility is equal to or less than a predetermined threshold, the meaningful information is information introducing spots that have been determined to have a comprehensive mobility degree, which is information indicating a comprehensive value of the degree of mobility of the entire subject that is higher than the degree of mobility,
Migration degree estimation device. The physical quantity includes at least one of the number of steps, walking distance, and walking speed.
The mobility estimation device according to claim 1 . The meaningful information includes health-related information.
The mobility estimation device according to claim 1 or 2 . The health-related information includes at least one of mental health-related information and physical health-related information.
The mobility estimation device according to claim 3 . The estimation unit estimates the degree of movement for each attribute of the subject person.
The mobility estimation device according to any one of claims 1 to 4 . an acquisition unit that acquires a learning physical quantity that is the physical quantity for learning and learning mobility information that is the mobility information for learning;
a learning unit that performs machine learning of the mobility estimation model using the learning physical quantity acquired by the acquisition unit as input information and learning mobility information as output information;
The mobility estimation device according to any one of claims 1 to 5 , further comprising: The mobility estimation device according to any one of claims 1 to 6 ,
a terminal including a transmitter that transmits the physical quantity to the estimation unit of the mobility degree estimation device, and a display controller that controls display of mobility-related information presented by the presentation unit of the mobility degree estimation device on a display unit;
A migration estimation system including: a mobility estimation model in which physical quantities relating to the walking of a person in a target area of movement are used as input information, and mobility information indicating the level of the person's subjective mobility when walking corresponding to the physical quantities is used as output information, and the mobility is estimated by inputting the actual physical quantities of the subject in the target area of movement;
presenting mobility-related information regarding the estimated mobility ;
Further presenting meaningful information that is meaningful to the subject and that is derived using the estimated degree of movement to the subject;
When the degree of mobility is equal to or less than a predetermined threshold, the meaningful information is information introducing spots that have been determined to have a comprehensive mobility degree, which is information indicating a comprehensive value of the degree of mobility of the entire subject that is higher than the degree of mobility,
A program that causes a computer to execute a process.