Running style analysis system and running style analysis method

Abstract

In a running style analysis system 100, a type determination unit 82 determines a type to which acquired motion data belongs from among a plurality of running style types classified in advance according to feature amounts corresponding to a plurality of kinds of motion analysis indices. A data storage unit 66 holds precedent data including the motion data acquired from a plurality of individuals subject to measurement and type determination results by the type determination unit 82 for the motion data. A data extraction unit 84 extracts, from the data storage unit, the precedent data of an individual subject to measurement whose goal is shared by a user 10 on the basis of the similarity of the running style type. An output unit 99 outputs a comparison result between the extracted precedent data and the motion data of the user 10.

Description

Running gait analysis system and running gait analysis method 【0001】 The present invention relates to a technique for analyzing running motion. 【0002】 In recent years, due to the increasing health consciousness of people, the number of runners has been increasing. In particular, with the improvement of various measurement technologies using information terminals and wearable devices, runners can easily measure running information (time, distance, altitude, heart rate, movement information such as pitch and stride), record it as a running log, and use it for analyzing their own running results. The utilization of such running logs serves as a motivation for continuing and habitualizing running, boosting the popularity of running. Technologies have been proposed to analyze the big data accumulated from the running logs of many runners, classify the running gait types of runners, and provide shoe selection, effective training, and training menus according to the running gait types (see, for example, Patent Document 1). 【0003】 Japanese Patent No. 7487345 【0004】 Here, even though it has become possible to easily obtain movement information during one's own running by recent measurement technologies, it is not easy to grasp whether one's current running form is sufficient to achieve the goal of the marathon completion time set by oneself. 【0005】 The present disclosure has been made in view of such problems, and its object is to provide a technique for estimating the possibility of achieving a goal based on a running form. 【0006】To solve the above problems, a running style analysis system in one aspect of the present disclosure includes: a goal acquisition unit that acquires a user's goal regarding the time it takes to complete a race; a measurement value acquisition unit that acquires motion data, which is a measurement value of feature quantities corresponding to a plurality of motion analysis indicators relating to the user's motion state, including the running form; a type determination unit that determines which of a plurality of running style types, which are pre-classified according to the feature quantities corresponding to the plurality of motion analysis indicators, the acquired motion data belongs to; a data storage unit that stores the type determination results from the type determination unit for motion data acquired from a plurality of subjects, precedent data including motion data, and the goals of the plurality of subjects; a data extraction unit that extracts precedent data of subjects whose goals are common to the user from the data storage unit based on the degree of similarity of the running style type; and an output unit that outputs the result of comparing the extracted precedent data with the user's motion data. 【0007】Another aspect of this disclosure is also a running style analysis system. This running style analysis system includes a goal acquisition unit that acquires the user's goal regarding the time it takes to complete a race, an attribute acquisition unit that acquires the user's attribute information, a measurement value acquisition unit that acquires motion data which is measured values ​​of feature quantities corresponding to multiple types of motion analysis indicators related to the user's motion state, including their running form, an evaluation calculation unit that calculates evaluation values ​​corresponding to multiple types of running style analysis indicators from the feature quantities corresponding to multiple types of motion analysis indicators included in the acquired motion data, and a unit that determines which of multiple running style types, which are pre-classified according to the combination of evaluation values ​​corresponding to multiple types of running style analysis indicators, the acquired motion data belongs to, and also performs multiple running style analysis The system includes: a type determination unit that determines which of several levels of running style type a person belongs to, based on the total value of evaluation values ​​corresponding to an indicator; a data storage unit that stores the type determination results from the type determination unit for motion data acquired from multiple subjects, along with information indicating the subject's goal, whether or not that goal was achieved, attribute information, and the time when the motion data was acquired; a data extraction unit that extracts type determination results from the data storage unit for subjects who share the same goal, attribute information, running time, and running style type as the user, and whose goal has been achieved; and an output unit that outputs the result of comparing the extracted type determination results with the user's motion data. 【0008】Another aspect of this disclosure is a running style analysis method. This method comprises: a process of obtaining a user's race completion time goal via a network; a process of obtaining motion data via a network, which is a measurement of feature quantities corresponding to multiple types of motion analysis indicators relating to the user's motion state, including running form; a process of a computer determining which of multiple running style types, pre-classified according to the feature quantities corresponding to multiple types of motion analysis indicators, the obtained motion data belongs to; a process of a computer extracting precedent data common to the user and the goal from precedent data including the type determination results for motion data obtained from multiple subjects and motion data, based on the degree of similarity of the running style type; and a process of outputting the comparison results between the extracted precedent data and the user's motion data via a network. 【0009】 Furthermore, any combination of the above components, or any substitution of the components or expressions of this disclosure between methods, apparatus, programs, temporary or non-temporary storage media storing programs, systems, etc., are also valid forms of this disclosure. 【0010】 According to this disclosure, the likelihood of achieving a goal can be estimated based on running form. 【0011】This is a diagram showing the configuration of the running style analysis system. This is a functional block diagram showing each component of the running style analysis system. This is a diagram showing the correspondence between running style analysis indicators and motion analysis indicators. This is a radar chart visually showing combinations of evaluation values ​​for running style analysis indicators. This is a functional block diagram showing each function of the running style analysis server. This is a flowchart illustrating the processing steps in the running analysis system in a general manner. This is a diagram showing the correspondence between nine types of running styles and five running style analysis indicators. This is a diagram showing the relationship between evaluation values ​​and normalized scores for each running style analysis indicator. This is a diagram showing the state after applying normalized scores for each running style analysis indicator to the nine types of running styles. This is a diagram showing a running style type determination table for determining the running style over the entire run. This is a flowchart showing in detail the filtering process of precedent data in S30 of Figure 6. This is a diagram showing a list of precedent data in which running style type determination tables for each user and run period are accumulated. This is a diagram showing an example screen that displays the comparison results with running style analysis indicators in precedent data. This shows the hardware configuration of the measuring device, information terminal, and running style analysis server. 【0012】 In this embodiment, based on various information acquired by wearable devices worn by the user while running and devices carried while running, feature quantities corresponding to multiple types of motion analysis indicators that indicate the runner's movement state are analyzed. The user's running style type is determined by the analysis of running motion. Information regarding the user's target race completion time, such as for marathons, and other attributes is obtained. This information is stored as precedent data. 【0013】 According to this disclosure, by presenting users with precedent data from runners with similar goals, they can use this information to determine whether their own running form is sufficient to achieve their goals. It is also possible to narrow down the presentation to include precedent data from runners with similar running styles or similar attributes. By referring to precedent data from other runners who share similar goals, running styles, and attributes, users can estimate their own likelihood of achieving their goals. 【0014】Multiple types of motion analysis indices are parameters represented by values ​​measured or detected by the measuring device itself, which is worn or carried by the user as a runner, or by values ​​calculated based on those measured or detected values. Examples include lap pace, lap time, running time, running speed, pitch, stride, stride-to-height ratio, trunk posterior tilt, vertical oscillation, vertical oscillation-to-height ratio, hip sinking, pelvic tilt, pelvic lift, pelvic rotation, pelvic rotation timing, lateral impact, push-off time, ground contact time, ground contact time ratio, landing impact, push-off acceleration, deceleration, and stiffness. 【0015】 In this embodiment, running techniques were classified into nine types. Techniques that suggest appropriate shoes and training menus according to such running technique classifications are known. In this embodiment, the concept of "goals" is added, and the runner user sets a goal for their marathon completion time. For example, for a 42.195 km marathon, the goal might be to complete it in under 3 hours, the so-called sub-3, or for a half marathon, the goal might be to complete it in 1 hour and 30 minutes. It is not easy to know whether one's current running form and technique are sufficient to achieve such completion time goals, and therefore it has not been easy to create training menus to improve or strengthen form and running technique. 【0016】 Therefore, by extracting precedent data from other runners with similar goals, running styles, and skill levels, and then narrowing it down to presenting precedent data with common attributes to the user, it can be used as a reference to estimate whether their own running form is sufficient to achieve their goals. 【0017】 The present disclosure will be described below with reference to the drawings, based on preferred embodiments. In embodiments and modifications, the same or equivalent components will be denoted by the same reference numerals, and redundant descriptions will be omitted as appropriate. 【0018】Figure 1 shows the configuration of the running style analysis system 100. The running style analysis system 100 comprises a wristwatch-type device 12, a waist-worn device 14, an information terminal-type device 16, and a running style analysis server 60, all of which can be worn by a user 10 who is a runner during running. The wristwatch-type device 12, the waist-worn device 14, and the information terminal-type device 16 are collectively referred to as the measuring device 20. Hereinafter, "user" mainly refers to the person who is the subject of running style analysis and running style type determination by the running style analysis system of this disclosure. 【0019】 The wristwatch-type device 12 is a sports watch or smartwatch that acquires location information, movement information, etc. The wristwatch-type device 12 includes sensors such as a positioning module, motion sensor, heart rate sensor, and barometer, and acquires information such as date and time, location coordinates, altitude, heart rate, temperature, and pitch. The motion sensor basically consists of an inertial sensor that combines an accelerometer and a gyroscope. The waist-worn device 14 is an electronic device that is worn near the user's waist to acquire location information, movement information, etc. The information terminal-type device 16 is a portable information terminal such as a smartphone that acquires location information and movement information while being held by the user 10 in a pocket or the like. 【0020】 User 10 wears one or more measuring devices 20 while running in a race such as a marathon, and acquires location information and motion information. If multiple measuring devices 20 are worn, location information may be acquired with a wristwatch-type device 12 and motion information with a waist-mounted device 14, and the devices may be used differently depending on the information to be acquired. 【0021】 The measuring device 20 is not limited to devices such as a wristwatch-type device 12, a waist-worn device 14, or an information terminal-type device 16, but may also be a device worn on or inside the runner's shoe. Alternatively, it may be a belt-type device that can be worn around the runner's chest, wrist, waist, or arm to acquire location information, movement information, and heart rate information. 【0022】Information such as distance traveled, time, heart rate, cadence, and stride measured by the measuring device 20 is displayed on the screens of the wristwatch-type device 12 and the information terminal-type device 16 while running. The user 10 can check their running status and activity status by looking at the screens of the wristwatch-type device 12 and the information terminal-type device 16 while running. 【0023】 The running style analysis system 100 can be implemented with various hardware and software configurations. For example, the running style analysis system 100 may consist of just one of the following devices: the wristwatch-type device 12, the waist-worn device 14, the information terminal 50, or the running style analysis server 60, or it may consist of a combination of two or more of these devices. 【0024】 For example, assuming that the measuring device 20 uses various general-purpose devices to detect the driving state and record it as a driving log, it may consist of only one of the information terminal 50 and the driving method analysis server 60, or a combination of both. Alternatively, it may be implemented as a single device that includes all the software configurations contained in the information terminal 50 and the driving method analysis server 60 shown in this figure. Therefore, regardless of the form of its hardware configuration, the driving method analysis system 100 only needs to include at least the software configurations of the information terminal 50 and the driving method analysis server 60 shown in this figure. 【0025】User 10 runs while wearing at least one or all of the following devices as measuring devices 20: a wristwatch-type device 12, a waist-worn device 14, and an information terminal-type device 16. The measuring devices 20 transmit information to the running method analysis server 60 via communication and receive analysis results from the running method analysis server 60 via communication. However, since the communication means of the wristwatch-type device 12 and the waist-worn device 14 of the measuring devices 20 is short-range wireless communication, they do not communicate directly with the running method analysis server 60, but rather synchronize information with the information terminal-type device 16, and the information terminal-type device 16 sends and receives information with the running method analysis server 60. As a variation, the waist-worn device 14 may first synchronize information with the wristwatch-type device 12 via short-range wireless communication, and the wristwatch-type device 12 may further synchronize information with the information terminal-type device 16 via short-range wireless communication. 【0026】 The information terminal device 16 can also function as an "information terminal 50," which will be described in detail later. However, in this embodiment, the information terminal 50 is primarily assumed to be a personal computer or tablet terminal, with the aim of displaying the analysis results from the running method analysis server 60 in detail on a larger screen. In this case, after the information acquired by the measuring device 20 is transmitted to the running method analysis server 60 by the information terminal device 16, the information terminal 50 will then display the analysis results received from the running method analysis server 60 on its screen. 【0027】 User 10 wears the measuring device 20 while running in a running race such as a marathon. Alternatively, measurements may be taken during long-distance or short-distance training runs simulating a race. User 10 starts measuring and recording the running log by operating a button on the measuring device 20 at the start of the run. During the run, the measuring device 20 uses a timer to measure the elapsed time from the start of recording as the running time and records location information for each date and time at predetermined time intervals. The measuring device 20 uses a built-in motion sensor to measure motion information such as pitch (steps per unit time, also called cadence), pelvic rotation and translation, and impact values. The measuring device 20 also uses a built-in optical heart rate monitor to measure User 10's heart rate. 【0028】 During the run and after the run log recording is completed, the measuring device 20 transmits information such as running time, location information, movement information, and heart rate to the running method analysis server 60. When transmitting during the run, it may be transmitted at predetermined measurement intervals. A "predetermined measurement interval" refers to a measurement interval based on a predetermined elapsed time, such as every minute or every five minutes, or a measurement interval based on a predetermined running distance, such as every 100m, every 1km, or every 5km. 【0029】 The measuring device 20 may calculate information such as running time, running distance, running speed, pitch, stride, altitude, and incline based on time information and location information, and include this calculated information in the running log and transmit it to the running style analysis server 60. The measuring device 20 may also calculate various motion information of the runner based on information detected by the motion sensor, and include this calculated information in the running log and transmit it to the running style analysis server 60. The measuring device 20 may also acquire weather information such as temperature, humidity, weather, wind direction, and wind speed corresponding to the time of running from a predetermined server, and include this weather information in the running log and transmit it to the running style analysis server 60. 【0030】 The information terminal 50 may be an information terminal such as a smartphone or tablet, or it may be a personal computer. The running method analysis server 60 is a server computer connected to the internet that sends and receives data with multiple user 10 information terminals 50. The running method analysis server 60 acquires information such as time information, location information, movement information, and heart rate as running log data of user 10 received from the information terminal 50, along with the user 10's identification information, attribute information, and goal information, and stores it together with various types of movement analysis index feature quantities calculated from each piece of information. The running method analysis server 60 may also acquire weather information such as temperature, humidity, weather, wind direction, and wind speed corresponding to the time of running from a predetermined server and include that weather information in the running log data for storage. In response to a request from the information terminal 50, the running method analysis server 60 transmits the accumulated running log data and analysis results based on the feature quantities of multiple types of movement analysis indexes to the information terminal 50. 【0031】The information terminal 50 and the running method analysis server 60 may be composed of a computer consisting of a CPU (Central Processing Unit), GPU (Graphics Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), auxiliary storage device, communication device, etc. The information terminal 50 and the running method analysis server 60 may each be composed of separate computers, or they may be implemented in a single computer or information terminal that combines the functions of both. In this embodiment, an example in which they are implemented in separate computers will be described. 【0032】 Figure 2 is a functional block diagram showing the various components of the running technique analysis system 100. In Figure 2, the functional blocks of the measuring device 20 and the information terminal 50 are depicted, each realized through the coordination of various hardware and software configurations. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various ways using hardware alone, software alone, or a combination thereof. The measuring device 20 is composed of a combination of hardware such as a microprocessor, display device, memory, communication module, positioning module, motion sensor, and optical heart rate monitor. The information terminal 50 is composed of a combination of hardware such as a microprocessor, touch panel, memory, communication module, positioning module, and motion sensor. The functions of the measuring device 20 and the information terminal 50 will be described below. 【0033】 The measuring device 20 includes a communication unit 21, a time measurement unit 22, a position measurement unit 24, a motion detection unit 26, and a calculation unit 28. For example, a waist-worn device 14 is used as the measuring device 20. The time measurement unit 22 measures the running start time, i.e., the running time from the measurement start time, by counting a timer. The position measurement unit 24 measures the current position of the runner, user 10, using position information received from a satellite positioning system by a positioning module such as a GPS module. The motion detection unit 26 detects motion information such as the pitch of the runner, user 10, the rotation and translational movement of the pelvis, or impact values ​​using a motion sensor. 【0034】The calculation unit 28 calculates feature quantities corresponding to multiple types of motion analysis indicators based on travel time, location information, and motion information. The location information, motion information, and feature quantities corresponding to multiple types of motion analysis indicators are transmitted to the information terminal 50 via the communication unit 21. Although an example has been described in which the calculation unit 28 in the measuring device 20 calculates the feature quantities corresponding to multiple types of motion analysis indicators, these may also be calculated in the measurement value acquisition unit 40 in the information terminal 50, which will be described later, instead of the measuring device 20. 【0035】 A wristwatch-type device 12 or an information terminal-type device 16 can also be used as the measuring device 20. When a wristwatch-type device 12 is used as the measuring device 20, the motion detection unit 26 of the wristwatch-type device 12 detects motion information such as the user 10's pitch using a motion sensor and detects physiological indicator information such as heart rate using an optical heart rate monitor. Physiological indicator information may also be included in the motion information. When an information terminal-type device 16 is used as the measuring device 20, the motion detection unit 26 of the information terminal-type device 16 detects motion information such as the user 10's pitch using a motion sensor. The information terminal 50 may also function as the information terminal-type device 16 used as the measuring device 20; in this case, for example, a single mobile terminal such as a smartphone may have all the functions of both the measuring device 20 and the information terminal 50. When the information terminal 50 is not used as the measuring device 20, the information terminal 50 is not limited to a smartphone; it may also be a tablet terminal or personal computer owned by the user 10. 【0036】The information terminal 50 includes an information acquisition unit 30, a measurement value acquisition unit 40, an input / output unit 51, and a communication unit 52. The information acquisition unit 30 receives location information and motion information of the user 10, who is a runner in a race, via the communication unit 52, which is measured or detected at each progress point during the run by a measuring device 20 worn by the user 10. Here, "progress point" refers to a point in time or distance during a run such as a race, and location information and motion information are recorded in the measuring device 20 for each time or distance progress point. Location information includes information such as the date and time of measurement, location coordinates, and altitude. Motion information includes information indicating pitch, rotation and translational motion of the pelvis, or impact value. The information acquisition unit 30 may synchronize information with the measuring device 20 during the run and acquire location information and motion information as information on the running state during the run by the measuring device 20, or it may acquire the location information and motion information for the entire run all at once from the measuring device 20 after the run is completed. 【0037】 The measurement value acquisition unit 40 acquires or calculates feature quantities of multiple types of motion analysis indices that indicate the user's driving motion state for each predetermined measurement interval, based on the time-series position information and motion information acquired by the information acquisition unit 30. 【0038】 The measurement acquisition unit 40 acquires, for example, lap pace, lap time, running time, running speed, pitch [steps / min], stride [m], stride-to-height ratio, trunk posterior tilt [°], vertical movement [cm], vertical movement-to-height ratio [%], hip sinking [%], pelvic lateral tilt [°], pelvic elevation [°], pelvic rotation [°], pelvic rotation timing, and lateral impact [m / s]. ２ ], push-off time [ms], ground contact time [ms], ground contact time percentage [%], landing impact [m / s] ２ ], kick-off acceleration [m / s²] ２ The system acquires feature quantities for multiple types of motion analysis indicators, such as [speed], deceleration [m / s], and stiffness [kN / m·kg]. The motion analysis indicators may also include heart rate. The measurement value acquisition unit 40 records the acquired feature quantities for multiple types of motion analysis indicators as time-series continuous motion data. 【0039】The evaluation calculation unit 42 calculates evaluation values ​​corresponding to multiple types of running style analysis indicators necessary for determining the running style type, using a predetermined calculation method based on the feature quantities of multiple types of running style analysis indicators included in the motion data acquired by the measurement value acquisition unit 40. The multiple types of running style analysis indicators in this embodiment are six items: "low-burden ground contact," "stable posture," "whole-body movement centered on the pelvis," "smooth center of gravity shift," "powerful movement," and "left-right symmetry." The evaluation calculation unit 42 calculates evaluation values ​​for each of these six items and also calculates an overall evaluation value using a predetermined calculation method. For motion data from a single run, the evaluation calculation unit 42 calculates evaluation values ​​for each running style analysis indicator and an overall evaluation value for each lap (i.e., every certain distance or every time). The overall evaluation value may be calculated by first calculating evaluation values ​​separately for the left and right sides of the user's body 10, and then integrating these evaluation values ​​based on the average or left-right difference. Each of the multiple types of motion analysis indicators is associated with at least one of the multiple types of running style analysis indicators. The correspondence between running style analysis indicators and motion analysis indicators will be described later. 【0040】 The input / output unit 51 transmits the position information and operation information acquired by the measuring device 20, the operation data recorded by the measurement value acquisition unit 40, and the evaluation value of the running analysis index calculated by the evaluation calculation unit 42 to the running method analysis server 60 via the communication unit 52 as a running log, along with the user 10's attribute information. The input / output unit 51 displays the position information and operation information acquired by the measuring device 20, the operation data recorded by the measurement value acquisition unit 40, and the evaluation value of the running analysis index calculated by the evaluation calculation unit 42 on the screen, and also displays information such as analysis results received from the running method analysis server 60 on the screen. The information received from the running method analysis server 60 includes reference information such as the running method type determination result and precedent data in which at least one of the targets, levels, or attributes is common, as determined by the running method analysis server 60 and presented to the user 10. The input / output unit 51 displays the determination results and reference information received from the running method analysis server 60 on the screen. The input / output unit 51 accepts operation input from the user 10. The input / output unit 51 may be configured as a touch panel in terms of hardware. 【0041】 In addition, in this embodiment, an example in which the measurement value acquisition unit 40 acquires or calculates feature amounts of a plurality of types of motion analysis indices has been described. However, by providing the functions of the measurement value acquisition unit 40 to the measuring device 20 or the gait analysis server 60, the information terminal 50 may not calculate the feature amounts of the motion analysis indices. Similarly, in this embodiment, an example in which the evaluation calculation unit 42 calculates evaluation values of a plurality of types of gait analysis indices has been described. However, by providing the functions of the evaluation calculation unit 42 to the measuring device 20 or the gait analysis server 60, the information terminal 50 may not calculate the evaluation values of the gait analysis indices. In the description of the gait analysis server 60 to be described later, an example in which the gait analysis server 60 has a measurement value acquisition unit 72 and an evaluation calculation unit 74 corresponding to the functions of the measurement value acquisition unit 40 and the evaluation calculation unit 42 will be described. 【0042】 FIG. 3 shows the correspondence between the gait analysis indices and the motion analysis indices. In the first column 110, six items of gait analysis indices are shown, and the evaluation values based on the combinations of the feature amounts of a plurality of types of motion analysis indices corresponding to each are calculated with 100 as the full score. In the second column 120, a plurality of types of motion analysis indices corresponding to the six items of gait analysis indices are shown. 【0043】 The first item 101 of the gait analysis index is "ground contact with less burden". For the first item 101, "pitch", "vertical movement", "landing impact", and "kicking acceleration" correspond as a plurality of types of motion analysis indices. The evaluation value of the first item 101 is calculated based on the respective feature amounts in combination with these motion analysis indices. 【0044】 The second item 102 of the gait analysis index is "stable posture". For the second item 102, "left - right tilt of the pelvis" and "rear tilt of the trunk" correspond as a plurality of types of motion analysis indices. The evaluation value of the second item 102 is calculated based on the respective feature amounts in combination with these motion analysis indices. 【0045】The third item 103 of the gait analysis index is "whole body movement with the pelvis as the axis". For the third item 103, "pelvic rotation timing", "sinking of the waist", and "kicking time" correspond as multiple types of motion analysis indexes. The evaluation value of the third item 103 is calculated based on the respective feature amounts by combining these motion analysis indexes. 【0046】 The fourth item 104 of the gait analysis index is "smooth center of gravity movement". For the fourth item 104, "deceleration amount" and "lateral impact" correspond as multiple types of motion analysis indexes. The evaluation value of the fourth item 104 is calculated based on the respective feature amounts by combining these motion analysis indexes. 【0047】 The fifth item 105 of the gait analysis index is "strength of movement". For the fifth item 105, "rotation of the pelvis", "lifting of the pelvis", and "stride" correspond as multiple types of motion analysis indexes. The evaluation value of the fifth item 105 is calculated based on the respective feature amounts by combining these motion analysis indexes. 【0048】 The sixth item 106 of the gait analysis index is "left - right symmetry". For the sixth item 106, "stiffness" and "all motion analysis indexes other than stiffness" correspond as multiple types of motion analysis indexes. The evaluation value of the sixth item 106 is calculated based on the respective feature amounts by combining these motion analysis indexes. 【0049】 Figure 4 is a radar chart visually showing the combination of the evaluation values of the gait analysis index. The input / output unit 51 visually shows the combination of the evaluation values of the first item 101, the second item 102, the third item 103, the fourth item 104, the fifth item 105, and the sixth item 106 by means of the gait evaluation graphic 107. The input / output unit 51 displays the overall evaluation value 108 numerically at the center of the gait evaluation graphic 107. From the deviation of the shape of the gait evaluation graphic 107 displayed in the radar chart, it is possible to grasp at a glance which index has a high evaluation value and which index has a low evaluation value. 【0050】Figure 5 is a functional block diagram showing the functions of the running method analysis server 60. It depicts the functional blocks of the running method analysis server 60 that are realized through the cooperation of various hardware and software configurations. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various ways by hardware alone, software alone, or a combination thereof. The running method analysis server 60 is composed of a combination of hardware such as a microprocessor, memory, display, and communication module. The running method analysis server 60 includes a communication unit 62, an information acquisition unit 64, a data storage unit 66, a determination unit 80, and an output unit 99. 【0051】 The information acquisition unit 64 acquires information such as driving logs from the information terminal 50 via the communication unit 62 and stores it in the data storage unit 66. The information acquisition unit 64 has functions equivalent to the information acquisition unit 30, measurement value acquisition unit 40, and evaluation calculation unit 42 of the measuring device 20. The information acquisition unit 64 includes a target acquisition unit 68, an attribute acquisition unit 70, a measurement value acquisition unit 72, an evaluation calculation unit 74, and an instruction acquisition unit 76. 【0052】 The goal acquisition unit 68 acquires goal information indicating the target race completion time set by the user 10, who is the subject of the evaluation. The user 10 sets the completion time of a marathon or other race that they wish to achieve as their goal. Here, "race" mainly refers to long-distance races such as marathons and half marathons. The race distance needs to be a fixed distance for comparison, and a predetermined distance such as 42.195 km is assumed. For example, as a marathon completion time of 42.195 km, round completion times that many runners aim for and that motivate them to take up the sport are provided as options, such as "sub-3" aiming to complete the race in under 3 hours, or "sub-4" aiming to complete it in under 4 hours. Setting goals like "sub-3" or "sub-4," which many runners aim for, means that there are many other runners with the same goal, and a lot of reference precedent data can be extracted. In addition, the goal may be further subdivided so that marathon completion times can be set as a goal in increments of 5 minutes or 10 minutes. 【0053】User 10 sets a goal the first time they use the analysis by the running style analysis system 100, and may also set a goal each time they use the analysis by the running style analysis system 100. User 10 may set a new goal each time they achieve a goal, set different goals for different periods, or change their goals as appropriate. It may be possible to set goals in stages, such as setting both a final goal for the season and intermediate goals during the season. Alternatively, it may be possible to specify the goal and the expected time of its achievement. The goal information of User 10 acquired by the goal acquisition unit 68 is stored in the data storage unit 66 as user information. 【0054】 The attribute acquisition unit 70 acquires attribute information of user 10. User attributes are elements that indicate physical characteristics and training patterns that affect running ability, and are elements that should be taken into consideration as preconditions when comparing running ability. User 10 registers attribute information including gender, date of birth or age, and training frequency, along with their own identification information. The attribute information of user 10 acquired by the attribute acquisition unit 70 is stored in the data storage unit 66 as user information along with the identification information. In addition to training frequency, attribute information may also include training patterns such as running time and monthly running distance, and characteristics of the shoes worn while running (for example, the model name of the shoes, whether or not there is a carbon plate, the material of the plate, etc.). 【0055】 The measurement value acquisition unit 72 acquires motion data, which is measured values ​​of feature quantities corresponding to multiple types of motion analysis indicators, as feature quantities that indicate the operating state, including the user's running form. The measurement value acquisition unit 72 has a function equivalent to the measurement value acquisition unit 40 of the information terminal 50. The user 10 may send motion data for one run to the running method analysis server 60 for analysis, or may send motion data for one month or several months to the running method analysis server 60 for comprehensive analysis. To specify the timing of each run for each piece of motion data, running logs and running date and time data measured by the measuring device 20 are also acquired along with the motion data. The motion data acquired by the measurement value acquisition unit 72 is stored in the data storage unit 66 in association with user information such as the user 10's identification information, target information, and attribute information. 【0056】 The evaluation calculation unit 74 calculates evaluation values ​​corresponding to six running style analysis indicators necessary for determining the running style type, using a predetermined calculation method based on feature quantities corresponding to multiple types of running style analysis indicators included in the running data acquired by the measurement value acquisition unit 72. The measurement value acquisition unit 72 is a function equivalent to the evaluation calculation unit 42 of the information terminal 50. The evaluation calculation unit 74 calculates an overall evaluation value along with the six evaluation values ​​using a predetermined calculation method. For the running data in a single run, the evaluation calculation unit 74 calculates evaluation values ​​for each running style analysis indicator and an overall evaluation value at regular intervals of distance or time. The evaluation value information calculated by the evaluation calculation unit 74 is stored in the data storage unit 66 along with the running data, associated with user information such as the user 10's identification information, target information, and attribute information. 【0057】 The instruction acquisition unit 76 acquires instructions from the information terminal 50 that the user 10 has entered into the information terminal 50. The user 10, for example, enters instructions for the filtering process described later into the information terminal 50 and sends them to the running method analysis server 60. 【0058】 The data storage unit 66 stores type determination results and operation data obtained in the past from multiple measurement subjects, including user 10, as precedent data from the type determination unit 82. In addition to precedent data, the data storage unit 66 also stores identification information and attribute information of the measurement subjects, target information and information indicating whether or not the target has been achieved, and information indicating the time when the operation data was acquired. Furthermore, the data storage unit 66 stores type determination results for each measurement subject at multiple time points. 【0059】 The data storage unit 66 holds information on a plurality of running style types that have been classified and defined in advance. In this embodiment, nine types of running style are predefined. In the radar chart of Figure 4, the running style type is classified by the combination of which indicator scores are high and which indicator scores are low. The nine types of running style are, for example, (1) "Balance", (2) "Sports Experience", (3) "Relaxed", (4) "Compact", (5) "Steady", and (6) "Jumper". 【0060】(1) The "Balanced" type has a long history of running and is often said to have exemplary running form, possessing a variety of appealing qualities. (2) The "Sports Experienced" type has experience in sports other than running and is good at utilizing their muscle strength, but is also prone to calf and thigh cramps. (3) The "Relaxed" type prefers to run naturally and without strain, is better at running longer distances than short distances like 5km or 10km, and is prone to muscle soreness when running at high speeds. 【0061】 (4) The "compact" type is confident in their ability to move their legs quickly and run steadily, but they are prone to pain in their shoulders and ankles. (5) The "steady" type lands each step carefully, lacks confidence in their own muscle strength, and is prone to pain in their lower back and knees. (6) The "jumping" type has a sharp landing and a flowing running style, feels like they are pushing off the ground firmly, and prefers stiff running shoes. 【0062】 The decision unit 80 determines the content of the analysis results and extraction results to be presented to the user 10. The decision unit 80 includes a type determination unit 82 and a data extraction unit 84. The type determination unit 82 determines which of the multiple running style types, which have been pre-classified according to the feature quantities corresponding to multiple types of motion analysis indicators, the acquired motion data of the user 10 belongs to. 【0063】 The type determination unit 82 determines which of the multiple running style types the user 10 belongs to, based on a combination of evaluation values ​​corresponding to multiple types of running style analysis indicators. The type determination unit 82 also determines which of the multiple levels within the determined running style type the user 10 belongs to, based on the sum of the evaluation values ​​corresponding to multiple types of running style analysis indicators. 【0064】The data extraction unit 84 extracts precedent data from the data storage unit 66 that have common targets with user 10, based on the degree of similarity of the driving style type. In extracting type determination results, the data extraction unit 84 extracts precedent data from the data storage unit 66 by comparing type determination results that have common driving timings with user 10's motion data. In addition, depending on the user 10's specifications, it extracts precedent data that have common attributes with user 10, at least one of several types. Along with type determination results that have common driving timings, the data extraction unit 84 further extracts type determination results corresponding to other driving timings for the same measurement subject as the type determination result. A detailed extraction method will be described later. 【0065】 The output unit 99 outputs the presented content to the information terminal 50 via the communication unit 62 based on the decision made by the decision unit 88. The output unit 99 outputs the comparison result between the extracted precedent data and the user 10's operation data. The output unit 99 further outputs the comparison result with the type determination result corresponding to other driving periods, in accordance with the instructions received from the user 10. The output unit 99 further outputs information on the training content for the person whose type determination result was extracted. 【0066】 Figure 6 is a flowchart illustrating the processing steps in the driving analysis system. User information such as the user's target information and attribute information set by the user 10 is acquired by the target acquisition unit 68 and the attribute acquisition unit 70 and stored in the data storage unit 66 (S10). The measurement value acquisition unit 72 acquires the motion data measured by the user 10 during one or more runs (S12) and calculates feature quantities for multiple types of motion analysis indicators (S14). The evaluation calculation unit 74 calculates evaluation values ​​and overall evaluation values ​​for multiple types of driving method analysis indicators based on the feature quantities of the motion analysis indicators and stores them in the data storage unit 66 (S16). 【0067】 The type determination unit 82 calculates a normalized score from the evaluation values ​​of each running style analysis index (S18), creates a type determination table (S20), calculates the difference calculation value for each running style type (S22), and determines the running style type and its level that best approximates the user 10's running style based on the difference calculation value (S24). 【0068】 The data extraction unit 84 extracts and outputs precedent data from the data storage unit 66 that is similar to the user 10's running style type and level, and that shares the same goal as the user 10 (S26). If the user 10 specifies filtering conditions such as attribute information (Y in S28), the precedent data is filtered and output according to the conditions (S30), and if no filtering conditions are specified, S30 is skipped (N in S28). 【0069】 Figure 7 shows the correspondence between nine running style types and five running style analysis indicators. Running style types are classified based on the combination of which indicators have high and low evaluation values ​​among the multiple types of running style analysis indicators. In other words, indicators with relatively high evaluation values ​​are considered the person's "strengths," indicators with relatively low evaluation values ​​are considered the person's "weaknesses," and indicators with intermediate evaluation values ​​are considered "average," and running style types are classified based on the differences in these combinations. A running style score table like the one in Figure 7 is prepared, in which the "strengths" and "weaknesses" indicators represent the characteristics of each running style type, and the combinations of "strengths," "weaknesses," and "average" are predetermined. The type determination unit 82 determines the running style type of user 10 by determining which running style type the user 10's running style is closest to in this running style score table. The classification of running style types may be intentionally based on expert knowledge to show the characteristics of various runners, or it may be classified mechanically by cluster analysis. 【0070】 (1) The "Balanced" type is a combination in which all running style analysis indicators are "average". (2) The "Sports Experienced" type is a combination in which items 102 (2nd item), 104 (4th item), and 105 (5th item) are "strengths", item 101 (1st item) is "average", and item 103 (3rd item) is a "weakness". (3) The "Relaxed" type is a combination in which items 101 (1st item), 102 (2nd item), and 104 (4th item) are "strengths", item 103 (3rd item) is "average", and item 105 (5th item) is a "weakness". 【0071】(4) The "Compact" type is a combination in which the first item 101 and the second item 102 are "strengths", the third item 103 and the fifth item 105 are "average", and the fourth item 104 is a "weakness". (5) The "Steady" type is a combination in which the first item 101 is a "strength", the third item 103 and the fifth item 105 are "average", and the second item 102 and the fourth item 104 are "weaknesses". (6) The "Jumper" type is a combination in which the second item 102 and the fourth item 104 are "strengths", the third item 103 and the fifth item 105 are "average", and the first item 101 is a "weakness". 【0072】 Figure 8 shows the relationship between the evaluation value and normalized score for each running style analysis index. In the first column 110, six types of running style analysis indexes, from item 101 to item 106, are shown, similar to Figure 3, and the overall evaluation value 108 is shown in the last row. In the evaluation value column 130, the evaluation values ​​corresponding to the six types of running style analysis indexes, from item 101 to item 106, and the overall evaluation value 108 are shown. For example, the evaluation value for item 101 is "X1", the evaluation value for item 202 is "X2", the evaluation value for item 303 is "X3", the evaluation value for item 404 is "X4", the evaluation value for item 505 is "X5", and the evaluation value for item 606 is "X6". The overall evaluation value 108 is "X7". 【0073】 Of the six types of running style analysis indicators, the scores of the six indicators are normalized by dividing the evaluation values ​​of all indicators by the evaluation value of the indicator with the highest evaluation value for five of the six types of running style analysis indicators, excluding the sixth item 106. The normalized score column 131 shows the normalized scores corresponding to the five types of running style analysis indicators from the first item 101 to the fifth item 105 and the overall evaluation value 108. For example, if "X1" is the highest evaluation value among the five types of running style analysis indicators from the first item 101 to the fifth item 105, dividing each by "X1" results in the normalized scores corresponding to the first item 101 to the fifth item 105 being "Y1" (note that in this example, Y1 is the value obtained by dividing X1 by X1, so the actual numerical example would be "1"), "Y2", "Y3", "Y4", and "Y5", and the normalized score corresponding to the overall evaluation value 108 is "Y7". 【0074】 Figure 9 shows the state in which normalized scores for each running style analysis index are applied to the nine types of running styles. The type determination unit 82 applies the highest score among the normalized scores of user 10 shown in Figure 8, for example "Y1", to the "strength" index in the running style score table shown in Figure 7. The type determination unit 82 applies the lowest score among the normalized scores of user 10 shown in Figure 8, for example "Y4", to the "weakness" index. The type determination unit 82 applies the overall score "Y7" among the normalized scores of user 10 shown in Figure 8 to the "average" index. 【0075】 Next, the type determination unit 82 determines that the running style with the smallest sum of the absolute values ​​of the differences between the score assigned to each running style type's index and the user 10's score for the same index is the running style with the smallest difference from the user 10's score, i.e., the closest running style. For example, in the case of the "Balance" type, the unit calculates the difference between the score corresponding to "Normal" assigned to the first item 101 to the fifth item 105 and the normalized score of the first item 101 to the fifth item 105 of the user 10 shown in Figure 8, and calculates the sum of their absolute values ​​as the difference calculation value 109. In the example in Figure 9, the difference calculation value 109 for the "Balance" type is "Z1". Similarly, the type determination unit 82 calculates "Z2", "Z3", "Z4", "Z5", and "Z6" as difference calculation values ​​109 for each running style. If the "Steady" type's "Z5" is the one with the smallest difference calculation value 109 among the nine running style types, the unit determines that the "Steady" type is the closest approximation of the user 10's score. 【0076】Figure 10 shows a running style type determination table for determining the running style type over the entire duration of a single run. The type determination unit 82 determines the running style type and its level for each lap based on the motion data for one run, and calculates the probability of occurrence as a distribution of running style type and level combinations for each lap. Each column of the running style type determination table indicates the type of running style, while each row indicates the level of the running style. The first level 141 indicates "R1" as the range of the overall evaluation value of the running style analysis index, the second level 142 indicates "R2" as the range of the overall evaluation value of the running style analysis index, the third level 143 indicates "R3" as the range of the overall evaluation value of the running style analysis index, and the fourth level 144 indicates "R4" as the range of the overall evaluation value of the running style analysis index. As a modification, the first level 141 to the fourth level 144 may be set as multiple stages such as "Level A" to "Level D" separated by some other value, or they may be expressed as a score out of 10 or 5 points. 【0077】 In the example shown in Figure 10, the proportion of laps that fell under the "steady" type "R3" was the highest at "24.6%", so the driving style throughout the entire run is determined to be the "steady" type, and the level is determined to be "R3". 【0078】 Figure 11 is a flowchart that shows in detail the process of narrowing down the precedent data in S30 of Figure 6. The precedent data described below is extracted by comparing it with precedent data that have the same driving period as the user 10's operation data. 【0079】 If user 10 specifies a condition to limit the output to precedent data with a common driving pace range (Y in S40), the data extraction unit 84 narrows the output target to precedent data common to user 10's driving pace range (S42). If no limitation to a driving pace range is specified, S42 is skipped and precedent data for all driving pace ranges becomes the output target (N in S40). As a variation, the specification may be such that if precedent data for the same driving pace range as user 10 exists, the filtering is performed, and if it does not exist, precedent data for all driving pace ranges is output without filtering. 【0080】If there is precedent data for a user whose running style type and level match that of user 10, that is, if there is precedent data for a user whose running style type and level have the highest probability of appearing in the running style type determination table (Y in S44), then the data is narrowed down to precedent data for a user whose running style type and level match (S46). If there is no precedent data for a user whose running style type and level have the highest probability of appearing in the running style type determination table, S46 is skipped (N in S44). 【0081】 For each of the precedent data selected as output targets, the difference in the probability of occurrence for each combination of driving style type and level is calculated between it and the user 10's driving style determination table, and the sum of the absolute values ​​of these differences is calculated as the difference calculation value (S48). The smaller the calculated difference calculation value of the precedent data, the higher the degree of similarity between the user 10's driving style type and level. 【0082】 If user 10 specifies a condition to limit the data to precedent data that matches at least one of several attributes such as gender, age, practice frequency, and whether or not goals have been achieved (Y in S50), the data extraction unit 84 narrows down the output target to precedent data that have the same attribute as specified by user 10 (S52). If no attribute restriction is specified, S52 is skipped (N in S50). 【0083】 The output unit 99 outputs precedent data from users whose goals are the same as user 10, which have been narrowed down by the processing in S40 to S52, as a comparison target with user 10's operation data (S54). 【0084】Figure 12 shows a list of precedent data, which is a table of running style type determinations for each user by running period. In the table shown in Figure 12, each row shows information for each user that has been accumulated as precedent data. Columns 151 to 154 show the user's attributes. Column 151 is the user's gender, indicated as "male" or "female". Column 252 is the user's age, indicated as "30s", "40s", or "50s". Column 353 is the user's training frequency, indicated as the average number of training sessions per week, such as "twice a week" or "three times a week". Column 454 is the user's goal, indicated as a target marathon completion time, such as "sub-3" or "sub-4". Although not shown in the diagram, information indicating whether the user achieved their goal for that marathon season is also registered along with the user's goal. 【0085】 Furthermore, although not shown in the diagram, the data is stored in the data storage unit 66 in a previously classified form based on the average pace during the run when the operational data was measured, categorized by running pace range. The running pace ranges are classified in increments of 30 seconds per kilometer, for example, "3:00 / km to 3:30 / km" and "3:30 / km to 4:00 / km". This is because even the same user will often run at various pace ranges from day to day, and their running form may differ depending on the pace range. 【0086】 Columns 155 and beyond in the fifth column register driving style type determination tables for each period of driving. Column 155 registers the driving style type determination table for "August," column 156 registers the driving style type determination table for "September," column 157 registers the driving style type determination table for "October," and column 158 registers the driving style type determination table for "November." In this way, one or more driving style type determination tables created as shown in Figure 10 are registered for each user on a monthly basis. In this embodiment, precedent data is classified by month, but it may also be classified by longer periods such as season or year, or by shorter periods such as week or day. 【0087】A large amount of precedent data from multiple users is accumulated in the data storage unit 66. The data extraction unit 84 extracts precedent data from the data storage unit 66 that have the same goal as user 10, based on the degree of similarity of the running style type. For example, if user 10's goal is "sub-3", then precedent data with a similar running style type is extracted from the precedent data registered as "sub-3" in the fourth column 154. Alternatively, precedent data whose predetermined running style type matches that of user 10 may be extracted, or if no precedent data with a matching running style type is found, precedent data from users with a relatively high probability of appearing for each running style type in the running style type determination table may be preferentially extracted. The data extraction unit 84 may also extract precedent data from the data storage unit 66 that have the same level as user 10, in addition to the same running style type, from among the precedent data that have the same goal as user 10. 【0088】 If user 10 specifies conditions such as driving pace range or attributes, the data extraction unit 84 extracts precedent data that matches the conditions of driving pace range or attributes. 【0089】 Figure 13 shows an example screen displaying the results of a comparison with running style analysis indicators in previous data. The results of a visual comparison of the evaluation values ​​of the running style analysis indicators of user 10 with the previous data of other users who share the same goals are displayed. 【0090】 In the radar chart 160, the first running style evaluation figure 161 shows the evaluation value of the running style analysis index for user 10, and the second running style evaluation figure 162 shows the evaluation value of the running style analysis index in the comparative precedent data. By observing the differences in shape and size between the first running style evaluation figure 161 and the second running style evaluation figure 162, user 10 can quickly grasp which running style analysis indexes are lacking or sufficient compared to the precedent data. 【0091】In the example in Figure 13, the comparison results between user 10's motion data and previous data from the same running period, "August," are displayed. When user 10 moves the first handle 166, which switches the running period of their own motion data, to another month such as "September" or "October" on the two horizontal sliders 165, the display switches to the first running style evaluation figure 161 corresponding to the running style analysis index in the motion data for that specified month. Similarly, when user 10 moves the second handle 167, which switches the running period of previous data, on the slider 165 to another month, the display switches to the second running style evaluation figure 162 corresponding to the running style analysis index in the previous data for that specified month. By moving the first handle 166 or the second handle 167 to the right on the slider 165 to switch months, it becomes possible to visualize how the form improves and how running ability, etc., develops as training progresses. By overlaying and comparing such desirable future changes with one's current form, it is possible to identify weaknesses and points that need improvement in one's own form. This allows you to predict your future form along a growth curve. 【0092】 The comparison unit specification 170 displays options for the comparison unit or switching unit with the precedent data to be compared, such as "month," "week," and "day," and the user 10 can specify the comparison unit. The gender specification 171 displays options for gender as a filtering condition by attribute, such as "male" and "female," and the user 10 can specify gender as a filtering condition. The age group specification 172 displays options for age group as a filtering condition by attribute, such as "teens," "20s," and "30s," and the user 10 can specify age group as a filtering condition. The practice frequency specification 173 displays options for practice frequency as a filtering condition by attribute, such as "twice a week or less" and "three times a week," and the user 10 can specify practice frequency as a filtering condition. 【0093】Although not shown in the diagram, a selection of running pace ranges is displayed, allowing user 10 to specify a running pace range as a filtering condition. Further specification may also be possible, allowing users to specify other attribute information such as running time, monthly running distance, and characteristics of the shoes worn during running (e.g., shoe model name, presence or absence of carbon plates, plate material, etc.). 【0094】 The comment section 180 may display comments that point out shortcomings in user 10's movement data based on a comparison with the provided precedent data. For example, a comment might be presented stating that, as of August, compared to the precedent data of runners with the same goal and similar attributes, user 10's "stable posture" is slightly weaker compared to the form of a runner who achieved a sub-3 marathon last year at the same time. 【0095】 The recommended training section 181 may display training methods to compensate for any deficiencies in user 10's movement data, based on a comparison with the provided precedent data. For example, it may display the training content of a runner from the precedent data at the same time and suggest what kind of training would be best to get closer to that runner's level. 【0096】 User 10 may choose to view a single precedent data point filtered by driving pace range and attributes as a point of comparison, or they may choose to view multiple precedent data points comprehensively by switching between them without filtering by driving pace range or attributes, allowing them to refer to data from various angles. If multiple precedent data points are extracted, the system may also be designed to present an average data point calculated by averaging the evaluation values ​​and scores of those data points as a point of comparison. 【0097】Figure 14 shows the hardware configuration of the measuring device, information terminal, and running method analysis server. The measuring device 20 is configured by connecting a communication interface 221, a timer 222, a positioning module 224, a motion sensor 226, a processor 228, a memory 230, an input / output interface 232, and an optical heart rate monitor 234 via a bus 210. The communication interface 221, timer 222, positioning module 224, motion sensor 226, and processor 228 correspond, for example, to the communication unit 21, time measurement unit 22, position measurement unit 24, motion detection unit 26, and calculation unit 28 in Figure 2. The various hardware elements constituting the measuring device 20 may be implemented as a whole or partially integrated SoC (System on a Chip). The measuring device 20 may be, for example, a wristwatch-type device 12 or a waist-worn device 14. 【0098】 The information terminal 50 is configured by connecting a communication interface 521, a timer 522, a positioning module 524, a motion sensor 526, a processor 528, a memory 530, and an input / output interface 532 via a bus 510. The communication interface 521, input / output interface 532, and processor 528 correspond, for example, to the communication unit 52, input / output unit 51, information acquisition unit 30, measurement value acquisition unit 40, and evaluation calculation unit 42 in Figure 2. The various hardware elements constituting the information terminal 50 may be implemented as an integrated System of Computers (SoC), either as a whole or in part. The information terminal 50 may be, for example, a smartphone, a tablet terminal, or a personal computer, or it may also function as an information terminal type device 16 as a measuring device 20. 【0099】The running method analysis server 60 is configured by connecting a communication interface 621, a processor 628, memory 630, an input / output interface 632, and storage 634 via a bus 610. The communication interface 621, input / output interface 632, storage 634, and processor 628 correspond, for example, to the communication unit 62, output unit 99, data storage unit 66, information acquisition unit 64, and determination unit 80 in Figure 5. The various hardware elements constituting the running method analysis server 60 may be implemented as an integrated SoC, either as a whole or in part. The running method analysis server 60 may be, for example, a server computer connected to a network. 【0100】 The present disclosure has been described above based on embodiments. The embodiments are illustrative, and it will be understood by those skilled in the art that various modifications are possible in combinations of their components and processing processes, and that such modifications are also within the scope of the present disclosure. Modifications will be described below. 【0101】 In the above embodiment, an example was described in which the running style type and level are determined based on the evaluation values ​​of six types of running style analysis indicators. In the first modified example, the running style type and level may be determined based on the evaluation value of only one of the running style analysis indicators. For example, if one wants to focus only on the "low-burden ground contact" item as a running style analysis indicator and compare it with previous data, the running style type determination table may extract the previous data in which the lap with the highest probability of occurrence corresponds to "R3" for the evaluation value of "low-burden ground contact". 【0102】 In the second modified example, the system may be configured to determine the running style type and level based on the evaluation values ​​of any two running style analysis indicators. For example, if we want to focus on two running style analysis indicators, "low-burden foot strike" and "stable posture," and compare them with previous data, we can define three types of running styles: a lap with a high evaluation value for "low-burden foot strike" but a low evaluation value for "stable posture"; a lap with a low evaluation value for "low-burden foot strike" but a high evaluation value for "stable posture"; and a lap with high evaluation values ​​for both "low-burden foot strike" and "stable posture." Thus, although six indicators are used in this embodiment, it is possible to implement the system with just one or two indicators, as shown in the modified example. 【0103】 Furthermore, the above-described embodiments can be generalized to obtain the following embodiments. 【0104】 [Aspect 1] A running style analysis system comprising: a target acquisition unit that acquires a target for the user's race completion time; a measurement value acquisition unit that acquires motion data which is a measurement value of feature quantities corresponding to a plurality of motion analysis indicators relating to the user's motion state, including the running form; a type determination unit that determines which of a plurality of running style types, which are pre-classified according to the feature quantities corresponding to the plurality of motion analysis indicators, the acquired motion data belongs to; a data storage unit that stores the type determination result by the type determination unit for motion data acquired from a plurality of measurement subjects, precedent data including the motion data, and the targets of the plurality of measurement subjects; a data extraction unit that extracts precedent data of measurement subjects whose targets are common to the user from the data storage unit based on the degree of similarity of the running style type; and an output unit that outputs the result of comparing the extracted precedent data with the user's motion data. 【0105】 In the running style analysis system of Embodiment 1, by presenting the user with precedent data that share common goals, the system can help them determine whether their own running form is sufficient to achieve their goals. 【0106】[Aspect 2] The running style analysis system according to aspect 1, wherein each of the multiple types of motion analysis indicators is associated with at least one of the multiple types of running style analysis indicators used to determine the running style type, the running style analysis system further comprises an evaluation calculation unit that calculates an evaluation value corresponding to the multiple types of running style analysis indicators from feature quantities corresponding to the multiple types of motion analysis indicators, the type determination unit determines which of the multiple types of running style types it belongs to based on a combination of evaluation values ​​corresponding to the multiple types of running style analysis indicators, and determines which of the multiple levels in the determined running style type it belongs to based on a total value of evaluation values ​​corresponding to the multiple types of running style analysis indicators, the data storage unit further stores the determined level, and the data extraction unit extracts type determination results from the data storage unit for which the level is common with the user in the extraction of the type determination results. 【0107】 In the running style analysis system of embodiment 2, by presenting precedent data that not only share common goals and running style types but also common skill levels, the accuracy of information regarding whether one's own running form is sufficient to achieve the goal can be improved. 【0108】 [Aspect 3] The running method analysis system according to aspect 1 or 2, further comprising an attribute acquisition unit for acquiring attribute information of the user, wherein the data storage unit further holds attribute information of the plurality of measurement subjects, and the data extraction unit, in extracting the type determination result, extracts from the data storage unit a type determination result in which at least a portion of the attribute information is common with the user. 【0109】 In the running style analysis system of embodiment 3, by narrowing down and presenting data on precedents of runners who share not only the same goals but also the same attributes, the accuracy of information on whether one's own running form is sufficient to achieve the goal can be improved. 【0110】[Aspect 4] The running method analysis system according to aspect 1 or 2, characterized in that, in extracting the type determination result, the data extraction unit extracts from the data storage unit a type determination result whose running pace range is common with that of the user's operation data. 【0111】 In the running style analysis system of embodiment 4, by narrowing down and presenting data on precedents of runners who not only share the same goals but also share the same range of running paces, the accuracy of information on whether one's own running form is sufficient to achieve the goal can be improved. 【0112】 [Aspect 5] The driving method analysis system according to aspect 1 or 2, characterized in that the data storage unit stores the type determination results for each of the multiple time periods for each of the subjects of measurement, and the data extraction unit extracts from the data storage unit type determination results for which the driving period is common with the user's operation data when extracting the type determination results. 【0113】 In the running style analysis system of embodiment 5, by narrowing down and presenting data on precedents of runners who not only share the same goal but also run at the same time, the accuracy of information on whether one's own running form is sufficient to achieve the goal can be improved. 【0114】 [Aspect 6] The driving method analysis system according to aspect 5, characterized in that the data extraction unit further extracts type determination results corresponding to other driving times for the same subject as the type determination result, along with type determination results for the same driving time, and the output unit further outputs a comparison result with the type determination results corresponding to the other driving times in response to instructions received from the user. 【0115】 In the running style analysis system of embodiment 6, by presenting not only the comparison running period but also precedent data from other running periods, it becomes easier to predict one's own running form and the likelihood of achieving goals in the future. 【0116】[Aspect 7] The running method analysis system according to aspect 1 or 2, characterized in that the data storage unit further stores information indicating whether the subject of measurement has achieved the goal of the type determination result, and the data extraction unit extracts the type determination result of a subject of measurement whose goal is the same as the user's and whose goal has already been achieved. 【0117】 In the running style analysis system of embodiment 7, the accuracy of the information presented to the user can be improved, and it is possible to make future predictions about the user's likelihood of achieving their goals easier. 【0118】 [Aspect 8] The running style analysis system according to aspect 1 or 2, characterized in that the output unit further outputs information on the training content of the person being measured based on the extracted type determination result. 【0119】 In the running style analysis system of embodiment 8, the usefulness of the information presented to the user can be increased, and the likelihood of the user achieving their goals can be improved. 【0120】[Aspect 9] A target acquisition unit that acquires a user's target race completion time; an attribute acquisition unit that acquires attribute information of the user; a measurement value acquisition unit that acquires motion data which is a measurement value of feature quantities corresponding to a plurality of motion analysis indicators related to the user's motion state, including running form; an evaluation calculation unit that calculates evaluation values ​​corresponding to a plurality of running style analysis indicators from the feature quantities corresponding to the plurality of motion analysis indicators included in the acquired motion data; a type determination unit that determines which of a plurality of running style types, which are classified in advance according to the combination of evaluation values ​​corresponding to the plurality of running style analysis indicators, the acquired motion data belongs to, and determines which of a plurality of levels in the determined running style type it belongs to based on the total value of the evaluation values ​​corresponding to the plurality of running style analysis indicators; a data storage unit that stores the type determination results by the type determination unit for motion data acquired from a plurality of measurement subjects, along with the measurement subject's target and whether or not that target has been achieved, attribute information, and information indicating the time of acquisition of motion data; a data extraction unit that extracts the type determination results from the data storage unit for measurement subjects who have the same target, attribute information, running time, and running style type as the user and whose target has been achieved. A running style analysis system comprising: an output unit that outputs a comparison result between the extracted type determination result and the user's operation data. 【0121】 In the running style analysis system of embodiment 9, the accuracy of information regarding whether one's running form is sufficient to achieve the goal can be improved, and it is possible to make future predictions about one's running form and the likelihood of achieving the goal. 【0122】[Aspect 10] A running method analysis method comprising: a process of acquiring a user's target race completion time via a network; a process of acquiring motion data via a network, which is a measurement value of feature quantities corresponding to a plurality of motion analysis indicators relating to the user's motion state, including running form; a process of a computer determining which of a plurality of running style types, which are pre-classified according to the feature quantities corresponding to the plurality of motion analysis indicators, the acquired motion data belongs to; a process of a computer extracting precedent data that the user and the target have in common based on the degree of similarity of running style types from the type determination results for motion data acquired from a plurality of measurement subjects and precedent data including the motion data; and a process of outputting the comparison result between the extracted precedent data and the user's motion data via a network. 【0123】 In the running technique analysis method of embodiment 10, by presenting the user with precedent data that share common goals, it is possible to use this as a reference to determine whether their own running form is sufficient to achieve their goals. 【0124】 This invention relates to a technique for analyzing running motion. 【0125】 40 Measurement value acquisition unit, 42 Evaluation calculation unit, 60 Running method analysis server, 66 Data storage unit, 68 Target acquisition unit, 70 Attribute acquisition unit, 72 Measurement value acquisition unit, 74 Evaluation calculation unit, 82 Type determination unit, 84 Data extraction unit, 99 Output unit, 100 Running method analysis system.

Claims

1. A running style analysis system comprising: a goal acquisition unit that acquires a user's goal regarding the time it takes to complete a race; a measurement value acquisition unit that acquires motion data which is a measurement value of a feature corresponding to a plurality of motion analysis indicators relating to the user's motion state, including its running form; a type determination unit that determines which of a plurality of running style types, which are pre-classified according to the feature corresponding to the plurality of motion analysis indicators, the acquired motion data belongs to; a data storage unit that stores the type determination result by the type determination unit for motion data acquired from a plurality of subjects, precedent data including the motion data, and the goals of the plurality of subjects; a data extraction unit that extracts precedent data of subjects whose goals are common to the user from the data storage unit based on the degree of similarity of the running style type; and an output unit that outputs the result of comparing the extracted precedent data with the user's motion data.

2. The running style analysis system according to claim 1, wherein each of the multiple types of motion analysis indicators is associated with at least one of the multiple types of running style analysis indicators used to determine the running style type, the running style analysis system further comprises an evaluation calculation unit that calculates an evaluation value corresponding to the multiple types of running style analysis indicators from feature quantities corresponding to the multiple types of motion analysis indicators, the type determination unit determines which of the multiple types of running style types it belongs to based on a combination of evaluation values ​​corresponding to the multiple types of running style analysis indicators, and determines which of the multiple levels in the determined running style type it belongs to based on a total value of evaluation values ​​corresponding to the multiple types of running style analysis indicators, the data storage unit further stores the determined level, and the data extraction unit extracts type determination results from the data storage unit for which the level is common with the user in the extraction of the type determination results.

3. The running method analysis system according to claim 1 or 2, further comprising an attribute acquisition unit for acquiring attribute information of the user, wherein the data storage unit further holds attribute information of the plurality of measurement subjects, and the data extraction unit, in extracting the type determination result, extracts from the data storage unit a type determination result in which at least a portion of the attribute information is common with the user.

4. The running method analysis system according to claim 1 or 2, characterized in that, in extracting the type determination result, the data extraction unit extracts from the data storage unit a type determination result whose running pace range is common with that of the user's operation data.

5. The driving method analysis system according to claim 1 or 2, characterized in that the data storage unit stores the type determination results for each of the multiple time periods for each of the subjects of measurement, and the data extraction unit extracts type determination results from the data storage unit that have a common driving period with the user's motion data when extracting the type determination results.

6. The driving method analysis system according to claim 5, characterized in that the data extraction unit further extracts type determination results corresponding to other driving periods for the same subject as the type determination result, along with type determination results for the same driving period, and the output unit further outputs a comparison result with the type determination results corresponding to the other driving periods in accordance with the instructions received from the user.

7. The running style analysis system according to claim 1 or 2, wherein the data storage unit further stores information indicating whether the subject of measurement has achieved the goal of the type determination result, and the data extraction unit extracts the type determination results of the subject of measurement whose goal is the same as the user's and whose goal has already been achieved.

8. The running style analysis system according to claim 1 or 2, characterized in that the output unit further outputs information on the training content of the person being measured based on the extracted type determination result.

9. A target acquisition unit that acquires the user's target race completion time; an attribute acquisition unit that acquires the user's attribute information; a measurement value acquisition unit that acquires motion data which is measured values ​​of feature quantities corresponding to multiple types of motion analysis indicators related to the user's motion state, including their running form; an evaluation calculation unit that calculates evaluation values ​​corresponding to multiple types of running style analysis indicators from the feature quantities corresponding to the multiple types of motion analysis indicators included in the acquired motion data; a type determination unit that determines which of multiple running style types the acquired motion data belongs to according to the combination of evaluation values ​​corresponding to the multiple types of running style analysis indicators, and determines which of multiple levels in the determined running style type it belongs to based on the overall value of the evaluation values ​​corresponding to the multiple types of running style analysis indicators; a data storage unit that stores the type determination results by the type determination unit for motion data acquired from multiple subjects, along with the subject's target and whether that target has been achieved, attribute information, and information indicating the time of acquisition of the motion data; and a data extraction unit that extracts the type determination results from the data storage unit for subjects who share the same target, attribute information, running time, and running style type as the user and whose target has been achieved. A running style analysis system comprising: an output unit that outputs a comparison result between the extracted type determination result and the user's operation data.

10. A running style analysis method characterized by comprising: a process of obtaining a user's target race completion time via a network; a process of obtaining motion data via a network, which is a measurement value of feature quantities corresponding to multiple types of motion analysis indicators related to the user's motion state, including running form; a process of a computer determining which of multiple running style types, which are pre-classified according to the feature quantities corresponding to the multiple types of motion analysis indicators, the obtained motion data belongs to; a process of a computer extracting precedent data that the user and the target have in common based on the degree of similarity of running style types from the type determination results for motion data obtained from multiple measurement subjects and precedent data including the motion data; and a process of outputting the comparison result between the extracted precedent data and the user's motion data via a network.

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