NETWORK ANALYSIS METHOD, NETWORK ANALYSIS SYSTEM AND SERVER

Abstract

To analyze a network between individuals in a group and visualize the individuals that perform effective motions on a game content.SOLUTION: A network analysis method for analyzing, by a computer including a processor and a memory, a network between individuals in a group includes: a sensing data collection step of storing, by the computer, time-series information on motions of individuals who exercise in the group, in sensor data; feature quantity calculation step of calculating, by the computer, feature quantities on motion of each individual from the sensor data; a network analysis step of analyzing, by the computer, relationships between the individuals on the basis of the feature quantities regarding combinations of the individuals, to generate networks between the individuals in time series, from a result of analyzing the relationships between the individuals; and a clique evaluation step of extracting, by the computer, as a clique, a group composed of multiple individuals mutually having relationships, out of individuals having relationships in the networks, and analyzing the clique in time series.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a network analysis technique in a population. [Background technology]

[0002] In recent years, with the advancement of sensing devices and video analysis technology, the use of sensing technology has also been attracting attention in the field of sports. In particular, professional sports teams and Olympic national teams are seeking quantitative evaluation of the performance of individual and group athletes using IoT in order to improve their competitiveness and win games.

[0003] When evaluating individual performance, there is increasing use of sensing technologies such as video analysis and wearable sensors that can record and analyze the amount of movement, position, motion, vital signs, etc. during competition.

[0004] In evaluating group performance, it is important to analyze the network that represents the relationships between individuals during the competition. For example, Patent Document 1 provides a technology that analyzes and visualizes the network of a group even when individuals in the group do not face each other but move in a related manner. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2018-81406 A Summary of the Invention [Problem to be solved by the invention]

[0006] In sports data analysis using conventional technology, reductionist statistical analysis is the mainstream. In sports such as soccer, rugby, and basketball, where the game situation changes drastically and the game situation is difficult to reproduce, it is difficult to effectively use the results of conventional analysis.

[0007] Conventional technology focuses on analyzing data about players related to the ball or those at the center of the game, and there was a problem in that it was difficult to visualize players who were making effective plays.

[0008] For example, in soccer, if a player on the right wing dribbles the ball up the field, a player who runs into the open space on the left wing can be evaluated as making an effective play even if he does not receive a pass. Also, an offensive player who keeps the defense engaged even without the ball can be evaluated as making an effective play.

[0009] In addition, conventional technology mainly focuses on analyzing past data, making it difficult to evaluate players or teams in real time.

[0010] The present invention has been made in consideration of the above problems, and aims to analyze the network between individuals in a population and visualize individuals that perform effective movements in relation to the content of a competition. [Means for solving the problem]

[0011] The present invention is a network analysis method in which a computer including a processor and a memory analyzes a network between individuals in a population, the method including: a sensing data collection step in which the computer collects time-series information on the movements of individuals exercising in a group as sensor data; a feature calculation step in which the computer calculates features related to the movement of each individual from the sensor data; a network analysis step in which the computer analyzes relationships between individuals for combinations of individuals in the population based on the features and generates a network between individuals in time series from the analysis results of the relationships between the individuals; and a clique evaluation step in which the computer extracts a group composed of a plurality of individuals having mutual relationships from among individuals having relationships in the network, as a clique, and analyzes the clique in time series. Effect of the Invention

[0012] Therefore, the present invention makes it possible to visualize individuals that perform effective movements by analyzing the network between individuals in a population and extracting a plurality of cliques.

[0013] The details of at least one implementation of the subject matter disclosed herein are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the disclosed subject matter will become apparent from the following disclosure, drawings, and claims. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 illustrates a first embodiment of the present invention, and is a diagram illustrating an example of a network analysis system applied to the field of sports. [Diagram 2] FIG. 1 is a block diagram illustrating an example of a configuration of a network analysis system according to a first embodiment of the present invention. [Diagram 3] 1 is a flowchart illustrating an outline of a process performed by a server according to the first embodiment of the present invention. [Figure 4] FIG. 2 illustrates an example of sensor data according to the first embodiment of the present invention. [Diagram 5] FIG. 2 is a diagram illustrating an example of a screen displayed by an environmental information input unit according to the first embodiment of the present invention. [Figure 6] FIG. 2 illustrates an example of environment information according to the first embodiment of the present invention. [Figure 7] 5 is a flowchart illustrating an example of processing performed by the feature amount calculation unit according to the first embodiment of the present invention. [Figure 8] FIG. 2 illustrates an example of feature amount information according to the first embodiment of the present invention. [Figure 9] 5 is a flowchart illustrating an example of processing performed by the network analysis unit according to the first embodiment of the present invention. [Figure 10] FIG. 2 illustrates an example of synchronous network time series data according to the first embodiment of the present invention. [Figure 11]5 is a flowchart illustrating an example of processing performed by the network evaluation unit according to the first embodiment of the present invention. [Figure 12] 5 is a flowchart illustrating details of a degree centrality calculation process performed by the network evaluation unit according to the first embodiment of the present invention. [Figure 13] 10 is a flowchart showing details of a closeness centrality calculation process performed by the network evaluation unit according to the first embodiment of the present invention. [Figure 14] 10 is a flowchart illustrating the details of a calculation process of betweenness centrality by a network evaluation unit according to the first embodiment of the present invention. [Figure 15] 10 is a flowchart illustrating an example of a process for calculating an individual that is a center of a population by a network evaluation unit according to the first embodiment of the present invention. [Figure 16] 5 is a flowchart illustrating an example of a process for calculating a group activity level by a network evaluation unit according to the first embodiment of the present invention. [Figure 17] FIG. 2 illustrates an example of an individual evaluation table according to the first embodiment of the present invention. [Figure 18] FIG. 2 illustrates an example of a group evaluation table according to the first embodiment of the present invention. [Figure 19] FIG. 2 illustrates an example of synchronous network time series data according to the first embodiment of the present invention. [Figure 20] FIG. 2 illustrates the first embodiment of the present invention and is a diagram illustrating an example of a relationship between sensor data and time-series data of a synchronous network. [Figure 21] FIG. 1 illustrates a first embodiment of the present invention and is a diagram showing an example of extracting a clique from a synchronization network. [Figure 22] 11 is a flowchart illustrating an example of processing performed by a clique evaluation unit according to the first embodiment of the present invention. [Figure 23] FIG. 13 illustrates the types of two cliques according to the first embodiment of the present invention. [Figure 24] FIG. 11 illustrates an example of two-clique information according to the first embodiment of the present invention. [Diagram 25]13 is a graph illustrating an example of time series data by type for two cliques according to the first embodiment of the present invention. [Figure 26] 13 is a graph illustrating an example of individual time series data of two cliques according to the first embodiment of the present invention. [Figure 27] FIG. 13 illustrates the types of three cliques according to the first embodiment of the present invention. [Figure 28] FIG. 11 illustrates an example of three-clique information according to the first embodiment of the present invention. [Figure 29] 13 is a graph illustrating an example of time series data by type for three cliques according to the first embodiment of the present invention. [Diagram 30] 13 is a graph illustrating another example of time series data by type of three cliques according to the first embodiment of the present invention. [Diagram 31] 13 is a graph illustrating an example of individual time series data of three cliques according to the first embodiment of the present invention. [Diagram 32] FIG. 11 is a block diagram illustrating an example of a configuration of a network analysis system according to a second embodiment of the present invention. [Diagram 33] 13 is a flowchart illustrating an outline of a process performed by a server according to a second embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings. EXAMPLES

[0016] As a first preferred embodiment of the present invention, an example of a network analysis system between players in a group sport will be described below with reference to the drawings.

[0017] 1 is a diagram showing an example in which the network analysis system of the first embodiment is applied to a group sport. The network analysis system of the first embodiment is a computer system that analyzes relationships based on the movements of individuals (players) constituting a group, creates a network (connections between individuals), and visualizes the evaluations of the individuals.

[0018] In the following, a system for visualizing a human network will be described, but the present invention may be applied to any system that analyzes and visualizes a network between moving individuals such as animals. In the following, the individuals that make up the network will be referred to as users.

[0019] The network visualization system includes a sensing device 1, a network 4, and a server 5.

[0020] An example of an athletic activity for which a network is displayed in Example 1 is soccer. However, the type of athletic activity for which a network is visualized may be any sport in which two or more individuals participate, and the network analysis system of Example 1 may be applied to group sports such as rugby, basketball, volleyball, goalball, baseball, tennis, handball, lacrosse, track and field, and speed skating.

[0021] Furthermore, the network analysis system of Example 1 is not limited to networks between individuals, and may be used to display networks between human bodies and objects, or between objects, by measuring the movements of objects such as tools and implements such as balls, bats, and rackets using the sensing device 1.

[0022] In the first embodiment, one or more sensing devices 1 are attached to the body of an individual 30 performing an athletic activity. The individual 30 in the first embodiment shows an example in which a wristwatch-type wearable device equipped with a three-axis acceleration sensor is directly attached to the human body as the sensing device 1. The individual 30 performs an athletic activity on a field 100, but the place where the individual performs the athletic activity is not limited to the field 100 and may be any place.

[0023] In addition, by specifying the location where the physical activity will be performed, it is possible to display a network of only the individuals in the specified location even if the individuals wearing the sensing device 1 are in multiple locations. In this embodiment, the individual 30 is an individual displayed in the network and is also an individual measured by the sensing device 1.

[0024] The sensing device 1 in this embodiment is equipped with at least one or more sensors among various sensors that measure the movement (exercise) of an individual. The various sensors include, but are not limited to, an acceleration sensor, a gyro sensor, a pedometer, a heart rate monitor, a position sensor, a shock sensor, a magnetometer, a temperature sensor, a humidity sensor, a wind sensor, a sound sensor, a pressure sensor, a positioning sensor, an infrared sensor, and a position information sensor. The results measured by these various sensors may be transmitted to the server 5 as sensing data.

[0025] The individual 30 may also wear the sensing device 1 so that it is physically connected to a body part of the individual 30, such as the head, neck, shoulders, back, arms, wrists, hands, fingers, waist, hips, legs, ankles, feet, heels, and toes. If there is one or more pieces of clothing, footwear, or athletic protective gear between the sensing device 1 and the body of the individual 30, the individual 30 may wear the sensing device 1 by various removable or non-removable connection means such as straps, adhesives, pockets, and clips, in a state where the sensing device 1 is integrated with the clothing, footwear, or athletic protective gear used in the athletic activity. The sensing device 1 may also be attached to or embedded in an object such as an implement or tool, such as a ball, bat, or racket.

[0026] In addition, the sensing device 1 is not limited to a wearable sensor, but may be any device that can measure the movement of an individual or object, such as an instrument that measures the movement of an individual or object through image analysis, a laser radar that can detect the position of an object by reflecting laser light, a force plate that can measure pressure and impact, or other installed sensors such as an acoustic sensor, a magnetic sensor, an RGB camera, a depth sensor, or a multi-array microphone.

[0027] Sensing data relating to the movements measured by the sensing device 1 is transmitted to the server 5 via the network 4, where it is subjected to analysis processing and the like, and is displayed on the display of the input / output device 56.

[0028] The server 5 of this embodiment analyzes the sensing data received from the sensing device 1 in real time to generate synchronous network time series data (described later) indicating the connections or relationships between the individuals 30, and displays the data on the display of the input / output device 56 as clique evaluation information (described later) that classifies the relationships between the individuals 30 in real time.

[0029] By referring to the clique evaluation information, it is possible to grasp in real time which individuals 30 have effective moves in a competition currently in progress.

[0030] 2 is a block diagram showing an example of the configuration of a network analysis system. A server 5 communicates with a sensing device 1 via a network 4. The server 5 is a computer having a CPU 51, a memory 54, an auxiliary storage device (storage device) 55, a communication unit 52, and an input / output device 56. In the following description, a network without a symbol is used to indicate cooperation between individuals, and is distinguished from the network 4 that connects computers.

[0031] The server 5 realizes the functions of the analysis unit 10 and the environmental information input unit 53 by executing, with the CPU 51, a program loaded from the auxiliary storage device 55 to the memory 54. The memory 54 of the server 5 can be configured with a ROM, which is a non-volatile storage element, or a RAM, which is a volatile storage element.

[0032] The ROM stores programs (e.g., BIOS), etc. The RAM is a high-speed, volatile storage element such as a DRAM (Dynamic Random Access Memory), and temporarily stores the programs stored in the auxiliary storage device 55 and data used when the programs are executed.

[0033] The database 20 is stored in the auxiliary storage device 55. The auxiliary storage device 55 is, for example, a large-capacity non-volatile storage device such as a magnetic storage device (HDD) or a flash memory (SSD). The auxiliary storage device 55 also stores the programs executed by the CPU 51 and data used by the programs. That is, the programs are read from the auxiliary storage device 55, loaded into the memory 54, and executed by the CPU 51.

[0034] The server 5 has an information collection unit (not shown) that stores the sensing data received from the sensing device 1 in the sensor data 22 of the database 20, and collects the sensing data transmitted at a predetermined cycle.

[0035] The analysis unit 10 is a functional unit that calculates time-series acceleration sensor information and position information based on the sensor data 22, calculates a network between individuals based on the movements and trajectories of the individuals detected from the acceleration sensor information and position information, and evaluates the network and cliques. When the cooperation or relationship between individuals in a competition or the like is expressed by edges, a group of vertices (or nodes) including one or more edges is considered to be a clique. In other words, a group consisting of multiple individuals among individuals having relationships in a network is considered to be a clique.

[0036] The analysis unit 10 mainly has functional units of a feature amount calculation unit 11, a network analysis unit 12, a network evaluation unit 13, a clique evaluation unit 15, and a display unit 14. Note that the location of an individual may be measured using location information detected by the GNSS 35 or the like in addition to the sensing device 1 attached to the individual. Also, the association between the time-series location information and the sensor data 22 may be performed by a computer connected to the server 5.

[0037] The display unit 14 and the environmental information input unit 53 output the data recorded in the database 20 to a computer or device or an input / output device 56 connected via the network 4, thereby making the data available to a user (a user of the analysis system) who evaluates the group network. The input / output device 56 is composed of a keyboard, a mouse, a display, etc.

[0038] The CPU 51 operates as a functional unit that provides a predetermined function by processing according to the program of each functional unit. For example, the CPU 51 functions as the analysis unit 10 by processing according to an analysis program. The same applies to other programs. Furthermore, the CPU 51 also operates as a functional unit that provides each function of a plurality of processes executed by each program. A computer and a computer system are devices and systems that include these functional units.

[0039] The communication unit 52 is a network interface that controls communication with other devices in accordance with a predetermined protocol, and is capable of communicating with other devices via the network 4 and transmitting and receiving data.

[0040] The analysis unit 10 and the environmental information input unit 53 may be implemented by a program or by a physical integrated circuit. In particular, the analysis unit 10 may be implemented by a plurality of programs or a plurality of integrated circuits for executing the functional units included in the analysis unit 10. Furthermore, the feature calculation unit 11, the network analysis unit 12, the network evaluation unit 13, and the display unit 14 may each be implemented by a plurality of programs or a plurality of integrated circuits for each process that they execute.

[0041] The programs executed by the CPU 51 are provided to the server 5 via a removable medium (such as a CD-ROM or a flash memory) or a network, and are stored in the auxiliary storage device 55, which is a non-transitory storage medium. For this reason, the server 5 may have an interface for reading data from the removable medium.

[0042] The server 5 is a computer system configured on one physical computer, or on multiple logical or physical computers, and the above-mentioned programs may be executed in different threads on the same computer, or may be executed on virtual computers constructed on multiple physical computers.

[0043] The database 20 stores sensor data 22 , feature amount information 21 , environmental information 23 , synchronous network time series data 24 , network evaluation information 25 , and clique evaluation information 40 .

[0044] The sensing data measured by the sensing device 1 for each individual is stored as sensor data 22. The sensor data 22 in this embodiment indicates the results of measuring the movement of each individual.

[0045] The feature amount information 21 indicates a feature amount related to the movement of each individual, which is calculated by executing the feature amount calculation unit 11 on the sensor data 22. Details will be described later.

[0046] The environmental information 23 is input through the environmental information input unit 53. The environmental information 23 indicates information related to the athletic activity, and includes the sensor data 22, the feature amount information 21, and information for identifying the synchronous network time series data 24 based on the athletic activity.

[0047] The environmental information 23 indicates, for example, the content of the athletic activity, the time and place where the athletic activity was performed, and participants (individuals) in the athletic activity. Details will be described later. Note that, when analyzing and displaying a network targeting all of the sensor data 22, the environmental information 23 and the environmental information input unit 53 are not necessarily required.

[0048] The synchronization network time series data 24 indicates information on the network between individuals calculated by executing the network analysis unit 12 on the feature amount information 21. Details will be described later.

[0049] The network evaluation information 25 is evaluation information on the network of a population calculated by executing the network evaluation unit 13 on the synchronous network time series data 24, and includes an individual evaluation table 250 and a population evaluation table 258. Details will be described later.

[0050] The clique evaluation information 40 is data in which the connections or relationships between individuals calculated from the synchronization network time series data 24 are classified by clique type, and the occurrence frequency of each clique type is expressed in time series, as described below.

[0051] The sensing device 1 is a device that measures the movement of a target individual for analyzing a network. In this embodiment, a wearable device worn by the target individual is taken as an example. Note that the sensing device 1 may measure any content as sensing data as long as the content changes in value as a result of the user's movement.

[0052] The sensing device 1 includes an acceleration sensor 31, a Global Navigation Satellite System (GNSS) 35, a memory 32, a microcomputer (MCU in the figure) 33, and a communication unit 34.

[0053] The acceleration sensor 31 measures the acceleration of the user, for example, about 20 to 1000 times per second. The GNSS 35 can be configured as a position information system such as a Global Positioning System (GPS) or a Quasi-Zenith Satellite System (QZSS), and measures the position information of the individual. In this embodiment, the position information from the GNSS 35 is used as the position information of the individual 30 at the start of the competition, and the sensing data of the acceleration sensor 31 is used to measure the movement of the individual 30 during the competition.

[0054] The microcomputer 33 records the measurement results obtained by the acceleration sensor 31 and the GNSS 35 as sensing data in the memory 32. Here, the microcomputer 33 records an identifier (user ID) unique to each user and the measurement results as the sensing data.

[0055] The microcomputer 33 transmits the sensing data and the position information recorded in the memory 32 to the server 5 via the communication unit 34. The communication unit 34 transmits the sensing data to the server 5, wirelessly or via wired communication, at a timing when communication is possible or at a predetermined timing set by a user or an administrator.

[0056] FIG. 3 is a flowchart showing an example of processing performed by the analysis unit 10 of the server 5.

[0057] First, the feature amount calculation unit 11 acquires sensing data of a target to be processed from the sensor data 22 based on the environmental information 23, and calculates the feature amount information 21 related to the movement of each individual 30 from the target sensor data 22 (S101).

[0058] The feature amount information 21 indicates a feature amount whose value changes according to the strength of the movement (or the magnitude of the energy of the movement) of the individual 30, such as the amount of body movement, and further indicates the change in the feature amount in a time series.

[0059] In this embodiment, the physical activity intensity is calculated as the characteristic amount, but any characteristic amount related to exercise such as amount of exercise, amount of body movement, moving speed, activity frequency, pitch, number of steps, etc. Furthermore, characteristic amounts such as calorie consumption, heart rate, pulse rate, amount of sweat, etc. that change with exercise may be calculated as the characteristic amount.

[0060] Next, the network analysis unit 12 uses the calculated feature amount information 21 to perform network analysis between the individuals 30 to generate synchronous network time series data 24 (S102). Then, the network evaluation unit 13 calculates the central individual of the group and group activity from the result of step S102, evaluates the network, and generates network evaluation information 25 (S103). The calculation of group activity will be described later.

[0061] The clique evaluation unit 15 acquires the synchronous network time series data 24, extracts a preset number of cliques, and generates clique evaluation information 40 by classifying the types of the extracted cliques as described below (S104).

[0062] Finally, the display unit 14 outputs the clique evaluation information 40, the synchronous network time series data 24 of the processing target population, and the network evaluation information 25 (S105). The display unit 14 visualizes and outputs the clique evaluation information 40, the synchronous network time series data 24, and the network evaluation information 25 at a predetermined time interval.

[0063] The output of the display unit 14 can be displayed on a display of the input / output device 56 or on an output device of a computer (hereinafter, referred to as an external computer) connected to the server 5 via the network 4. Note that the information visualized and output by the display unit 14 may include the synchronous network time series data 24 or the network evaluation information 25 in the clique evaluation information 40.

[0064] The visualization process performed by the display unit 14 can graph the clique evaluation information 40 or output the synchronous network time series data 24 as a map. The visualization of the clique evaluation information 40 and the synchronous network time series data 24 will be described later.

[0065] By executing the process shown in FIG. 3, the analysis unit 10 can analyze the network between the individuals 30 being analyzed using the sensor data 22 and the environmental information 23, generate synchronous network time series data 24, and perform clique analysis processing to generate clique evaluation information 40.

[0066] The analysis unit 10 can identify combinations of individuals with strong relationships from the generated synchronization network time series data 24 and output them in the form of, for example, a graph. This makes it possible to visualize the network between individuals in a population.

[0067] The analysis unit 10 can extract predetermined cliques from the synchronous network time series data 24 obtained by chronologically aggregating the network, generate clique evaluation information 40 by classifying the cliques by type, and output the information in a visualized form such as a graph. The clique evaluation information 40 makes it possible to visualize individuals that perform effective moves in relation to the content of the competition, with regard to the network between individuals in a group.

[0068] FIG. 4 is a diagram showing the sensor data 22 according to the first embodiment.

[0069] The sensor data 22 includes a user information table 201 that records information about the individual 30 wearing the sensing device 1, and a movement information table 202 that records activity amount information of each individual 30.

[0070] The user information table 201 includes a user ID 2011, a user name 2012, and a user type 2013. The user ID 2011 records an identifier assigned to each individual 30 in order to recognize the individual 30 wearing the sensing device 1. The user ID stored in the user ID 2011 is stored in sensing data transmitted from the sensing device 1.

[0071] The user name 2012 records the name or nickname of the individual 30 wearing the sensing device 1. The user type 2013 records various information of the individual 30, including but not limited to age and sex, as user information.

[0072] For example, if the athletic activity to be measured is soccer, the user type 2013 records the team the user belongs to, the position, and the like, which enables the display unit 14 to evaluate and display the individual 30 by type.

[0073] One row of the movement information table 202 indicates information on one time (measurement date and time) of one individual 30. The movement information table 202 includes a user ID 2021, a measurement date and time 2022, an acceleration X-axis 2023, an acceleration Y-axis 2024, an acceleration Z-axis 2025, a latitude 2026, and a longitude 2027. The user ID 2021 records an ID for identifying the individual 30, and corresponds to the user ID 2011.

[0074] The measurement date and time 2022 stores the date and time when the sensing data was measured. The acceleration x-axis 2023, the acceleration y-axis 2024, and the acceleration z-axis 2025 store the measurement results of each axis of the three-axis acceleration sensor. The latitude 2026 and the longitude 2027 store the measurement values ​​of the GNSS35.

[0075] As described above, the sensor data 22 stores the user information table 201 for each individual 30 and the time-series movement information table 202.

[0076] FIG. 5 is a diagram showing an environment display input screen 301 displayed by the environment information input unit 53 of the first embodiment.

[0077] The environmental information input unit 53 provides an environmental display input screen to the display of the input / output device 56 or an external computer, and receives environmental information input to the input / output device 56 or the server 5 from an operator of the external computer. Then, the environmental information input unit 53 stores the input environmental information in the environmental information 23 in the auxiliary storage device 55.

[0078] The analysis unit 10 can analyze data in short-term athletic activities by referring to the environmental information 23. The analysis unit 10 can also analyze and display a network in athletic activities such as training in which various practice menus are performed in a short period of time while changing the participants.

[0079] The environment display input screen 301 includes an environment information input page 302 , a participant selection page 303 , and a grouping selection page 304 .

[0080] The environmental information input page 302 allows for input of an activity ID 3021 which stores an identifier for the athletic activity, an activity name 3022 which stores the name of the athletic activity, a start time 3023, an end time 3024, an area name 3025 of the area in which the athletic activity was performed, participants in the athletic activity 3026, and a grouping of participants 3027.

[0081] The environmental information input page 302 is an example of a screen for inputting environmental information related to the athletic activity that the operator wishes to analyze. The operator inputs an activity name 3022, a start time 3023, an end time 3024, and the like into each line of the environmental information input page 302. The operator also uses tabs to select an area name 3025 of the area where the activity was performed from candidate locations registered in advance. The operator can also input the area name 3025. The operator then inputs individuals 30 participating in the athletic activity into participants 3026.

[0082] In the case of participants 3026, if all of the wearers (individuals 30) of the sensing device 1 who have been registered in advance are participants, the operator checks the "all" check box. In addition, when the operator selects a participant (individual 30), he or she selects "individual selection" for participants 3026.

[0083] When "individual selection" for participant 3026 is selected, the environment information input unit 53 displays a participant selection page 303. The participant selection page 303 is a screen that displays a list of individuals 30 wearing a sensing device 1 that has been registered in advance, and allows the operator to select a participant (individual 30). A function for newly registering an individual 30 wearing a sensing device 1 may be added to this screen.

[0084] The participant selection page 303 includes selections 3031 as check boxes for selecting a record, and information such as a user ID 3032, a user name 3033, a uniform number 3034, a position 3035, and a grade 3036 for one record.

[0085] Grouping 3027 displays an interface that allows the operator to select how to group the participants. If the operator does not need to group the participants, the operator checks the "none" check box in Grouping 306. If the operator wants to group the participants, the operator selects Advanced Settings.

[0086] When the detailed setting of grouping 3027 is selected, the environmental information input unit 53 displays a grouping selection page 304. The grouping selection page 304 displays a list of individuals 30 equipped with sensing devices 1 that have been registered in advance. The grouping selection page 304 then displays an interface that allows the operator to select a team for the individuals 30. The grouping selection page 304 may also have a function that allows the operator to freely set and change team names.

[0087] The grouping selection page 304 includes information on a user ID 3041, a user name 3042, a team A 3043, a team B 30434, and a team C 3045 in one record.

[0088] In addition, in this embodiment 1, the individuals 30 are grouped according to the team to which they belong, but this is not limited to this, and they may be grouped according to the position or grade of the individuals 30, etc., and further, grouping may be performed using various information registered in the user information table 201.

[0089] The environmental information received by the environmental information input unit 53 is stored in the environmental information 23 of the database 20. In addition, the environmental information input unit 53 may store the grouping of the individuals 30 input via the grouping selection page 304 in the user type 2013 of the user information table 201.

[0090] FIG. 6 is a diagram showing the environment information 23 according to the first embodiment.

[0091] The environmental information 23 includes an activity information table 203 that records information about the activity measured using sensor data 22, an environmental information table 204 that records the environment in which the activity was performed, such as the activity period and location, for each activity ID in order to identify the environment in which the activity was performed, and a group information table 205 that records the composition and characteristics of the group in which the activity was performed.

[0092] The activity information table 203 includes an activity ID 2031, an activity name 2032, and a group ID 2033. The activity ID 2031 records an ID (identifier) ​​set for each athletic activity in order to recognize the athletic activity for which the sensor data 22 was measured. The activity ID 2031 may be the same as the activity ID entered in the environmental information input page 302, or may be an ID assigned based on the activity ID.

[0093] The activity name 2032 records the name of the exercise activity for which the sensor data 22 was measured. This allows the display unit 14 to display a screen showing the area name. The activity name 2032 stores the activity name 3022 entered in the environmental information input page 302.

[0094] The environmental information table 204 includes an activity ID 2041, a start time 2042, an end time 2043, an area ID 2044, a user ID 2045, and an area name 2046. The activity ID 2041 corresponds to the activity ID 2031 in the activity information table 203. The start time 2042 and the end time 2043 indicate the start time and end time of the exercise activity indicated by the activity ID 2041. The start time 2042 and the end time 2043 store the start time 3023 and the end time 3024 inputted to the environmental information input page 302.

[0095] The area ID 2044 indicates the location where the athletic activity indicated by the activity ID 2041 was performed. The area ID 2044 stores an ID assigned to the area name 3025 entered in the environmental information input page 302. The user ID 2045 stores the user ID of the individual 30 who is a participant in the athletic activity indicated by the activity ID 2041 and wears the sensing device 1. The area name 2046 stores the name of the area corresponding to the area ID 2044.

[0096] The group information table 205 includes a group ID 2051, a group name 2052, a user ID 2053, and a group type 2054. The group ID 2051 corresponds to the group ID 2033. The group name 2052 records the name or nickname of the group. The user ID 2053 stores the user ID of the individual 30 who is a member of the group indicated by the group ID 2051 and wears the sensing device 1. The group type 2053 may record the composition and characteristics of the group as group information, and also records various information of the group that is not limited to these.

[0097] For example, when the group activity to be measured is soccer, the group type 2053 records the formation or the like, and the display unit 14 can display by type, such as by mapping and displaying a network on the formation.

[0098] 7 is a flowchart showing an outline of step S101 performed by the feature amount calculation unit 11 in the embodiment 1. The feature amount calculation unit 11 calculates feature amounts related to the movement of the individual 30 from the sensor data 22.

[0099] First, in step S1011, the feature amount calculation unit 11 acquires the sensor data 22 to be subjected to the network analysis and display processing from the DB 20 based on the environment information 23 inputted by the environment information input unit 53. In the present embodiment 1, the sensor data 22 is three-axis acceleration data (the acceleration X-axis 2023, the acceleration Y-axis 2024, and the acceleration Z-axis 2025 of the movement information table 202) measured at a predetermined sampling rate (for example, 20 Hz). The sensor data 22 to be processed can be specified from the input / output device 56 or an external computer. In addition to the three-axis acceleration data, the latitude 2026 and the longitude 2027 can be added as position information.

[0100] Next, in step S1012, the feature calculation unit 11 applies a predetermined filter to the sensor data 22 acquired in step S1011 to remove noise. In the first embodiment, a band-pass filter is designed and applied, but the present invention is not limited to the band-pass filter, and any processing such as moving average, low-pass filter, high-pass filter, etc. may be executed according to the sensing data. Parameters such as the order of the filter, pass band, stop band, etc. may also be set according to the sensing data. Also, depending on the sensing data, it is not necessarily necessary to execute step S1012.

[0101] In step S1013, the feature amount calculation unit 11 calculates feature amounts from the sensing data to which the above filter has been applied. The feature amounts calculated here are information relating to the movement of the individual 30.

[0102] The above steps S1011, S1012, and S1013 are applied to the sensor data 22 of all the individuals 30 that make up the population.

[0103] The feature amount information 21 in the first embodiment is METs (Metabolic Equivalents) indicating exercise intensity. In step S1013, the feature amount information 21 (METs) for each time point is calculated.

[0104] The feature calculation unit 11 first calculates a scalar S of the accelerations on three axes, the X-axis, the Y-axis, and the Z-axis, after application of the filter. Here, the scalar S is calculated by the following formula (1) when the accelerations on the respective axes are written as Xg, Yg, and Zg.

[0105] Scalar quantity S = (Xg^2 + Yg^2 + Zg^2)^(1 / 2) (1)

[0106] Next, METs is calculated from the scalar quantity S according to the following formula (2).

[0107] METs = a × S + 1 (2)

[0108] Here, a is a constant. This METs value is calculated at rest, i.e., when the scalar quantity S = 0, METs = 1, and indicates how many times the exercise intensity is at rest.

[0109] The sensor data 22 described above includes three-axis acceleration, but may include any measurement results that measure the strength of the energy (intensity) of human movement, such as heart rate, breathing rate, and amplitude. The feature calculation unit 11 calculates feature amounts related to movement, such as breathing rate, heart rate, walking or running pattern, or moving speed, based on these various sensor data 22, which enables the network analysis unit 12 of this embodiment to analyze the group network more effectively.

[0110] Furthermore, a value collected at a specific time may be used as the feature information 21. For example, by calculating the feature from the sensor data 22 measured at a sampling frequency of 20 Hz and using the value collected every second, noise due to sudden changes can be reduced. Whether to use the collected value of the sensor data 22 and to what extent to use the collected value are determined depending on the sensing device 1 used, the measurement environment, etc.

[0111] Through the above process, METs is calculated as an example of the feature amount information 21 from the filtered sensor data 22.

[0112] 8 is a diagram showing an example of data stored in the feature amount information 21 of the first embodiment. The feature amount information 21 stores features at each date and time 222 of all individuals 223 for which network analysis was performed. In this embodiment, METs are calculated as the feature amount from the sensor data 22 measured at a sampling frequency of 20 Hz, and values ​​tallied every second are stored.

[0113] Fig. 9 is a flowchart showing an outline of step S102 in Fig. 3 performed by the network analysis unit 12 in the embodiment 1. The network analysis unit 12 analyzes the relationships between the individuals constituting the population from the feature amounts calculated in step S101.

[0114] First, in step S1021, the network analysis unit 12 acquires the time interval of data used to analyze the network between individuals 30. A preset value may be used as the time interval of the data, so long as it is equal to or greater than the measurement interval (sampling period or a predetermined time interval) of the sensing device 1.

[0115] The time interval of the data may be a value already written in the program. Also, an input section (not shown) may be provided in the environmental information input section 53 to refer to the input value. In the present embodiment 1, the time interval of the data is set to one minute, and the network can be analyzed and displayed every minute.

[0116] In step S1022, the network analysis unit 12 selects two individuals for network analysis. In this embodiment 1, two individuals are selected one by one to analyze the presence or absence of correlation between the two individuals for all combinations of the individuals that make up the population.

[0117] In step S1023, the feature amount calculated in step S101 of FIG. 3 is obtained from the feature amount information 21 for the individual selected in step S1022 above.

[0118] In steps S1024, S1025, and S1026, the relationship between the two individuals is analyzed to determine whether or not a network exists. In this embodiment 1, the amount of information transmitted between the individuals 30 is calculated using transfer entropy to determine whether or not a network exists.

[0119] Transfer entropy is a type of information amount, and is a numerical value of the information flow between random variables as an average information amount. Unlike mutual information, which indicates only the correlation between variables and does not include the flow of information, transfer entropy can specify the flow of information and is an index that measures the relationship between variables. In this embodiment 1, transfer entropy is used, but any index that measures the relationship between multiple variables, such as mutual information, correlation analysis, regression analysis, multivariate analysis, etc., and any method for calculating the index may be used.

[0120] In step S1024, the network analysis unit 12 calculates a probability distribution from the time series data of the variables (feature amounts) of each individual 30. A method of approximating the probability distribution using a histogram is adopted, but a method of estimating by assuming a Gaussian distribution or a mixed Gaussian distribution may also be adopted, and the method is not limited to these. When approximating using a histogram, the number of divisions of the histogram uses the Akaike Information Criterion or Sturges's formula, but is not limited to these.

[0121] In step S1025, the network analysis unit 12 calculates the amount of information transmission between the variables of the two individuals from the transfer entropy. Here, when the elements of the random variables X and Y at time t are xt and yt, the transfer entropy T(Y→X), which indicates the influence of Y on X, is calculated by the following formula (3).

[0122]

number

[0123] T(Y → X) takes a value between 0 and 1, with 0 indicating no influence at all, i.e., no relationship at all, and the closer it is to 1, the greater the influence, i.e., the greater the relationship between individuals.

[0124] In this Example 1, the influence of element yt of random variable Y on element xt+1 of random variable X is calculated. In other words, when feature amount information 21 collected every second is used, the influence of the movement of individual Y on individual X one second later is calculated. In addition, by setting the element of random variable X to xt+n, it is also possible to calculate the influence of the movement of individual Y on individual X n seconds later. The value of n can be set arbitrarily.

[0125] In step S1026, the network analysis unit 12 uses the value of the transfer entropy calculated in step S1025 to determine whether or not a network exists between two individuals. The network analysis unit 12 determines that information has been transmitted between individuals 30 whose transfer entropy is equal to or greater than a predetermined threshold, and that a network exists. In this embodiment 1, the threshold is a predetermined constant. The threshold may be a threshold determination method using a x2 distribution, a gamma distribution, or the like, and is set arbitrarily depending on the sensing device 1 used, the environment in which the measurement was performed, and the like.

[0126] In step S1027, the network analysis unit 12 stores the presence or absence of the network determined in step S1026 in the synchronous network time series data 24.

[0127] In this embodiment 1, if a network exists between two individuals, the synchronization network time series data 24 is set to 1, and if not, the synchronization network time series data 24 is set to 0. In addition, multiple thresholds may be set and the network strength may be determined and stored in several stages, and the transfer entropy value may also be stored as it is.

[0128] In step S1028, the above steps S1022, S1023, S1024, S1025, S1026, and S1027 are repeated until analysis of all combinations of individuals constituting the population is completed.

[0129] Through the above process, the magnitude of the relationship between the individuals 30 is calculated based on the magnitude of the transfer entropy, the individuals that make up the network are identified, and a network within the population is generated.

[0130] 10 is a diagram showing data stored in the synchronous network time series data 24 of Example 1. The synchronous network time series data 24 is a brute force diagram of networks at a certain time, and describes the presence or absence (strength or weakness) of a network at a certain time t for all individuals subjected to network analysis.

[0131] The synchronous network time series data 24 is time series data generated at a predetermined time interval. In the illustrated example, the synchronous network time series data 24 is generated in real time at a predetermined time interval ΔT from time T1, and is analyzed and evaluated by the clique evaluation unit 15.

[0132] The first column of the synchronous network time series data 24 is the user ID 242 of the individual who gives information and influence, and the first row is the user ID 243 of the individual who gives information and is influenced. The value between two individuals shown by the row and column indicates the strength of the network on a five-point scale.

[0133] When the relationship between the individuals 30 is strong, "5" is set, and when the relationship between the individuals 30 is weak, "1" is set. By storing the strength of the relationship, it becomes possible to display the relationship between the individuals in more detail on the display unit 14 described below. In this embodiment, the strength of the relationship is described in five stages, but it may be stored in any number of stages. In the above, the strength of the relationship is described in five stages, but it may be stored in any number of stages.

[0134] The strength of the relationship can be expressed by degree centrality and closeness centrality, as described later. An example of the relationship between individuals 30 visualized on the display unit 14 is shown in Fig. 19. Fig. 19 is a diagram showing an example in which degree centrality and closeness centrality of network evaluation information 25 are added to synchronization network time series data 24. The example shown in the figure shows the results of a time series network analysis between individuals 30 in a soccer game.

[0135] In the illustrated example, the synchronous network time series data 24 at time t1 is displayed in an area 81, the network at time t2 after a time interval Δt is displayed in an area 82, and the network at time t3 is displayed in an area 83.

[0136] Individuals 30 in team A (first group) are displayed as solid circles, and individuals 30 in team B (second group) are displayed as dashed circles. Relationships between individuals 30 are displayed as edges, the size of the degree centrality is displayed as the size of the circle, and the size of the closeness centrality is displayed as the density of the hatching.

[0137] In addition, although FIG. 19 shows an example in which the individuals 30 are displayed as a directed graph, the graph may be an undirected graph (same below).

[0138] 11 is a flowchart showing an example of the process performed in step S103 by the network evaluation unit 13. The network evaluation unit evaluates the central individual of the population and the population activity level from the synchronous network time series data 24 calculated in step S102.

[0139] In step S1031, the network evaluation unit 13 acquires the synchronous network time series data 24 calculated in step S102 in Fig. 3. In this embodiment, the network shown in Fig. 11 is used.

[0140] In step S1032, the network evaluation unit 13 calculates the degree centrality, closeness centrality, betweenness centrality, and number of edges as evaluation indices of the network using the synchronized network time series data 24 acquired. The calculation method of each index will be described later. In this embodiment 1, the degree centrality, closeness centrality, betweenness centrality, and number of edges are used as evaluation indices of the network, but are not limited to these.

[0141] In step S1033, the network evaluation unit 13 estimates an individual at the center of the network using the degree centrality, closeness centrality, and betweenness centrality calculated in step S1032. Details will be described later.

[0142] In step S1034, the network evaluation unit 13 estimates the group activity level of the network in the team using the number of edges calculated in step S1032. Details will be described later.

[0143] In step S1035, the network evaluation unit 13 stores the evaluation information calculated in the above steps S1032, S1033, and S1034 in the network evaluation information 25 (the individual evaluation table 250 and the population evaluation table 258).

[0144] Through the above process, network evaluation information 25 is generated using the degree centrality, closeness centrality, betweenness centrality, and number of edges for the calculated synchronization network time series data 24.

[0145] 12, 13 and 14 are flow charts showing the details of the process performed by the network evaluation unit 13 in the first embodiment.

[0146] A flowchart 60 in Fig. 12 shows the process of calculating degree centrality. Degree centrality is an index that highly evaluates vertices (individuals) that have more relationships within a network.

[0147] In step S1036, the network evaluation unit 13 counts the in-degree of an individual at a certain time. The in-degree is the number of individuals from which an individual has received information.

[0148] In step S1037, the network evaluation unit 13 counts the out-degree of an individual at a certain time. The out-degree is the number of individuals to which an individual has transmitted information.

[0149] In step S1038, the network evaluation unit 13 calculates the in-degree, out-degree, and degree centrality from the in-degree calculated in step S1036 above, the out-degree calculated in step S1037, and the total degree, which is the sum of the in-degree and the out-degree, since the degree centrality is the integrated value of the degrees.

[0150] In step S1039, since degree centrality differs depending on the number of vertices included in the network, the network evaluation unit 13 standardizes the degree by dividing it by the theoretical maximum value of the population to be processed. In a population with n individuals, the maximum degree is n-1, so the network evaluation unit 13 divides each degree centrality calculated in step S1038 by n-1.

[0151] In step S1040, the network evaluation unit 13 repeatedly executes the processes of steps S1036 to S1039 to calculate the in-degree, out-degree, and degree centrality for all individuals (vertices) that have been subjected to the network analysis.

[0152] A flow chart 61 in Fig. 13 shows a process for calculating closeness centrality. Closeness centrality is an index that evaluates a vertex (individual) higher as the distance between the vertex (individual) and another vertex (individual) becomes smaller.

[0153] In step S1041, the network evaluation unit 13 calculates the sum of the shortest distances from one individual to another individual. The shortest distance is the number of edges that must be traversed in order to reach from one vertex to another vertex in the shortest time.

[0154] In step S1042, since closeness centrality is the inverse of the shortest distance from one individual to another individual, the network evaluation unit 13 calculates the closeness centrality using the shortest distance from one individual 30 to another individual 30 calculated in step S1040 above.

[0155] In step S1043, since closeness centrality differs depending on the number of vertices included in the network, the network evaluation unit 13 standardizes it by dividing it by the theoretically maximum closeness centrality value in the population to be processed. In a population with n individuals, the maximum closeness centrality value is 1 / (n-1), so the network evaluation unit 13 divides the closeness centrality calculated in step S1040 by 1 / (n-1).

[0156] In step S1044, the network evaluation unit 13 repeatedly executes the above steps S1040 to calculate the closeness centrality for all individuals that have been subjected to the network analysis.

[0157] A flowchart 62 in Fig. 14 shows the process of calculating betweenness centrality in the network evaluation unit 13. Betweenness centrality is an index that evaluates a vertex (individual) more highly on the shortest path between other vertices (individuals). An individual with high betweenness centrality can be considered to be an important individual for maintaining the connectivity of the network.

[0158] In step S1045, the network evaluation unit 13 calculates the sum of the shortest distances from one individual to another individual. The shortest distance is the number of edges that must be traversed in order to reach from one vertex to another vertex in the shortest time.

[0159] In step S1046, the network evaluation unit 13 calculates the number of shortest paths from one individual to another individual. The number of shortest paths is calculated as follows. The number of shortest paths from the start point to the start point itself is set to 1. The number of paths to a vertex reached in the process of the search is equal to the number of paths to the immediately preceding vertex. If there are multiple immediately preceding vertices, it is equal to the sum of the numbers of paths to those immediately preceding points.

[0160] In step S1047, the network evaluation unit 13 repeatedly executes steps S1045 and S1046 for all individuals 30 (vertices) in order to calculate the sum of the shortest distances and the number of shortest paths for all individuals for which network analysis has been performed.

[0161] In step S1048, using the information about the shortest paths examined in steps S1045 and S1046, network evaluation unit 13 examines the extent to which each vertex is located on the shortest path between other vertices.

[0162] Here, vertex w is the vertex immediately following vertex v on the shortest path starting from vertex s, and g sv , g sw are the number of shortest paths to vertex v and vertex w, respectively. In this case, the extent to which vertex v is on the shortest path to other vertices is expressed by b sv is calculated by the following formula (4).

[0163]

number

[0164] The network evaluation unit 13 uses the above formula (4) to calculate whether each vertex included in the graph of each individual 30 is located on the shortest path to other vertices when the vertex is taken as the starting point.

[0165] In step S1049, the network evaluation unit 13 evaluates the degree b of each vertex located on the shortest path between other vertices calculated in step S1048. sv The betweenness centrality of each vertex is calculated by adding the above for each vertex.

[0166] In step S1050, since betweenness centrality differs depending on the number of vertices included in the network, the network evaluation unit 13 standardizes it by dividing it by the theoretically maximum betweenness centrality value in the group. In a group with n individuals, the maximum betweenness centrality is (n-2)×(n-1), so the network evaluation unit 13 divides the closeness centrality calculated in step S1049 above by (n-2)×(n-1).

[0167] Through the above processing, the network evaluation unit 13 calculates the betweenness centrality of each vertex (individual).

[0168] Flowchart 63 in Fig. 15 shows an example of the process of calculating an individual that is the center of the population in step S1033 shown in Fig. 11. In step S1051, the network evaluation unit 13 calculates an individual that has the maximum centrality shown in the following formula (5) by using the degree centrality x, closeness centrality y, and betweenness centrality z of each individual 30 at each time calculated in step S1032 in Fig. 11.

[0169] ax+by+cz ···(5)

[0170] In the above formula (5), a, b, and c are coefficients. In this embodiment, the individual that maximizes formula (5) is calculated as the central individual of the population, but the method is not limited to this.

[0171] A flow chart 64 in FIG. 16 shows the process of calculating group activity in step S1034 shown in FIG.

[0172] In step S1052, the network evaluation unit 13 counts the degree of the entire group for which the group activity is calculated. In addition, when multiple individuals 30 exist on the same field 100, such as in soccer, the degree within the group, the out-degree from within the group to another group, the in-degree from outside the other group to the group, and the degree outside the group can be calculated to evaluate the relationship between groups.

[0173] In step S1053, the network evaluation unit 13 standardizes the degree by dividing it by the theoretically maximum value, and calculates the population activity. The population activity takes a value between 0 and 1, for example, and the closer to 1, the more activated the population is. In the first embodiment, the population activity is calculated using the degree of the entire population, but the method is not limited to this.

[0174] 17 is a diagram showing an example of an individual evaluation table 250 stored in the network evaluation information 25 of the embodiment 1. The individual evaluation table 250 of the network evaluation information 25 stores evaluation results for each date and time 251 of each individual on which the network evaluation was performed in step S103 of FIG.

[0175] The individual evaluation table 250 includes a date and time 251, an indegree centrality 252, an outdegree centrality 253, a degree centrality 254, a closeness centrality 255, an betweenness centrality 256, and a centrality 257 in one entry.

[0176] Indegree centrality 252, outdegree centrality 253, and degree centrality 254 store the indegree, outdegree, and degree centralities calculated in the flowchart 60 shown in FIG. 12, closeness centrality 255 stores the closeness centrality calculated in the flowchart 61 shown in FIG. 13, betweenness centrality 256 stores the betweenness centrality calculated in the flowchart 62 shown in FIG. 14, and centrality 257 stores the centrality calculated in the flowchart 63 shown in FIG. 15.

[0177] The individual evaluation table 250 of the network evaluation information 25 is a table that is generated for each date of the individual 30 .

[0178] 18 is a diagram showing data of the group evaluation table 258 stored in the network evaluation information 25 of the embodiment 1. The group evaluation table 258 stores the evaluation results for each date of each group for which the network evaluation was performed in step S103 of FIG.

[0179] The group evaluation table 258 includes, in each entry, a date and time 259, an in-group degree 260, an out-group degree 261, an in-group degree 262, an out-group degree 263, a total degree 264, a liveness level 265, an individual with the highest degree centrality 266, an individual with the highest closeness centrality 267, an individual with the highest betweenness centrality 268, and a central individual 269.

[0180] The date and time 259 in the population evaluation table 258 corresponds to the date and time 251 in the individual evaluation table 250 .

[0181] The degree within a group 260, the out-degree to outside a group 261, the in-degree to a group 262, the out-degree outside a group 263, the total degree 264, and the activity 265 store the degree within a group, the out-degree from within a group to another group, the in-degree from outside another group to the group, the degree outside the group, the total degree, and the group activity calculated in the flowchart 64 of FIG. 16.

[0182] The individual with the highest degree centrality 266 stores the individual with the highest degree centrality calculated in the flowchart 60 of FIG. 12, the individual with the highest closeness centrality 267 stores the individual with the highest closeness centrality calculated in the flowchart 61 of FIG. 13, the individual with the highest betweenness centrality 268 stores the individual with the highest betweenness centrality calculated in the flowchart 62 of FIG. 14, and the central individual 269 stores the individual with the highest centrality calculated in the flowchart 63 of FIG. 15.

[0183] 20 is a diagram showing the relationship between the sensor data 22 and the synchronous network time series data 24. In the illustrated example, a part of the synchronous network time series data 24 generated from the acceleration data acquired from the movement information table 202 of the sensor data 22 is displayed as a graph, and the time-series change of the relationship between multiple individuals 30 is shown.

[0184] At time t1, the individual 30 with ID="1" has relationships with two individuals 30 with ID="2" and "4", and the individual 30 with ID="3" has a relationship with an individual 30 with ID="6". At time t2, after a predetermined time interval Δt (e.g., one minute) has elapsed, ID="1" changes the individual 30 with which it is related from ID="4" to ID="5", and has relationships with two individuals 30 with ID="2" and "5". At time t3, ID="1" has only one individual 30 with ID="5" as its related entity.

[0185] The synchronous network time series data 24 is a network based on synchronization between individuals 30, and by comparing the synchronous networks between individuals 30 over time, it is possible to analyze sports involving intense group movements, such as soccer, rugby, or basketball.

[0186] Fig. 21 shows an example of extracting a clique from the synchronization network time series data 24. As shown in Fig. 20, a graph can be generated from the synchronization network time series data 24, and changes in the connections of an individual 30 can be extracted in time series.

[0187] The clique evaluation unit 15 of the analysis unit 10 extracts a predetermined number of cliques from the synchronous network time series data 24, and aggregates and outputs the cliques at each time in time series. As shown in the figure, the clique evaluation unit 15 of this embodiment extracts two cliques consisting of two individuals 30 and three cliques consisting of three individuals 30 as evaluation targets.

[0188] The graph at time t1 in FIG. 20 can be decomposed into a two-clique and three-clique relationship shown in FIG.

[0189] In FIG. 21, the clique evaluation unit 15 connects related individuals 30 with one edge, and extracts a graph with the nodes at both ends of the edge as 2 cliques. That is, a combination of nodes with degree 1 forms a 2 clique. For example, in the graph at time t1, IDs "1" and "2" form 2 cliques, and further, IDs "1" and "4", IDs "2" and "4", and IDs "3" and "6" are extracted as 2 cliques. Note that in this embodiment, the direction of the edges is not evaluated and the graph is treated as an undirected graph.

[0190] When a node at the destination of two cliques is related to a third node and a node at the source of the relationship is also related to the same third node, the clique evaluation unit 15 extracts these three nodes as a three-clique.

[0191] Each node in a 3-clique has degree 2, and the three nodes are related to each other. In the graph at time t1, in a 2-clique with IDs "1" and "2", if node "1" is the source of the relationship and node "2" is the destination of the relationship of "1", then the destination node "2" has a relationship with another (third) node "4", and node "1" of the source of the relationship has a relationship with the same other node "4". In this way, a configuration in which the edges connecting the three nodes are closed is called a 3-clique.

[0192] 22 is a flowchart showing an example of the process performed in the clique evaluation unit 15. This process is performed in step S104 in FIG.

[0193] The clique evaluation unit 15 first acquires data to be analyzed from the synchronous network time series data 24 (S1141). The clique evaluation unit 15 extracts two cliques for each date and time (or time) of the synchronous network time series data 24 (S1142).

[0194] The clique evaluation unit 15 classifies the data of the extracted two cliques into three types as shown in Fig. 23 (S1143). When the competition of the individual 30 is being held by two teams, Team A and Team B, the types of the two cliques are the following three types.

[0195] A combination consisting of two individuals 30 only from team A is type A2, a combination consisting of only individuals 30 from team B is type B2, and a combination consisting of one each of individuals 30 from teams A and B is type A1B1. In the case of type A2 or B2, it can be considered that the teammates are working together. In addition, in the case of the clique type A1B1, it can be considered that the enemy and the teammate are facing each other and marking each other.

[0196] The clique evaluation unit 15 can detect the type of relationship between a group of individuals 30 by classifying the type of clique according to the group to which the individuals belong and the number of individuals 30 for the extracted clique.

[0197] The clique evaluation unit 15 aggregates the data of the two cliques classified by type for each time and stores the data in the two clique information 410 constituting the clique evaluation information 40 (S1143). Fig. 24 is a diagram showing an example of the two clique information 410. The two clique information 410 includes, in one record, a date and time 4101 that stores the date and time of the synchronous network time series data 24, a first node 4102 that stores the identifier of the node (individual 30) that is the source of the relationship, a second node 4103 that stores the identifier of the individual 30 of the node that is the destination of the relationship, and a classified clique type 4104.

[0198] Next, the clique evaluation unit 15 chronologically aggregates the data of the two cliques classified by type, and generates and outputs time series data for each type (S1144). Fig. 25 is a graph showing an example of the time series data for each type 420 of two cliques. The time series data for each type 420 of two cliques is also data constituting the clique evaluation information 40. The time series data for each type 420 of two cliques is displayed on the display of the input / output device 56 or the display of an external computer.

[0199] The time series data 420 for each type of two cliques is a time series graph for each type of the two clique information 410, with the horizontal axis representing time and the vertical axis representing the number of cliques. The time series data 420 for each type of two cliques can represent the frequency of occurrence of a state in which each individual 30 cooperates with a teammate (type A2 or B2) and a state in which each individual marks another (type A1B1) in the entire group. In particular, in the case of soccer, types A2 and B2 can be considered as states in which the ball is kept.

[0200] Next, the clique evaluation unit 15 generates and outputs time series data for each individual by chronologically aggregating the identifiers of the individuals 30 from the two clique information 410 classified by type (S1145). Fig. 26 is a graph showing an example of the individual time series data 430 for the two cliques. The individual time series data 430 for the two cliques is displayed on the display of the input / output device 56 or the display of an external computer.

[0201] The individual time series data 430 of the two cliques is a time series graph with the horizontal axis representing time and the vertical axis representing the number of cliques, for each identifier of the individual 30 (first node 4102 of the two clique information 410). In the illustrated example, time series data for each type of individual 30 with ID="1" is displayed.

[0202] The individual time series data 430 of the two cliques can represent the frequency of occurrence of a state in which each individual 30 cooperates with an ally (type A2) and an attack or defense state (type A1B1) in time series. By displaying the individual time series data 430 of the two cliques on the display of the input / output device 56 or the display of an external computer, the user of the server 5 can observe the change in the manner in which the individual 30 cooperates with the group in time series and evaluate each individual 30.

[0203] Through the above processing of steps S1142 to S1145, the clique evaluation unit 15 generates the two-clique information 410, the species-specific time series data 420, and the individual-specific time series data 430 that constitute the clique evaluation information 40.

[0204] In steps S1142 to S1145, the three cliques are processed in the same manner as in steps S1142 to S1145. The clique evaluation unit 15 can execute the processes of steps S1142 to S1145 and steps S1142 to S1145 in parallel. However, the clique evaluation unit 15 may execute the processes of steps S1142 to S1145 and steps S1142 to S1145 sequentially.

[0205] The clique evaluation unit 15 extracts three cliques for each date and time (or ) of the synchronous network time series data 24 (S1146).

[0206] The clique evaluation unit 15 classifies the extracted data of the three cliques into four types as shown in Fig. 27 (S1147). When the competition of the individual 30 is being held by two teams, Team A and Team B, the types of the three cliques are the following four types.

[0207] A combination in which the three individuals 30 are only from Team A is type A3, a combination in which the three individuals 30 are only from Team B is type B3, a combination in which the three individuals 30 are only from Team A is type A2B1, and a combination in which the three individuals 30 are only from Team B is type A1B2.

[0208] When the clique type is A3 or B3, it can be understood as a state where teammates are working together. When it is type A2B1, it can be understood as a state where team A has a numerical advantage. When it is type A1B2, it can be understood as a state where team B has a numerical advantage.

[0209] The clique evaluation unit 15 collects the data of the three cliques classified by type for each time and stores the collected data in the three clique information 440 constituting the clique evaluation information 40. Fig. 28 is a diagram showing an example of the three clique information 440. The three clique information 440 includes, in one record, a date and time 4401 that stores the date and time of the synchronous network time series data 24, a first node 4402 that stores the identifier of the individual 30 of the node that is the source of the relationship, a second node 4403 that stores the identifier of the individual 30 of the node that is the destination of the relationship, a third node 4404 that stores the identifier of the individual 30 of another node that is the destination of the relationship, and a type 4405 of the classified clique.

[0210] Next, the clique evaluation unit 15 chronologically aggregates the data of the three cliques classified by type, and generates and outputs time series data for each type (S1148). Figures 29 and 30 are graphs showing an example of time series data for each type 450 for three cliques. The time series data for each type 450 for three cliques is also data constituting the clique evaluation information 40. The time series data for each type 450 for three cliques is displayed on the display of the input / output device 56 or the display of an external computer.

[0211] FIG. 29 shows time series data 450 by type for types A3 and B3, and FIG. 30 shows time series data 450 by type for types A2B1 and A1B2. The graphs are divided into two for ease of viewing, but may be one graph.

[0212] The three clique type time series data 450 is a time series graph of the three clique information 440 type, with the horizontal axis representing time and the vertical axis representing the number of cliques. The three clique type time series data 450 can represent the frequency of occurrence of a state in which the individual 30 cooperates with teammates in the entire group (type A3 or B3) and a numerically superior state (type A2B1, A1B2) in a time series. In particular, in the case of soccer, types A3 and B3 can be considered to be states in which ideal cooperative play is possible, avoiding the opponent's mark.

[0213] Next, the clique evaluation unit 15 generates and outputs time series data for each individual by chronologically aggregating the identifiers of the individuals 30 from the 3 clique information 440 classified by type (S1149). Fig. 31 is a graph showing an example of individual time series data 460 for the 3 cliques. The individual time series data 460 for the 3 cliques is displayed on the display of the input / output device 56 or the display of an external computer.

[0214] The individual time series data 460 of the three cliques is a time series graph with the horizontal axis representing time and the vertical axis representing the number of cliques, for each identifier of the individual 30 (first node 4402 of the three clique information 440). In the illustrated example, time series data for each type of individual 30 with ID="1" is displayed.

[0215] The individual time series data 460 of the 3 cliques can represent, in time series, the frequency of occurrence of a state in which the individual 30 (ID="1") cooperates with allies (type A3) and a numerically superior state (types A2B1, A1B2).

[0216] Through the above processing of steps S1146 to S1149, the clique evaluation unit 15 generates three clique information 440 constituting the clique evaluation information 40, time series data by type 450 of the three cliques, and time series data by individuals 460 of the three cliques.

[0217] By using the above processing, relationships between individuals 30 are extracted from the sensing data collected by the sensing device 1 and clique evaluation information 40 is generated and output in real time, making it possible to grasp the relationships between the individuals 30 during the competition in real time and to send accurate instructions to each individual 30.

[0218] A manager or coach of a group can plan a strategy for a game by referring to each piece of information in the clique evaluation information 40. For example, in the case of soccer, the two most frequently used cliques are considered to dominate the rhythm of the entire network. In addition, the organizational play of a team and the rhythm of the entire team can be understood from the diversity of each type of the three cliques.

[0219] Therefore, if the two cliques with high frequency of appearance among the individuals 30 of the opposing team are suppressed, it is expected that the diversity of the three cliques of the opposing team will be destroyed. Therefore, by sending an instruction to the team to mark or press the two cliques with high frequency of appearance, it is possible to improve the content of the game.

[0220] Furthermore, by acquiring the movements of each individual 30 in real time from the various clique evaluation information 40 of 2-clique and 3-clique, it is possible to extract individuals 30 that make effective movements regardless of whether or not there is a ball.

[0221] For example, from the information on three cliques, it can be estimated that an individual 30 that frequently appears in type A3 is a central player. Also, from the information on three cliques, an individual 30 that appears in types A1B2 and A2B1 can be evaluated as making effective moves such as marking an opponent or attracting an opponent, regardless of whether or not the ball is in hand.

[0222] As described above, the network analysis system of this embodiment provides clique evaluation information 40 in real time from sensing data from the sensing device 1 attached to the individual 30, making it possible to visualize the individual 30 that is making effective moves in relation to the content of the competition, and making it possible to plan strategies and evaluate the individual 30 in real time.

[0223] Furthermore, with the two-clique or three-clique clique evaluation information 40, it is possible to evaluate an individual 30 regardless of whether or not there is a ball or the centrality of the game, making it possible to accurately evaluate players in games with high levels of activity.

[0224] In addition, the network analysis system of the present embodiment has versatility that allows it to be applied to various sports. As described above, the analysis target of the present embodiment is not limited to soccer, but can also be applied to rugby, volleyball, and the like.

[0225] Furthermore, in the network analysis system of this embodiment, since the only explanatory variable is the number of cliques, there is no need to refer to a large number of parameters as in the prior art, and it is possible to easily understand and quickly evaluate the behavior of individuals 30.

[0226] In addition, in the above Example 1, examples of two and three cliques are described as the cliques extracted from the synchronous network time series data 24, but the number of cliques to be extracted is not limited to the above and can be changed as appropriate depending on the type of sport to be analyzed, etc.

[0227] In addition, in the above Example 1, an example was shown of classifying the clique types of individuals 30 competing in two teams, Team A (first group) and Team B (second group), but the clique types can be set according to the number of teams competing simultaneously, and are not limited to the above two teams. EXAMPLES

[0228] 32 is a block diagram showing an example of the configuration of a network analysis system, illustrating a second embodiment of the present invention. In the first embodiment, an example was shown in which the server 5 generates clique evaluation information 40 in real time based on acceleration and position information acquired by the sensing device 1 attached to the individual 30, but there are cases in which it is not possible to attach the sensing device 1 to all of the individuals 30 participating in the competition.

[0229] Therefore, in this embodiment, an example is shown in which, without using the sensing device 1, cameras are installed in a stadium or the like to collect video data of the competition and generate clique evaluation information 40. In this embodiment, one or more cameras 70 are installed on the field 100 to obtain video data of the individuals 30 competing, and the video data is stored in the video data 27 of the server 5.

[0230] In the server 5, the image tracking unit 16 reads the video data 27, identifies the individual 30, extracts the position information, calculates the moving speed, etc. The other configurations are the same as those in the first embodiment, and the same configurations are denoted by the same reference numerals and the duplicated explanations will be omitted.

[0231] 33 is a flowchart showing an example of processing performed by the server 5. First, in the server 5, the image tracking section 16 acquires the video data 27 stored in the auxiliary storage device 55 (S211).

[0232] The image tracking unit 16 identifies the individual 30 from the loaded video data 27 and calculates the position on the field 100 (S212). Note that the individual 30 can be identified by applying a known technique such as face recognition. Also, the identified individual 30 may be associated with the user ID 2021 (movement information table 202) by an administrator who operates the input / output device 56 or an external computer.

[0233] The image tracking unit 16 calculates the acceleration (or movement speed) from the position information of the individual 30 detected for each frame of the video data 27, and sets it as the accelerations 2023 to 2025 in the movement information table 202 (S213).

[0234] When the analysis unit 10 of the server 5 completes the calculation of the acceleration for each individual, the feature calculation unit 11 calculates the feature information 21 using the movement information table 202 as an input, in the same manner as in the embodiment 1. Then, the network analysis unit 12 generates the synchronous network time series data 24 based on the feature information 21, in the same manner as in the embodiment 1 (S214).

[0235] Next, the clique evaluation unit 15 acquires the synchronous network time series data 24 (S215), and extracts two-cliques and three-cliques in the same manner as in the first embodiment, to generate the clique evaluation information 40 (S216).

[0236] The display unit 14 outputs the generated clique evaluation information 40 to the display of the input / output device 56 or an external computer, and presents a two-clique graph or a three-clique graph to a group manager or the like.

[0237] As described above, in this embodiment, even in a situation in which the sensing device 1 cannot be attached to the individual 30, it is possible to evaluate the individual 30 based on the clique evaluation information 40 in the same manner as in the first embodiment.

[0238] <Conclusion> As described above, the network analysis systems of the first and second embodiments can be configured as follows.

[0239] (1) A network analysis method in which a computer (server 5) including a processor (CPU 51) and a memory (memory 54) analyzes a network between individuals (30) in a population, the method comprising: a sensing data collection step in which the computer (5) collects time-series information (sensing data) regarding the movements of individuals exercising in a population as sensor data (22); a feature calculation step (feature calculation unit 11) in which the computer (5) calculates feature amounts (feature information 21) regarding the movements of each of the individuals from the sensor data (22); a network analysis step (network analysis unit 12) in which the computer (5) analyzes relationships between individuals (30) for combinations of individuals in the population based on the features and generates a network between the individuals in a time series from the analysis results of the relationships between the individuals; and a clique evaluation step (clique evaluation unit 15) in which the computer (5) extracts a group consisting of a plurality of individuals (30) from among the individuals (30) having relationships in the network as a clique, and analyzes the clique in a time series.

[0240] With the above configuration, it is possible to analyze the network between the individuals 30 in the population and visualize the individuals 30 that make effective moves from multiple cliques.

[0241] (2) The network analysis method according to (1) above, wherein the individual (30) belongs to either a first group (Team A) or a second group (Team B), and the clique evaluation step (15) classifies the clique by a clique type that is preset according to a combination of the individual (30) and the group to which the individual (30) belongs.

[0242] With the above configuration, the clique evaluation unit 15 can detect the type of relationship between a group of individuals by classifying the type of clique for the extracted clique depending on the group to which the individuals 30 belong and the number of individuals 30.

[0243] (3) The network analysis method according to (2) above, wherein the clique evaluation step (15) comprises aggregating the classified types of cliques in time series for each individual (30) and outputting the aggregated types as individual time series data (430, 460).

[0244] With the above configuration, by displaying the individual time series data 430 on the display of the input / output device 56 or the display of an external computer, users of the server 5 can observe changes in the manner in which the individuals 30 interact with the group over time and evaluate each individual 30.

[0245] (4) The network analysis method according to (2) above, wherein the clique evaluation step (15) comprises aggregating the classified types of cliques in time series and outputting the types as time series data (420, 450).

[0246] With the above configuration, by displaying the species-specific time series data 420 on the display of the input / output device 56 or the display of an external computer, the user of the server 5 can observe the changes in the individuals 30 cooperating within the group over time.

[0247] The present invention is not limited to the above-mentioned embodiment, and various modifications are included. For example, the above-mentioned embodiment is described in detail to easily explain the present invention, and is not necessarily limited to including all the configurations described. In addition, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. In addition, any addition, deletion, or replacement of other configurations can be applied to a part of the configuration of each embodiment, either alone or in combination.

[0248] In addition, the above-mentioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole by hardware, for example, by designing them as integrated circuits. In addition, the above-mentioned configurations and functions, etc. may be realized in software by a processor interpreting and executing a program that realizes each function. Information such as the program, table, file, etc. that realizes each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0249] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are connected to each other. [Explanation of symbols]

[0250] 1. Sensing device 5 Server 10 Analysis Department 11 Feature Calculation Unit 12 Network Analysis Department 13 Network Evaluation Department 14 Display section 15 Clique evaluation section 21 Feature information 22 Sensor Data 24 Synchronous network time series data 25 Network Evaluation Information 40 Creek Rating Information 51 CPU 52 Memory 55 Auxiliary storage device

Claims

1. A network analysis method in which a computer including a processor and a memory analyzes a network between individuals in a population, comprising: a sensing data collection step in which the computer collects, as sensor data, time-series information on the movements of individuals exercising in a group; a feature calculation step in which the computer calculates a feature related to the movement of each of the individuals from the sensor data; a network analysis step in which the computer analyzes relationships between individuals for combinations of individuals in the population based on the feature amount, and generates a network between individuals in time series from the analysis result of the relationships between the individuals; a clique evaluation step in which the computer extracts a group of a plurality of individuals having mutual relationships among the individuals having relationships in the network as a clique, and analyzes the clique in a time series; A network analysis method comprising:

2. 2. The network analysis method of claim 1, comprising: The individual is Belonging to either the first group or the second group, The clique evaluation step includes: A network analysis method comprising: classifying the cliques into predetermined clique types according to a combination of the individuals and a group to which the individuals belong.

3. 3. A network analysis method according to claim 2, comprising: The clique evaluation step includes: A network analysis method comprising: tabulating the classified types of cliques for each individual in time series and outputting the results as individual time series data.

4. 3. A network analysis method according to claim 2, comprising: The clique evaluation step includes: A network analysis method comprising: tabulating the classified types of cliques in time series and outputting the types as time series data.

5. a server including a processor and a memory; A network analysis system including a sensing device that acquires time-series information regarding the movement of an individual, The sensing device transmits time-series information regarding the movements of individuals exercising in a group to the server; The server, an information collecting unit that collects, as sensor data, time-series information regarding the movement of the individual received from the sensing device; a feature extraction unit that calculates a feature related to the movement of each individual from the sensor data; a network analysis unit that analyzes relationships between individuals for combinations of individuals in the population based on the feature amount, and generates a network between individuals in time series from a result of the analysis of the relationships between the individuals; a clique evaluation unit that extracts a group of a plurality of individuals having mutual relationships among the individuals having relationships in the network as a clique and analyzes the clique in a time series; A network analysis system comprising:

6. 6. The network analysis system of claim 5, The individual is Belonging to either the first group or the second group, The clique evaluation unit includes: A network analysis system comprising: a network analysis unit that classifies the cliques into predetermined clique types according to a combination of the individuals and a group to which the individuals belong.

7. 7. The network analysis system of claim 6, The clique evaluation unit includes: A network analysis system characterized in that the classified types of cliques are compiled in time series for each individual and output as individual time series data.

8. 7. The network analysis system of claim 6, The clique evaluation unit includes: A network analysis system characterized in that the classified types of cliques are compiled in time series and output as time series data for each type.

9. A server including a processor and a memory, an information collecting unit that receives time-series information on the movements of individuals exercising in a group and collects the information as sensor data; a feature extraction unit that calculates a feature related to the movement of each individual from the sensor data; a network analysis unit that analyzes relationships between individuals for combinations of individuals in the population based on the feature amount, and generates a network between individuals in time series from a result of the analysis of the relationships between the individuals; a clique evaluation unit that extracts a group of a plurality of individuals having mutual relationships among the individuals having relationships in the network as a clique and analyzes the clique in a time series; A server comprising:

10. 10. The server of claim 9, The individual is Belonging to either the first group or the second group, The clique evaluation unit includes: A server which classifies the cliques into predetermined clique types according to a combination of the individuals and a group to which the individuals belong.

11. 11. The server of claim 10, The clique evaluation unit includes: A server that counts the classified types of cliques for each individual in time series and outputs the counted types of cliques as individual time series data.

12. 11. The server of claim 10, The clique evaluation unit includes: A server that aggregates the classified clique types in time series and outputs the aggregated types as time series data.

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