Migratory Degree Estimation Device, Migratory Degree Estimation System, and Program

Abstract

To provide a migration degree estimation device, a migration degree estimation system and a program that can estimate a migration degree with higher accuracy.SOLUTION: A migration degree estimation device 10 comprises: an estimation part 11C which estimates a migration degree by inputting an actual quantity by an object person in an object place of migration to a migration degree estimation model 13E, in which the migration degree estimation model receives a physical quantity related to a walk of a person in an object place of migration as input information, and generates migration degree information representing a height of a subjective migration degree that the person corresponding to the physical quantity has during the walk as output information; and a presentation part 11D which presents migration degree-related information associated with the migration degree estimated by the estimation part 11C.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a mobility estimation device, a mobility estimation system, and a program. [Background technology]

[0002] In recent years, many television programs have been broadcast that introduce spots around the country, such as travel programs, gourmet programs, and city walking programs (so-called "machibura" programs). As a result, there is a growing demand to rediscover unexpected sights and aspects of spots in familiar places and faraway places.

[0003] The following techniques can be applied to meet this demand.

[0004] Patent Document 1 discloses a technology for acquiring feature feature information that characterizes features related to a location determined by a user based on the location information of the location, and transmitting the acquired feature feature information to a network system that records third-party information sent by a third party other than the user.

[0005] With this technology, when related information related to the transmitted 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.

[0006] In cities, tourist destinations, and other areas, it is extremely important to encourage not only local residents but also people from far away to visit in large numbers in order to revitalize the area.

[0007] In contrast, the technology described in Patent Document 1 can present unexpected information to users, but does not necessarily effectively encourage users to browse.

[0008] As a technology that can be applied to solve this problem, Patent Document 2 by the present applicant discloses a migration promotion support device that aims to effectively promote migration. This migration promotion support device includes an acquisition unit that acquires a physical quantity that indicates the subject's degree of interest in a target location based on the subject's actual behavior in the target location, and a derivation unit that derives the subject's evaluation value for the target location using the physical quantity acquired by the acquisition unit. 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 can be referenced by others. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-11550 [Patent Document 2] Japanese Patent Application Publication No. 2020-109600 Summary of the Invention [Problem to be solved by the invention]

[0010] However, in the technology disclosed in Patent Document 2, the evaluation value for a target location is derived using physical quantities based on the actual behavior of the subject at the target location, more specifically, physical quantities such as the subject's stay time at the target location and the distance from a specified starting point to the target location.

[0011] As described above, the technology disclosed in Patent Document 2 derives an evaluation value using only indirect physical quantities, and therefore has the problem that it is not always possible to estimate the evaluation value with high accuracy. This problem is particularly pronounced with respect to an evaluation value indicating the degree to which a subject felt that they had been wandering around a target location (hereinafter referred to as "wandering degree"). Note that the wandering degree (the degree to which a subject felt that they had been wandering around) here refers to the degree to which a subject walked around a target location, such as a city or a tourist spot, to see and hear about objects of interest, rather than walking in a straight line toward a destination.

[0012] The present invention has been made in consideration of the above circumstances, and aims to provide a mobility estimation device, a mobility estimation system, and a program that can estimate mobility with higher accuracy. [Means for solving the problem]

[0013] The mobility estimation device according to the present invention as set forth in claim 1 comprises an estimation unit that estimates the mobility by inputting actual physical quantities of a subject person in a target location of mobility to a mobility estimation model in which input information is a physical quantity related to the walking of a person in a target location of mobility, and output information is mobility information indicating a level of the subjective mobility of the person when walking corresponding to the physical quantity, and a presentation unit that presents mobility-related information related to the mobility estimated by the estimation unit. The presentation unit further presents to the subject meaningful information that is meaningful to the subject and that is derived using the degree of mobility estimated by the estimation unit, and when the degree of mobility is equal to or less than a predetermined threshold, the meaningful information is information that introduces spots that have been determined to have an overall degree of mobility that is higher than the degree of mobility, and is information that indicates an overall value of the degree of mobility of all subjects. .

[0014] 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 person 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 the subject's motivation to migrate.

[0015] A mobility estimation device according to the present invention as set forth in claim 2 is the mobility estimation device as set forth in claim 1, wherein the physical quantity includes at least one of the number of steps, walking distance, and walking speed.

[0016] According to the mobility estimation device of the present invention as set forth in claim 2, by using at least one of the number of steps, walking distance, and walking speed as the physical quantity, it is possible to estimate the mobility more easily compared to the case where a qualitative physical quantity is used as the physical quantity.

[0019] Claim 3 The mobility estimation device according to the present invention described in Claim 1 or Claim 2 In the mobility estimation device according to the above, the meaningful information includes information related to health.

[0020] Claim 3 According to the mobility estimation device of the present invention described above, by including health-related information in the meaningful information, it is possible to further increase motivation to migrate.

[0021] Claim 4 The mobility estimation device according to the present invention described in claim 3 In the mobility estimation device according to the above, the health-related information includes at least one of information on mental health and information on physical health.

[0022] Claim 4 According to the mobility estimation device of the present invention described above, by including at least one of information on mental health and information on physical health in the health-related information, it is possible to allow the subject to understand the included health-related information.

[0023] Claim 5 The mobility estimation device according to the present invention described in claims 1 to 5 is 4 10. The mobility estimation device according to claim 9, wherein the estimation unit estimates the mobility for each attribute of the subject.

[0024] Claim 5 According to the mobility estimation device of the present invention described above, the mobility can be estimated with higher accuracy by estimating the mobility for each attribute of a subject.

[0025] Claim 6 The mobility estimation device according to the present invention described in claims 1 to 5 is 5 The mobility estimation device according to any one of claims 1 to 5, further comprising: 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; and 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 the learning mobility information as output information.

[0026] Claim 6 According to the mobility estimation device of the present invention described above, the mobility estimation model is obtained by machine learning, so that the mobility can be estimated with higher accuracy than when the mobility estimation model is obtained by statistical methods.

[0027] Claim 7 The mobility estimation system according to the present invention described in claims 1 to 5 is 6 and a terminal including: a transmission unit that transmits the physical quantity to the estimation unit of the mobility degree estimation device; and a display control unit that controls display of the mobility-related information presented by the presentation unit of the mobility degree estimation device on a display unit.

[0028] Claim 7 According to the mobility estimation system of the present invention described above, physical quantities relating to people's walking in a target area for 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 for the mobility estimation model. By inputting actual physical quantities of the subject person in the target area for movement into the model, the mobility can be estimated, thereby making it possible to reflect the subjective mobility of the 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 system of the present invention as set forth 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.

[0029] Claim 8 The program according to the present invention described in (1) above estimates the degree of movement by inputting actual physical quantities of a subject in a target place of movement into a movement degree estimation model in which input information is physical quantities related to the walking of a person in a target place of movement, and output information is movement degree information indicating the level of the person's subjective degree of movement when walking corresponding to the physical quantities, and presents movement degree-related information related to the estimated degree of movement. The process further presents meaningful information that is meaningful to the subject, derived using the estimated degree of mobility, to the subject, and when the degree of mobility is equal to or less than a predetermined threshold, the meaningful information is information that introduces spots that have been determined to have an overall degree of mobility, which is information that indicates an overall value of the degree of mobility of all subjects and is higher than the degree of mobility. The processing is executed by a computer.

[0030] Claim 8 According to the program of the present invention described above, physical quantities relating to people's walking in a target area for movement are used as input information, and mobility estimation model in which mobility information indicating the level of a person's subjective degree of movement when walking corresponding to said physical quantities is used as output information, by inputting actual physical quantities of a person in a target area for movement into the model, the mobility can be estimated by reflecting the person's subjective degree of movement in the estimation of the mobility, and as a result, the mobility can be estimated 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. [Effects of the Invention]

[0031] As described above, according to the present invention, it is possible to estimate the degree of movement with higher accuracy. [Brief explanation of the drawings]

[0032] [Figure 1] 1 is a block diagram showing an example of a hardware configuration of a mobility estimation system according to an embodiment. [Figure 2] 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. [Figure 3] 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. [Figure 4] FIG. 2 is a schematic diagram illustrating an example of a configuration of a target region information database according to the embodiment. [Figure 5] FIG. 2 is a schematic diagram illustrating an example of a configuration of a subject information database according to the embodiment. [Figure 6] 10 is a flowchart illustrating an example of a learning process according to the embodiment. [Figure 7] 10 is a flowchart illustrating an example of a terminal process according to the embodiment. [Figure 8] 10 is a flowchart illustrating an example of a migration degree estimation process according to the embodiment. [Figure 9] FIG. 2 is a front view showing an example of the configuration of an initial screen according to the embodiment. [Figure 10] FIG. 10 is a front view showing an example of a configuration of a mobility degree display screen according to the embodiment. [Figure 11] FIG. 10 is a front view showing an example of the configuration of a meaningful information display screen according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0033] Hereinafter, an example of an 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 including a mobility estimation device configured by a server computer or the like and a plurality of subject terminals, each of which is a terminal used individually by a subject.

[0034] First, the configuration of a mobility estimation system 90 according to this embodiment will be described with reference to Fig. 1. As shown in Fig. 1, the mobility estimation system 90 according to this embodiment includes a mobility estimation device 10 and a plurality of target person 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 a personal computer and a server computer. Examples of the target person terminals 30 include portable terminals such as smartphones, tablet terminals, and PDAs (Personal Digital Assistants, mobile information terminals).

[0035] The subject terminal 30 according to this embodiment is a terminal carried by each of a plurality of subjects who are intended to use the mobility estimation system 90. The subject terminal 30 includes a CPU (Central Processing Unit) 31, a memory 32 serving 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 a liquid crystal display, and a medium 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, medium read / write device 36, camera 38, microphone 39, GPS 40, acceleration sensor 41, and wireless communication unit 42 are connected to one another via a bus B1. The medium read / write device 36 reads information written in a recording medium 37 and writes information to the recording medium 37.

[0036] The storage unit 33 is realized by an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like. 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 in the memory 32, and sequentially executes the processes of the terminal processing program 33A.

[0037] On the other hand, the mobility estimation device 10 is a device that collectively stores and uses various information handled by the mobility estimation system 90. The mobility estimation device 10 includes a CPU 11, a memory 12 as a temporary storage area, a non-volatile storage unit 13, an input unit 14 such as a keyboard and a mouse, a display unit 15 such as a liquid crystal display, a medium reading and writing device 16, and a communication interface (I / F) unit 18. The CPU 11, memory 12, storage unit 13, input unit 14, display unit 15, medium reading and writing device 16, and communication I / F unit 18 are connected to one another via a bus B2. The medium reading and writing device 16 reads information written in a recording medium 17 and writes information to the recording medium 17.

[0038] The storage unit 13 is realized by an HDD, an SSD, a flash memory, or the like. A learning program 13A and a mobility estimation program 13B are stored in the storage unit 13 as a storage medium. The learning program 13A and the mobility estimation program 13B are stored (installed) in the storage unit 13 by setting a recording medium 17, on which each of these programs is written, in the medium reading and writing device 16, and the medium reading and writing device 16 reading each of the programs from the recording medium 17. The CPU 11 reads the learning program 13A and the mobility estimation program 13B from the storage unit 13, expands them in the memory 12, and sequentially executes the processes that each of the learning program 13A and the mobility estimation program 13B has.

[0039] Furthermore, a target area information database 13C and a target person information database 13D are stored in the storage unit 13. The target area information database 13C and the target person information database 13D will be described in detail later.

[0040] Furthermore, a mobility estimation model 13E is stored in the storage unit 13. The mobility estimation model 13E according to this embodiment receives input information of a physical quantity (hereinafter simply referred to as a "physical quantity") related to a person's walking in a target place of movement, and outputs output information of the mobility information indicating the level of the person's subjective mobility degree when walking, which corresponds to the physical quantity.

[0041] In the mobility estimation system 90 according to this embodiment, three types of physical quantities, namely, the number of steps, walking distance, and walking speed per predetermined period (10 minutes in this embodiment), are applied as physical quantities, but the present invention is not limited to this. 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 as the physical quantities in addition to these three types.

[0042] The mobility estimation model 13E according to this embodiment is an AI (Artificial Intelligence) model using an MLP (Multilayer Perceptron), but is not limited thereto. A machine learning model such as an AI other than an MLP, such as an AI model using an RNN (Recurrent Neural Network), may also be applied as the mobility estimation model 13E.

[0043] Next, a functional configuration of the mobility estimation device 10 according to this embodiment when learning the mobility estimation model 13E will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example of a functional configuration of the mobility estimation device 10 according to this embodiment when learning the mobility estimation model 13E.

[0044] 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.

[0045] The acquiring unit 11A according to the present embodiment acquires learning physical quantities, which are physical quantities for learning, and learning mobility information, which is mobility information for learning. In the present embodiment, the learning physical quantities and learning mobility information are acquired by reading out the physical quantities and mobility information registered by a mobility estimation process described later from the subject information database 13D (see also FIG. 5 ). However, the present embodiment is not limited to this configuration, and the physical quantities and mobility information related to the subjects may be sequentially stored in the subject terminals 30 used by the subjects, and the learning physical quantities and learning mobility information may be acquired by acquiring them from each of the subject terminals 30.

[0046] The learning unit 11B according to this embodiment performs machine learning of the mobility estimation model 13E using the learning physical quantity acquired by the acquisition unit 11A as input information and the learning mobility information as output information.

[0047] Next, a functional configuration of the mobility degree estimation device 10 according to this embodiment when the mobility degree estimation model 13E is in operation will be described with reference to Fig. 3. Fig. 3 is a block diagram showing an example of a functional configuration of the mobility degree estimation device 10 according to this embodiment when the mobility degree estimation model 13E is in operation.

[0048] 3, the mobility estimation device 10 when the mobility estimation model 13E is in operation 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.

[0049] The estimation unit 11C according to this embodiment estimates the degree of mobility by inputting actual physical quantities of a subject person in a target location of mobility to the mobility estimation model 13E. Then, the presentation unit 11D according to this embodiment presents mobility-related information regarding the mobility estimated by the estimation unit 11C.

[0050] In this embodiment, the mobility-related information is the mobility itself estimated by the estimation unit 11C, and an image simulating a facial expression according to the magnitude of the mobility (hereinafter referred to as a "face image"), but is not limited to this. For example, only one of the mobility and the face image may be applied as the mobility-related information. Furthermore, in this embodiment, presentation by the presentation unit 11D is performed by displaying the display unit, but 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.

[0051] Furthermore, the presentation unit 11D according to this embodiment further presents meaningful information (hereinafter simply referred to as "meaningful information") that is meaningful to the subject using the mobility degree estimated by the estimation unit 11C. 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 embodiment. For example, when the mobility degree 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 mobility degree than the estimated mobility degree and are determined to be a total mobility degree (described later) 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.

[0052] Here, the estimation unit 11C according to this embodiment estimates the degree of movement for each attribute of the subject. In this embodiment, three types of attributes, namely, gender, age, and height, are applied as the attributes, but this is not limiting. For example, one or a combination of two of these three types of attributes may be applied as the attributes, or other attributes such as weight may be applied in addition to these three types as the attributes.

[0053] Furthermore, the CPU 31 of the target person terminal 30 executes the terminal processing program 33A, thereby functioning as a transmission unit and a display control unit of the present invention.

[0054] Next, the target area information database 13C according to this embodiment will be described with reference to Fig. 4. Fig. 4 is a schematic diagram showing an example of the configuration of the target area information database 13C according to this embodiment.

[0055] As shown in FIG. 4, the target area information database 13C according to this embodiment stores information such as target area names, target area positions, social heat map images, migration routes, spot names, spot positions, and overall migration degrees.

[0056] The target area name is information indicating the name of a target area (hereinafter simply referred to as "target area") that is the target of 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 positions 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 limiting, and for example, instead of the circle, other shapes such as an ellipse or a rectangle may be applied, or instead of the coordinate positions in the two-dimensional coordinate system, latitude and longitude may be applied.

[0057] The social heat map image is an image showing a map that highlights locations with a large amount of information that matches the target user's category by displaying areas with different densities or colors overlaid on a map image that is normally displayed for the corresponding target area. In other words, in this embodiment, each target user is asked to answer multiple questions in advance, and the responses are analyzed and classified to determine each target user's category in advance. The social heat map image according to this embodiment is then displayed overlaid on the map image so that the density increases with the amount of information (in this embodiment, information posted on a social networking service (SNS)) that matches the target user's category. However, this density change is not limited to this, and the color may also be changed in order from high density to low density, such as red → yellow → green.

[0058] 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 of 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.

[0059] Furthermore, the travel route is information indicating a route recommended for 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 a 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 a rectangle, or latitude and longitude may be used instead of the coordinate position in the two-dimensional coordinate system.

[0060] In this embodiment, the above-mentioned travel routes and spots are extracted from information posted on SNS using AI technology, but the present invention is not limited to this. For example, travel routes and spots may be extracted from the homepage of a television program that introduces spots in various locations, such as a travel program, a gourmet program, or a city walking program (a so-called city stroll program).

[0061] 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, but it goes without saying that the subject does not necessarily have to travel exactly along the presented route, and can simply walk around to see and hear about objects of interest.

[0062] Furthermore, the above-mentioned overall mobility degree is information indicating the overall value of the above-mentioned mobility degree for the corresponding spot. 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 an important attribute 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."

[0063] Next, the subject information database 13D according to this embodiment will be described with reference to Fig. 5. Fig. 5 is a schematic diagram showing an example of the configuration of the subject information database 13D according to this embodiment.

[0064] As shown in FIG. 5, the subject information database 13D according to this embodiment stores information such as subject terminal ID (Identification), subject name, attribute, visited spot, visit date and time, physical quantity, and degree of movement.

[0065] The subject terminal ID is information assigned to identify the subject terminal 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.

[0066] Furthermore, the attributes are information indicating the above-mentioned 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. Furthermore, the physical quantities are information indicating the above-mentioned 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 about the corresponding spots when the corresponding subject visited the corresponding spots. Note that, hereinafter, the information stored in the subject information database 13D is collectively referred to as "subject information."

[0067] Next, the operation of the mobility estimation system 90 according to this embodiment will be described with reference to FIGS.

[0068] First, the operation of the mobility estimation device 10 when executing the learning process according to this embodiment will be described with reference to Fig. 6. Fig. 6 is a flowchart showing an example of the learning process according to this embodiment.

[0069] The learning process shown in Fig. 6 is executed by the CPU 11 of the mobility estimation device 10 executing the learning program 13A. The learning process shown in Fig. 6 is executed when an instruction to start execution of the learning program 13A is input by the operator of the mobility estimation device 10 via the input unit 14. Note that, in order to avoid confusion, a case will be described here in which the number of pieces of information on physical quantities and mobility required to learn the mobility estimation model 13E are registered in the subject information database 13D by the mobility estimation process (see also Fig. 8) described later.

[0070] 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 above-mentioned learning physical quantity and learning mobility information) from the subject information database 13D.

[0071] In step 104, the CPU 11 uses the read information on the attributes and learning physical quantities as input information and the read learning mobility information as output information (correct answer information) to machine-learn the mobility estimation model 13E.

[0072] In step 106, the CPU 11 determines whether or not the machine learning in step 104 has been completed for all physical quantities and mobility degrees corresponding to the processing subject stored in the subject information database 13D, and if the determination is negative, the process returns to step 102, whereas if the determination is positive, the process proceeds to step 108. When repeatedly executing the processes of steps 102 to 104, the CPU 11 processes physical quantities and mobility degrees that have not been the subject up to that point.

[0073] In step 108, CPU 11 determines whether or not machine learning in step 104 has been completed for all subjects stored in subject information database 13D, and 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 selects subjects who have not been considered as subjects up to that point as processing subjects.

[0074] Through the above learning process, the migration degree estimation model 13E is learned.

[0075] Next, the operation of the subject terminal 30 will be described with reference to Fig. 7. The CPU 31 of any of the subject terminals 30 executes the terminal processing program 33A, thereby executing the terminal processing shown in Fig. 7. The terminal processing shown in Fig. 7 is executed, for example, when an instruction to execute the terminal processing is input from any of the subjects (hereinafter referred to as "subject") via the input unit 34 of their own subject terminal 30.

[0076] In step 200 of FIG. 7, the CPU 31 acquires information indicating the position (hereinafter referred to as "position information") from the GPS 40, and in the next step 202, the CPU 31 transmits the acquired position information to the mobility estimation device 10 via the wireless communication unit 42.

[0077] In a later-described migration degree estimation process (see also FIG. 8 ), the migration degree estimation device 10 reads, from the target area information database 13C, target area information (hereinafter referred to as “implementation target area information”) related to a target area (hereinafter referred to as “implementation target area”) that includes the position indicated by the position information received from the target user terminal 30. Furthermore, the migration degree estimation device 10 creates information indicating an initial screen (hereinafter referred to as “initial screen information”), which will be described later, based on the implementation target area information. Then, the migration degree estimation device 10 transmits the implementation target area information and the initial screen information to the target user terminal 30 (hereinafter referred to as “implementation target user terminal”) that transmitted the position information.

[0078] Therefore, in the next step 204, the CPU 31 waits to receive the initial screen information and the implementation target 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.

[0079] FIG. 9 shows an example of an initial screen according to this embodiment. As shown in FIG. 9, the initial screen according to this embodiment displays the names and travel routes of each spot included in the target area superimposed on the corresponding position 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. 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 FIG. 9, the initial screen according to this embodiment displays the circumscribing circles of each spot included in the target area with dashed lines, but it goes without saying that this is not limited to this.

[0080] As mentioned above, the subject does not necessarily have to move exactly along the displayed route, but can just walk around aimlessly to see and hear about objects of interest.

[0081] 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 at the area of ​​one of the spots in the implementation target area.

[0082] In the next step 212, the CPU 31 starts counting the number of steps of the subject using the output signal from the acceleration sensor 41, and also starts timing using a built-in timing unit (not shown), and in the next step 214, the CPU 31 acquires position information from the GPS 40. In the next step 216, the CPU 31 waits until the position indicated by the acquired position information becomes a position where the subject leaves the spot determined to have been reached in the processing of step 210 (hereinafter referred to as the "leaving spot").

[0083] In the next step 218, the CPU 31 stops counting the number of steps and timing that was started by the processing of step 212. By the processing of the above steps 212 to 218, the CPU 31 can obtain the number of steps PH of the target person at the departure spot and the staying time PV of the target person at the departure spot.

[0084] In the next step 220, the CPU 31 calculates the walking distance SD and walking speed SS of the activity target person at the departure 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 activity target person, 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 activity target person's movement route, which is sequentially obtained by the GPS 40.

[0085] Then, in step 220, the CPU 31 controls to transmit the spot name, the number of steps PH, the walking distance SD, and the walking speed SS corresponding to the exit spot (hereinafter, the spot name, the number of steps PH, the walking distance SD, and the 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 to transmit the time when clocking started by the processing of step 212 (hereinafter, referred to as "start time") and the time when clocking stopped by the processing of step 218 (hereinafter, referred to as "end time") together with the basic information to the mobility degree estimation device 10. When the mobility degree estimation device 10 receives the basic information from the target person terminal, it derives a mobility degree for the target person using the received basic information in a mobility degree estimation process described later, and transmits the deriving information to the target person terminal.

[0086] Therefore, in the next step 222, the CPU 31 waits to receive the mobility from the mobility estimation device 10, and in the next step 224, the CPU 31 controls the display unit 35 to display a mobility display screen that mainly displays the received mobility.

[0087] An example of a migration degree display screen according to this embodiment is shown in Fig. 10. As shown in Fig. 10, the migration degree display screen according to this embodiment displays the name of the exit spot and the name of the target participant, and also displays the migration degree of the target participant in an adjustable manner. Note that, as shown in Fig. 10, the migration degree display screen according to this embodiment also displays a face image 35D that simulates a facial expression according to the magnitude of the migration degree.

[0088] When the mobility display screen shown in Fig. 10 is displayed on the display unit 35, if the displayed mobility does not match the mobility for the exit spot that the subjectively felt, the subject inputs an instruction to adjust the mobility. In this embodiment, the instruction to adjust the mobility is input by specifying the increase button 35C when wanting to increase the mobility and by specifying the decrease button 35B when wanting to decrease the mobility, but this is not limited to this. For example, the adjusted value may be directly input in the display area of ​​the mobility. Then, if the subject wants to end the display of the mobility display screen, the subject selects the end button 35A.

[0089] Therefore, in the next step 226, the CPU 31 waits until the end button 35A is designated. In the next step 228, the CPU 31 transmits information indicating that the end button 35A has been designated (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 designated 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.

[0090] When the mobility degree estimation device 10 receives the end information from the target person terminal, it derives meaningful information, which is information that is meaningful to the target person, and transmits the information to the target person terminal in a mobility degree estimation process described later.

[0091] 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 mainly displays the received meaningful information.

[0092] An example of a meaningful information display screen according to this embodiment is shown in Figure 11. 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 target person, as well as meaningful information.

[0093] In this embodiment, 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") are applied as meaningful information. Also, in this embodiment, information indicating the level of leisure and enjoyment at the departure spot for the participant is also applied as meaningful information. However, it goes without saying that meaningful information is not limited to these pieces of information.

[0094] When the meaningful information display screen shown in FIG. 11 is displayed on the display unit 35, the subject understands the content of the displayed information and then presses the end button 35A.

[0095] Therefore, in the next step 234, CPU 31 waits until the end button 35A is designated. In the next step 236, CPU 31 determines whether or not the above processing has been completed for all spots in the target area, and if the determination is negative, CPU 31 returns to step 206, but if the determination is positive, CPU 31 ends this terminal processing.

[0096] Next, with reference to Fig. 8, the operation of the mobility estimation device 10 when the mobility estimation model 13E is in operation will be described. The CPU 11 of the mobility estimation device 10 executes the mobility estimation program 13B, thereby executing the mobility estimation process shown in Fig. 8. The mobility estimation process shown in Fig. 8 is executed, for example, when an instruction to execute the mobility estimation process is input from the operator of the mobility estimation device 10 via the input unit 14. Note that, in order to avoid confusion, the case will be described here where the subject ID, subject name, and attribute information are registered in advance in the subject information database 13D.

[0097] In step 300 of Figure 8, the CPU 11 waits until location information is received from any of the target terminals 30, and in the next step 302, the CPU 11 reads out target area information (=implementation target area information) regarding the target area that includes the location indicated by the received location information from the target area information database 13C.

[0098] In the next step 304, the CPU 11 creates the above-mentioned initial screen information using the read-out implementation target area information. In the next step 306, the CPU 11 controls 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 transmits, by the above-mentioned terminal processing, information on the basic information, start time, and end time to the mobility degree estimation device 10 when the implementation target person leaves each spot in the visited target area.

[0099] Therefore, in the next step 308, the CPU 11 waits until the basic information, start time, and end time are received from the exercise target terminal. In the next step 310, the CPU 11 reads out the attributes of the exercise target corresponding to the exercise target terminal from the exercise target information database 13D. Then, the CPU 11 inputs the read-out attributes and the physical quantities of the number of steps PH, walking distance SD, and walking speed SS in the received basic information to 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 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.

[0100] In the next step 312, the CPU 11 controls to transmit the acquired mobility degree to the target user terminal. When the mobility degree is received, the target user terminal displays a mobility degree display screen by terminal processing as described above, and is able to accept adjustment of the mobility degree on the mobility degree display screen. Then, if the adjustment of the mobility degree is accepted, the CPU 11 transmits the above-described adjusted mobility degree and end information to the mobility degree estimation device 10. If the adjustment of the mobility degree is not accepted, the CPU 11 transmits only the end information to the mobility degree estimation device 10.

[0101] Therefore, in the next step 314, the CPU 11 waits until it receives the end information from the target terminal. In the next step 316, the CPU 11 determines whether or not the adjusted mobility degree has been received together with the end information, thereby determining whether or not the mobility degree has been adjusted by the target terminal. If the determination is affirmative, the process proceeds to step 318.

[0102] In step 318, the CPU 11 stores (registers) the received start time and end time, the spot name and each physical quantity contained in the received basic information, and the adjusted mobility in the corresponding memory area in the subject information database 13D, and then proceeds to step 322.

[0103] On the other hand, if the determination in step 316 is negative, that is, if the adjusted mobility degree has not been received from the implementation target terminal, the process proceeds to step 320 .

[0104] In step 320, 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 acquired by the processing of step 310 in the corresponding storage areas of subject information database 13D, and then proceeds to step 322. When registering the physical quantities in subject information database 13D in steps 318 and 320, CPU 11 converts each of the physical quantities, namely, the number of steps PH, the walking distance SD, and the walking speed SS, into values ​​per the above-mentioned predetermined period (10 minutes in this embodiment) and registers them.

[0105] In step 322, the CPU 11 reads all the migration degrees that have been 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 migration degree as described above using the read migration degrees, and stores the calculated overall migration degree in the corresponding area in the target area information database 13C, thereby updating the overall migration degree of the corresponding spot.

[0106] In step 324, the CPU 11 derives the meaningful information. In this embodiment, the mental health level and physical health level included in the meaningful information are derived as values ​​indicating the level of each of the mental health level and physical health level out of a predetermined number of levels (10 levels in this embodiment). In this embodiment, the mental health level is derived as a value obtained by taking 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) as the median (5 in this embodiment). In addition, in this embodiment, the physical health level is derived as a value obtained by taking the relative magnitude of one type of physical quantity (the number of steps per predetermined period in this embodiment) of the subject to the average value of the same type of physical quantity corresponding to all subjects at the corresponding spot as the median (5 in this embodiment). However, it goes without saying that the mental health level and physical health level are not limited to these examples.

[0107] In step 326, the CPU 11 transmits the derived meaningful information to the subject terminal.

[0108] In the next step 328, the CPU 11 determines whether processing from step 308 onwards has been completed for all spots in the target area, and if the determination is negative, the process returns to step 308, but if the determination is positive, the process proceeds to step 330.

[0109] In step 330, the CPU 11 determines whether a predetermined end timing has arrived, and if the determination is negative, the process returns to step 300, whereas when the determination is positive, the process ends the mobility degree estimation process. Note that in this embodiment, the end timing is the timing when the operator of the mobility degree estimation device 10 inputs an instruction to end the mobility degree estimation process via the input unit 14, but the invention is not limited to this.

[0110] 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, regional planning (so-called town development), and the like.

[0111] 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," that is, the more attractive the target area is, the higher its safety is, and ultimately the higher its economic activity is, and it can be used as an index to judge the attractiveness of a target area.

[0112] As described above, according to this embodiment, the mobility is estimated by inputting the actual physical quantities of a person in a target area of ​​mobility to a mobility estimation model in which input information is physical quantities related to the person's walking in the target area of ​​mobility, and output information is mobility information indicating the level of the person's subjective mobility when walking corresponding to the physical quantities. Therefore, the subjective mobility can be reflected in the estimation of the mobility, and as a result, the mobility can be estimated with higher accuracy.

[0113] Furthermore, according to this embodiment, the number of steps, walking distance, and walking speed are used as the physical quantities, which makes it possible to estimate the degree of movement more easily than when qualitative physical quantities are used as the physical quantities.

[0114] Furthermore, according to this embodiment, meaningful information that is meaningful to the target person is further presented to the target person using the estimated degree of movement, which makes it possible to make the target person feel the significance of their movement, thereby increasing their motivation to move around.

[0115] Furthermore, according to this embodiment, the meaningful information includes information about health, which can further increase motivation to wander around.

[0116] Furthermore, according to this embodiment, the health-related information includes both mental health-related information and physical health-related information, so that the subject can understand this health-related information.

[0117] Furthermore, according to this embodiment, the degree of movement is estimated for each attribute of the subject, which allows the degree of movement to be estimated with higher accuracy.

[0118] Furthermore, according to this embodiment, the mobility estimation model is obtained by machine learning, which allows for more accurate estimation of mobility compared to when the mobility estimation model is obtained by a statistical method.

[0119] 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 the present invention is not limited to this. 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 can be given.

[0120] In the above embodiment, the case where an area including the location of the target user terminal is automatically applied as the target area has been described, but this is not limited to this. For example, the target area may be specified by the target user specifying a remote location away from the location of the target user terminal on an electronic map or by inputting the name of the remote location. In addition, the mobility estimation device 10 may acquire information indicating the target user's preference trends in advance, and provide the target user with information on the target area according to the target user's preference as needed.

[0121] In the above embodiment, the case where the mobility of the target person for each spot is derived and displayed at the timing when the target person leaves the spot has been described, but the present invention is not limited to this. For example, the mobility may be derived and displayed at the timing when the target person leaves the target area.

[0122] In the above embodiment, the case where the mobility estimation process is executed in the mobility estimation device 10 has been described, but the present invention is not limited to this. For example, the mobility estimation process may be executed by each subject terminal 30. In this case, the mobility estimation device of the present invention is included in the subject terminal 30.

[0123] In the above embodiment, the social heat map image is an image showing a map that highlights locations with a lot of information that matches the target user's category, but the present invention is not limited to this. For example, the social heat map image may be an image in which the higher the overall visitor frequency of a spot, the deeper the red color.

[0124] In the above embodiment, the mobility estimation model 13E is a single model in which the attributes and physical quantities of the subject are used as input information, but this is not limiting. For example, a mobility estimation model 13E may be prepared in advance for each type of attribute of the subject, and the mobility estimation model 13E corresponding to the attribute of the subject may be selectively applied, and only the physical quantities may be input to the model.

[0125] In the above embodiment, the case where the mobility estimation model 13E is constructed as a single model for all spots has been described, but the present invention is not limited to this. For example, the mobility estimation model 13E may be constructed and applied to each spot.

[0126] Furthermore, in the above embodiment, for example, the following various processors can be used as the hardware structure of the processing units that execute the processes of the acquisition unit 11A, the learning unit 11B, the estimation unit 11C, and the presentation unit 11D. As described 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 dedicated electrical circuits that are processors having a circuit configuration specifically designed to execute specific processes, such as a programmable logic device (PLD) that is a processor whose circuit configuration can be changed after manufacture, such as an FPGA (Field-Programmable Gate Array), and an ASIC (Application Specific Integrated Circuit).

[0127] 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.

[0128] Examples of configuring a processing unit with a single processor include, first, a form in which one processor is configured with a combination of one or more CPUs and software, and this processor functions as the processing unit, as typified by computers such as client and server. 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 using one or more of the above-mentioned various processors as a hardware structure.

[0129] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. [Explanation of symbols]

[0130] 10. Mobility estimation device 11 CPU 11A Acquisition Department 11B Learning Department 11C Estimation part 11D Presentation section 12 Memory 13 Storage section 13A Study Program 13B Migration Estimation Program 13C Subject Area Information Database 13D Subject Information Database 13E Migration estimation model 14 Input section 15 Display 16 Media reading and writing device 17 Recording Media 18 Communication I / F section 30 Target Device 31 CPU 32 memory 33 Storage section 33A Terminal Processing Program 34 Input section 35 Display section 36 Media reading and writing device 37 Recording Media 38 Camera 39. Mike 40 GPS 41 Acceleration sensor 42 Radio Communication Department 80 Network 90 Mobility Estimation System

Claims

1. 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 having 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.

2. 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 .

3. The meaningful information includes health-related information. The mobility estimation device according to claim 1 or 2.

4. 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 .

5. 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.

6. 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:

7. 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:

8. 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 having 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.

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