Gait analysis device, gait analysis method, and computer-readable storage medium

Abstract

A running method analysis device, a running method analysis method, and a computer-readable storage medium are provided. In the running method analysis device, a first step frequency acquisition unit (76) acquires a step frequency at a first running speed as a first step frequency with respect to running of a subject. A second step frequency acquisition unit (77) acquires a step frequency at a second running speed different from the first running speed as a second step frequency with respect to running of the subject. A determination unit (80) determines which of a plurality of running pattern types including a stride pattern and a step frequency pattern the running of the subject conforms to, based on a magnitude of a difference between the first step frequency and the second step frequency with respect to a difference between the first running speed and the second running speed. A result output unit (92) outputs a result of the determination.

Description

Technical Field

[0001] This invention relates to a technique for analyzing the running patterns of marathon runners, and more particularly to a running pattern analysis device, a running pattern analysis method, and a computer-readable storage medium. Background Technology

[0002] In long-distance running such as marathons, there are known running styles for runners: "cadence running" and "stride running." The former is generally considered to be characterized by a relatively high cadence (number of steps per unit time) and a relatively short stride (distance of one step), while the latter is characterized by a relatively low cadence and a relatively long stride, but there is no clear definition yet.

[0003] In recent years, running shoes suitable for cadence-based running and stride-based running have also been developed. Therefore, runners can sometimes choose more suitable shoes by understanding whether their running style is cadence-based or stride-based (see, for example, Patent Document 1).

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent No. 4856427 Summary of the Invention

[0007] The problem that the invention aims to solve

[0008] However, in the past, except for cases where the tendency of cadence or stride length was particularly significant, there was no clear benchmark as to whether a runner's running style conformed to either cadence type or stride length type, and it could only rely on subjective judgment.

[0009] In this context, the inventors analyzed a large number of runners' running records and discovered a method for distinguishing between tendencies based on running style and objective criteria.

[0010] This invention was made in view of this problem, and its purpose is to provide a technique for analyzing a runner's running style.

[0011] Technical means to solve the problem

[0012] To address the aforementioned problem, a running style analysis apparatus according to one embodiment of the present invention includes: a first step frequency acquisition unit, which acquires the step frequency at a first running speed as the first step frequency for the subject's running; a second step frequency acquisition unit, which acquires the step frequency at a second running speed different from the first running speed as the second step frequency for the subject's running; a determination unit, which determines which of a plurality of running styles, including stride type and step frequency type, the subject's running style conforms to based on the magnitude of the difference between the first step frequency and the second step frequency relative to the difference between the first running speed and the second running speed; and a result output unit, which outputs the determination result.

[0013] Here, the "running style analysis device" can be implemented through a combination of server-side programs and servers running on a web server or in the cloud, or through a combination of programs and devices running on information terminals such as smartphones or tablets, or personal computers. Alternatively, it can be implemented through a combination of programs and wearable devices running on wearable devices with built-in sensors. The "first cadence acquisition unit" and the "second cadence acquisition unit" can acquire cadence information from pre-measured running data of the test subject, or from information acquired by specified sensors about the test subject during running. The "first running speed" and the "second running speed" can be relatively fast speeds of experienced runners who are primarily assumed to have completed a marathon in under 3 hours. For example, two running speeds within the range of 4.17 m / s (equivalent to a 4-minute pace for 1000 meters) to 5.56 m / s (equivalent to a 3-minute pace for 1000 meters) with an interval of 1 m / s can be designated as the first running speed and the second running speed. The increase in stride length relative to the increase in running speed may tend to be greater for a stride length type than a cadence type, and the increase in cadence relative to the increase in running speed may tend to be greater for a cadence type than a stride length type. The "determination unit" can determine the running style type based on the increase in cadence relative to the increase in running speed during the subject's running. "Multiple running styles" may include not only stride length types and cadence types, but also intermediate types falling between these running styles. According to the embodiment described, by acquiring cadence at various running speeds and analyzing the cadence change relative to speed changes, it is possible to determine whether a runner's running style is cadence-based or stride length-based, thus obtaining information useful for shoe selection.

[0014] The determination unit can determine a runner to be of cadence type if at least one of the first and second cadences is above a determination benchmark value calculated according to a prescribed benchmark, regardless of the magnitude of the difference in cadences. The "determination benchmark value calculated according to the prescribed benchmark" can be a variable value calculated using a prescribed linear equation with running speed as the variable, or it can be a predetermined fixed value determined in advance based on experiments, analysis, or insights. According to the embodiment described, when a runner's cadence is above the determination benchmark value, the runner can be directly determined to be of cadence type without further calculations or analysis, simplifying the determination method.

[0015] The determination unit can determine whether a subject's cadence type is compliant if the difference in cadence frequency falls within a predetermined first reference range, and determine whether it is compliant with stride length type if the difference in cadence frequency falls within a predetermined second reference range below the first reference range. Here, "the magnitude of the difference in cadence frequency" can refer to the increase in cadence frequency relative to the increase in running speed during the subject's running. The overall range of cadence frequency can be divided into a "first reference range" and a "second reference range" with a predetermined value to determine which type it conforms to, or it can be divided into multiple reference ranges that also include other reference ranges to determine which type it conforms to. According to the above embodiment, as long as the cadence frequency difference at various running speeds can be obtained during the subject's running, it is possible to determine with high accuracy whether it is a cadence type or a stride length type.

[0016] The determination unit can determine whether a running style conforms to a cadence type if the difference in cadence is within a predetermined first reference range, a stride length type if the difference is within a predetermined second reference range below the first reference range, and an intermediate type if the difference is within a predetermined third reference range below the first reference range and above the second reference range. Here, "intermediate type" can refer to a running style that cannot be definitively identified as either a cadence type or a stride length type, or a running style whose characteristics are not significant but can be tentatively considered to conform to either a cadence type or a stride length type. According to the embodiment described, as long as the cadence difference at various running speeds can be obtained during the subject's running, it is possible to determine with high precision whether the running style is cadence type, stride length type, or intermediate type.

[0017] It may also include a recommendation output unit, which, based on the determination result, outputs information recommending at least one type of shoe from a variety of shoes, including shoes suitable for cadence-type runners and shoes suitable for stride-type runners.

[0018] According to the embodiment, as long as the cadence difference at various running speeds can be obtained during the subject's running, information on shoes suitable for the running style can be obtained with high precision.

[0019] Other embodiments of the present invention are running style analysis methods. The method includes: for a subject's running, obtaining the stride frequency at a first running speed as the first stride frequency; for the subject's running, obtaining the stride frequency at a second running speed different from the first running speed as the second stride frequency; determining, based on the magnitude of the difference between the first and second stride frequencies relative to the difference between the first and second running speeds, which of several running styles, including stride length and stride frequency, the subject's running conforms to; and outputting the determination result.

[0020] According to the embodiment, by acquiring the cadence at various running speeds and analyzing the cadence change relative to speed changes, it is possible to determine whether a runner's running style is cadence-based or stride-based, thus obtaining useful information for shoe selection.

[0021] Furthermore, any combination of the above-mentioned structural elements, or any substitution of the structural elements of the present invention with each other in a method, apparatus, program, transient or non-transient storage medium storing the program, system, etc., is also valid as an embodiment of the present invention.

[0022] The effects of the invention

[0023] According to the present invention, a simple analysis of a runner's running style can be performed to obtain information that is beneficial to the user. Attached Figure Description

[0024] Figure 1 This is a diagram representing the basic structure of a running pattern analysis system.

[0025] Figure 2 It is a graph that compares stride length and cadence changes between stride types and cadence types within the same running speed range.

[0026] Figure 3 This is a scatter plot illustrating the relationship between running speed and stride length, and the relationship between running speed and cadence for multiple runners with different stride lengths.

[0027] Figure 4 This is a scatter plot illustrating the relationship between running speed and stride length, and the relationship between running speed and cadence for multiple runners with different cadence levels.

[0028] Figure 5 It is a functional block diagram representing the basic structure of a user terminal.

[0029] Figure 6 This is a functional block diagram representing the basic structure of the running analysis server.

[0030] Figure 7 This is a flowchart representing the basic processing in the running analysis server.

[0031] Figure 8 It is a detailed representation Figure 7 The flowchart of the decision-making process in S20.

[0032] Figure 9 This graph compares the results of determining running style based on the cadence of 14 subjects with the experimental results.

[0033] Explanation of symbols

[0034] 20: Runtime Analysis Server

[0035] 30: Running style analysis system

[0036] 74: Step Frequency Acquisition Unit

[0037] 76: First step frequency acquisition department

[0038] 77: Second step frequency acquisition unit

[0039] 80: Judgment Department

[0040] 90: Output Department

[0041] 92: Result Output Section

[0042] 94: Recommended Output Department Detailed Implementation

[0043] Hereinafter, the present invention will be described based on suitable embodiments and with reference to the accompanying drawings. In the embodiments and modifications, the same or equivalent structural elements are labeled with the same symbols, and repeated descriptions are omitted where appropriate. In the accompanying drawings, parts that are not important to the description of the embodiments are omitted.

[0044] In this embodiment, the premise is that the user, as a runner who wants to determine whether running shoes for cadence-based running (hereinafter referred to as "shoes") or stride-based running shoes are more suitable for them, personally performs the running style analysis. First, the user wears various wearable devices while running, and the information required for analysis is acquired through various sensors and sent to the user's terminal. Then, the user sends information from the terminal to the server and obtains the analysis results from the server.

[0045] Figure 1This describes the basic structure of a running style analysis system. The running style analysis system 30 includes, for example, a user terminal 10, a wearable device 16, and a running style analysis server 20. The user runs while wearing the wearable device 16, such as a running watch 12 or a motion sensor 14, on their arm or waist, acquiring various detection data through the running watch 12 or the motion sensor 14. The running watch 12 or the motion sensor 14 includes sensors such as a positioning module or a 9-axis motion sensor. Running speed is obtained based on the relationship between time and location information detected by the positioning module, and cadence is obtained based on information detected by the 9-axis motion sensor. Furthermore, stride length (stride length = running distance ÷ cadence) is obtained based on the running distance and cadence measured by the positioning module. The running logs acquired by the user terminal 10 are sent to the running style analysis server 20 via network 18. The running style analysis server 20 analyzes the running style to determine which of several running styles, including cadence-based and stride-based, it conforms to.

[0046] In a variation, instead of the wearable device 16, a positioning module or motion sensor built into a smartphone serving as the user terminal 10 can be used. In other variations, the data representing the subject's running status can be formatted as data on running speed and cadence obtained from images captured by a high-speed camera using techniques such as motion capture or by detecting ground reaction force from a force plate. In this case, an operator other than the user (e.g., a shop clerk) can operate the user terminal 10 to obtain running status data for the subject and have the running analysis server 20 perform running analysis.

[0047] "Calibratory frequency" is a numerical value measured in steps per minute (spm). For runners who complete a marathon in 3 hours and 30 minutes or less, running at race pace, cadence typically falls within the range of 175 spm to 205 spm. "Stride length" is the average stride length (m), calculated by dividing the distance run in one minute by cadence.

[0048] Figure 2 It is a graph that compares stride length and cadence changes between stride types and cadence types within the same running speed range. Figure 2 (a) is a scatter plot illustrating the relationship between running speed and stride length and the relationship between running speed and cadence for an example stride-type runner (whose personal best time to complete a marathon is 2 hours, 36 minutes and 7 seconds). Figure 2(b) is a scatter plot illustrating the relationship between running speed and stride length, and between running speed and cadence, for an example cadence-type runner (whose personal best time to complete a marathon is 2 hours 40 minutes and 0 seconds). The horizontal axis represents running speed [m / s], and the vertical axis represents stride length [m] or cadence [spm]. In this plot, stride length (marked with "×") and cadence (marked with "·") are shown for running within a range of approximately 4.0 m / s to 6.5 m / s, including speeds from 4.17 m / s (4 minutes per kilometer pace) to approximately 5.56 m / s (3 minutes per kilometer pace).

[0049] exist Figure 2 In the case of the stride type runner shown in (a), the stride length increases significantly within a range of approximately 0.6m from about 1.4m to about 2m, in a manner proportional to the increase in running speed. This indicates that the slope of the regression line representing the increase in stride length relative to the increase in running speed, i.e., the stride length change 110, is relatively large. In particular, with the increase in running speed from 4.17m / s (4 minutes per kilometer pace) to approximately 5.56m / s (3 minutes per kilometer pace), the stride length increases from 1.49m to 1.88m, reaching +0.39m.

[0050] In contrast, the cadence of stride-oriented runners gradually increases within a small range of 169 spm to 183 spm in a manner proportional to the increase in running speed. However, the slope of the regression line representing the increase in stride length relative to the increase in running speed—the cadence change—is very small, almost constant. In particular, with the increase in running speed from 4.17 m / s (4 minutes per kilometer pace) to approximately 5.56 m / s (3 minutes per kilometer pace), the cadence increases from 169 spm to 177 spm, a mere +8 spm.

[0051] exist Figure 2 In the case of the cadence-type runner shown in (b), the stride length increases proportionally to the increase in running speed, ranging from approximately 1.4m to approximately 1.8m by about 0.4m. The slope of the regression line representing the increase in stride length relative to the increase in running speed, i.e., the stride length change 112, is smaller than that of the stride length type. In particular, with the increase in running speed from approximately 4.17m / s (4 minutes per kilometer pace) to approximately 5.56m / s (3 minutes per kilometer pace), the stride length increases from 1.48m to 1.68m, a mere +0.2m.

[0052] In contrast, cadence-type runners show a significant increase in cadence across a wide range from 170 spm to 230 spm, proportional to the increase in running speed. This means the slope of the regression line (cadence change 113) relative to the increase in running speed is greater than that of stride-type runners. In particular, with the increase in running speed from 4.17 m / s (4 minutes per kilometer pace) to approximately 5.56 m / s (3 minutes per kilometer pace), cadence increases from 170 spm to 198 spm, a gain of +28 spm.

[0053] Figure 3 This is a scatter plot illustrating the relationship between running speed and stride length, and the relationship between running speed and cadence for multiple runners with different stride lengths. Below, the horizontal axis represents running speed [m / s], and the vertical axis represents stride length [m] or cadence [spm].

[0054] Figure 3 (a) represents an example of a runner running at a pace ranging from approximately 4.67 m / s (approximately 3 minutes 34 seconds per kilometer) to approximately 5.37 m / s (approximately 3 minutes 6 seconds per kilometer). In this case, the stride length plotted with “×” increases significantly in proportion to the increase in speed, ranging from approximately 1.44 m to approximately 1.63 m in about 0.19 m, indicating that the slope of the regression line for the change in stride length relative to the change in running speed, i.e., stride length change 100, is relatively large. In contrast, the cadence plotted with “·” increases only in a small range of 194 to 198 with the increase in speed, indicating that the regression line for the change in pace relative to the change in running speed, i.e., cadence change 101, remains approximately unchanged or has a very small slope.

[0055] Figure 3 (b) represents an example of a second runner running at a pace ranging from approximately 5.23 m / s (approximately 3 minutes 11 seconds per kilometer) to approximately 5.78 m / s (approximately 2 minutes 53 seconds per kilometer). In this case, the stride length, marked with "×", increases significantly within a range of approximately 0.2 m from approximately 1.73 m to approximately 1.93 m, proportional to the increase in speed. The slope of the regression line representing the increase in stride length relative to the increase in running speed, i.e., stride length change 102, is relatively large. In contrast, the cadence, marked with "·", varies only within a small range of 178 to 183 with the increase in speed, and the regression line representing the change in pace relative to the increase in running speed, i.e., cadence change 103, remains approximately unchanged or, more accurately, decreases slightly.

[0056] Figure 3(c) represents an example of a third runner running at a pace ranging from approximately 5.35 m / s (approximately 3 minutes 7 seconds per kilometer) to approximately 6.42 m / s (approximately 2 minutes 36 seconds per kilometer). In this case, the stride length plotted with “×” increases significantly in proportion to the increase in speed, ranging from approximately 1.75 m to approximately 2.11 m in about 0.26 m, indicating a relatively large slope in the regression line representing the increase in stride length relative to the increase in running speed, i.e., stride length change 104. In contrast, the cadence plotted with “·” varies only within a small range of 182 to 185 with the increase in speed, indicating that the regression line representing the change in pace relative to the increase in running speed, i.e., cadence change 105, remains approximately unchanged.

[0057] Figure 4 This is a scatter plot illustrating the relationship between running speed and stride length, and the relationship between running speed and cadence for several runners with different cadence patterns. Below, the horizontal axis represents running speed [m / s], and the vertical axis represents stride length [m] or cadence [spm].

[0058] Figure 4 (a) represents an example of a fourth runner running at a pace ranging from approximately 3.0 m / s (approximately 5 minutes 33 seconds per kilometer) to approximately 5.5 m / s (approximately 3 minutes 2 seconds per kilometer). In this case, the stride length, marked with "×", increases significantly in proportion to the increase in speed, ranging from approximately 0.95 m to approximately 1.65 m, indicating a relatively large slope in the regression line of stride length change relative to the change in running speed, i.e., stride length change 106. On the other hand, the cadence, marked with "·", also increases in a large range from 177 to 203 along with the increase in speed, indicating a large cadence change in the regression line of pace change relative to the change in running speed, i.e., cadence change 107.

[0059] Figure 4 (b) represents an example of a fifth runner running at a pace ranging from approximately 4.1 m / s (approximately 4 minutes 39 seconds per kilometer) to approximately 5.3 m / s (approximately 3 minutes 9 seconds per kilometer). In this case, the stride length, marked with "×", increases significantly within a range of approximately 0.3 m from approximately 1.3 m to approximately 1.6 m, proportional to the increase in speed. This indicates that the slope of the regression line for the change in stride length relative to the change in running speed, i.e., stride length change 108, is relatively large. On the other hand, the cadence, marked with "·", also increases significantly within a wide range of 184 to 194, accompanying the increase in speed. This indicates that the regression line for the change in pace relative to the change in running speed, i.e., cadence change 109, is also large.

[0060] As mentioned above, it is known that stride-type runners tend to significantly increase their stride length to improve running speed, rather than significantly increasing their cadence. Conversely, cadence-type runners also tend to increase their stride length to improve running speed, but they also tend to significantly increase their cadence. In the running style analysis system of this embodiment, the stride-type or cadence-type runner is determined by analyzing which of the two comparative approaches the subject uses to improve running speed.

[0061] Figure 5 This is a functional block diagram illustrating the basic structure of a user terminal. In this diagram, a block diagram focusing on functions is shown; these functional blocks can be implemented in various forms through hardware, software, or a combination thereof. The user terminal 10 can be a device such as an information terminal like a smartphone or tablet, or a personal computer, which serves as hardware. The user terminal 10 includes at least the functions of a running log recording unit 50, a display unit 52, a data processing unit 54, an operation processing unit 56, and a data communication unit 58. Furthermore, as a variation, the functions of the user terminal 10 shown in this diagram can also be integrated into a wearable device 16, thus realizing it as an integrated device.

[0062] The running log recording unit 50 acquires various detection data from the wearable device 16 via a communication module such as near-field communication and records it in the form of a running log. The detection data acquired from the wearable device 16 includes, for example, location information received from a satellite positioning system such as the Global Positioning System (GPS), information indicating the date and time of acquisition, and cadence per unit time (e.g., steps per minute). Based on the detection data acquired from the wearable device 16, the running log recording unit 50 records information such as running time, running distance, running speed at specified distances or times, and cadence per unit time in the form of a running log in a designated storage area.

[0063] The operation processing unit 56 receives operation input from the user's instructions. The display unit 52 displays the running log recorded by the running log recording unit 50 on the screen based on the user's instructions via the operation processing unit 56. The data processing unit 54 selects cadence data at various running speeds from the running logs recorded by the running log recording unit 50 based on the user's instructions via the operation processing unit 56, and sends the selected data to the running analysis server 20 via the data communication unit 58 in the form of the subject's running log. For example, the data processing unit 54 selects cadence data at running speeds of 4.17 m / s (4 minutes per kilometer pace) or higher from the overall running logs and sends it to the running analysis server 20 in the form of the subject's running log. The reason for removing data below 4.17 m / s is that the relationship between running speed and cadence is more stable at relatively higher speeds, and a clearer trend is observed. In addition, in a modified example, the data processing unit 54 may be configured to send the entire running log to the running analysis server 20 via the data communication unit 58, and select the necessary data on the running analysis server 20 side.

[0064] Figure 6 This is a functional block diagram illustrating the basic structure of a running analysis server. The diagram focuses on functionality, and these functional blocks can be implemented in various forms through hardware, software, or a combination thereof. The running analysis server 20 can be, for example, a server computer as hardware. The running analysis server 20 includes at least the functions of a data receiving unit 70, a data storage unit 72, a step frequency acquisition unit 74, a decision unit 80, and an output unit 90.

[0065] The data receiving unit 70 receives running speed and cadence data from the user terminal 10, which are included in the subject's running log, and stores them in the data storage unit 72. In this embodiment, the cadence data at running speeds of 4.17 m / s or higher selected by the data processing unit 54 of the user terminal 10 is received by the data receiving unit 70 as the running log. However, in a variant, the data receiving unit 70 may receive the entire running log of the subject and store it in the data storage unit 72. Regarding the subject's running, the cadence acquisition unit 74 acquires cadence data at multiple running speeds from the subject's running log stored in the data storage unit 72. The cadence data acquired by the cadence acquisition unit 74 is, for example, cadence data at running speeds of 4.17 m / s or higher. The cadence acquisition unit 74 includes a regression analysis unit 75, a first cadence acquisition unit 76, and a second cadence acquisition unit 77.

[0066] The regression analysis unit 75, based on the running logs stored in the data storage unit 72, derives a regression equation by using cadence as the target variable and running speed as the explanatory variable. The regression analysis unit 75 performs regression analysis on at least two data points that represent the relationship between running speed and cadence. However, since the more data points analyzed, the smaller the error of the regression equation and the higher the accuracy, it is ideal to analyze data points of three or more.

[0067] The first step frequency acquisition unit 76, based on the regression equation obtained by the regression analysis unit 75, acquires the step frequency at a predetermined running speed of 4.17 m / s (4 minutes per kilometer pace) or higher as the first running speed. The second step frequency acquisition unit 77, based on the regression equation obtained by the regression analysis unit 75, acquires the step frequency at a running speed 1 m / s faster than the first running speed as the second running speed. Furthermore, in this embodiment, the second running speed is set to be 1 m / s faster than the first running speed, but the speed difference is not limited to 1 m / s. A larger interval can be set between the first and second running speeds; as long as a certain degree of interval can be ensured, an interval of less than 1 m / s can also be set between the first and second running speeds.

[0068] The determination unit 80 determines which of several running styles the subject's running conforms to, including stride length type and cadence type. The determination unit 80 includes a cadence change calculation unit 82 and a determination processing unit 84. The cadence change calculation unit 82 calculates the difference between the first cadence and the second cadence relative to the difference between the first running speed and the second running speed.

[0069] The determination processing unit 84 determines which of several running styles, including stride type and cadence type, the subject's running style conforms to based on the difference between the first running speed and the second running speed, specifically the difference between the first and second running speeds. First, if the cadence at any running speed is above a predetermined determination benchmark value, the determination processing unit 84 determines that the subject's running style conforms to the cadence type, regardless of the magnitude of the change in cadence relative to the change in running speed. More specifically, the running speed is set as x, and the variable value obtained through the linear equation "9.6x + 145 spm" is set as the determination benchmark value. The determination processing unit 84 determines the subject's running style to be cadence type when the cadence is set as y and y ≥ 9.6x + 145 spm. Furthermore, as a variation, a fixed value, such as "190 spm," predetermined through experimentation or analysis, can be set as the determination benchmark value. In this case, the fixed value can be limited to a cadence relative to a running speed of, for example, 5.56 m / s (3 minutes per kilometer). Generally, the higher the running speed, the more the cadence is perceived to match that of a cadence-type runner, even for those with a longer stride. Therefore, in terms of accuracy, it is ideal to use a variable value obtained from a linear equation that is proportional to the running speed as the criterion, rather than a fixed value. However, if the running speed (pace) is limited to within 3 minutes for 1000 meters, even using a fixed value as the criterion can result in a highly accurate assessment.

[0070] When the cadence y is less than the benchmark value (y < 9.6x + 145 spm), if the difference (increase) between the first and second cadences before and after a 1 m / s increase in running speed is within the first range (e.g., more than 16 times), it is classified as cadence-type. If it is within the second range (less than 10 times) below the first range, it is classified as stride-type. If the difference between the first and second cadences is within the third range (more than 10 times but less than 16 times) below the first benchmark range but above the second benchmark range, it is classified as intermediate. In the intermediate case, if the difference between the first and second cadences is more than 10 times but less than 13 times, it is classified as "barely stride-type," i.e., "intermediate type close to stride-type." If the difference between the first and second cadences is more than 13 times but less than 16 times, it is classified as "barely cadence-type," i.e., "intermediate type close to cadence-type." In the "intermediate" case, it is believed that wearing either shoes suitable for cadence or shoes suitable for stride length will not have a significant impact on running, but it is difficult to determine how much performance improvement is possible when wearing either type.

[0071] The output unit 90 includes a result output unit 92, a recommendation output unit 94, and a data transmission unit 96. The result output unit 92 outputs the determination result of the determination unit 80 to the user terminal 10 via the data transmission unit 96. That is, the result output unit 92 displays the determination result on the screen of the user terminal 10 by sending the determination result to the user terminal 10 which type of running style the subject conforms to: cadence type, stride type, or intermediate type.

[0072] Based on the determination result of the determination unit 80, the recommendation output unit 94 selects shoes suitable for the test subject's running style from multiple options. The recommendation output unit 94 generates information recommending at least one shoe from multiple candidate shoes and outputs this information to the user terminal 10 via the data transmission unit 96. If the test subject is determined to be a stride type, the recommendation output unit 94 selects shoes suitable for stride type runners; if determined to be a cadence type runner, it selects shoes suitable for cadence type runners. Stride type running shoes, compared to cadence type running shoes, have features such as superior sole rebound. If the test subject is determined to be an intermediate type and "barely stride type," the unit generates recommendations for shoes suitable for both stride type and cadence type, with a stronger recommendation for stride type shoes. Similarly, if the test subject is determined to be an intermediate type and "barely cadence type," the unit generates recommendations for shoes suitable for both stride type and cadence type, with a stronger recommendation for cadence type shoes.

[0073] Figure 7 This is a flowchart illustrating the basic processing in the running style analysis server. The data receiving unit 70 acquires the subject's running log (S10), the regression analysis unit 75 performs regression analysis on the subject's running log (S12), the first cadence acquisition unit 76 acquires the first cadence at a first running speed based on regression (S14), and the second cadence acquisition unit 77 acquires the second cadence at a second running speed 1 m / s faster than the first running speed based on regression (S16). The cadence change calculation unit 82 calculates the magnitude of the cadence change (i.e., the cadence increase) when the running speed difference between the first and second running speeds is 1 m / s (S18), and the judgment processing unit 84 determines the running style based on the magnitude of the cadence or the magnitude of the cadence change (S20). The result output unit 92 outputs the judgment result from the judgment processing unit 84 to the user terminal 10 (S22), and the recommendation output unit 94 determines the recommended shoes and generates recommendation information based on the judgment result from the judgment processing unit 84 (S24), and outputs it to the user terminal 10 (S26).

[0074] Figure 8 It is a detailed representation Figure 7The flowchart of the determination process in S20 is as follows. When the running speed is set to x and the cadence is set to y, if the subject's cadence y is at or above the determination benchmark value (9.6x + 145 spm) (S30 "Yes"), the determination processing unit 84 determines that the subject's running style is cadence-type (S32). Even if the subject's cadence y is less than the determination benchmark value (S30 "No"), if the increase in cadence with a running speed difference of 1 m / s is in the first range (+16 spm or more) (S34 "Yes"), the subject's running style is determined to be cadence-type (S32). In S34, if the increase in cadence with a running speed difference of 1 m / s is less than +16 spm (S34 "No") and the increase in cadence is in the second range (less than +10 spm) (S36 "Yes"), the subject's running style is determined to be stride-type (S38). If the increase in cadence is +10 spm or more (No in S36) and +13 spm or more but less than +15 spm (less than 16 spm) (Yes in S40), the subject's running style is determined to be an intermediate type close to the cadence type (S42). If the increase in cadence is not +13 spm or more but less than +15 spm (less than 16 spm), that is, +10 spm or more but less than +13 spm (No in S40), the subject's running style is determined to be an intermediate type close to the stride type (S44).

[0075] Figure 9 This graph compares the results of judging running style based on cadence of 14 subjects with experimental results. Running logs were collected from the 14 subjects numbered "1" to "14". Cadence was used as the target variable, and running speed as the explanatory variable. Regression analysis was performed, and the results are shown in the first column 120. The first column 120 shows the slope and intercept of the regression equations obtained through regression analysis. Based on these regression equations, the cadence at each subject's first and second running speeds, and the differences between these cadences, can be obtained. The measured running speeds (race pace) included in the running logs are shown in the second column 121, and the measured cadences at those running speeds are shown in the third column 122. The benchmark value (9.6x + 145 spm) for the running speed in the second column 121 is shown in the fourth column 123. When the cadence shown in the third column 122 is above the benchmark value in the fourth column 123, the following criteria are also considered: Figure 8 The "yes" of S30 directly determines that the test subject's running style is cadence-based ( Figure 8(S32). Column 5, 124, shows the judgment result of the subject's running style. For example, subjects "5", "6", and "11" are directly judged as cadence type because their cadence in column 3, 122, is above the judgment benchmark value in column 4, 123. Their respective cadence type judgment results are shown in column 5, 124. Other subjects "1" to "4", "7" to "10", and "12" to "14", after obtaining the cadence increase under a running speed difference of 1 m / s based on the regression formula shown in column 1, 120, will be based on... Figure 8 The results of the judgment process are shown in column 5, 124. In the judgment results shown in column 5, 124, “(step frequency type)” in parentheses means the judgment result of “intermediate type close to step frequency type”, and “(stride length type)” in parentheses means the judgment result of “intermediate type close to stride length type”.

[0076] Column 6, section 125, shows the results of running experiments conducted by each participant, wearing shoes suitable for cadence and shoes suitable for stride length, indicating which type of shoe resulted in a faster run. The results from column 5, section 124, are compared with those from column 6, section 125, with "○" indicating agreement and "×" indicating disagreement, and the correctness is shown in column 7, section 126. Furthermore, participant "13"'s result shows "-" indicating that neither cadence nor stride length is acceptable because there is no difference between shoes suitable for cadence and shoes suitable for stride length. Based on the above, except for participant "8," the results of 13 out of 14 participants are consistent with the judgment results, confirming a pass. Figure 8 The judgment and processing can make judgments with high precision.

[0077] The present invention has been described above based on embodiments. Those skilled in the art will understand that the embodiments are illustrative, and various modifications may exist in the combination of these structural elements or processing techniques; moreover, such modifications are also within the scope of the present invention. Modifications will be described below.

[0078] In the described embodiment, an example of acquiring running logs through wearable device 16 and performing running style analysis based on the running logs was explained. In a variation, the user may send the cadence values ​​at at least two running speeds to the running style analysis server 20 by entering text in a designated field on a web browser, and the running style analysis server 20 performs running style analysis.

[0079] In the described embodiment, an example of running analysis being performed in the form of a running analysis system 30 including a user terminal 10 and a running analysis server 20 was explained. In a variation, the various functions for running analysis can be implemented in a form that is executed on a device such as a smartphone, tablet, or personal computer that is directly operated by the user, rather than being implemented on the running analysis server 20.

[0080] Industrial availability

[0081] This invention relates to a technique for analyzing the running patterns of marathon runners.

Claims

1. A running pattern analysis device, characterized in that, include: The first step frequency acquisition unit acquires the step frequency at the first running speed as the first step frequency for the subject's running. The second step frequency acquisition unit acquires the step frequency at a second running speed that is different from the first running speed as the second step frequency for the subject's running. The determination unit determines, based on the magnitude of the difference between the first step frequency and the second step frequency relative to the difference between the first running speed and the second running speed, whether the subject's running conforms to stride type or step frequency type. The stride type is a running style with relatively few steps per unit time and relatively long distance per step, and the step frequency type is a running style with relatively many steps per unit time and relatively short distance per step. and The result output section outputs the result of the determination. If the difference in step frequency is greater than 16 steps per minute, it is determined to conform to the step frequency type; if the difference in step frequency is less than 10 steps per minute, it is determined to conform to the stride type.

2. The running pattern analysis device according to claim 1, characterized in that: If the determination unit determines that the step frequency conforms to the step frequency type when at least one of the first step frequency and the second step frequency is above the determination benchmark value of 9.6x + 145 steps per minute, regardless of the magnitude of the difference in step frequency, where x is the running speed.

3. The running pattern analysis device according to claim 1 or 2, characterized in that: The determination unit determines that the difference in step frequency is greater than 10 steps per minute and less than 16 steps per minute if it meets the intermediate type.

4. The running pattern analysis device according to claim 1 or 2, characterized in that, Also includes: The recommendation output unit, based on the determination result, outputs information recommending at least one type of running shoe from a variety of running shoes, including shoes suitable for cadence-type runners and shoes suitable for stride-type runners.

5. A running style analysis method, characterized in that, include: Regarding the subject's running, the process of obtaining the step frequency at the first running speed as the first step frequency; Regarding the subject's running, the process of obtaining the step frequency at a second running speed that is different from the first running speed as the second step frequency; Based on the magnitude of the difference between the first step frequency and the second step frequency relative to the difference between the first running speed and the second running speed, the process of determining whether the subject's running conforms to either stride type or step frequency type is used. The stride type is a running style with relatively few steps per unit time and relatively long distance per step, while the step frequency type is a running style with relatively many steps per unit time and relatively short distance per step. The process of outputting the result of the determination. If the difference in step frequency is greater than 16 steps per minute, it is determined to conform to the step frequency type; if the difference in step frequency is less than 10 steps per minute, it is determined to conform to the stride type.

6. A computer-readable storage medium storing a running analysis program, the running analysis program being characterized in that it enables a computer to perform the following functions: Regarding the subject's running, the function is to obtain the step frequency at the first running speed as the first step frequency; Regarding the subject's running, the function is to obtain the step frequency at a second running speed that is different from the first running speed as the second step frequency; Based on the magnitude of the difference between the first step frequency and the second step frequency relative to the difference between the first running speed and the second running speed, it is determined whether the subject's running conforms to either stride type or step frequency type. The stride type is a running style with relatively few steps per unit time and relatively long distance per step, while the step frequency type is a running style with relatively many steps per unit time and relatively short distance per step. The function to output the result of the determination. If the difference in step frequency is greater than 16 steps per minute, it is determined to conform to the step frequency type; if the difference in step frequency is less than 10 steps per minute, it is determined to conform to the stride type.

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