Driving analysis system and driving analysis method

Abstract

To provide a technique of facilitating running analysis based on information about running.SOLUTION: In a running analysis system 100, an information acquisition unit 70 acquires position information and motion information of a user 10 being a runner measured at each passage point in running by a prescribed measurement device 20, a measurement value acquisition unit 80 acquires measurement values of plural types of motion analysis indexes indicating a running motion state of the user 10 for each measurement section of a prescribed unit on the basis of the position information and motion information that are continuous in time series, a classification processing unit 85 classifies measurement values of the plurality of measurement sections by means of a prescribed classification method for classification with a property for each type of a motion analysis index, a mode decision unit 90 decides the output mode of the classified measurement value on the basis of comparison with a prescribed comparison target, an output unit may output at least information about the measurement value classified on the basis of the decided output mode.SELECTED DRAWING: Figure 2

Description

【Technical Field】 【0001】 The present invention relates to a running analysis system. In particular, it relates to a system for analyzing the results of running. 【Background Art】 【0002】 In recent years, due to the increasing health awareness of people, the number of running enthusiasts has been increasing. Especially in recent years, with the popularization of smartphones and wristwatches equipped with GPS (Global Positioning System) modules, anyone can easily record the log of running exercise (hereinafter also referred to as "running log"). The utilization of such running logs serves as a motivation to continue and habitualize running, boosting the popularity of running. 【0003】 With the increase in the number of running enthusiasts, the number of participants and races in marathons has also been increasing. By recording the running log during the race, runners can review their running results after the race. As technologies for displaying such running data, a running data display method and an exercise support device are known (see, for example, Patent Documents 1 and 2). 【Prior Art Documents】 【Patent Documents】 【0004】 【Patent Document 1】 Japanese Patent No. 7031234 【Patent Document 2】 Japanese Patent No. 5984002 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0005】 However, with prior technologies, even if various information such as running time and distance, as well as running form, could be obtained during a race, it was not always easy to find out which information to focus on in order to improve one's own level. Therefore, for users who could not fully utilize the various information they obtained, it was difficult to motivate them to review their race results based on that information. 【0006】 This invention has been made in view of these circumstances, and its purpose is to provide a technology that facilitates driving analysis based on driving information. [Means for solving the problem] 【0007】 To solve the above problems, a running analysis system according to one aspect of the present invention includes: an information acquisition unit that acquires location information and motion information of a runner measured at each intermediate point during running using a predetermined measuring device; a measurement value acquisition unit that acquires measurement values ​​of multiple types of motion analysis indicators indicating the user's running motion state for each predetermined measurement interval based on the location information and motion information that follows in chronological order; a classification processing unit that classifies the measurement values ​​of multiple measurement intervals according to a predetermined classification method for distinguishing them by characteristics for each type of motion analysis indicator; a mode determination unit that determines the output mode of the classified measurement values ​​based on comparison with a predetermined comparison target; and an output unit that can output at least information relating to the classified measurement values ​​based on the determined output mode. 【0008】 Another aspect of the present invention is a running analysis method. This method comprises the steps of: acquiring location information and motion information of a runner measured at each intermediate point during running using a predetermined measuring device; acquiring measured values ​​of multiple types of motion analysis indicators indicating the user's running motion state for each predetermined measurement interval based on the chronologically sequential location information and motion information; classifying the measured values ​​of multiple measurement intervals for each type of motion analysis indicator using a predetermined classification method for distinguishing them by characteristics; determining the output mode of the classified measured values ​​based on comparison with a predetermined comparison target; and outputting at least information relating to the classified measured values ​​based on the determined output mode. [Effects of the Invention] 【0009】 According to the present invention, it is possible to provide a technology that facilitates driving analysis based on driving information. [Brief explanation of the drawing] 【0010】 [Figure 1] This is a diagram showing the configuration of the driving analysis system. [Figure 2] This is a functional block diagram showing the various components of the driving analysis system. [Figure 3] This is a flowchart showing the processing steps in the driving analysis system. [Figure 4] This figure shows an example of classifying indicator measurements using the first classification method. [Figure 5] This figure shows the preparatory process when classifying indicator measurements using the second classification method. [Figure 6] This diagram shows the process of classifying the indicator measurements using a second classification method. [Figure 7] This is a bar graph showing the proportion of each classification for each motion analysis indicator. [Figure 8] Figure 7 shows the results of evaluating the divided indicator measurements using yet another criterion, as shown in the horizontal bar graph. [Figure 9] This figure shows an example of narrowing the output to display only the indicator measurements detected as values ​​of interest. [Figure 10] This figure shows an example of a motion analysis index that has the longest interval in which a significantly negative trend persists. [Figure 11] This figure shows an example of a screen displaying race results. [Figure 12] This figure shows an example of a screen displaying race evaluations. [Figure 13] This figure shows an example screen displaying a detailed explanation of the evaluation points. [Modes for carrying out the invention] 【0011】 Hereinafter, the present invention will be described with reference to the drawings based on preferred embodiments. In the embodiments and variations, the same or equivalent components will be denoted by the same reference numerals, and repeated explanations will be omitted as appropriate. 【0012】 FIG. 1 shows the configuration of a running analysis system 100. The running analysis system 100 includes a wristwatch-type device 12, a waist-mounted device 14, an information terminal-type device 16 that a user 10, who is a runner in running motion, can wear, and a running analysis server 60. The wristwatch-type device 12, the waist-mounted device 14, and the information terminal-type device 16 are collectively referred to as a measuring device 20. The wristwatch-type device 12 is a sports watch or a smartwatch that can measure position information, motion information, etc. The waist-mounted device 14 is a motion sensor that can be worn near the user 10's waist to measure position information and motion information. The information terminal-type device 16 is a portable information terminal such as a smartphone that can measure position information and motion information while the user 10 holds it in a pocket or the like. The user 10 wears one or more measuring devices 20 and performs running in a race or the like to acquire position information and motion information. When wearing a plurality of measuring devices 20, the devices may be used separately according to the information to be acquired, for example, acquiring position information with the wristwatch-type device 12 and acquiring motion information with the waist-mounted device 14. 【0013】 Note that the measuring device 20 is not limited to devices such as the wristwatch-type device 12, the waist-mounted device 14, and the information terminal-type device 16, and may be a device worn on or in the runner's shoes. Alternatively, it may be a belt-type device that can be wound around the runner's chest, wrist, waist, or arm to acquire position information and motion information. In addition, various wearable devices such as smart glasses can be considered as the measuring device 20. Also, cameras may be installed near each passing point of the running course to photograph the runner, and skeletal information such as the joint positions of the runner and motion information such as pitch and stride that can be calculated from such information may be acquired by image recognition. Instead of installing cameras near each passing point, the runner may be photographed by a drone. 【0014】 User 10 runs while wearing at least one or all of the wristwatch-type device 12, waist-worn device 14, and information terminal-type device 16 as the measuring device 20. The measuring device 20 synchronizes information via communication with the running analysis server 60. However, since the communication means of the wristwatch-type device 12 and the waist-worn device 14 among the measuring devices 20 is short-range wireless communication, they do not communicate directly with the running analysis server 60, but synchronize information with the information terminal-type device 16 (which also functions as the "information terminal 50" to be described in detail later), and the information terminal 50 synchronizes information with the running analysis server 60. In this way, the wristwatch-type device 12 and the waist-worn device 14 transmit information to the running analysis server 60 via synchronization with the information terminal 50, on the premise of possessing the information terminal 50. However, it is not necessary to always wear the information terminal 50 during running, and it is sufficient if synchronization with the information terminal 50 can be achieved after the exercise. As a modification, the waist-worn device 14 may synchronize information with the wristwatch-type device 12 once via short-range wireless communication, and the wristwatch-type device 12 may further synchronize information with the information terminal-type device 16 (information terminal 50) via short-range wireless communication. 【0015】 User 10 mainly wears the measuring device 20 and runs in a running race such as a marathon. However, the measurement is not limited to during the race, and it may be measured during long-distance practice runs assuming a race, or during shorter-distance runs. User 10 operates a button or the like of the measuring device 20 at the start of running to start measurement and recording of the running log. During the execution of the running exercise, the measuring device 20 measures the elapsed time from the start of recording as the running time with a timer, and records the position information for each date and time at a predetermined time interval. The measuring device 20 measures motion information such as pitch (the number of steps per unit time, also called cadence), rotational and translational motions of the pelvis, or impact values, using the built-in motion sensor. The measuring device 20 measures the heart rate of user 10 using the built-in optical heart rate monitor. 【0016】 After the run and recording of the run log are completed, the measuring device 20 transmits information such as run time, location information, movement information, and heart rate as a run log to the run analysis server 60. The measuring device 20 may also calculate information such as run time, distance, speed, cadence, and stride based on the time information and location information, and include this calculated information in the run log before transmitting it to the run analysis server 60. 【0017】 The driving analysis server 60 is a server computer connected to the internet that sends and receives data with multiple user information terminals 50 10. The driving analysis server 60 receives driving log data from the information terminals 50 of the users 10, including time information, location information, movement information, heart rate, etc., along with the user's identification information and attribute information, and stores it along with the measured values ​​and evaluation values ​​of various types of driving analysis indicators calculated from each piece of information. In response to requests from the information terminals 50, the driving analysis server 60 sends the stored driving log data, measured values, evaluation values, etc., to the information terminals 50. 【0018】 Figure 2 is a functional block diagram showing the various components of the driving analysis system 100. In this embodiment, the driving analysis system 100 consists of a measuring device 20, an information terminal 50, and a driving analysis server 60. However, the driving analysis system 100 can be implemented with various hardware and software configurations. For example, the driving analysis system 100 may consist only of the information terminal 50, or it may consist of a combination of the information terminal 50 and the measuring device 20, or it may consist of a combination of the information terminal 50 and the driving analysis server 60. Alternatively, it may consist of a combination of the information terminal 50, the measuring device 20, and the driving analysis server 60, or it may consist of a combination of the measuring device 20 and the driving analysis server 60, or it may consist only of the driving analysis server 60. 【0019】 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, the driving analysis system 100 may be configured with a combination of the information terminal 50 and the driving analysis server 60, or with the driving analysis server 60 alone, or it may be implemented as a single device that includes all the software configurations included in the information terminal 50 and the driving analysis server 60 shown in this figure. Therefore, regardless of the form of its hardware configuration, the driving analysis system 100 only needs to include at least the software configurations of the information terminal 50 and the driving analysis server 60 shown in this figure. 【0020】 Figure 2 illustrates the functional blocks realized by the coordination of various hardware and software configurations for the measurement device 20, information terminal 50, and driving analysis server 60. 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 measurement 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 driving analysis server 60 is composed of a combination of hardware such as a microprocessor, memory, display, and communication module. The functions of the measurement device 20, information terminal 50, and driving analysis server 60 will be described below. 【0021】 The measuring device 20 is, for example, a waist-worn device 14. The measuring device 20 includes a communication unit 21, a time measuring unit 22, a position measuring unit 24, and a motion detection unit 26. The time measuring unit 22 measures the start time of travel, i.e., the travel time from the start time of measurement, by counting a timer. The position measuring unit 24 measures the current position using position information received from the satellite positioning system by a GPS module. The motion detection unit 26 detects the user 10's pitch, pelvic rotation / translational movement, or impact value using a motion sensor. 【0022】 A wristwatch-type device 12 or an information terminal-type device 16 can also be used as the measuring device 20. When the wristwatch-type device 12 is used as the measuring device 20, the motion detection unit 26 of the wristwatch-type device 12 detects the user 10's pitch etc. using a motion sensor and detects the heart rate etc. using an optical heart rate monitor. When the 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 the user 10's pitch etc. using a motion sensor. The information terminal 50 may also serve as the information terminal-type device 16 as the measuring device 20, in which 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, but may also be a tablet terminal or personal computer owned by the user 10. 【0023】 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, measured at each progress point during the run by a measuring device 20 worn by the user 10, via the communication unit 52. 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 such as 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. 【0024】 The measurement value acquisition unit 40 acquires measurement values ​​of multiple types of motion analysis indicators that indicate the user's running motion state for each predetermined measurement interval, based on the information acquired by the information acquisition unit 30 and based on the time-series position information and motion information (hereinafter, the measurement values ​​of motion analysis indicators that indicate the running motion state are referred to as "indicator measurement values"). A "predetermined measurement interval" refers to a measurement interval with a predetermined elapsed time unit, such as every second or every minute, or a measurement interval with a predetermined elapsed distance unit, such as every 100m or every 1km. 【0025】 The measurement acquisition unit 40 calculates indicator measurements for multiple types of motion analysis indicators, such as lap pace, lap time, running time, running speed, pitch, stride, stride-to-height ratio, trunk posterior tilt, vertical movement, vertical movement-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, stiffness, and stiffness-to-body weight ratio. The measurement acquisition unit 40 records the indicator measurements that continue in a time series throughout the entire running process from start to finish as time-series data. 【0026】 The input / output unit 51 transmits the location information and operation information acquired by the measuring device 20, and the index measurement values ​​acquired by the measurement value acquisition unit 40, along with the user 10's attribute information, to the driving analysis server 60 via the communication unit 52 as a driving log. The input / output unit 51 displays the location information and operation information acquired by the measuring device 20 and the index measurement values ​​acquired by the measurement value acquisition unit 40 on the screen, and also displays the information received from the driving 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. 【0027】 In this embodiment, an example was described in which the measurement value acquisition unit 40 acquires multiple types of indicator measurement values. However, by assigning the functions of the measurement value acquisition unit 40 to the driving analysis server 60, the information terminal 50 may not calculate the indicator measurement values. In the following description of the driving analysis server 60, an example will be described in which the driving analysis server 60 has a measurement value acquisition unit 80 that corresponds to the functions of the measurement value acquisition unit 40. 【0028】 The driving analysis server 60 includes a communication unit 62, a log acquisition unit 64, an information acquisition unit 70, a measurement value acquisition unit 80, a classification processing unit 85, a pattern determination unit 90, a data storage unit 66, and an output unit 99. 【0029】 The log acquisition unit 64 acquires the running log via the communication unit 62 and stores it in the data storage unit 66. The information acquisition unit 70 has a function equivalent to the information acquisition unit 30 of the measuring device 20. That is, the information acquisition unit 70 acquires the location information and movement information of the user 10, who is the runner in the race, measured at each point along the run by the measuring device 20 worn by the user 10, from the running log stored in the data storage unit 66. 【0030】 The measurement value acquisition unit 80 has a function equivalent to the measurement value acquisition unit 40 of the information terminal 50. That is, based on the information acquired by the information acquisition unit 70, the measurement value acquisition unit 80 acquires indicator measurements of multiple types of motion analysis indicators that indicate the user's driving motion state for each predetermined measurement interval, based on the time-series position information and motion information. The measurement value acquisition unit 80 stores the indicator measurements that continue in time series throughout the entire journey from the start to the end of the journey as time-series data in the data storage unit 66. 【0031】 Furthermore, race courses may be hilly, and external environmental factors such as season, weather, temperature, humidity, time of day, and number of participants can significantly affect the measured values ​​of the indicators. Therefore, the measurement value acquisition unit 80 may acquire information on course conditions and environmental conditions and normalize the data based on such information to represent conditions unaffected by these conditions. In this case, the comparison data described later may also be normalized in the same way to achieve a comparison under equal conditions and improve the accuracy of the analysis. 【0032】 The classification processing unit 85 classifies the index measurements of multiple measurement intervals for each type of motion analysis index according to a predetermined classification method for distinguishing them by their characteristics. There are two classification methods. The first classification method classifies the index measurements based on the range obtained from the mean and standard deviation of the index measurements for each type of motion analysis index. The second classification method classifies the index measurements for each type of motion analysis index using cluster analysis. These classification methods allow for the classification of index measurements based on their degree of similarity to each other for each type of motion analysis index. 【0033】 In the first classification method, the classification processing unit 85 classifies the indicator measurements using the mean and standard deviation. The classification processing unit 85 classifies the time-series data of the indicator measurements based on whether or not they fall within the range of mean ± standard deviation. This makes it possible to distinguish between normal and abnormal indicator measurements, or between indicator measurements that indicate a stable running form and those that indicate an unstable running form. 【0034】 In the second classification method, the classification processing unit 85 classifies the indicator measurements using cluster analysis. As a preparatory process for cluster analysis, the classification processing unit 85 calculates the mean and standard deviation of data in predetermined units from the start point to the goal point in the time series data of the indicator measurements. That is, it sets a window of a predetermined length from the start point to the goal point in the time series data of the indicator measurements, and then performs a windowing process that sequentially calculates the mean and standard deviation of the indicator measurements within the window while sliding the window over the entire range to the goal point. By clustering the calculated mean and standard deviation values ​​into a predetermined number of groups using cluster analysis, the indicator measurements are classified into multiple groups and labeled with group labels. Details of cluster analysis will be described later. 【0035】 Depending on the index measurement value, the classification method may differ between the first and second classification methods, or the classification method may be predetermined based on the index measurement value. Alternatively, the classification processing unit 85 may perform classification processing using both the first and second classification methods for each index measurement value and decide which to adopt based on the results. For example, if the results of one classification method do not allow for classification into an appropriate number, the other classification method may be adopted. 【0036】 The mode determination unit 90 determines the output mode of the classified index measurement values ​​based on a comparison with a predetermined comparison target. Here, "output mode" refers to the mode of information to be output to the information terminal 50, including the type of information to be displayed on the screen of the information terminal 50, the display content, the display format, the data content to be transmitted to the information terminal 50, etc. The mode determination unit 90 determines the type of information to be output, the comparison target for the information, the output format of the information, etc., based on instructions from the user 10 via input to the input / output unit 51 of the measuring device 20. The mode determination unit 90 includes an instruction acquisition unit 91, an output target determination unit 92, a comparison target setting unit 93, a comparison processing unit 94, a change condition setting unit 95, and a change detection unit 96. 【0037】 The instruction acquisition unit 91 acquires instructions from the user 10 via input to the input / output unit 51 of the measuring device 20. The instruction acquisition unit 91 may also allow the user 10 to select an analysis perspective of interest, and set the indicator measurement values ​​corresponding to the selected analysis perspective as output targets or comparison targets. For example, seven types of analysis perspectives are provided for the user 10 to select: "ground contact with minimal strain," "stable posture," "whole-body coordination around the pelvis," "smooth center of gravity shift," "powerful movement," "symmetry," and "race speed." Each of these is an analysis perspective that individually affects one or more indicator measurement values. 【0038】 The output target determination unit 92 sets the indicator measurement values ​​corresponding to the analysis viewpoint selected by the user 10 as the output targets. For example, the indicator measurement values ​​corresponding to the analysis viewpoint of "low-burden ground contact" are vertical-to-height ratio, landing impact, and push-off acceleration. The indicator measurement values ​​corresponding to the analysis viewpoint of "stable posture" are lateral pelvic tilt and trunk posterior tilt. The indicator measurement values ​​corresponding to the analysis viewpoint of "whole-body coordination around the pelvis" are push-off time, pelvic rotation timing, and hip sinking. The indicator measurement values ​​corresponding to the analysis viewpoint of "smooth center of gravity shift" are deceleration and lateral impact. The indicator measurement values ​​corresponding to the analysis viewpoint of "powerful movement" are stride-to-height ratio, pelvic rotation, and pelvic lift. All indicator measurement values ​​correspond to the analysis viewpoint of "left-right symmetry." The indicator measurement value corresponding to the analysis viewpoint of "race speed" is running speed. 【0039】 The output target determination unit 92 may output all indicator measurements unless otherwise instructed by the user 10. Alternatively, the output target determination unit 92 may output all measurement intervals for a single indicator measurement, or, if the instruction acquisition unit 91 acquires a selection instruction from the user 10 regarding a measurement interval of interest, it may narrow the output target to a specific measurement interval based on the user 10's instruction. The output target determination unit 92 may also narrow the output target to measurement intervals where abnormalities are particularly observed or where particularly excellent results are shown, as detected by the change detection unit 96 (described later). 【0040】 The output data generation unit 98 generates output data such that it includes the index measurement values ​​that have been determined to be output by the output target determination unit 92. The output data includes at least the content that will be displayed on the screen by the input / output unit 51 of the measuring device 20. 【0041】 The mode determination unit 90 can output the index measurement values ​​in different output modes depending on what values ​​are used for comparison. The comparison target setting unit 93 sets some reference value as the comparison target. The comparison target setting unit 93 may, for example, be set to compare the average values ​​for each group label classified by the classification processing unit 85. The comparison target setting unit 93 may, for example, use the average value in the time series data of the index measurement values ​​or the average value within a range narrowed to a predetermined measurement interval as the comparison target, or it may use the same type of index measurement values ​​in other measurement intervals as the comparison target. If the comparison target setting unit 93 is using the index measurement value of one of the left or right legs as the comparison target, it may use the index measurement value of the other leg as the comparison target. The comparison target setting unit 93 may also use the same type of index measurement values ​​from other dates or other users as the comparison target. 【0042】 The comparison processing unit 94 compares the indicator measurement values ​​to be output with the comparison targets set by the comparison target setting unit 93. For example, if the comparison targets are the average values ​​for each group label classified by the classification processing unit 85, the comparison processing unit 94 compares the average values ​​for each group label. The evaluation determination unit 97 evaluates each group of indicator measurement values ​​classified by the classification processing unit 85 based on the comparison results from the comparison processing unit 94. With respect to the indicator measurement values ​​that have been designated as output targets by the output target determination unit 92, the evaluation determination unit 97 determines the evaluation of the indicator measurement values ​​based on the comparison results from the comparison processing unit 94 as well as the detection results from the change detection unit 96. 【0043】 The change detection unit 96 detects changes that meet predetermined change conditions in the time-series data of the indicator measurement values ​​to be output. The change condition setting unit 95 sets the change conditions to be detected by the change detection unit 96. The change condition setting unit 95 may set specific change conditions as standard detection conditions, or it may set conditions specified by the user 10 as change conditions. As change conditions, for example, a condition may be defined that when the difference between a predetermined reference value and an indicator measurement value exceeds a predetermined range, that indicator measurement value is detected as a value of interest. For example, the indicator measurement value may be detected as a value of interest when it is significantly better or significantly worse than the reference value. The reference value to be compared may be a comparison target set by the comparison target setting unit 93. 【0044】 The evaluation determination unit 97 determines the evaluation of the index measurement values, mainly with respect to the index measurement values ​​that have been designated as output targets by the output target determination unit 92, based on the comparison results from the comparison processing unit 94 and the detection results from the change detection unit 96. 【0045】 The output data generation unit 98 generates output data in a manner that distinguishes the measurement interval of the index measurement value in which a change has been detected by the change detection unit 96 from other index measurement values, i.e., measurement intervals in which no change was detected. Furthermore, the output data generation unit 98 generates output data in a manner that distinguishes the motion analysis index of the index measurement value in which a change has been detected by the change detection unit 96 from other motion analysis indexes, i.e., the index measurement values ​​of motion analysis indexes in which no similar change was detected. If an evaluation has been determined by the evaluation determination unit 97, the output data generation unit 98 generates output data that includes the evaluation result. 【0046】 Figure 3 is a flowchart showing the processing steps in the driving analysis system. The information acquisition unit 70 acquires position information and motion information measured by the measuring device 20 during driving such as a race (S10), and the measurement value acquisition unit 80 acquires measurement values ​​of various motion analysis indicators based on the acquired position information and motion information (S12). The classification processing unit 85 classifies the indicator measurement values ​​corresponding to the various motion analysis indicators according to a predetermined classification method (S14). The instruction acquisition unit 91 acquires information on the analysis viewpoint if the user 10 inputs an analysis viewpoint through operation (S16), the output target determination unit 92 determines the output target of the indicator measurement values ​​according to the analysis viewpoint (S18), the change detection unit 96 detects the motion analysis indicator or its indicator measurement value that corresponds to a predetermined change condition (S22), and the evaluation determination unit 97 determines the evaluation of the indicator measurement values ​​(S24). The output data generation unit 98 generates output data for the target indicator measurement values, along with comparison results, detection results, and evaluations. Based on the output data generated by the output data generation unit 98, the output unit 99 outputs the indicator measurement values ​​(S26). Note that the processes S12 to S26 can be executed in various orders, and the order of S12 to S26 in this flowchart is merely a convenient one. 【0047】 Figure 4 shows an example of classifying index measurements using the first classification method. In the first classification method, the classification processing unit 85 classifies the index measurements using the mean and standard deviation. In the graph of Figure 4, time-series data of index measurements indicating the amount of pelvic rotation are plotted in correspondence with distance points as the index measurements to be classified. The vertical axis shows the amount of pelvic rotation [deg], and the horizontal axis shows the distance points [km]. The solid line 110 shows the mean value of the time-series data of the amount of pelvic rotation, and the dashed line 112 shows the range of the mean value ± standard deviation of the time-series data of the number of pelvic rotations. Index measurements within the range of the two dashed lines 112 are plotted as circles, and index measurements outside the range of the dashed lines 112 are plotted as diamonds. In this example, the diamond plots of plot groups 114a to k are index measurements outside the range of the dashed lines 112, and are classified in a way that distinguishes them from the index measurements of the circular plots within the range of the dashed lines 112. Index values ​​shown in circular plots are classified as index values ​​indicating stable driving form within the standard deviation, while index values ​​shown in diamond plots are classified as index values ​​indicating unstable or disordered driving form outside the standard deviation. 【0048】 In this way, by classifying the time-series data of the indicator measurements based on whether or not they fall within the range of mean ± standard deviation, it is possible to distinguish between indicator measurements that indicate a stable running form and those that indicate an unstable running form. 【0049】 Figure 5 shows the preparatory process when classifying indicator measurements using the second classification method. In the second classification method, the classification processing unit 85 classifies the indicator measurements using cluster analysis. In the graph of Figure 5, time-series data of indicator measurements indicating running pace are plotted in correspondence with distance points as the indicator measurements to be classified. The vertical axis shows running pace [min / km], and the horizontal axis shows distance points [km]. Figure 5(a) shows the indicator measurements plotted as they are. As a preparatory process, a window W of a predetermined length is set as shown in the figure, and a windowing process is executed to calculate the mean and standard deviation of the indicator measurements included in the window W while sliding the window W from the start point to the finish point. The window W covers a range that may include multiple time-series consecutive indicator measurements, for example, 5 indicator measurements. If indicator measurements are taken every 100m, a window of 500m is set. Alternatively, if indicator measurements are taken in time units such as every 10 seconds, a time window of 60 seconds per section is set. 【0050】 The first window W1 covers the 1st to 5th indicator measurements, and the second window W2 covers the 2nd to 6th indicator measurements, setting the windows so that the indicator measurements slide one by one. Slide towards the goal, and the (n-1)th window W n-1 From the last nth window W n The windowing process ends when this point is reached. Figure 5(b) plots the time-series data of the average value of the index measurements within each window calculated by the windowing process, corresponding to the distance traveled in each window. Figure 5(c) plots the time-series data of the standard deviation of the index measurements within each window calculated by the windowing process, corresponding to the distance traveled in each window. 【0051】 Figure 6 shows the process of classifying the indicator measurements using the second classification method. The graph in Figure 6(a) is a scatter plot with the mean values ​​from Figure 5(b), calculated for each window in Figure 5, plotted on the horizontal axis and the standard deviation from Figure 5(c) plotted on the vertical axis. All plots are clustered, for example, using the K-means method. The number of clusters K can be set to an estimated value using the elbow method, for example, or a fixed value such as "4" can be set if you want to uniformly divide the data into four stages regardless of the type of driving evaluation value. As shown in the figure, all plots are clustered by their distance from the K centroid points and classified into four groups: Group 120 with centroid point 121 as the nucleus, Group 22 with centroid point 123 as the nucleus, Group 324 with centroid point 125 as the nucleus, and Group 426 with centroid point 127 as the nucleus. Each plot is labeled with the group label to which it belongs. The graph in Figure 6(b) shows the mean values ​​from Figure 5(b). Each plot is classified into four groups: Group 1 (120), Group 2 (122), Group 3 (124), and Group 4 (126), as labeled in Figure 6(a). The number of clusters may vary depending on the index measurement values, and may also vary depending on the variability of index measurement values ​​for each user or for each run. 【0052】 In this way, by classifying time-series data of indicator measurements using cluster analysis, it is possible to categorize the indicator measurements based on the degree of similarity of their characteristics, thereby objectively capturing and detecting the variability of the indicator measurements. 【0053】 Figure 7 is a bar graph showing the proportion of classification for each motion analysis index. For each motion analysis index, the measured values ​​are classified according to the first or second classification method described above, and the time series data is divided at the points where the classification changes. The vertical axis shows the measured values ​​for each index, and the horizontal axis shows the distance points [km]. In the case of a marathon, the total distance is 42km, and the bar graph of the measured values ​​for each motion analysis index is divided according to the proportion of each classification. For example, for the motion analysis index "stiffness," it is divided into two classifications at around 34.5km. For the motion analysis index "pelvic rotation timing," it is divided into three classifications at around 4km and around 14.5km. For example, for motion analysis indexes classified using the first classification method, the time series data of the measured values ​​is divided at the boundary between whether the value is within or outside the standard deviation range. For motion analysis indexes classified using the second classification method, the time series data of the measured values ​​is divided at the points where the group label changes. 【0054】 Figure 8 shows the evaluation of the index measurements divided in the horizontal bar graph of Figure 7 using yet another criterion. The comparison processing unit 94 evaluates the classification of each motion analysis index using the comparison target set by the comparison target setting unit 93 as the reference value. For example, if the average value for each group label classified by the classification processing unit 85 is used as the comparison target, the comparison processing unit 94 compares the average values ​​for each group label. Based on the comparison results by the comparison processing unit 94, the evaluation determination unit 97 evaluates each group of index measurements classified by the classification processing unit 85 and displays them in color according to the order of the average values ​​for each group, distinguishing them from other groups of index measurements as shown in Figure 8. The first color 130 indicates the classification of the best value (e.g., shown in blue). The second color 131 indicates the classification of a slightly good value (e.g., shown in green). The third color 132 indicates the classification of a slightly bad value (e.g., shown in yellow). The fourth color indicates the classification of the worst value (e.g., shown in red). As a variation, the comparison processing unit 94 may calculate the average value of the indicator measurements for the entire race, compare it with the average value for the entire race for each group label, and display the comparison results in a color-coded manner to distinguish them. 【0055】 Figure 9 shows an example of displaying only the indicator measurements detected as values ​​of interest. Figures 9(a) and 9(b) show the comparison results from the comparison processing unit 94, limiting the display to categories that are significantly better or significantly worse than a predetermined reference value being compared. Significantly better categories are shown in the fourth color 134 (e.g., red), and significantly worse categories are shown in the fifth color 135 (e.g., blue). In Figure 9(a), the comparison targets are, for example, the user 10's own data and the data of other runners. The comparison targets may be set from the indicator measurements of the same runner within the same race, from the indicator measurements of the same runner in past races, or from the indicator measurements of other runners. When using the indicator measurements of other runners as the comparison targets, the indicator measurements of other runners within the same measurement interval may be used as the comparison targets. In Figure 9(b), the comparison targets may be, for example, the value of the previous window in each window of Figure 5(a), or the value of the previous lap in each lap. 【0056】 The change detection unit 96 detects motion analysis indicators that have the longest duration as a period in which a significantly positive trend continues (in the example of Figure 9(a), this is "left-right tilt of the pelvis") and motion analysis indicators that have the longest duration as a period in which a significantly negative trend continues (in the example of Figure 9(a), this is "deceleration amount"). 【0057】 The change detection unit 96 may detect a combination of motion analysis indicators where the longest interval is a period in which all of the multiple motion analysis indicators show a significantly good trend, or a combination of motion analysis indicators where the longest interval is a period in which all of the multiple motion analysis indicators show a significantly bad trend. For example, the change detection unit 96 may detect a combination of landing impact / push-off acceleration (low-burden ground contact) and lateral impact (smooth center of gravity movement) as a combination of motion analysis indicators where the longest interval in a period of significantly good trend occurs towards the end of the race. In this case, the evaluation determination unit 97 may decide as an evaluation result that the runner is able to run by exerting force without waste. Alternatively, the change detection unit 96 may detect a combination of pelvic rotation amount (strength of movement) and push-off time (coordination of the whole body around the pelvis) as a combination of motion analysis indicators where the longest interval in a period of significantly bad trend occurs towards the end of the race. In this case, the evaluation determination unit 97 may decide as an evaluation result that the runner should reduce the push-off motion and use their pelvis to move their legs forward when running. 【0058】 Figure 10 shows an example of a motion analysis index with the longest interval showing a significantly poor trend. This figure graphs the amount of pelvic rotation, which has the longest interval showing a significantly poor trend towards the end of the race. As an index measurement value, time-series data of the index measurement value showing the amount of pelvic rotation is plotted in correspondence with the distance points. The vertical axis shows the amount of pelvic rotation [deg], and the horizontal axis shows the distance point [km]. In particular, the amount of pelvic rotation of user 10 in interval 139, which is towards the end of the race, shows a significantly poor trend and is detected as an important change by the change detection unit 96. While user 10 may often be aware of such an important change through their senses during the race, they may not be able to fully recognize at what stage the change actually began as a warning sign. According to this embodiment, important changes in objective data and the timing at which the change began can also be detected as warning signs, providing useful data for improving user 10's level. 【0059】 Figure 11 shows an example of a screen displaying race results. When user 10 instructs to display a summary of the race results, the race summary screen 140 generated by the mode determination unit 90 is displayed. The race summary screen 140 displays information indicating running conditions such as distance, running time, average pace, average stride, and average cadence, as well as a running form score based on measured values ​​of various motion analysis indicators, displayed in radar chart format. Course information based on the location information of the run is also displayed. At the bottom, evaluation content including advice for user 10 is displayed. 【0060】 Figure 12 shows an example of a screen displaying a race evaluation. When user 10 instructs the system to display the race evaluation, the race review screen 142 generated by the mode determination unit 90 is displayed. In the race review screen 142, the first column 143 displays an evaluation related to the running pace. The second column 144 displays a graph showing the progress of the running pace. The third column 145 displays evaluations of the pace and form, divided into the early, middle, and late stages of the race. 【0061】 The fourth column 146 displays evaluations based on important changes detected by the change detection unit 96, as well as evaluations related to motion analysis indicators corresponding to the viewpoint selected by the user 10. The fourth column 146 displays multiple evaluation points determined by the evaluation determination unit 97, and the screen switches to a screen like the following figure, which displays detailed explanations and graphs related to each evaluation point, at the user 10's instruction. 【0062】 Figure 13 shows an example of a screen that displays a detailed explanation of the evaluation points. When user 10 gives a command to display a detailed explanation of the evaluation points, the point explanation screen 150 generated by the mode determination unit 90 is displayed. On the point explanation screen 150, explanations of the motion analysis indicator itself, explanations of changes when important changes are detected, and explanations of comparison results with predetermined comparison targets are displayed in text and graphs. 【0063】 The present invention has been described above based on embodiments. These embodiments are illustrative, and it will be understood by those skilled in the art that various modifications are possible in combinations of their respective components and processing processes, and that such modifications also fall within the scope of the present invention. Furthermore, the above-described embodiments can be generalized to obtain the following embodiments. 【0064】 [Aspect 1] An information acquisition unit that acquires location information and movement information of the runner, who is the user, measured at each point along the route during the run using a predetermined measuring device, A measurement value acquisition unit acquires measurement values ​​of multiple types of motion analysis indicators that indicate the user's driving motion state for each predetermined measurement interval, based on the location information and motion information that are sequentially followed over time. A classification processing unit that classifies the measured values ​​of multiple measurement intervals according to their characteristics for each type of motion analysis index, A mode determination unit that determines the output mode of the classified measurement values ​​based on a comparison with a predetermined comparison target, An output unit capable of outputting at least information regarding the classified measured values ​​based on the determined output mode, A driving analysis system characterized by having the following features. 【0065】 [Aspect 2] The driving analysis system according to embodiment 1, characterized in that the classification processing unit classifies the measured values ​​by a predetermined classification method for classifying the measured values ​​according to the degree of similarity between them for each type of motion analysis index. 【0066】 [Aspect 3] The driving analysis system according to embodiment 1 or 2, characterized in that the classification processing unit classifies the measured values ​​based on a range determined by the mean and standard deviation of the measured values ​​for each type of motion analysis index. 【0067】 [Aspect 4] The driving analysis system according to any one of embodiments 1 to 3, characterized in that the classification processing unit classifies the measured values ​​by cluster analysis for each type of motion analysis index. 【0068】 [Aspect 5] The driving analysis system according to any one of embodiments 1 to 4, characterized in that the mode determination unit determines the output mode in a manner that distinguishes a measured value in which a change corresponding to a predetermined change condition is detected from other measured values. 【0069】 [Aspect 6] The driving analysis system according to any one of embodiments 1 to 5, characterized in that the mode determination unit determines the output mode in a manner that distinguishes a motion analysis index in which a change corresponding to a predetermined change condition has been detected from other motion analysis indexes. 【0070】 [Aspect 7] The driving analysis system according to any one of embodiments 1 to 6, characterized in that the mode determination unit determines the output mode in a manner that distinguishes a measured value in which a change corresponding to a change condition specified by the user is detected from other measured values. 【0071】 [Aspect 8] The driving analysis system according to any one of embodiments 1 to 7, characterized in that the mode determination unit determines the output mode by distinguishing measured values ​​in which a change corresponding to a predetermined change condition is detected in a motion analysis index specified by the user from other measured values. 【0072】 [Aspect 9] The driving analysis system according to any one of embodiments 1 to 8, characterized in that the mode determination unit determines the output mode in a manner that distinguishes measured values ​​in which a change corresponding to a predetermined change condition is detected in a measurement section specified by the user from other measured values. 【0073】 [Aspect 10] The driving analysis system according to any one of embodiments 1 to 9, characterized in that the mode determination unit determines the output mode of the classified measurement values ​​based on a comparison with a comparison target specified by the user. 【0074】 [Aspect 11] The process of acquiring location information and movement information of the runner, who is the user, measured at each point along the route during the run using a predetermined measuring device, A process of acquiring measured values ​​of multiple types of motion analysis indicators that indicate the user's driving motion state for each predetermined measurement interval, based on the location information and motion information that are sequentially followed over time, A process of classifying the measured values ​​of multiple measurement intervals for each type of motion analysis index according to a predetermined classification method for distinguishing them by their characteristics, A process for determining the output mode of the classified measurement values ​​based on a comparison with a predetermined comparison target, A process of outputting information regarding the classified measured values ​​based on the determined output mode, A driving analysis method characterized by comprising the following features. [Explanation of symbols] 【0075】 10 User, 20 Measuring device, 30 Information acquisition unit, 40 Measurement value acquisition unit, 50 Information terminal, 60 Driving analysis server, 70 Information acquisition unit, 80 Measurement value acquisition unit, 85 Classification processing unit, 90 Pattern determination unit, 99 Output unit, 100 Driving analysis system.

Claims

[Claim 1] An information acquisition unit that acquires location information and movement information of the runner, who is the user, measured at each point along the route during the run using a predetermined measuring device, A measurement value acquisition unit acquires measurement values ​​of multiple types of motion analysis indicators that indicate the user's driving motion state for each predetermined measurement interval, based on the location information and motion information that are sequentially followed over time. A classification processing unit that classifies the measured values ​​of multiple measurement intervals according to their characteristics for each type of motion analysis index, A mode determination unit that determines the output mode of the classified measurement values ​​based on a comparison with a predetermined comparison target, An output unit capable of outputting at least information regarding the classified measured values ​​based on the determined output mode, Equipped with, The aforementioned classification processing unit is characterized by classifying the measured values ​​according to a predetermined classification method for classifying the measured values ​​based on the degree of similarity between them for each type of motion analysis index, in a driving analysis system. [Claim 2] The driving analysis system according to claim 1, characterized in that the classification processing unit classifies the measured values ​​based on a range determined by the mean and standard deviation of the measured values ​​for each type of motion analysis index. [Claim 3] The driving analysis system according to claim 1, characterized in that the classification processing unit classifies the measured values ​​by cluster analysis for each type of motion analysis index. [Claim 4] The driving analysis system according to any one of claims 1 to 3, characterized in that the mode determination unit determines the output mode in a manner that distinguishes a measured value in which a change corresponding to a predetermined change condition is detected from other measured values. [Claim 5] The driving analysis system according to any one of claims 1 to 3, characterized in that the mode determination unit determines the output mode in a manner that distinguishes the motion analysis index in which a change corresponding to a predetermined change condition is detected from other motion analysis indexes. [Claim 6] The driving analysis system according to any one of claims 1 to 3, characterized in that the mode determination unit determines the output mode in a manner that distinguishes a measured value in which a change corresponding to a change condition specified by the user is detected from other measured values. [Claim 7] The driving analysis system according to any one of claims 1 to 3, characterized in that the mode determination unit determines the output mode in such a way that it distinguishes a measured value in which a change corresponding to a predetermined change condition is detected in a motion analysis index specified by the user from other measured values. [Claim 8] The driving analysis system according to any one of claims 1 to 3, characterized in that the mode determination unit determines the output mode in such a way that it distinguishes a measured value in which a change corresponding to a predetermined change condition is detected in a measurement section specified by the user from other measured values. [Claim 9] The driving analysis system according to any one of claims 1 to 3, characterized in that the mode determination unit determines the output mode of the classified measurement values ​​based on a comparison with a comparison target specified by the user. [Claim 10] The process involves a computer acquiring location and movement information of the runner (user) measured at each point along the route using a predetermined measuring device, and A process in which a computer acquires measured values ​​of multiple types of motion analysis indicators that show the user's driving motion state for each predetermined measurement interval, based on the location information and motion information that are sequentially followed over time, The process involves a computer classifying the measured values ​​of multiple measurement intervals according to their characteristics for each type of motion analysis index, using a predetermined classification method. The process by which the computer determines the output mode of the classified measurement values ​​based on a comparison with a predetermined comparison target, A process in which the computer outputs information regarding the classified measurement values ​​based on the determined output mode, Equipped with, The aforementioned classification process is characterized by classifying the measured values ​​according to a predetermined classification method for classifying the measured values ​​based on the degree of similarity between them for each type of motion analysis index.

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