JP2019204451A - Physical fitness measurement method, activity support method, program, and physical fitness measurement system - Google Patents

Abstract

To provide a physical fitness measurement method easy-to-measure physical fitness data of intrinsic physical fitness data of a subject, an activity support method, a program, and a physical fitness measurement system.SOLUTION: The physical fitness measurement method includes a series of life data acquisition processing and a series of estimation processing. The life data acquisition processing is a series of processing to acquire a piece of life data relevant to activities in the living space of a subject. The estimation processing is a series of processing to estimate a piece of physical fitness data corresponding to the results of a certain exercise menu from the life data.SELECTED DRAWING: Figure 1

Description

  The present disclosure generally relates to a physical fitness measurement method, an activity support method, a program, and a physical fitness measurement system, and more specifically, a physical fitness measurement method, an activity support method for measuring physical fitness data indicating an execution result of an exercise menu by a subject, The present invention relates to a program and a physical strength measurement system.

  Patent Document 1 describes a health support device for supporting improvement of sports ability while maintaining the health of a subject (user). In the health support device described in Patent Document 1, by inputting various data such as body characteristic data such as height, exercise ability data such as flexibility, and sports ability measurement data, the exercise menu is based on this data. Offering and dietary guidance are provided.

JP 2014-183867 A

  The technique described in Patent Document 1 uses physical strength data (exercise ability data and sports ability measurement data) measured with an exercise device or a grip strength meter at a training facility or the like. There is a problem that it is difficult to measure physical fitness data.

  The present disclosure has been made in view of the above-described reasons, and an object thereof is to provide a physical strength measurement method, an activity support method, a program, and a physical strength measurement system that can easily measure the subject's original physical strength data.

  The physical strength measurement method according to an aspect of the present disclosure includes a lifestyle data acquisition process and an estimation process. The life data acquisition process is a process of acquiring life data related to the movement of the subject in the living space. The said estimation process is a process which estimates the physical strength data equivalent to the implementation result of a specific exercise menu from the said life data.

  An activity support method according to an aspect of the present disclosure includes an acquisition process for acquiring the physical fitness data estimated by the physical fitness measurement method, a generation process, and an output process. The generation process is a process of generating related information related to the subject based on the behavior of the subject. The output process is a process of outputting recommendation information generated based on the physical strength data and the related information.

  A program according to an aspect of the present disclosure is a program for causing a computer system to execute the physical strength measurement method.

  The physical strength measurement system according to an aspect of the present disclosure includes a life data acquisition unit and an estimation unit. The life data acquisition unit acquires life data related to the movement of the subject in the living space. The estimation unit estimates physical fitness data corresponding to a specific exercise menu execution result from the life data.

  According to the present disclosure, there is an advantage that it is easy to measure the subject's original physical strength data.

FIG. 1 is a schematic block diagram illustrating configurations of an activity support system and a physical strength measurement system according to the first embodiment. FIG. 2 is a flowchart showing the operation of the activity support system described above. FIG. 3 is a schematic diagram showing an example of an input screen used in the activity support system of the above. FIG. 4 is a schematic diagram showing an example of an output screen used in the activity support system of the above. FIG. 5 is a schematic view showing an example of another output screen used in the activity support system. 6A and 6B are schematic views showing examples of still another output screen used in the activity support system of the above. FIG. 7 is a flowchart showing the operation of the above-described physical strength measurement system. 8A to 8F are explanatory diagrams of a method for acquiring life data used in the physical strength measurement system. FIG. 9A and FIG. 9B are explanatory diagrams of a method for acquiring life data used in the physical fitness measuring system. FIG. 10 is an explanatory diagram of a method for acquiring life data used in the physical strength measurement system described above. FIG. 11 is a schematic diagram illustrating an example of an output screen used in the activity support system according to the modification of the first embodiment. FIG. 12 is a schematic block diagram illustrating a configuration of the physical strength measurement system according to the second embodiment.

(Embodiment 1)
(1) Overview Hereinafter, an overview of the physical strength measurement method according to the present embodiment will be described.

  The physical strength measurement method according to the present embodiment is a method for measuring physical strength data indicating the result of the exercise menu performed by the subject. The “exercise menu” referred to in the present disclosure indicates the content of exercise performed by the subject, and is a menu for measuring one or more items related to the exercise capability of the subject. In the present embodiment, as an example, the exercise menu includes a plurality of menus for measuring five items of muscle strength, walking ability, agility, balance, and flexibility. The “subject” as used in the present disclosure is a person whose physical fitness data is measured by the physical fitness measurement method, and may be any person regardless of age, sex, nationality, health status, or the like.

  By the way, the physical strength measurement method according to the present embodiment includes a life data acquisition process and an estimation process. The life data acquisition process is a process for acquiring life data related to the movement of the subject in the living space. The estimation process is a process of estimating physical fitness data corresponding to a specific exercise menu execution result from life data.

  That is, according to the physical strength measurement method according to the present embodiment, it is possible to estimate physical fitness data corresponding to a specific exercise menu implementation result from the movement of the subject in the living space. As an example, if attention is paid to the movement of the door knob by the subject in the living space, the exercise menu called “grip strength” is implemented from the life data by acquiring the magnitude of the load applied to the door knob from the subject as life data. The physical strength data corresponding to the result can be estimated. For this reason, the physical strength data obtained by the physical strength measurement method is less likely to cause the subject's strength at the time of measurement compared to physical strength data measured with exercise equipment or a grip strength meter at a training facility, etc. The ability to exercise is easily reflected. As a result, the physical strength measurement method according to the present embodiment has an advantage that it is easy to measure the subject's original physical strength data.

(2) Details The activity support method according to the present embodiment can be realized, for example, by a program, a non-transitory recording medium that records the program, or the activity support system 1 (see FIG. 1). That is, for example, the activity support method according to the present embodiment is embodied by a program for causing a computer system to execute the activity support method. Further, for example, the activity support method according to the present embodiment is embodied in the activity support system 1 mainly composed of a computer system that executes such a program (for causing the computer system to execute the activity support method). The Hereinafter, a case where the activity support method according to the present embodiment is embodied in the activity support system 1 will be described as an example.

  Similarly, the physical strength measurement method according to the present embodiment can be realized by, for example, a program, a non-transitory recording medium that records the program, or the physical strength measurement system 2 (see FIG. 1). That is, for example, the physical strength measurement method according to the present embodiment is embodied by a program for causing a computer system to execute the physical strength measurement method. In addition, for example, the physical strength measurement method according to the present embodiment is embodied in the physical strength measurement system 2 mainly including a computer system that executes such a program (for causing the computer system to execute the physical strength measurement method). The Below, the case where the physical strength measuring method according to the present embodiment is embodied in the physical strength measuring system 2 will be described as an example.

  The activity support method according to the present embodiment is a method for supporting the activity of the target person. “Activity” as used in this disclosure includes not only the act of moving the body (hand, foot, neck, waist, etc.) by the subject as in exercise, but also, for example, eating and drinking performed by the subject, purchase of goods (exercise equipment, etc.) Also includes actions such as conversation. In the activity support method, the target person's activity is supported by outputting recommendation information for recommending the target person's activity from the target person. “Output” in the present disclosure is not only a notification to the target person or other person by means such as display, audio output, printout (printing), or data transmission to a terminal such as a smartphone, but also non-temporary Includes output as data, such as writing to a recording medium.

  The activity support method according to the present embodiment basically outputs the recommendation information based on the physical fitness data indicating the result of the exercise menu performed by the subject, so that the subject is found from the physical fitness data of the subject. It is a way of presenting activities to be performed. In the case where the subject performs the exercise menu, for example, if the evaluation of walking ability is particularly low, for this subject, for example, information on meals, equipment or exercise (gym exercises) for enhancing walking ability is recommended. Output as information. Thereby, in a subject, it is possible to aim at improvement of walking ability by performing activity according to recommendation information.

  The activity support method according to the present embodiment includes an acquisition process, a generation process, and an output process. The acquisition process is a process of acquiring the physical strength data estimated by the physical strength measurement method according to the present embodiment. The generation process is a process for generating related information related to the subject based on the behavior of the subject. The output process is a process for outputting recommendation information based on physical fitness data and related information. The term “behavior” as used in the present disclosure means to perform a certain thing, a work or an overall action, and in particular, an observable action and reaction exhibited by a human (subject). In short, various information unique to the subject, such as the subject's life sphere, preferences, lifestyle, presence / absence of smoking, and family structure, can be identified from the behavior of the subject. The “living area” here means a range and a region where the subject acts in daily life. For example, stores (including retail stores and restaurants), transportation (station and station) that the subject often uses. (Including bus stops, etc.), hospitals, pharmacies or nursing facilities. In addition, the “preference” here includes a subject's preference for food or design (including color). Further, the “life style” referred to here includes a night type / day type, a restaurant group / a restaurant group, or the like.

  Further, in the present embodiment, as an example, a case where an elderly person whose physical ability has decreased to a level called a so-called “frail” and who intends to live an independent daily life is targeted. To do. “Frail” as used in this disclosure is a decrease in vitality of the mind and body (motor function and cognitive function, etc.) with aging, influence of coexistence of multiple chronic diseases, impaired life function, and mental and physical vulnerability. It is a state that has appeared. On the other hand, “flail” refers to a state in which maintenance and improvement of living functions can be achieved with appropriate intervention and support. In other words, “flail” means an intermediate state between a healthy state and a care state that needs support in daily life. According to the activity support method according to the present embodiment, by performing appropriate intervention and support for a subject who is in a frail state, for example, the subject can be less likely to be in a care-needed state.

(2.1) Overall Configuration The activity support system 1 according to the present embodiment, as shown in FIG. 1, recommend information D3 based on at least physical strength data D1 (see FIG. 3) measured (estimated) by the physical strength measurement system 2. This is a system for outputting (see FIG. 5). The physical strength measurement system 2 is a system for measuring physical strength data D1 indicating the result of the exercise menu performed by the subject H1 (see FIG. 8A). In the present embodiment, the activity support system 1 is described as not including the physical strength measurement system 2 as a component, but the activity support system 1 may include the physical strength measurement system 2 as a component. That is, the physical strength measurement system 2 may be a part of the activity support system 1.

  Each of the activity support system 1 and the physical fitness measurement system 2 is mainly composed of a computer system having one or more memories as hardware and one or more processors. The function as the activity support system 1 or the physical fitness measurement system 2 is realized by the processor executing the program recorded in the memory of the computer system. The program may be recorded in advance in a memory of the computer system, may be provided through an electric communication line, or may be provided by being recorded in a non-transitory recording medium readable by the computer system. In this embodiment, as an example, it is assumed that the activity support system 1 and the physical strength measurement system 2 are built in one server system 10.

  In the present embodiment, the server system 10 is connected to the information terminal 3, the facility management server 50, and the sensor server 60 via a network 4 such as the Internet, as shown in FIG. Here, the server system 10 should just be comprised so that communication with each of the information terminal 3, the facility management server 50, and the sensor server 60 is possible bidirectionally. That is, the server system 10, the information terminal 3, the facility management server 50, and the sensor server 60 may be directly connected to the network 4 or indirectly via other networks, gateways, repeaters, or the like. It may be connected to. The network 4 may be composed of a plurality of networks.

  The information terminal 3 is a terminal having a function of accepting a user operation and a function of presenting (displaying) information to the user. The “user” here may include the target person H1. That is, the information terminal 3 may accept the operation of the target person H1 or present information to the target person H1. The information terminal 3 mainly includes a computer system having a CPU (Central Processing Unit) and a memory. In the present embodiment, as an example, the information terminal 3 will be described as a mobile information terminal such as a smartphone or a tablet terminal. The information terminal 3 installs dedicated application software and activates the application software to realize the functions described below.

  The information terminal 3 includes a presentation unit 31, an operation unit 32, and an expansion unit 33. The information terminal 3 is connected to the network 4 to perform bidirectional communication with the server system 10 via the network 4. Here, since the information terminal 3 is a portable information terminal such as a smartphone or a tablet terminal, the information terminal 3 is connected to the network 4 via a router or the like, for example, by wireless communication using radio waves as a medium. The communication method of the information terminal 3 is, for example, wireless communication such as Wi-Fi (registered trademark) and low power wireless (specific low power wireless) that does not require a license. For this type of low-power radio, specifications such as the frequency band to be used and the antenna power are defined in each country depending on the application. In Japan, low-power radio that uses radio waves in the 920 MHz band or 420 MHz band is defined. Further, the information terminal 3 is connected to the network 4 even outdoors, for example, via a mobile phone network (carrier network) or a public wireless LAN (Local Area Network) provided by a communication carrier. The mobile phone network includes, for example, a 3G (third generation) line, an LTE (Long Term Evolution) line, and the like.

  In the present embodiment, the presentation unit 31 displays a screen for presenting information to the user as an example. The “screen” in the present disclosure is an image (image or the like) displayed on the presentation unit 31. The presentation part 31 is implement | achieved by image display apparatuses, such as a liquid crystal display and an organic EL (Electro Luminescence) display, for example.

  As an example, the presentation unit 31 is configured to display an input screen 311 and output screens 312 and 313 as shown in FIGS. The input screen 311 and the output screens 312 and 313 of the presentation unit 31 will be described in detail in the column “(3.1) Activity support method”.

  The operation unit 32 has a function of accepting a person (user) operation. In the present embodiment, the information terminal 3 is equipped with a touch panel display, and the touch panel display functions as the presentation unit 31 and the operation unit 32. For this reason, the information terminal 3 operates the object such as the button when the operation unit 32 detects the operation (tap, swipe, drag, etc.) of the object such as the button on each screen displayed on the presentation unit 31. Judge that it was done. That is, the presentation unit 31 and the operation unit 32 function as a user interface that receives operation inputs from the user in addition to various displays. However, the operation unit 32 is not limited to a touch panel display, and may be a keyboard, a pointing device, a mechanical switch, or the like.

  The extension unit 33 controls external devices. Specifically, the extension unit 33 is connected to a printer as an external device indirectly or directly via a network, for example. The expansion unit 33 causes the printer to execute printing by outputting print data to the printer. As will be described in detail later, the print data is image data including recommendation information D3 as an example.

  The facility management server 50 manages information regarding a plurality of facilities 5. “Facility” as used in the present disclosure is, for example, facilities such as stores (including retail stores and restaurants), transportation facilities (including stations and bus stops), hospitals, pharmacies, or nursing care facilities that can be used by the target person H1. 5. The facility management server 50 accumulates information regarding the plurality of facilities 5 in association with identification information (user ID) unique to the target person H1 for identifying the target person H1.

  Information managed by the facility management server 50 (information regarding the facility 5) broadly includes information representing the usage status of the facility 5 by the target person H1 and information representing the attribute of the facility 5. The “usage status of the facility 5” in the present disclosure is information that can be specified from the usage history of the facility 5 by the target person H1, for example, the number of times of use of the facility 5 by the target person H1, the contents of use, the usage frequency, Includes information such as date and time of use (stay). The “usage content” here includes, for example, a purchased product if the facility 5 is a retail store, an order content if the facility 5 is a restaurant, and a prescription if the facility 5 is a hospital or a pharmacy. In addition, the “attribute of the facility 5” in the present disclosure is a characteristic and property unique to the facility 5, and includes, for example, the name, the type of business, the handling content, the location (including the address, the nearest station, etc.) of the facility 5 and Includes business hours. For example, if the facility 5 is a store, the “handling content” includes inventory information indicating the number of stocks by product, sales information indicating sales by product, information on sales promotion products, and the like.

  Here, the usage status of the facility 5 by the subject person H1 is based on the behavior of the subject person H1, and so to speak, is determined by the convenience of the subject person H1, so for example, the living area, preference, lifestyle, smoking of the subject person H1 Various information unique to the target person H1, such as presence / absence and family structure, is reflected. Therefore, the usage status of the facility 5 by the target person H1 is used to generate personal property information D41 (see FIG. 1) that represents the attribute of the target person H1 among the related information D4 (see FIG. 1) related to the target person H1. Used. On the other hand, the information representing the attribute of the facility 5 is not based on the behavior of the target person H1 and is determined by the convenience of the facility 5, so basically does not reflect information specific to the target person H1. Therefore, the information representing the attribute of the facility 5 is used to generate facility property information D42 (see FIG. 1) representing the attribute of the facility 5 used by the target person H1 among the related information D4. The “related information” will be described in detail in the section “(3.1) Activity support method”.

  The facility management server 50 stores information on the facility 5 as described above, for example, a plurality of facility terminals installed in the plurality of facilities 5, or a smartphone or a wearable terminal possessed by the plurality of target persons H1, respectively. Obtained by collecting information from multiple mobile devices. In particular, the usage status and the like of the facility 5 by the target person H1 can be easily acquired even from a portable terminal possessed by the target person H1. Therefore, FIG. 1 conceptually shows a state in which a plurality of facilities 5 are connected to the facility management server 50, but strictly speaking, the facility terminal or the target person H1 installed in each facility 5 possesses. A portable terminal is connected to the facility management server 50. That is, the facility management server 50 is connected to the facility terminal or the portable terminal directly or indirectly via a network or the like so as to be able to communicate with the facility terminal or the portable terminal. The mobile terminal may also be used as the information terminal 3.

  The sensor server 60 manages the measurement data of the plurality of sensors 6. The plurality of sensors 6 measure (detect) pressure, acceleration, speed, light, vibration, temperature, magnetism, ultrasonic waves, electromagnetic waves, and the like in the living space of the subject H1, and output measurement data representing the measurement results. The “living space” in the present disclosure is a space in which the daily life of the subject H1 is operated. For example, the subject H1's residence, the area where the residence is located, the workplace of the subject H1, and the subject H1 are often used. Including 5 facilities. The sensor server 60 stores the measurement data of the plurality of sensors 6 in association with identification information (user ID) unique to the target person H1 for identifying the target person H1.

  The plurality of sensors 6 are roughly classified into an installation type sensor 6 installed in the living space of the subject H1 and a portable sensor 6 carried or worn by the subject H1. As an example, the installation type sensor 6 includes a floor, a wall, a ceiling, a staircase, a door and the like in a living space, and a sensor built in or attached to a chair and a desk disposed in the living space. On the other hand, the portable sensor 6 includes, for example, a sensor built in or externally attached to a user terminal such as a smartphone or a wearable terminal possessed by the target person H1. The user terminal may also be used as the information terminal 3.

  Specifically, the plurality of sensors 6 are image sensors (cameras), pressure sensors, acceleration sensors, Doppler sensors, angular velocity sensors, vibration sensors, radio wave sensors, ultrasonic sensors, temperature sensors, fingerprint sensors, depth sensors, and the like. Includes a wide variety of sensors. A position sensor such as GPS (Global Positioning System) is also included in the sensor 6. Each sensor 6 may include a signal processing circuit that performs appropriate signal processing on the sensor output (electrical signal).

  Here, the measurement data of the plurality of sensors 6 is based on the movement of the subject H1 in the living space, and includes, for example, daily activities such as the walking movement of the subject H1, the movement of standing and sitting, and the movement of grasping an object. Reflects the behavior of a typical target person H1. Therefore, the measurement data of the plurality of sensors 6 are used for estimating the physical fitness data D1 as life data related to the movement of the subject H1 in the living space. The “life data” will be described in detail in the section “(3.2) Physical strength measurement method”. Furthermore, since the measurement data of the plurality of sensors 6 is based on the behavior of the subject person H1, and so to speak, it is determined by the convenience of the subject person H1, so for example, the living area, taste, lifestyle, smoking presence and family of the subject person H1 Various information unique to the target person H1, such as the configuration, is reflected. Accordingly, the measurement data of the plurality of sensors 6 is used to generate personal property information D41 representing the attribute of the target person H1 among the related information D4.

  The sensor server 60 acquires measurement data of the plurality of sensors 6 as described above, for example, from the plurality of sensors 6 as needed. Therefore, the sensor server 60 is connected to the plurality of sensors 6 directly or indirectly via a network or the like so as to be able to communicate with the plurality of sensors 6.

  In FIG. 1, one server system 10, one information terminal 3, one facility management server 50, and one sensor server 60 are provided, but the present invention is not limited to this configuration. That is, a plurality of at least one of the server system 10, the information terminal 3, the facility management server 50, and the sensor server 60 may be provided. In particular, when the activity support system 1 and the physical strength measurement system target a plurality of subjects H1, it is preferable that the information terminals 3 are prepared for the number of subjects H1.

(2.2) Configuration of Activity Support System The activity support system 1 includes an acquisition unit 11, a generation unit 12, and an output unit 13, as shown in FIG. The acquisition part 11 acquires the physical strength data D1 which shows the implementation result of the exercise menu by the subject person H1. The generation unit 12 generates related information D4 related to the target person H1 based on the behavior of the target person H1. The output unit 13 outputs recommendation information D3 based on the physical strength data D1 and the related information D4. Here, the activity support system 1 further includes a first storage unit 14.

  In the present embodiment, as described above, the activity support system 1 mainly includes a computer system (server system 10) having one or more memories as hardware and one or more processors. That is, the functions as the acquisition unit 11, the generation unit 12, and the output unit 13 are realized by the processor executing the program recorded in the memory of the server system 10.

  The acquisition unit 11 acquires the physical strength data D1 from the physical strength measurement system 2. That is, the acquisition unit 11 acquires the physical strength data D1 estimated by the physical strength measurement method according to the present embodiment. The physical strength data D1 is data obtained from the result of exercise menu exercise by the subject H1, that is, the result of the subject H1 exercising according to the exercise menu.

  The generation unit 12 generates the related information D4 based on information managed by the facility management server 50 and the sensor server 60, for example. That is, when the target person H1 is specified, the generation unit 12 acquires information stored in association with identification information (user ID) unique to the target person H1 from the facility management server 50 and the sensor server 60. To do. And the production | generation part 12 produces | generates the relevant information D4 relevant to this object person H1 based on the information acquired from the facility management server 50 and the sensor server 60. FIG.

  The output unit 13 generates recommendation information D3 based on the physical strength data D1 acquired by the acquisition unit 11 and the related information D4 generated by the generation unit 12, and outputs the recommendation information D3. That is, since the recommendation information D3 is information based on the related information D4 related to the subject person H1, the output unit 13 can output the recommendation information D3 suitable for the subject person H1, for example.

  In the present embodiment, the output unit 13 includes an evaluation unit 131, a selection unit 132, and an adjustment unit 133. The functions of the evaluation unit 131, the selection unit 132, and the adjustment unit 133 will be described in detail in the column “(3.1) Activity support method”.

  The first storage unit 14 is composed of a readable / writable non-transitory recording medium such as a hard disk drive or a flash memory. The first storage unit 14 stores at least a plurality of pieces of primary candidate information. The plurality of pieces of primary candidate information are stored in association with any one of the plurality of exercise menus. The primary candidate information is information that is a candidate for the recommendation information D3. That is, the output unit 13 selects information to be recommended information D3 from the plurality of primary candidate information stored in the first storage unit 14 based on the physical fitness data D1 and the related information D4.

(2.3) Configuration of Physical Strength Measurement System The physical strength measurement system 2 includes a life data acquisition unit 21 and an estimation unit 22, as shown in FIG. The life data acquisition unit 21 acquires life data related to the movement of the subject H1 in the life space. The estimation unit 22 estimates the physical strength data D1 corresponding to the execution result of the specific exercise menu from the life data. Here, the physical strength measurement system 2 further includes a second storage unit 23.

  In the present embodiment, as described above, the physical strength measurement system 2 mainly includes a computer system (server system 10) having one or more memories as hardware and one or more processors. That is, the functions of the life data acquisition unit 21 and the estimation unit 22 are realized by the processor executing the program recorded in the memory of the server system 10.

  The life data acquisition unit 21 acquires life data from, for example, the sensor server 60. When the target person H1 is specified, the life data acquisition unit 21 uses information (measurement data of the plurality of sensors 6) accumulated in association with identification information (user ID) unique to the target person H1 to Acquired from the sensor server 60 as data.

  The estimation unit 22 estimates the physical strength data D1 corresponding to the exercise menu implementation result from the life data acquired by the life data acquisition unit 21. That is, the life data related to the motion of the subject H1 in the living space includes data related to the motion (exercise) of the subject H1 corresponding to the exercise menu implementation. Physical fitness data D1 is estimated from the data. In other words, the estimation unit 22 generates the physical strength data D1.

  In the present embodiment, the estimation unit 22 includes a first processing unit 221 and a second processing unit 222. The first processing unit 221 extracts specific information related to the exercise menu from the life data. The second processing unit 222 estimates physical fitness data based on the specific information extracted by the first processing unit 221. The functions of the first processing unit 221 and the second processing unit 222 will be described in detail in the section “(3.2) Physical strength measurement method”.

  The second storage unit 23 is composed of a readable / writable non-temporary recording medium such as a hard disk drive or a flash memory. The second storage unit 23 stores at least the physical fitness data D1 generated (estimated) by the estimation unit 22. The physical strength data D1 is stored for each subject H1. The second storage unit 23 may also be used as the first storage unit 14.

(3) Operation Next, an activity support method embodied in the activity support system 1 and a physical strength measurement method embodied in the physical strength measurement system 2 will be described in detail.

(3.1) Activity Support Method First, an activity support method embodied by the operation of the activity support system 1 according to the present embodiment will be described.

  Hereinafter, the exercise menu includes a menu for measuring at least one of muscle strength, walking ability, agility, balance, and flexibility among items related to the exercise ability of the subject H1. Here, in particular, a case is assumed in which a plurality of exercise menus are set so that all five items of muscle strength, walking ability, agility, balance, and flexibility can be measured. As specific examples of this type of exercise menu, as shown in FIG. 3, “Time up & go”, “Functional reach”, “Opening one leg standing”, “Grip strength”, “5 m walking” and “Stepping” There is one. In the following description, “Time up & go” may be abbreviated as “TUG”. However, these six exercise menus are merely examples, and exercise menus may be appropriately set according to the subject H1.

  Each of the plurality of exercise menus is classified into a plurality of groups according to their functions. Here, each of the plurality of (here, six) exercise menus is classified into a first group, a second group, a third group, a fourth group, or a fifth group. The first group is a group for evaluating walking ability, and includes exercise menus such as “TUG” and “5 m walking”. The second group is a group for evaluating flexibility, and includes an exercise menu such as “functional reach”. The third group is a group for evaluating balance, and includes an exercise menu such as “standing with one eye open”. The fourth group is a group for evaluating muscular strength, and includes an exercise menu such as “grip strength”. The fifth group is a group for evaluating agility, and includes an exercise menu such as “stepping”.

  FIG. 2 is a flowchart showing an outline of the operation of the activity support system 1 described above, that is, an activity support method.

  In the activity support method, first, processing for specifying the target person H1 is performed (S1). In the present embodiment, the target person H1 is specified by identification information unique to the target person H1.

  When the target person H1 is specified, the acquisition unit 11 executes an acquisition process for acquiring physical strength data D1 indicating the result of the exercise menu performed by the target person H1 from the physical strength measurement system 2 (S2). The physical strength data D1 acquired by the acquisition unit 11 is associated with each of a plurality of (here, six) exercise menus as described above. That is, since the physical strength data D1 is an execution result of each exercise menu by the subject H1, the acquisition unit 11 acquires a plurality of physical fitness data D1 corresponding to a plurality of exercise menus.

  The acquisition unit 11 determines whether or not all physical strength data D1 has been acquired every time physical strength data D1 corresponding to one exercise menu is acquired (S3). If acquisition has not been completed for all of the physical strength data D1, that is, all of the plurality of physical strength data D1 corresponding to the plurality of exercise menus (S3: No), the acquisition unit 11 returns to the process S2 and stores the physical strength data D1. Continue acquisition. On the other hand, if acquisition of all the physical strength data D1 is completed (S3: Yes), the production | generation part 12 will perform the production | generation process which produces | generates the relevant information D4 (S4).

  At this time, the production | generation part 12 produces | generates the relevant information D4 relevant to the subject H1 specified by process S1 based on this subject's H1 action. As an example, the subject H1's “meal” behavior in daily life can identify the subject's H1 meal preference (preference), and the subject H1's “going out” behavior in daily life is a target. The living sphere of the person H1 (facility 5 etc. that is often used) can be specified.

  Here, the related information D4 broadly includes personal property information D41 and facility property information D42. The personal property information D41 is information representing the attribute of the target person H1 in the related information D4. That is, the personal property information D41 is various information unique to the target person H1, such as, for example, the life sphere of the target person H1, the preference, the lifestyle, the presence or absence of smoking, and the family structure. Information determined by the convenience of the person H1. On the other hand, the facility property information D42 is information representing the attribute of the facility 5 used by the target person H1 in the related information D4. That is, the facility property information D42 is information unique to the facility 5 such as, for example, the name, type of business, handling content, location (including address and nearest station), business hours, etc. Is information determined by the convenience of the facility 5. In short, in the present embodiment, the related information D4 includes personal property information D41 representing the attribute of the subject H1. Further, the related information D4 includes facility property information D42 representing the attributes of the facility 5 used by the target person H1.

  The generation unit 12 generates the personal property information D41 based on, for example, information indicating the usage status of the facility 5 by the target person H1 in the information (information on the facility 5) managed by the facility management server 50. . As an example, when it is found from the usage status of the facility 5 by a certain target person H1 that the target person H1 frequently uses a specific store (for example, every day), it is specified as the related information D4 of the target person H1. Personal property information D41 including the store in the living area is generated. Furthermore, the generation unit 12 generates personal property information D41 based on information managed by the sensor server 60 (measurement data of the plurality of sensors 6), for example. As an example, when it is determined that the subject H1 dislikes natto from the state of the subject H1 taken with an image sensor, the related information D4 of the subject H1 is “dislikes natto”. Personal property information D41 is generated.

  Moreover, the production | generation part 12 produces | generates the facility property information D42 based on the information etc. which represent the attribute of the facility 5 among the information (information regarding the facility 5) managed by the facility management server 50, for example. As an example, when it is determined that a store that a certain target person H1 uses for daily shopping is in a state where there is no natto stock, the related information D4 of the target person H1 is “out of stock of natto”. The facility property information D42 is generated.

  When the acquisition process (S2) and the generation process (S4) are completed, the output unit 13 executes an output process for outputting the recommendation information D3 based on the physical fitness data D1 and the related information D4 (S8). Although there are various methods for generating the recommendation information D3 based on the physical strength data D1 and the related information D4, the following procedure (S5 to S7) is adopted in the present embodiment.

  That is, the output unit 13 first generates evaluation data D2 (see FIG. 4), which is an evaluation result of the physical strength data D1, by evaluating the physical strength data D1 in the evaluation unit 131 (S5). In the present embodiment, the evaluation data D2 is data including the evaluation result of the physical strength data D1 for each of the plurality of exercise menus. Specifically, the evaluation unit 131 ranks each of the plurality of exercise menus based on the comparison result between the physical strength data D1 and the reference value to obtain an evaluation score. The “reference value” here refers to, for example, an average value obtained from the measurement results of a specific number of people for each gender and age (age). However, the reference value is not limited to the average value, and may be any value that can obtain an evaluation score for each exercise menu, and may be a representative value other than the average value such as a median value or a mode value.

  In this embodiment, as an example, for each of the plurality of exercise menus, the evaluation unit 131 sets the evaluation score to “C rank” when the physical strength data D1 is within “± 5%” of the reference value. The evaluation unit 131 sets the evaluation score to “B rank” when the physical strength data D1 is greater than the reference value “+ 5%” and less than or equal to the reference value “+ 15%”. The evaluation unit 131 sets the evaluation score to “A rank” when the physical strength data D1 is larger than “+ 15%” of the reference value. Further, the evaluation unit 131 sets the evaluation score to “D rank” when the physical strength data D1 is smaller than “−5%” of the reference value and is not less than “−15%” of the reference value. The evaluation unit 131 sets the evaluation score to “E rank” when the physical strength data D1 is smaller than “−15%” of the reference value. These evaluation points are “A rank”, “B rank”, “C rank”, “D rank”, and “E rank” in descending order of evaluation.

  The evaluation data D2 is data including the evaluation points for a plurality of exercise menus. That is, when there are six exercise menus as in the present embodiment, data with any of the “A rank” to “E rank” assigned to each of these six exercise menus is evaluated. Generated as data D2.

  Next, the selection unit 132 selects one or more secondary candidate information corresponding to the evaluation data D2 from the plurality of primary candidate information (S6). Here, the number of secondary candidate information selected is equal to or less than the number of primary candidate information, and is preferably smaller than the number of primary candidate information. That is, the selection unit 132 performs the first-stage narrowing down based on the evaluation data D2 generated by the evaluation unit 131 from the plurality of primary candidate information. The one or more secondary candidate information selected by the selection unit 132 corresponds to the evaluation data D2, and is information based on at least the physical fitness data D1.

  In the present embodiment, the selection unit 132 selects and selects two or more optimal primary candidate information from among a plurality of primary candidate information stored in the first storage unit 14 based on the evaluation data D2. The above primary candidate information is set as secondary candidate information. Specifically, the selection unit 132 selects two or more pieces of primary candidate information associated with the exercise menu having the lowest evaluation score among the plurality of exercise menus included in the evaluation data D2 as secondary candidate information. Select as.

  Next, the adjustment unit 133 selects the recommendation information D3 corresponding to the related information D4 from the one or more secondary candidate information (S7). Here, the number of recommended information D3 to be selected is equal to or less than the number of secondary candidate information, and is preferably smaller than the number of secondary candidate information. That is, the adjustment unit 133 performs second-stage narrowing based on the related information D4 generated by the generation unit 12 from one or more pieces of secondary candidate information. The recommendation information D3 selected by the adjustment unit 133 is information based on at least the physical fitness data D1 and the related information D4.

  Here, the adjustment unit 133 selects secondary candidate information determined to be optimal for the target person H1 from the related information D4 among the one or more secondary candidate information, and selects the selected secondary candidate information as recommendation information. Let D3. Specifically, for the one or more secondary candidate information, the adjustment unit 133 scores the feasibility for the target person H1 that can be identified from the related information D4 as an implementation point, and the implementation point is the highest, that is, the implementation Secondary candidate information with high possibility is selected as recommendation information D3. As an example, for the subject H1 who is not good at natto, the implementation point of secondary candidate information that suggests ingestion of natto is relatively low, suggesting ingestion of natto substitutes (eg okra) The implementation point of such secondary candidate information is relatively high. As a result, when the secondary candidate information is information that suggests intake of natto and information that suggests intake of a substitute for natto, the adjustment unit 133 has a high implementation point. Secondary candidate information that suggests the intake of a substitute is selected as recommendation information D3.

  Here, in the present embodiment, the related information D4 includes personal property information D41 representing the attributes of the subject H1, and facility property information D42 representing the attributes of the facility 5 used by the subject H1. The recommendation information D3 is preferably generated based on at least a combination of the personal property information D41 and the facility property information D42. That is, in the present embodiment, the related information D4 includes two types of information, that is, personal property information D41 and facility property information D42. The adjustment unit 133 uses a combination of the personal property information D41 and the facility property information D42 when generating (selecting) the recommendation information D3 based on the related information D4.

  Thereby, both the personal property information D41 determined by the convenience of the target person H1 and the facility property information D42 determined by the convenience of the facility 5 can be reflected in the recommendation information D3. However, it is preferable that which one of the personal property information D41 and the facility property information D42 is preferentially used can be adjusted by, for example, the respective weighting factors of the personal property information D41 and the facility property information D42. Thereby, the state in which the facility property information D42 is given priority over the personal property information D41 and the recommendation information D3 is generated, and the state in which the personal property information D41 is given priority over the facility property information D42 and the recommendation information D3 is generated. Switchable. In other words, a state in which the facility property information D42 is prioritized and a state in which the personal property information D41 is prioritized can be alternatively selected.

  In short, the output unit 13 narrows down the secondary candidate information from the primary candidate information by the selection unit 132 (S6), and further narrows down the recommendation information D3 from the secondary candidate information by the adjustment unit 133 (S7). To generate recommendation information D3. In other words, the recommendation information D3 is information selected based on the related information D4 from one or more candidate information (secondary candidate information) associated with the physical fitness data D1.

  The output unit 13 executes output processing for outputting the generated recommendation information D3 by an appropriate means (S8). In the present embodiment, as an example, the output unit 13 presents the recommendation information D3 from the presentation unit 31 of the information terminal 3 by transmitting the recommendation information D3 to the information terminal 3 (displayed here).

  As described above, according to the activity support system 1, the acquisition process (“S2” in FIG. 2), the generation process (“S4” in FIG. 2), the output process (“S8” in FIG. 2), An activity support method is realized. An acquisition process is a process which acquires the physical strength data D1 which shows the implementation result of the exercise menu by the subject H1. The generation process is a process of generating related information D4 related to the target person H1 based on the behavior of the target person H1. The output process is a process of outputting recommendation information D3 based on the physical strength data D1 and the related information D4.

  In particular, in the present embodiment, the physical strength data D1 acquired by the acquisition process is the physical strength data D1 estimated by the physical strength measurement method embodied by the physical strength measurement system 2. That is, the acquisition process is a process of acquiring the physical strength data D1 estimated by the physical strength measurement method.

  However, the flowchart shown in FIG. 2 is only an example of the activity support method according to the present embodiment. For example, the order of some processes may be changed, and some processes may be omitted as appropriate.

  By the way, in this embodiment, when implementing the activity support method, the information terminal 3 is used as a user interface. That is, the activity support system 1 communicates with the information terminal 3 to present information to the user (including the target person H1) at the presentation unit 31 of the information terminal 3, and to the operation unit 32 of the information terminal 3. To accept user operations.

  Hereinafter, specific examples of the activity support method will be described with reference to FIGS. 3 to 6B showing examples of screens (input screen 311 and output screens 312 and 313) displayed on the presentation unit 31 of the information terminal 3. In the following, description will be given using the top, bottom, left, and right when viewing each of the screens (input screen 311 and output screens 312 and 313) displayed on the presentation unit 31 from the front. It is not intended to limit the orientation when the terminal 3 is used.

  When implementing the activity support method, first, an input screen 311 as illustrated in FIG. 3 is displayed on the presentation unit 31 of the information terminal 3. The input screen 311 is a screen for inputting identification information of the target person H1.

  In the center of the input screen 311, a data display column A1 for displaying physical fitness data D1 is arranged. On the left side of the data display column A1 on the input screen 311, a menu display column A2 for displaying a plurality of (here, six) exercise menus is arranged. On the right side of the data display column A1 on the input screen 311, an input column A4 that functions as the operation unit 32 is arranged. Identification information of the subject person H1 is displayed in the identification column A3 arranged in the upper right corner of the input screen 311.

  A first button B1, a second button B2, and a third button B3 that function as the operation unit 32 are displayed side by side in the left-right direction at the lower end of the input screen 311. The first button B1 is an object for shifting to an input mode for inputting identification information (user ID) unique to the target person H1. The second button B2 is an object for reading the physical strength data D1. The third button B3 is an object for changing the display screen.

  When the first button B1 is tapped on the input screen 311, the identification information (user ID) of the target person H1 can be input (input mode). In this state, when the user inputs the identification information of the target person H1 using the numeric keypad in the input field A4, the target person H1 is specified.

  When the second button B2 is tapped in a state where the subject person H1 is specified, the acquisition unit 11 reads the physical strength data D1 corresponding to the subject person H1, that is, the physical strength data D1. At this time, the physical strength data D1 to be read is associated with each of a plurality of (here, six) exercise menus as described above.

  Specifically, “TUG” is an exercise menu that performs a series of actions of standing up from a posture in which the subject H1 is sitting on a chair, turning back at a mark point ahead of a predetermined distance (3 m), and sitting on the chair again. The time required for this series of operations is the physical strength data D1 corresponding to “TUG”. In the example of FIG. 3, the physical strength data D1 corresponding to “TUG” is “7.5” (seconds). “Functional reach” is an exercise menu in which the subject H1 stands upright and raises both arms to the shoulder height, and tilts the upper body forward without breaking the balance. Is the physical fitness data D1 corresponding to “functional reach”. In the example of FIG. 3, the physical strength data D1 corresponding to “functional reach” is “45.5” (cm).

  “Opening one leg standing” is an exercise menu in which the subject H1 stands with one eye open, and the time during which one leg can be continued becomes the physical strength data D1 corresponding to “opening one leg standing”. In the example of FIG. 3, the physical strength data D1 (in the case of standing on the right foot) corresponding to “one eye stand with open eyes” is “32.0” (seconds). The “grip strength” is an exercise menu in which the subject H1 holds the grip strength meter in an upright posture, and the magnitude of the grip strength measured by the grip strength meter becomes the physical strength data D1 corresponding to the “grip strength”. In the example of FIG. 3, the physical strength data D1 (left hand) corresponding to “grip strength” is “45.2” (kg).

  “5 m walking” is an exercise menu in which the subject H1 walks straight 5 m on a flat floor, and the time required to move 5 m is the physical strength data D1 corresponding to “5 m walking”. In the example of FIG. 3, the physical strength data D1 corresponding to “5 m walking” is “5.6” (seconds). “Stepping” is an exercise menu in which the subject H1 repeatedly opens and closes both legs in a predetermined time (for example, 20 seconds) while sitting in a chair, and the number of times the target person can open and close the predetermined time corresponds to “stepping” To become physical strength data D1. In the example of FIG. 3, the physical strength data D1 corresponding to “stepping” is “32” (times).

  Here, on the input screen 311, editing work can be performed on the acquired physical strength data D1, that is, the physical strength data D1 displayed in the data display column A1. Specifically, the user can edit the physical strength data D1 using the numeric keypad in the input field A4 in a state where any physical strength data D1 displayed in the data display field A1 is selected. . However, the function of editing the physical fitness data D1 is not essential for the activity support method.

  When the third button B3 on the input screen 311 is tapped in the state where reading of the corresponding physical strength data D1 is completed for all of the plurality of (six here) exercise menus, the information is displayed on the presentation unit 31 of the information terminal 3 The displayed screen is changed to an output screen 312 as shown in FIG. The output screen 312 is a screen for displaying the evaluation data D2.

  In the center of the output screen 312, evaluation data D2 is displayed. In the example shown in FIG. 4, the evaluation data D2 includes first evaluation data D21 and second evaluation data D22. The first evaluation data D21 is, for example, advice on the exercise result of the exercise menu by the subject H1, and here is advice on exercise and meal. The second evaluation data D22 is, for example, data obtained by graphing the evaluation of the exercise menu implementation result by the subject H1, and is a radar chart as an example.

  In the radar chart shown in FIG. 4, the solid line is the current result (evaluation data D2) for a certain target person H1, and the broken line is the result of the first time or the previous time (for example, the previous month) for the same target person H1. . That is, in the example illustrated in FIG. 4, the presentation unit 31 presents a plurality of evaluation data D2 that changes in time series for the same subject at the same time, and can present the degree of change to the subject.

  In the identification column A3 arranged in the upper right corner of the output screen 312, the identification information of the subject H1 is displayed. At the lower end of the output screen 312, a fourth button B4, a fifth button B5, and a sixth button B6 that function as the operation unit 32 are displayed side by side in the left-right direction. The fourth button B4 is an object for shifting to an input mode for inputting identification information (user ID) unique to the target person H1. The fifth button B5 is an object for returning the display of the presentation unit 31 to the previous screen (input screen 311). The sixth button B6 is an object for changing the display screen.

  Here, it is preferable that the display mode is different between the first evaluation result that is the evaluation result for the exercise menu having the highest priority among the plurality of exercise menus and the second evaluation result other than the first evaluation result. In the example shown in FIG. 4, the evaluation result for “5 m walking” is the first evaluation result, and the evaluation results for “TUG”, “functional reach”, “one-leg stand with eyes open”, “grip strength”, and “stepping” are the first evaluation results. 2 is an evaluation result. In the example shown in FIG. 4, “★ mark” is displayed with respect to the first evaluation result, and the display mode is different from that of the second evaluation result. Thereby, there exists an advantage that it is easy to distinguish a 1st evaluation result and a 2nd evaluation result.

  When the sixth button B6 is tapped on the output screen 312, the screen displayed on the presentation unit 31 of the information terminal 3 changes to an output screen 313 as shown in FIG. The output screen 313 is a screen for displaying the recommendation information D3.

  In the center of the output screen 313, recommendation information D3 is displayed. In the example illustrated in FIG. 5, the recommendation information D3 includes first recommendation information D31 and second recommendation information D32. The first recommendation information D31 includes a first icon I1, a second icon I2, and a third icon I3. The 1st icon I1 is an icon for selecting the recommendation (recommendation) regarding a meal. The 2nd icon I2 is an icon for selecting the recommendation regarding an instrument. The third icon I3 is an icon for selecting a recommendation regarding exercise.

  The second recommendation information D32 is information representing information associated with the icon selected by the user among the first icon I1, the second icon I2, and the third icon I3 as image (image) data and text data. In the example shown in FIG. 5, the case where the 1st icon I1 is selected and the information showing the recommendation regarding a meal is shown (displayed) as 2nd recommendation information D32 is illustrated.

  In the identification field A3 arranged in the upper right corner of the output screen 313, the identification information of the subject H1 is displayed. At the lower end of the output screen 313, a seventh button B7, an eighth button B8, and a ninth button B9 that function as the operation unit 32 are displayed side by side in the left-right direction. The seventh button B7 is an object for shifting to an input mode for inputting identification information (user ID) unique to the target person H1. The eighth button B8 is an object for returning the display of the presentation unit 31 to the previous screen (output screen 312). The ninth button B9 is an object for printing the output result.

  When the ninth button B9 is tapped on the output screen 313, an image signal including the recommendation information D3 is transmitted from the expansion unit 33 to the printer, and a report including the recommendation information D3 is printed out by the printer. Furthermore, in the present embodiment, when the ninth button B9 is tapped, the display of the output screen 313 in the presentation unit 31 ends.

  In the example of FIG. 4, it is assumed that “5 m walking” included in the first group among the plurality of exercise menus is “E rank”, which is the lowest evaluation. The recommendation information D3 shown in FIG. 5 is data associated with “5m walking”, and is a recommendation for improving walking ability. That is, in the example shown in FIGS. 4 and 5, the evaluation score of “5m walking” included in the first group among the plurality of exercise menus is the lowest, and the recommendation information D3 associated with this “5m walking” is displayed in the presentation unit. 31. In other words, in the activity support method, in the output process, the recommendation information D3 associated with the exercise menu having the lowest evaluation score among the plurality of exercise menus is output. However, the content of the recommendation information D3 shown in FIG. 5 is only an example.

  Here, it is assumed that “TUG” and “5m walking” included in the first group among the first group, the second group, the third group, the fourth group, and the fifth group are both “E rank”. To do. In other words, “TUG” and “5 m walking” are caution items that are exercise menus with the lowest evaluation score in the first group. That is, in this case, a plurality of items requiring attention are included in the first group. In this case, it is preferable to determine a priority in advance among a plurality of items requiring attention. For example, when “TUG” has a higher priority than “5 m walking” and both have the same “E rank”, the output unit 13 presents the recommendation information D3 associated with “TUG” to the presentation unit 31. Let

  By the way, since the recommendation information D3 displayed on the output screen 313 is information based on the related information D4 unique to the target person H1, the activity proposed in the recommendation information D3 is customized according to the target person H1. It will be.

  As an example, even when the evaluation data D2 similar to that in the example of FIG. 4 is obtained, depending on the subject H1, the output screen 313 of FIG. 6A or 6B may be used instead of the recommendation information D3 shown in the output screen 313 of FIG. The recommendation information D3 as shown in FIG. Here, it is assumed that the physical strength data D1 is the same, that is, the same evaluation data D2 is obtained for three subjects H1 whose identification information is “12345”, “11111”, and “22222”, respectively. Further, it is assumed that the related information D4 that it is difficult to obtain “Okra” at the store in the living area is obtained for the subject H1 whose identification information is “11111”. Further, it is assumed that the related information D4 that “dislikes natto” is obtained for the target person H1 whose identification information is “22222”.

  In this case, for the subject H1 whose identification information is “12345”, as shown in FIG. 5, recommendation information D3 that suggests intake of “yamabuchi, okra, natto” is output. On the other hand, for the subject H1 whose identification information is “11111”, as shown in FIG. 6A, “Okra” is omitted, and recommendation information D3 that suggests the intake of “yamato, natto” is output. Is done. Also, for the subject H1 whose identification information is “22222”, as shown in FIG. 6B, “natto” is omitted, and recommendation information D3 that suggests the intake of “yamatake, okra” is output.

  As described above, according to the activity support method according to the present embodiment, even if the physical fitness data D1 of the plurality of target persons H1 is the same, when the life areas and preferences of the plurality of target persons H1 are different, the target persons Different recommendation information D3 can be output for each H1. Therefore, for example, by avoiding activities that are difficult for each target person H1 to implement and recommending to the target person H1 activities that are easy for each target person H1 to perform, the recommended information D3 by the target person H1 is followed. Can improve the feasibility of such activities. Therefore, in the activity support method, for example, by recommending to the target person H1 activities that can be easily performed by the individual target person H1 as the recommendation information D3, the possibility of performing the activities according to the recommendation information D3 by the target person H1 is improved. This also leads to improvement in the motivation of the target person H1. As a result, the activity support method according to the present embodiment has an advantage that it is easy to provide useful information for the target person H1.

  As an example, instead of the recommendation information D3 that suggests walking for the subject H1 who has a disability in the lower limb, exercise that can be performed in a sitting position, exercise that trains the upper body, exercise that trains the trunk, etc. It is preferable to output the recommendation information D3 as proposed. Furthermore, it is preferable to output the recommendation information D3 that suggests a meal suited to these symptoms to the subject H1 having symptoms such as hypertension or hyperlipidemia.

  In addition, the target person H1 who uses the same type of facility 5 may be classified for each target person H1 who uses the same type of facility 5 on the assumption that the target person H1 has the same characteristics. For example, the subject H1 who frequently uses a supermarket is often healthy, and the subject H1 who frequently uses a pharmacy is often worried about health. Moreover, there are many cases where there is a dietary restriction for the subject person H1 who frequently uses hospitals or care facilities. Focusing on such characteristics, the target person H1 may be categorized for each facility 5 being used, and the category may be reflected in the proposal related to meals in the recommendation information D3, for example.

(3.2) Physical Strength Measuring Method Next, a physical strength measuring method embodied by the operation of the physical strength measuring system 2 according to the present embodiment will be described.

  FIG. 7 is a flowchart showing an operation of the above-described physical strength measuring system 2, that is, a general flow of the physical strength measuring method.

  In the physical strength measurement method, first, a process for specifying the subject H1 is performed (S11). In the present embodiment, the target person H1 is specified by identification information unique to the target person H1.

  When the target person H1 is specified, the life data acquisition unit 21 executes a life data acquisition process for acquiring life data (measurement data of the plurality of sensors 6) corresponding to the target person H1 from the sensor server 60 ( S12). When the life data is acquired by the life data acquisition unit 21, the estimation unit 22 executes an estimation process for estimating the physical fitness data D1 (S13, S14). At this time, the estimation unit 22 estimates the physical strength data D1 corresponding to the exercise menu execution result from the life data acquired by the life data acquisition unit 21. There are various methods for estimating the physical strength data D1, but in the present embodiment, the following procedures (S13, S14) are employed.

  That is, the estimation unit 22 first executes a first process of extracting specific information related to the exercise menu from the life data in the first processing unit 221 (S13). In short, since the life data related to the movement of the subject H1 in the life space includes data not related to the exercise menu, the first processing unit 221 removes such data not related to the exercise menu. . At this time, the 1st processing part 221 extracts only specific information relevant to an exercise menu by performing predetermined filtering processing about life data, for example.

  Next, the 2nd processing part 222 performs the 2nd processing which estimates physical fitness data D1 based on the specific information extracted by the 1st processing (S14). At this time, the second processing unit 222 identifies an exercise menu related to the specific information, digitizes the specific information using a conversion formula corresponding to the exercise menu, and obtains physical fitness data D1. The “conversion formula” here is an expression for converting the specific information into a value equivalent to the physical strength data D1 derived from the specific information when the specific information is applied to the related exercise menu. It is preset for each menu.

  Here, in the present embodiment, the physical fitness data D1 is estimated based on at least image data of life data. That is, in the present embodiment, measurement data of a plurality of sensors 6 managed by the sensor server 60 is used as life data. In particular, measurement data (image data) of a sensor including an image sensor (camera) is used. Is used for the estimation of the physical fitness data D1. Thereby, the physical strength data D1 corresponding to the execution results of various exercise menus can be estimated without touching the subject H1. The image data here is, as an example, image data including the subject H1 who is performing some kind of operation in the living space in the field of view.

  Each time the estimation unit 22 estimates the physical strength data D1 corresponding to one exercise menu, the estimation unit 22 determines whether all the physical strength data D1 has been estimated (S15). If the estimation is not completed for all the physical fitness data D1, that is, all of the plurality of physical fitness data D1 corresponding to a plurality of (here 6) exercise menus (S15: No), the process returns to the processing S12 and the life data Acquisition and estimation of physical strength data D1 are continued. On the other hand, if the estimation of all the physical strength data D1 has been completed (S5: Yes), the estimation unit 22 outputs the estimated physical strength data D1 to the second storage unit 23 (S16). Thereby, the physical strength data D1 is memorize | stored in the 2nd memory | storage part 23 for every subject H1.

  As described above, according to the physical strength measurement system 2, the physical strength measurement method including the life data acquisition process ("S12" in FIG. 7) and the estimation process ("S13" and "S14" in FIG. 7). Embodied. The life data acquisition process is a process for acquiring life data related to the movement of the subject H1 in the living space. The estimation process is a process of estimating physical fitness data D1 corresponding to the result of executing a specific exercise menu from life data.

  However, the flowchart shown in FIG. 7 is merely an example of the physical strength measurement method according to the present embodiment. For example, the order of some processes may be changed, and some processes may be omitted as appropriate.

  By the way, in this embodiment, when implementing the physical strength measurement method, the measurement data of the plurality of sensors 6 managed by the sensor server 60 are used as life data related to the movement of the subject H1 in the living space. An example of a method for estimating the physical fitness data D1 corresponding to the result of executing a specific exercise menu from such life data will be described below with reference to FIGS. 8A to 10.

  With respect to “TUG”, for example, as shown in FIG. 8A, the physical strength of the subject H1 can be calculated from the life data representing the motion of the subject H1 when standing up from the chair T1 and sitting on the chair T1, and the motion of fast walking. Data D1 can be estimated. That is, it is possible to estimate the time required for the subject person H1 to stand up from the posture of sitting on the chair and to sit again on the chair from the speed of movement of the subject person H1 when standing up from the chair T1 and sitting on the chair T1. . Further, it is possible to estimate the time required for the round trip between the chair and the landmark point ahead of the predetermined distance from the average speed of the subject H1 walking fast. Among the motions of the subject H1, the speed of motion when standing up from the chair T1 and sitting on the chair T1 is, for example, a Doppler sensor or a pressure sensor installed (including built-in) on the seat surface of the chair T1. Is measured. The average speed at the time of fast walking is measured by, for example, a sensor built in or externally attached to a user terminal such as a smartphone or a wearable terminal possessed by the target person H1.

  With regard to “functional reach”, for example, as shown in FIG. 8B, the physical fitness data D1 can be estimated from the life data representing the motion of the target person H1 when picking up the object T2 in the living space. That is, it can be estimated from the posture of the subject H1 when picking up the object T2, especially the waist bending angle θ1, how much the subject H1 can lean forward without breaking the balance in an upright posture. Such a posture of the subject H1 is measured by, for example, an image sensor (camera) or a thermal image sensor installed indoors. Further, the posture of the subject H1 can be measured by an acceleration sensor, an angular velocity sensor, or the like attached to the upper body of the subject H1.

  As for “standing with one eye open”, for example, as shown in FIG. 8C, the physical fitness data D1 can be estimated from the life data representing the motion of the subject H1 when going up the stairs T3 in the living space. That is, when going up the stairs T3, it is possible to estimate how long the subject H1 can continue standing on one foot from the posture of the subject H1 at the moment when the subject H1 stands on one foot. Such a posture of the target person H1 is measured by, for example, a pressure sensor or the like installed (including the built-in) on the staircase T3 (particularly a footboard) or the handrail T4. Specifically, these sensors 6 can measure, for example, the fluctuation of the center of gravity when standing on one foot or the magnitude of the load applied to the handrail T4. Further, not only the movement when going up the stairs T3, but also from the life data representing the movement of the target person H1 when straddling an object such as a sill or a bathtub, similarly, the physical fitness data corresponding to "one leg standing with eyes open" D1 can be estimated.

  As for “grip strength”, for example, as shown in FIG. 8D, the physical strength data D1 can be estimated from the life data representing the motion of the subject H1 when operating the door knob T5 in the living space. That is, when operating the door knob T5, the magnitude of the grip force of the subject H1 can be estimated from the magnitude of the load applied from the subject H1 to the doorknob T5. The magnitude of such a load is measured by, for example, a pressure sensor or the like installed (including built-in) on the door knob T5. Further, not only the door knob T5 but also the physical strength data D1 of “grip strength” can be estimated from the magnitude of the load applied to the lid of a bottle or a plastic bottle beverage, for example. Furthermore, when the subject H1 is performing an operation involving gripping an object such as weeding or farming, the physical strength data D1 of “grip strength” is also determined from the magnitude of the impact applied to the shoulder of the subject H1. Can be estimated.

  As for “5 m walking”, for example, as shown in FIG. 8E, the physical fitness data D1 can be estimated from life data representing the motion of the subject H1 walking (excluding running and fast walking) in the living space. That is, the time required to move 5 m can be estimated from the average walking speed of the target person H1. The average speed at the time of walking is measured, for example, by a sensor 6 built in or externally attached to a user terminal T6 such as a smartphone or a wearable terminal possessed by the subject H1. Whether the subject H1 is walking (walking) or running (running) can be distinguished by the magnitude of vibration applied to the user terminal T6.

  As for “stepping”, as shown in FIG. 8F, for example, the physical fitness data D1 can be estimated from the life data representing the motion of the subject H1 when the door T7 is opened in the living space. That is, when opening the door T7, the subject H1 moves in the direction opposite to the traveling direction (door T7 side), so the agility is determined based on the moving speed of the subject H1 at that time. The number of steppings for can be estimated. In short, it is possible to estimate the agility of the subject H1 from the switching operation during walking of the subject H1. The operation of the target person H1 at this time is measured by a sensor or the like incorporated in or externally attached to a user terminal such as a smartphone or a wearable terminal that the target person H1 has. Furthermore, by using the open / close sensor installed (including built-in) on the door T7, the operation of the subject H1 can be measured with higher accuracy.

  Since various life data as described above are acquired for each subject H1, the life data acquisition unit 21 needs to identify which of the subject H1 is the life data. The identification of the subject person H1 is realized by the following method, for example.

  For example, the measurement data of the portable sensor 6 that is carried or worn by the subject person H1 can be identified by providing the identification information of the subject person H1 for each sensor 6. Further, with respect to the measurement data of the installation type sensor 6, the target person H1 can be identified by a biometric authentication technique such as fingerprint authentication, vein authentication, iris authentication or face authentication. As an example, when the lifestyle data corresponding to the above-mentioned “grip strength” is acquired, the fingerprint of the subject H1 can be acquired by a fingerprint sensor installed (including the built-in) on the door knob T5. Furthermore, in a space used only by a limited number of people, such as a residence, the target person H1 can be identified by weight or height. In addition, with respect to life data corresponding to an exercise menu (for example, grip strength, functional reach, etc.) that is unlikely to cause a rapid change in the physical strength data D1, it is also possible to identify the subject H1 based on the physical strength data D1 estimated from now on. It is.

  Here, the physical fitness data D1 may be estimated based on at least information indicating whether or not a specific task can be performed by the subject H1 among the life data. In short, it is possible to roughly estimate the physical strength data D1 depending on whether or not a specific work requiring a predetermined athletic ability has been performed.

  9A and 9B illustrate a specific operation of opening the bottle T8. In this case, the subject H1 opens the bottle T8 by rotating the lid T82 while holding the main body T81 and the lid T82 of the bottle T8. At this time, since it is necessary to hold the main body T81 and the lid T82 so as not to slip, a gripping force of a certain value or more is required for the operation of opening the bottle T8. Therefore, as shown in FIG. 9A, when the subject H1 fails to execute the operation of opening the bottle T8 within a predetermined work time, that is, when the opening of the bottle T8 fails, The grip strength is estimated to be less than a certain value. On the other hand, as shown in FIG. 9B, when the subject H1 has been able to execute the operation of opening the bottle T8 within a predetermined work time, that is, when the bottle T8 has been successfully opened, the gripping force of the subject H1 Is estimated to be above a certain value.

  As shown in FIGS. 9A and 9B, whether the work can be performed can be monitored by a sensor 6 including an image sensor (camera), but is monitored by other sensors (such as a pressure sensor or a thermal image sensor). May be. The physical strength data D1 estimated from work availability is not limited to the physical strength data D1 corresponding to “grip strength”, but may be physical strength data D1 corresponding to an exercise menu other than “grip strength”.

  In the present embodiment, as described above, the estimation process for estimating the physical fitness data D1 from the life data is performed by the first process (“S13” in FIG. 7) and the second process (“S14” in FIG. 7). Since the process includes two steps, it is possible to improve the estimation accuracy of the physical strength data D1. This point will be described with reference to FIG. FIG. 10 is a diagram schematically illustrating the movement route R1 of the subject H1 when the subject H1 is taking a walk in the neighborhood of the residence. Here, the process in the case of estimating the physical strength data D1 corresponding to “5 m walking” from the operation of the subject H1 at this time will be described as an example.

  That is, the moving speed of the target person H1 when the target person H1 moves on the movement route R1 is, for example, incorporated in or externally attached to the user terminal T6 such as a smartphone or a wearable terminal possessed by the target person H1. Measurement is performed by the sensor 6. More specifically, for example, the moving speed of the target person H1 on the movement route R1 can be estimated from the time series data of the position of the target person H1 measured by the position sensor of the user terminal T6. Here, in the first process, only the information of the 5-m straight section Z1 in which the subject H1 keeps walking at a constant speed in the movement route R1 is extracted as the specific information related to the exercise menu (5m walking). As a result, in the second process, except for information that is not related to the exercise menu (5m walk), such as the period during which the subject H1 was stopped, the period when the subject started walking, the period immediately before stopping, and the direction change direction, The data is used for estimating the physical fitness data D1.

  Furthermore, it is preferable that the physical strength measurement system 2 executes the estimation of the physical strength data D1 in parallel for a large number of subjects H1. In this case, it is preferable that the reference value for ranking is updated at any time based on the representative values (average value, median value, mode value, etc.) of the physical fitness data D1 of many subjects H1.

  As described above, according to the physical strength measurement method according to the present embodiment, it is possible to estimate the physical strength data D1 corresponding to the execution result of the specific exercise menu from the motion of the subject H1 in the living space. For this reason, the physical strength data D1 obtained by the physical strength measurement method is less likely to cause a force or the like in the target person H1 at the time of measurement compared to physical strength data measured with an exercise equipment or a grip strength meter at a training facility or the like. The original motor ability of H1 is easily reflected. As a result, according to the physical strength measuring method according to the present embodiment, there is an advantage that it is easy to measure the original physical strength data of the subject H1.

  In addition, in order to obtain physical fitness data D1, the subject person H1 only needs to behave as usual in the living space. Therefore, an instrument (exercise instrument and measuring instrument such as a dynamometer) and a space for performing the exercise menu Is unnecessary. Here, it is preferable that the estimation of the physical strength data D1 by the physical strength measurement method is performed regularly or irregularly at predetermined intervals. As an example, when the physical fitness data D1 is estimated every day, the physical fitness data D1 can be averaged to measure the physical fitness data D1 with higher accuracy. Furthermore, by accumulating physical strength data D1 for a predetermined period (for example, about several months to one year) along a time series, it is possible to see a trend of changes in physical strength data D1.

  Moreover, according to the physical strength measurement method according to the present embodiment, an assistant for measuring the physical strength data D1 is not required, and the physical strength data D1 can be obtained even if the subject H1 is alone. Furthermore, compared with the case where physical fitness data is measured at an event such as a physical fitness measurement meeting held at a specific facility 5, for example, the subject H1 does not need to go to the facility 5 in the first place, and the burden on the subject H1 is reduced. It is reduced. Furthermore, the physical fitness data D1 can be obtained for the subject H1 who does not want to participate in an event such as a physical fitness measurement meeting.

(4) Modification Example 1 is only one of various embodiments of the present disclosure. The first embodiment can be variously modified according to the design or the like as long as the object of the present disclosure can be achieved. Moreover, the function similar to the activity support system 1 may be embodied by an activity support method, a computer program, or a non-transitory recording medium recording the computer program.

  In the activity support method according to one aspect, the physical strength measurement method includes a life data acquisition process (“S12” in FIG. 7) and an estimation process (“S13” and “S14” in FIG. 7). The life data acquisition process is a process for acquiring life data related to the movement of the subject H1 in the living space. The estimation process is a process of estimating physical fitness data D1 corresponding to the result of executing a specific exercise menu from life data. The program which concerns on 1 aspect is a program for making a computer system perform the said physical strength measurement method.

  Hereinafter, modifications of the first embodiment will be listed. Various modifications described below can be applied in appropriate combination.

  The execution subject of the activity support system 1 and the physical strength measurement system 2, and the activity support method and the physical strength measurement method in the present disclosure includes a computer system. The computer system mainly includes a processor and a memory as hardware. The functions of the activity support system 1 and the physical fitness measurement system 2 according to the present disclosure are realized by the processor executing the program recorded in the memory of the computer system. The program may be recorded in advance in a memory of a computer system, may be provided through a telecommunication line, or recorded in a non-transitory recording medium such as a memory card, an optical disk, or a hard disk drive that can be read by the computer system. May be provided. A processor of a computer system includes one or more electronic circuits including a semiconductor integrated circuit (IC) or a large scale integrated circuit (LSI). An integrated circuit such as an IC or an LSI here is called differently depending on the degree of integration, and includes an integrated circuit called a system LSI, a VLSI (Very Large Scale Integration), or a ULSI (Ultra Large Scale Integration). Furthermore, an FPGA (Field-Programmable Gate Array) programmed after manufacturing the LSI, or a logic device capable of reconfiguring the junction relationship inside the LSI or reconfiguring the circuit partition inside the LSI should be adopted as the processor. Can do. The plurality of electronic circuits may be integrated on one chip, or may be distributed on the plurality of chips. The plurality of chips may be integrated into one device, or may be distributed and provided in a plurality of devices.

  In the first embodiment, the information selected based on the related information D4 from the one or more secondary candidate information selected by the selection unit 132 based on the evaluation result (evaluation data D2) of the physical fitness data D1 is recommended information. Although it is used as D3, it is not limited to this configuration. That is, if the recommendation information D3 is information based on the physical fitness data D1 and the related information D4, for example, even if the recommendation information D3 is generated by a procedure such as the first modification, the second modification, or the third modification described below. Good.

  In the first modification, the output unit 13 generates evaluation data D2 at the evaluation unit 131 based on both the physical strength data D1 and the related information D4. In this case, the evaluation data D2 already reflects both the physical strength data D1 and the related information D4. Therefore, the recommendation information D3 based on the physical fitness data D1 and the related information D4 can be obtained simply by the selection unit 132 selecting primary candidate information corresponding to the evaluation data D2 from the plurality of primary candidate information. That is, in the first modification, it is not necessary to narrow down the secondary candidate information from the primary candidate information in the selection unit 132 and further narrow down the recommendation information D3 from the secondary candidate information in the adjustment unit 133, In the selection unit 132, recommendation information D3 is obtained. Therefore, the adjustment part 133 can be omitted in the first modification.

  In the second modification, the output unit 13 generates recommendation information D3 instead of the evaluation data D2 based on both the physical strength data D1 and the related information D4 in the evaluation unit 131. In this case, the recommendation information D3 based on the physical strength data D1 and the related information D4 is generated directly from the physical strength data D1 and the related information D4 without using the evaluation data D2 serving as intermediate data. That is, in the second modified example, it is not necessary to narrow down the secondary candidate information from the primary candidate information by the selection unit 132 and further narrow down the recommendation information D3 from the secondary candidate information by the adjustment unit 133. The evaluation unit 131 obtains recommendation information D3. Therefore, in the second modification, the selection unit 132 and the adjustment unit 133 can be omitted. However, also in the second modification, the evaluation data D2 may be generated separately from the recommendation information D3, and the evaluation data D2 may be presented together with the recommendation information D3.

  In the third modification, the output unit 13 generates the evaluation data D2 based on the related information D4 in the evaluation unit 131, and the selection unit 132 converts the evaluation data D2 into the evaluation data D2. One or more corresponding secondary candidate information is selected. In this case, since the secondary candidate information reflects the related information D4, the adjustment unit 133 selects the recommended information D3 corresponding to the physical fitness data D1 from the one or more secondary candidate information, so that the physical strength Recommendation information D3 based on data D1 and related information D4 is obtained.

  The information terminal 3 is not limited to a portable information terminal such as a smartphone or a tablet terminal, and may be an information terminal that can be connected to the network 4 such as a wearable terminal, a smart speaker, a personal computer, or a smart TV. The communication method of the information terminal 3 is not limited to wireless communication using radio waves as a medium, and may be, for example, optical communication using infrared or visible light, wired communication, or the like. Similarly, the communication method of the server system 10, the facility management server 50, and the sensor server 60 is not limited to wired communication, and may be, for example, wireless communication or optical communication.

  The activity support system 1 may output at least one of the physical strength data D1 and the recommendation information D3 to an external system, and may control the external system based on at least one of the physical strength data D1 and the recommendation information D3.

  Moreover, in Embodiment 1, although the presentation part 31 and the operation part 32 are combined with the touch panel display, the presentation part 31 and the operation part 32 may be provided separately.

  In the first embodiment, the evaluation data D2 includes a radar chart. However, the present invention is not limited to this example, and a graph or a table other than the radar chart may be included. That is, the evaluation data D2 may be in other forms as long as it is data created based on the physical strength data D1.

  In the first embodiment, the presentation unit 31 presents the evaluation data D2 and the recommendation information D3 by video (image). For example, the presentation data 31 may present the evaluation data D2 and the recommendation information D3 by voice.

  In the first embodiment, the presentation unit 31 simultaneously presents a plurality of evaluation data D2 that changes in time series for the same subject H1, but for example, presents the evaluation data D2 of a plurality of subjects H1 at the same time. May be. Thereby, it can compare with the other subject H1.

  In addition, the ranking described in the first embodiment is an example, and for example, the ranking may be performed based on a quartile method. For example, the top 25% is “A rank”, the intermediate value (median) or higher is “B rank”, the intermediate value to the third quartile is “C rank”, the third quartile to the fourth quartile The order (a value smaller than the third quartile) is designated as “D rank”, and the order below the fourth quartile is designated as “E rank”. Also, half of the intermediate values may be “C rank”, the first quartile or higher may be “A rank”, and the fourth quartile or lower may be “E rank”.

  In the first embodiment, ranking is performed based on an index of the same age as the subject H1 (see FIG. 4). For example, as shown in FIG. 11, the physical fitness age is presented (displayed) for each exercise menu. May be. In this case, as shown in FIG. 4, it is not displayed in ranks A to E for each exercise menu. For example, if “grip strength”, the actual value of physical strength data D1 (for example, “◯ kg”) is used. It is preferable to display. According to this configuration, it is possible to present the physical strength age for each exercise menu, and the subject H1 who has seen the presented content can know his / her physical strength age for each exercise menu.

(Embodiment 2)
The physical strength measurement system 2A (see FIG. 12) according to the present embodiment is different from the physical strength measurement system 2 according to the first embodiment in that the estimated physical strength data D1 is not used in the activity support system 1 (see FIG. 1). Hereinafter, the same configurations as those of the first embodiment are denoted by common reference numerals, and description thereof is omitted as appropriate.

  In the present embodiment, as shown in FIG. 12, the activity support system 1 (see FIG. 1) is omitted in the server system 10A. That is, in the physical strength measurement method embodied in the physical fitness measurement system 2A according to the present embodiment, the physical fitness data D1 as a result is not used in the activity support method embodied in the activity support system 1.

  In the physical strength measurement system 2A according to the present embodiment, the physical strength data D1 estimated by the estimation unit 22 is output to the information terminal 3, for example. Thereby, in the presentation part 31 of the information terminal 3, the physical strength data D1 estimated by the physical strength measurement system 2A can be presented. Further, the expansion unit 33 of the information terminal 3 may transfer the physical strength data D1 to a printer or other device. The extension unit 33 transfers the physical strength data D1 to the exercise equipment or the like, so that the physical fitness data D1 can be used for controlling the exercise equipment, for example.

  As a modification of the second embodiment, the physical fitness data D1 estimated by the physical fitness measurement system 2A may be used in an activity support system different from the activity support system 1 according to the first embodiment. This activity support system does not have a function of generating related information D4 (see FIG. 1), and outputs recommendation information D3 based only on physical fitness data D1. That is, in this case, the recommendation information D3 is generated based only on the physical strength data D1 estimated by the physical strength measurement system 2A.

  The configuration (including modifications) described in the second embodiment can be used in appropriate combination with the various configurations (including modifications) described in the first embodiment.

(Summary)
As described above, the physical strength measurement method according to the first aspect includes the life data acquisition process and the estimation process. The life data acquisition process is a process of acquiring life data related to the movement of the subject (H1) in the living space. The estimation process is a process of estimating physical fitness data (D1) corresponding to the result of executing a specific exercise menu from life data.

  According to this aspect, it is possible to estimate the physical strength data (D1) corresponding to the execution result of the specific exercise menu from the motion of the subject (H1) in the living space. Therefore, the physical strength data (D1) obtained by the physical strength measurement method is less likely to cause tension or the like in the subject (H1) at the time of measurement compared to physical strength data (D1) measured by an exercise device or a grip strength meter, for example. The original motor ability of the subject (H1) is easily reflected. As a result, the physical strength measurement method has an advantage that it is easy to measure the original physical strength data (D1) of the subject (H1).

  In the physical strength measurement method according to the second aspect, in the first aspect, the physical strength data (D1) is estimated based on at least information indicating whether or not the specific work by the subject (H1) is included in the life data.

  According to this aspect, if the physical strength data (D1) is roughly estimated, it can be estimated relatively easily from the monitoring result of whether or not the specific work by the subject (H1) is possible.

  In the physical strength measurement method according to the third aspect, in the first or second aspect, the physical fitness data (D1) is estimated based on image data of at least life data.

  According to this aspect, it is possible to estimate the physical strength data (D1) corresponding to the execution results of various exercise menus without touching the subject (H1) with the contact sensor.

  In the physical strength measurement method according to the fourth aspect, in any one of the first to third aspects, the estimation process includes a first process and a second process. The first process is a process for extracting specific information related to the exercise menu from the life data. The second process is a process for estimating the physical fitness data (D1) based on the specific information extracted in the first process.

  According to this aspect, since the physical strength data (D1) is estimated based only on the specific information related to the exercise menu, it is possible to improve the estimation accuracy of the physical strength data (D1) in the second process.

  In the physical strength measurement method according to the fifth aspect, in any one of the first to fourth aspects, the exercise menu includes muscle strength, walking ability, agility, balance, and flexibility among items related to the exercise ability of the subject (H1). Includes a menu for measuring at least one sex item.

  According to this aspect, it is possible to estimate physical strength data (D1) for measuring at least one item of muscle strength, walking ability, agility, balance, and flexibility that is closely related to a person's daily life. is there.

  The activity support method according to the sixth aspect includes an acquisition process, a generation process, and an output process for acquiring physical fitness data (D1) estimated by the physical fitness measurement method according to any one of the first to fifth aspects. Have. The generation process is a process of generating related information (D4) related to the target person (H1) based on the behavior of the target person (H1). The output process is a process for outputting recommendation information (D3) generated based on the physical strength data (D1) and the related information (D4).

  According to this aspect, since the recommendation information (D3) is information based on the related information (D4) unique to the target person (H1), the activity proposed in the recommendation information (D3) is the target person (H1 ) Will be customized to match. Therefore, according to the activity support method, for example, the recommendation information (D3) by the target person (H1) is recommended by recommending to the target person (H1) as recommendation information (D3) an activity that can easily be performed by the individual target person (H1). ) Will improve the feasibility of implementing activities. As a result, according to the activity support method, there is an advantage that it is easy to provide useful information for the target person (H1).

  A program according to a seventh aspect is a program for causing a computer system to execute the physical strength measurement method according to any one of the first to fifth aspects.

  According to this aspect, it is possible to estimate the physical strength data (D1) corresponding to the execution result of the specific exercise menu from the motion of the subject (H1) in the living space. Therefore, the physical strength data (D1) obtained by the above program is less likely to cause tension or the like in the subject (H1) at the time of measurement compared to physical strength data (D1) measured by an exercise device or a grip strength meter, for example. The original athletic ability of the subject person (H1) is easily reflected. As a result, according to the program, there is an advantage that it is easy to measure the original physical strength data (D1) of the subject (H1).

  The physical strength measurement system (2, 2A) according to the eighth aspect includes a life data acquisition unit (21) and an estimation unit (22). A life data acquisition part (21) acquires the life data regarding the motion in the living space of the subject (H1). An estimation part (22) estimates the physical strength data (D1) equivalent to the implementation result of a specific exercise menu from life data.

  According to this aspect, it is possible to estimate the physical strength data (D1) corresponding to the execution result of the specific exercise menu from the motion of the subject (H1) in the living space. Therefore, the physical strength data (D1) obtained by the physical strength measurement system (2, 2A) is compared with the physical strength data (D1) measured by, for example, an exercise device or a grip strength meter, and the force (H1) is measured on the subject during measurement. The original athletic ability of the subject person (H1) is easily reflected. As a result, the physical strength measurement system (2, 2A) has an advantage that it is easy to measure the original physical strength data (D1) of the subject (H1).

  Not only the said aspect but the various aspects (a modification example) of the physical strength measuring method which concerns on Embodiment 1 and Embodiment 2 are realizable with a physical strength measuring system (2, 2A) etc.

  About the structure which concerns on the 2nd-5th aspect, it is not an essential structure for the physical strength measurement method, and can be abbreviate | omitted suitably.

2,2A Physical Strength Measurement System 21 Life Data Acquisition Unit 22 Estimation Unit D1 Physical Fitness Data D3 Recommendation Information D4 Related Information H1 Target Person

Claims (8)

Life data acquisition processing for acquiring life data related to movement in the living space of the target person,
An estimation process for estimating physical fitness data corresponding to an implementation result of a specific exercise menu from the life data,
Physical strength measurement method. The physical fitness data is estimated based on at least information indicating whether or not a specific work can be performed by the subject among the life data.
The physical strength measuring method according to claim 1. The physical fitness data is estimated based on image data of at least the life data.
The physical strength measuring method according to claim 1 or 2. The estimation process includes
A first process for extracting specific information related to the exercise menu from the life data;
A second process for estimating the physical fitness data based on the specific information extracted in the first process,
The physical strength measuring method according to any one of claims 1 to 3. The exercise menu includes a menu for measuring at least one of muscle strength, walking ability, agility, balance, and flexibility among items related to the exercise ability of the subject.
The physical strength measuring method according to any one of claims 1 to 4. An acquisition process for acquiring the physical fitness data estimated by the physical fitness measurement method according to any one of claims 1 to 5,
A generation process for generating related information related to the target person based on the action of the target person;
An output process for outputting recommendation information generated based on the physical fitness data and the related information.
Activity support method.   The program for making a computer system perform the physical strength measuring method of any one of Claims 1-5. A life data acquisition unit for acquiring life data related to movements in the living space of the target person;
An estimation unit that estimates physical fitness data corresponding to an implementation result of a specific exercise menu from the life data,
Physical strength measurement system.

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