JP2019181040A - Landing determination method, program and landing determination device - Google Patents

Abstract

To provide technique capable of evaluating landing on locomotive movement of a user with a simple facility.SOLUTION: An information processor acquires acceleration in a movement direction and a longitudinal direction of legs of a subject, from an accelerometer attached to a leg part of a subject who is in locomotive movement, then outputs a determination result 400 about landing in locomotive movement of the subject. One embodiment of locomotive movement is running. The information processor acquires drive speed of a tread mill, for example, for acquiring running speed of the subject, then the running speed may be used for determination. The determination result 400 includes a landing pattern 421 determined based on the acceleration in a running direction, and level 422 of impact.SELECTED DRAWING: Figure 4

Description

  The present disclosure relates to a landing determination method, a program, and a landing determination device for evaluating landing in a moving motion of a user.

  Conventionally, various techniques for automatically determining a pattern related to a user's travel have been proposed. For example, Japanese Patent No. 5314224 (Patent Document 1) acquires body motion information about a user who runs on a treadmill, extracts features from the acquired body motion information, and uses the extracted features as a given calculation. We propose a running form diagnosis system that calculates the running form score of the user by applying the formula.

Japanese Patent No. 5314224

  In the above system, a user wearing a marker is photographed with a high-speed camera, and then a running form is diagnosed using motion capture technology. In such a background, there is a demand for a technique that can evaluate the landing of a user's moving motion with simpler equipment.

  The present disclosure has been conceived in view of such circumstances, and an object of the present disclosure is to provide a technique that makes it possible to evaluate a user's landing in a moving motion with simple equipment.

  According to an aspect of the present disclosure, a step of obtaining a detection output of an acceleration sensor attached to a user's leg during a movement exercise, a step of generating a determination result regarding the user's landing using the detection output, and a determination And a step of outputting a result.

The determination result may include a determination result regarding the landing mode of the user.
The step of generating the determination result may include generating a determination result regarding the landing mode using the detection output of the acceleration in the traveling direction of the user.

  The method may further include a step of acquiring the moving speed of the user. Generating the determination result for the landing mode includes the landing determination standard obtained based on the acceleration and the landing mode of each of the two or more moving speeds, the moving speed, and the detection output of the acceleration in the traveling direction of the user. And generating a determination result for the landing mode.

  The landing determination criterion may include a relational expression between the moving speed and the acceleration for distinguishing two or more landing modes.

  Generating the determination result for the landing mode depends on whether or not the detection output of the acceleration in the user's direction of travel is included in the acceleration range associated with the given landing mode. It may include determining whether or not it is a landing mode.

The determination result may include a determination result regarding the impact when the user lands.
Generating the determination result for the impact may include determining the impact level at the time of landing of the user using an impact determination criterion based on an average value and standard deviation of accelerations of a plurality of subjects.

  Generating a determination result for an impact includes determining an impact level at the time of landing of the user using an impact criterion based on an average value and a standard deviation of logarithmically transformed values of a plurality of subjects. May be included.

  The method may further include a step of acquiring the moving speed of the user. The impact determination criterion may include a relational expression between the moving speed and the acceleration. Generating the determination result for the impact may include determining a level of the impact at the time of landing of the user using a portion corresponding to the moving speed of the user in the impact determination criterion.

  According to another aspect of the present disclosure, a program that is executed by a computer and that causes the computer to realize the landing determination method is provided.

  According to still another aspect of the present disclosure, a landing determination apparatus including an acceleration sensor, a computer, and a memory storing the program is provided.

  According to the present disclosure, a determination result about the landing in the moving motion of the user is generated using the acceleration sensor attached to the leg of the user.

It is a figure which shows an example of a structure of the landing determination system by which the landing determination method is implement | achieved. It is a figure which shows the specific example of the mounting aspect of the accelerometer 90 in a user's leg part. 2 is a diagram illustrating an example of a hardware configuration of an information processing device 30. FIG. 7 is a diagram illustrating an example of a determination result regarding a user's travel that is output from the information processing device 30 to the output device 40. It is a flowchart of the process for producing | generating the reference | standard for landing pattern determination. It is a figure which shows an example of the regression line _RAP . It is a flowchart of the process for producing | generating the reference | standard for impact level determination at the time of landing. It is a diagram illustrating an example of a regression line R _VER and level determination of a straight line LD1 to LD4. It is a flowchart of the process which outputs the determination result with respect to a user's run. Other examples of the regression line R _VER and level determination of a straight line LD1~LD4 is a diagram showing a.

  Hereinafter, embodiments of a landing determination method will be described with reference to the drawings. In the following description, the same parts and components are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated.

[Outline of landing judgment system]
FIG. 1 is a diagram illustrating an example of a configuration of a landing determination system in which a landing determination method is realized. As illustrated in FIG. 1, the landing determination system 100 includes a treadmill 10, an accelerometer 90 attached to a user 900, an information processing device 30 that acquires acceleration detected by the accelerometer 90, and movement of the user 900. And an output device 40 that outputs a determination result regarding landing during exercise. The accelerometer 90 is attached to the heel (distal end of the tibia) of the user 900, for example. At least one of the output device 40 and the accelerometer 90 may be configured as a part of the information processing device 30.

  The information processing device 30 is an example of a landing determination device, and is, for example, a general-purpose computer in which a given application (hereinafter also referred to as “landing determination app” as appropriate) is installed. The information processing apparatus 30 acquires the driving speed of the treadmill 10 (the moving speed of the user 900 on the treadmill 10) as the speed of the user's moving motion. The output device 40 may be a monitor that displays a screen, or may be a printer that outputs a sheet on which an evaluation result is printed. In addition, although this specification mentions "running" as a mere example of "moving movement", what is determined in the landing determination system is not limited to landing in running, but in other forms of movement such as walking. It may be a landing.

[Specific example of direction of acceleration detected by accelerometer 90]
FIG. 2 is a diagram illustrating a specific example of the manner in which the accelerometer 90 is mounted on the leg of the user. As shown in FIG. 2, the accelerometer 90 is attached to the heel of the leg portion 901 of the user 900 by a belt 90A.

  The accelerometer 90 measures accelerations in two directions indicated by arrows AP and VER in FIG. The arrow AP is along the traveling direction of the user 900 (hereinafter also referred to as “front-rear direction”). The arrow VER is along the longitudinal direction of the user's 900 foot (hereinafter also referred to as “vertical direction”).

  The accelerometer 90 may be worn on each of both legs of the user. In this case, the information processing apparatus 30 can acquire acceleration on each of the user's right foot and left foot, and thereby output a determination regarding landing on each of the right foot and left foot.

[Hardware Configuration of Information Processing Apparatus 30]
FIG. 3 is a diagram illustrating an example of a hardware configuration of the information processing apparatus 30. The information processing apparatus 30 includes a CPU (Central Processing Unit) 300, a graphic controller 310, a VRAM (Video RAM (Random Access Memory)) 312, an I / O (input / output) controller 316, an interface 324, and a communication device (interface). 326, a main memory 328, a BIOS (Basic Input Output System) 330, a USB (Universal Serial Bus) board 336, and a bus line 338.

  CPU 300 acquires detection outputs from accelerometer 90 and treadmill 10 via communication device 326, for example, by wireless communication. The communication device 326 may be connected to the treadmill 10 by wire. The CPU 300 outputs a screen to a monitor that is an example of the output device 40 via the graphic controller 310.

  The BIOS 330 stores a boot program executed by the CPU 300 when the information processing apparatus 30 is started up, a program depending on the hardware of the information processing apparatus 30, and the like. Storage devices such as the hard disk 318, the optical disk drive 322, and the semiconductor memory 320 are connected to the I / O controller 316. The interface 324 is a device for inputting information to the information processing device 30 such as a touch panel and a keyboard.

  The information processing apparatus 30 further includes a wireless unit 334 and a Bluetooth (registered trademark) module 314. The information processing apparatus 30 can communicate with an external device wirelessly via the wireless unit 334. By using the Bluetooth module 314, the information processing device 30 can communicate with an external device using the Bluetooth method (an example of a short-range wireless communication method).

  Examples of the optical disk drive 322 include a CD-ROM (Compact Disc-ROM (Read Only Memory)) drive, a DVD (Digital Versatile Disc) -ROM drive, a DVD-RAM drive, and a BD (Blu-ray (registered trademark) Disk). -A ROM drive is adopted. The optical disc 500 is a recording medium having a format corresponding to the optical disc drive 322. The CPU 300 reads a program or data from the optical disc 500 using the optical disc drive 322. The CPU 300 can load the read program or data into the main memory 328 via the I / O controller 316, and can be installed in the hard disk 318. The communication device 326 is a device mounted on the information processing apparatus 30 to communicate with other devices such as a LAN (Local Area Network) card.

  The CPU 300 can execute a program that can be stored in the optical disc 500 or a recording medium (such as a memory card) and provided to the user. The CPU 300 may execute a program stored in a recording medium other than the optical disc 500, or may execute a program downloaded via the communication device 326.

[Example of landing judgment result output]
FIG. 4 is a diagram illustrating an example of a determination result regarding the user's travel that is output from the information processing apparatus 30 to the output apparatus 40. An example of the output mode of the determination result is display on a display device, and another example is printing on paper. The contents of an example of the determination result will be described with reference to FIG.

  The determination result 400 in FIG. 4 includes fields 410, 420, and 430. A field 410 indicates user information (ID, name) and measurement date and time. The information shown in the field 410 is input via the interface 324 or the like, for example. CPU 300 reads the information and places it in field 410.

  The field 420 includes a landing pattern 421 and an impact level 422 when landing. The landing pattern 421 indicates a pattern corresponding to a user's landing mode from among preset patterns. In one example, two patterns (“踵 landing” and “midfoot forefoot landing”) are preset. A landing pattern 421 in FIG. 4 represents a result (character string “踵 landing mode”) when the user's landing mode is determined to be “踵 landing”.

  Generally, landing patterns in traveling are classified into three types: “forefoot (front foot)”, “midfoot (medium foot)”, and “heel strike (heel)”. The “forefoot” is a pattern that lands from the base of the finger on the sole of the foot. “Midfoot” is a pattern that lands from the center of the sole of the foot. The “heel strike (heel part)” is a pattern that lands from the heel. In this specification, the landing pattern “heel strike (heel part)” is referred to as “heel landing”, and the landing pattern “midfoot (medium foot part)” and the landing pattern “forefoot (forefoot part)” are combined. This is called “Midfoot forefoot landing”.

  The level 422 represents the magnitude of impact at the time of landing of the user as a level determined by comparison with other users (subjects). In one example, five levels are set, and the lower the level, the smaller the impact. A level 422 in FIG. 4 represents a result (character string “level 2 (somewhat small)”) when it is determined that the magnitude of the impact at the time of landing of the user is the second level.

  In the example of FIG. 4, the field 420 further includes the magnitude of the impact when the user lands (the graph “impact value applied to your leg”) and the magnitude of the magnitude of the impact of another user (subject). And a graph generated by statistical processing (graph “impact value distribution”).

  The field 430 includes three types of shoes (models A to C) and a mark (check mark) given to the best fit for the user among the three types of shoes. The information processing apparatus 30 refers to information associating the determination result with the recommended shoe type, and determines a shoe suitable for the user according to the determination result. In one example, the information associates the landing pattern 421 with the recommended shoe type. In another example, the information associates an impact level (level 422) with a shoe type. In yet another example, the information associates a combination of landing pattern 421 and level 422 with a recommended shoe type.

[Generation of landing pattern judgment criteria]
FIG. 5 is a flowchart of a process for generating a reference for determining a landing pattern. The processing in FIG. 5 is realized, for example, when the CPU 300 of the information processing apparatus 30 executes a given program. The contents of the process will be described with reference to FIG. In one example, a plurality of sets of travel data (acceleration detection results) are used for generating the reference.

  In the present embodiment, a reference used to determine the landing in the user's moving motion is generated using a plurality of travel data. In this specification, a person who uses travel data for generating a reference is called a “subject”, and a person who is given a judgment on landing using the reference is called a “user”.

  One set of data consists of the running direction (“AP direction” in FIG. 2) and the longitudinal direction of the foot (“VER direction” in FIG. 2) for a predetermined time (for example, 30 seconds) on one leg of the subject. Each acceleration data is included. The acceleration data may be associated with a running speed and a landing pattern. The running speed is acquired as the driving speed of the treadmill 10, for example. The landing pattern is given by an expert who visually recognizes the traveling of each subject. In one example, the pattern to be given can be categorized as either “Amber landing”, “Midfoot forefoot landing” or “Difficult to judge”. It is one of).

  For example, when the subject wears the accelerometer 90 on the right foot and travels on the treadmill 10 for 30 seconds, one set of data is generated by the travel. The data of the set includes acceleration data for 30 seconds in the AP direction, acceleration data for 30 seconds in the VER direction, and the driving speed of the treadmill 10 in the traveling for 30 seconds. Furthermore, when the expert gives the landing pattern “spot landing” to the subject's travel, the data of the set includes the landing pattern “spot landing”.

  In step S500, CPU 300 cuts out data for one cycle from the acceleration data in the traveling direction from the data for one set. One cycle represents an operation from when the foot to be measured kicks the ground until it lands. For example, when the subject requires 2 seconds for one cycle, the CPU 300 extracts data for 15 cycles from the acceleration data for 30 seconds. In one example, the CPU 300 recognizes data of each cycle from one set of data by pattern recognition or the like.

In step S502, CPU 300 extracts data included in the period from 5 seconds to 25 seconds after the start of traveling from the data extracted in step S500. In the extracted data, the CPU 300 selects the peak value immediately after landing of the acceleration (acceleration G _{AP in the} traveling direction) in each cycle. Then, CPU 300 calculates the average value of the peak of the acceleration G _AP of each cycle.

  In step S504, CPU 300 reads the running speed associated with the set of acceleration data to be processed, and stores the running speed and the average value calculated in step S502 in hard disk 318.

In step S506, CPU 300 determines whether all sets of data prepared for reference generation have been processed in steps S500 to S504. If CPU 300 determines that there is a set that has not yet been processed (NO in step S506), it returns control to step S500, and starts processing for unprocessed set data. On the other hand, when it is determined that all sets of data have been processed (YES in step S506), CPU 300 advances the control to step S508. At this point, the hard disk 318, the same number as the processed set, a set of the average value of the peak of the run speed and acceleration G _AP is stored.

In step S508, CPU 300 utilizes a set of the average value of the peak of the stored run speed and acceleration G _AP at step S504 obtains the regression line R _AP. The regression line R _AP may be determined only for a given landing pattern. In step S510, CPU 300 obtains a straight line PD for pattern determination. Thereafter, the CPU 300 ends the process of FIG.

FIG. 6 is a diagram illustrating an example of the regression line _RAP . With reference to FIG. 6, how to obtain the regression line R _AP (step S508) and the pattern determination line PD (step S510) will be described.

In the graph shown in FIG. 6, the average value of the peak of the acceleration _GAP is plotted against the running speed. In the example of FIG. 6, since 73 sets of acceleration data are used, the graph includes 73 plots. The graph of FIG. 6 includes three types of plots (◯, Δ, ×). ○ is a plot for a set having a landing pattern “spot landing”. X is a plot for a set having a landing pattern “Midfoot forefoot landing”. Δ is a plot for a set having a landing pattern “difficult to determine”.

In FIG. 6, the line R _AP (v) represents the regression line R _AP generated by the set of the running speed and the average value of the peak of the acceleration G _AP for the plot of the landing pattern “spot landing”. Line PD represents relative regression line R _AP, three times the value (3SD _AP) was added the value of the landing pattern "heel landing" standard deviation calculated for the plot of (SD _AP).

The standard deviation SD _AP is calculated as a function of the running speed (v). In this case, first, a regression line R _AP of acceleration G _{AP represented by} the following equation (1) is used. In the regression line R _AP , the acceleration G _AP is expressed as a function of the running speed (v). In the formula (1), a _AP and b _AP are coefficients used in the regression line R _AP .

G _AP (v) = a _AP · v + b _AP (1)
When the coefficient of variation (CV _AP ) is used, the standard deviation SD _AP is expressed as the following equation (2).

SD _AP (v) = G _AP (v) × CV _AP (2)
Incidentally, the coefficient of variation CV _AP are those calculated for any run speed is utilized over the entire run speed. The traveling speed for which the coefficient of variation _CVAP is calculated is, for example, a traveling speed having data of two or more accelerations _GAP . Coefficient of variation CV _AP, such as the average of a plurality of values calculated for each of two or more run speed (speed range), may be generated based on the calculated values for two or more speed zone. In the example of FIG. 6, the regression line R _AP is generated using the acceleration G _AP measured at the running speeds of 8, 8.5, 9, 9.5, 10, 12, 12.5 [km / h]. For example, the coefficient of variation is calculated using data with a traveling speed of 10 km with a large number of acceleration _GAP data.

By substituting G _AP (v) in equation (1) into equation (2), the standard deviation SD _AP is expressed as a function of running speed (v) as shown in the following equation (3). .

SD _AP = (a _AP · v + b _AP ) × CV _AP
= A _AP · v · CV _AP + b _AP · CV _AP (3)
Using equation (3), the line PD is expressed as a function of the running speed (v) as shown as the following equation (4).

PD = G _AP (v) + 3SD _AP
= A _AP · v + b _AP +3 (a _AP · v · CV _AP + b _AP · CV _AP )
= (3CV _AP +1) · a _AP · v + (3CV _AP +1) · b _AP (4)
As shown in FIG. 6, most of the plots (◯) of the landing pattern “踵 landing” are located below the line PD. Most of the plots (×) of the landing pattern “Midfoot forefoot landing” are located above the line PD. Using this relationship, in the present embodiment, the user's landing pattern is determined using the line PD.

In this sense, the landing pattern is an example of a determination result regarding the user's landing mode, and the line PD is an example of a landing determination standard that represents the relationship between the acceleration and the landing mode for each of two or more traveling speeds. The landing criterion is derived using acceleration data to which a landing pattern is added. The acceleration data may be obtained by logarithmically converting the acceleration value. The method for deriving the landing determination criterion is not limited to the processing of FIG. Be the average value of the acceleration G _AP is used, the value to be added to the regression line is not limited to 3SD, it may be a 2SD, or may be a 4-SD.

  In the present embodiment, the landing determination reference (line PD) is obtained using the data of the landing pattern “踵 landing”, and the landing pattern of the user is determined using the line PD. Note that the landing determination criterion may be obtained by using data of the landing pattern “midfoot / forefoot landing”. In one example, a straight line PD ′ obtained by adding 3SD to the regression line obtained from the data of the landing pattern “midfoot / forefoot landing” may be used as the landing determination criterion. When the user's data has a predetermined relationship with the straight line PD ′, the user's landing pattern is determined as “mid-foot-for-foot landing”, and when the user's data does not have the predetermined relationship with the straight line PD ′. The landing pattern of the user may be determined as “spot landing”.

In the present embodiment, when the running speed and the acceleration G _AP are plotted as shown in FIG. 6, when the user's plot is located on the line PD or below the line PD, the landing pattern of the user is expressed as “ When the user's plot is located above the line PD, the user's landing pattern is determined as “mid-foot-fore-foot landing”. Line PD defines the range of acceleration. In this sense, depending on whether or not the detection output of the acceleration in the traveling direction of the user is included in the range of acceleration associated with the given landing mode, the user's landing mode is determined based on the given landing mode (“踵 landing”). ) Is determined. As the landing determination reference, a reference for specifying a user's landing pattern from one or more types of landing patterns to one landing pattern may be generated.

  As shown in FIG. 6, a line PD is given as a function of running speed (v). For this reason, if line PD is used, even if a user's running speed and a test subject's running speed do not correspond, the landing pattern with respect to a user's driving | running | working can be determined. For example, in the example of FIG. 6, there is no data on the subject having the landing pattern “踵 landing” at the running speed of 15 [km / h]. However, by extending the line PD to a portion corresponding to the traveling speed of 15 [km / h], even if the user's traveling speed is 15 [km / h], the user's traveling is performed on the landing pattern “ It can be determined whether or not it is a “spot landing”.

For example, when the user's running speed is 15 [km / h] and the acceleration _GAP is 5 [G], it is determined that the user's landing pattern is “spot landing”. Run speed 15 [km / h], a plot of the acceleration G _AP 5 [G] is to positioned lower than the line PD in FIG.

On the other hand, when the running speed of the user is 15 [km / h], if the acceleration _GAP is 15 [G], it is determined that the landing pattern of the user is “mid-foot-fore-foot landing”. This is because the plots of the running speed 15 [km / h] and the acceleration G _AP 15 [G] are located above the line PD in FIG.

[Standard generation of impact level judgment]
FIG. 7 is a flowchart of a process for generating a reference for determining an impact level at the time of landing. The processing in FIG. 7 is realized, for example, when the CPU 300 of the information processing device 30 executes a given program. The contents of the process will be described with reference to FIG.

  In one example, a plurality of sets of travel data (acceleration detection results) are used for generating the reference. The CPU 300 may generate a reference for each landing pattern.

  In step S700, CPU 300 extracts travel data of a landing pattern for generating a reference. For example, when generating a reference for the landing pattern “spot landing”, the CPU 300 extracts data of a set having “spot landing” as a landing pattern value from a plurality of sets of data stored in the hard disk 318.

  In step S702, CPU 300 cuts out data for one cycle from the acceleration data in the longitudinal direction of the foot for one set of data among the plurality of sets of data extracted in step S700.

In step S704, CPU 300 extracts data included in the period from 5 seconds to 25 seconds after the start of traveling from the data extracted in step S702. In the extracted data, the CPU 300 selects the peak value immediately after the landing of the acceleration (acceleration G _{VER in} the longitudinal direction of the foot) in each cycle. Then, the CPU 300 calculates the average value of the acceleration G _VER peaks for each cycle.

  In step S706, CPU 300 reads the running speed associated with the set of acceleration data to be processed, and stores the running speed and the average value calculated in step S704 in hard disk 318.

In step S708, CPU 300 determines whether all sets of data extracted in step S700 have been processed in steps S702 to S706. If CPU 300 determines that there is a set that has not yet been processed (NO in step S708), it returns control to step S702, and starts processing for unprocessed set data. On the other hand, when it is determined that all sets of data have been processed (YES in step S708), CPU 300 advances the control to step S710. At this point, the hard disk 318 stores as many pairs of average values of the traveling speed and acceleration G _VER as the number of processed sets.

In step S710, CPU 300 obtains regression line R _VER using a set of the running speed stored in step S706 and the average value of the peak of acceleration G _VER . In step S712, CPU 300 obtains level determination straight lines LD1 to LD4. Thereafter, the CPU 300 ends the process of FIG.

FIG. 8 is a diagram showing an example of the regression line R _VER and level determination lines LD1 to LD4. Referring to FIG. 8, it will be described how to obtain the regression line R _VER and level determination of a straight line LD1 to LD4.

In the graph shown in FIG. 8, the average value of the peak of acceleration G _VER is plotted against the running speed. In the example of FIG. 8, the data associated with the landing pattern “踵 landing” among the 73 sets of acceleration data is plotted. The line R _VER (v) represents the regression line R _VER generated by the set of the running speed and the average value of the peak of acceleration G _VER .

In FIG. 8, the line LD3 is derived by adding the standard deviation (SD _VER ) calculated for the plot of the landing pattern “踵 landing” to the regression line R _VER .

The standard deviation SD _VER is calculated as a function of the running speed (v). In this case, first, a regression line R _VER of acceleration G _{VER represented by} the following equation (5) is used. In the regression line R _VER , the acceleration G _VER is expressed as a function of the running speed (v). In equation (5), a _VER and b _VER are coefficients used in the regression line R _VER .

G _VER (v) = a _VER · v + b _VER (5)
When the coefficient of variation (CV _VER ) is used, the standard deviation SD _VER is expressed as the following equation (6).

SD _VER (v) = G _VER (v) × CV _VER (6)
The coefficient of variation CV _VER calculated for an arbitrary running speed is used over the entire running speed. The traveling speed for which the coefficient of variation CV _VER is calculated is, for example, a traveling speed having data of acceleration G _VER of 2 or more. The variation coefficient CV _VER may be generated based on values calculated for two or more speed bands, such as an average value of a plurality of values calculated for each of two or more running speeds (speed bands). In the example of FIG. 8, the regression line R _VER is generated using the acceleration G _VER measured at the running speeds of 8, 8.5, 9, 9.5, 10, 12, 12.5 [km / h]. For example, the coefficient of variation is calculated using data with a running speed of 10 km with a large number of data of acceleration G _VER .

By substituting equation (5) into equation (6), standard deviation SD _VER is expressed as a function of running speed (v) as shown in equation (7) below.

SD _VER = (a _VER · v + b _VER ) x CV _VER
= A _VER・ v ・ CV _VER + b _VER・ CV _VER (7)
Using the equation (7), the line LD3 is expressed as a function of the running speed (v) as shown as the following equation (8).

LD3 = G _VER (v) + SD _VER (v)
= A _VER · v + b _VER + (a _VER · v · CV _VER + b _VER · CV _VER )
= (CV _VER +1) · a _VER · v + (CV _VER +1) · b _VER (8)
8, line LD4, to the regression line R _VER, is derived by the addition of 1.5SD _VER. That is, the line LD4 is expressed as a linear function of the running speed (v) as shown in the following equation (9).

LD4 = G _VER (v) +1.5 SD _VER (v)
= (1.5 CV _VER +1) · a _VER · v + (1.5 CV _VER +1) · b _VER (9)
In FIG. 8, a line LD2 is derived by subtracting SD _VER from the regression line R _VER . That is, the line LD2 is expressed as a linear function of the running speed (v) as shown in the following equation (10).

LD2 = G _VER (v) −SD _VER (v)
= (1- _CVVER ) _.aVER.v + (1- _CVVER ) _.bVER (10)
8, line LD1, to the regression line R _VER, is derived by being subtracted 1.5SD _VER. That is, the line LD1 is expressed as a linear function of the running speed (v) as shown in the following equation (11).

LD1 = G _VER (v) −1.5 SD _VER (v)
= (1-1.5 CV _VER ) · a _VER · v + (1−1.5 CV _VER ) · b _VER (11)
In the present embodiment, as shown in FIG. 8, the range of the traveling speed of 5 to 20 [km / h] is divided into five regions in the vertical direction by the lines LD1 to LD4. Each of the five areas is assigned a level for impact during running. “Level 3” of levels 1 to 5 is assigned to a region between the lines LD2 and LD3. “Level 1” is assigned to the area below the line LD1. “Level 2” is assigned to the region between the lines LD1 and LD2. “Level 4” is assigned to the region between the lines LD3 and LD4. “Level 5” is assigned to the region above the line LD4. That is, each level represents that the greater the numerical value assigned to the level, the greater the impact received upon landing.

  In this sense, the impact level is an example of a determination result regarding the impact at the time of the user's landing, and the lines LD1 to LD4 indicate the impact determination criteria used for determining the impact level at the time of the user's landing. It is an example.

[Landing judgment]
FIG. 9 is a flowchart of a process for outputting a determination result for the user's travel. The process of FIG. 9 is realized by the CPU 300 executing a given program (for example, “landing determination application”), for example. The processing of FIG. 9 uses the acceleration data of the user 900 for 30 seconds (the acceleration G _{AP in the} running direction and the acceleration G _{VER in} the longitudinal direction of the foot) and the running speed. These data are acquired, for example, when the user 900 travels on the treadmill 10 for 30 seconds. The contents of the process will be described with reference to FIG.

In step S900, CPU 300 from the acceleration G _AP in the running direction of the user of the acceleration data, cuts out data one cycle minutes.

In step S902, CPU 300 extracts a cycle included in a period from 5 seconds to 25 seconds after the start of traveling from among the cycles cut out in step S900. In the extracted cycles, CPU 300 determines the acceleration G _{AP of} each cycle. The peak value is selected, and the average value of the selected peaks is calculated.

  In step S904, CPU 300 reads the running speed of the user from treadmill 10.

In step S906, CPU 300 determines the user's landing pattern using the average value of the acceleration _GAP peak calculated in step S902 and the running speed read in step S904. The CPU 300 may use a criterion such as that described with reference to FIG. 6 for determining the landing pattern. That, CPU 300 includes a speed run and the average value for the user of the acceleration G _AP if plotted in the graph of FIG. 6, if the plot is located below the line PD on or line PD, the user of the landing pattern is " It is determined that it is a “spot”. On the other hand, when the plot is positioned above the line PD, the CPU 300 determines that the user's landing pattern is “mid-foot-for-foot landing”.

In step S908, CPU 300 cuts out data for each cycle from acceleration G _{VER in} the longitudinal direction of the foot from the acceleration data of the user.

In step S910, cycles included in the period from 5 seconds to 25 seconds after the start of traveling are extracted from the cycles extracted in step S908. In the extracted cycles, the peak acceleration G _VER of each cycle is extracted. Select a value and calculate the average value of the selected peaks.

In step S912, CPU 300 is a landing pattern in step S906 the determination result, and, by using the run speed read in acceleration G mean and step S904 of _VER calculated in step S910, when the user of the landing Determine the level of impact.

The CPU 300 may use the lines LD1 to LD4 described with reference to FIG. In this case, the CPU 300 first reads a level determination standard corresponding to the user's landing pattern. When the user's landing pattern is “spot landing”, the lines LD1 to LD4 in FIG. 8 are read. When the user's landing pattern is “midfoot forefoot landing”, the level determination reference corresponding to the midfoot forefoot landing is read out. Then, the CPU 300 determines the level of the user's impact based on the relationship between the average value of the user's acceleration G _VER and the combination of the running speed and the level determination criterion. For example, when the user's landing pattern is “spot landing”, the CPU 300, when the average value and the running speed of the user ’s acceleration G _VER are plotted in the graph of FIG. Based on the positional relationship, the impact level of the user is determined. If the plot is located on the line LD1 or below the line LD1, the determination result is “level 1”. If the plot is located on the line LD2 or between the lines LD1 and LD2, the determination result is “level 2”. If the plot is located on the line LD3 or between the lines LD2 and LD3, the determination result is “level 3”. If the plot is located on the line LD4 or between the lines LD3 and LD4, the determination result is “level 4”. If the plot is located above the line LD4, the determination result is “level 5”.

  In step S914, CPU 300 outputs a determination result. An example of the determination result is shown as a determination result 400 in FIG. The determination result includes at least one of the determination result in step S906 and the determination result in step S912. Note that the determination result output by the landing determination system of the present embodiment may include only one of the determination result in step S906 or the determination result in step S912. That is, the determination result may include only the landing pattern or may include only the impact level at the time of landing.

  Through the process of FIG. 9 described above, the user 900 can obtain the determination result about the landing pattern and the impact level by wearing the accelerometer 90 and traveling on the treadmill 10. In this case, if the user 900 is wearing the accelerometer 90, it is not necessary to wear a marker. Also, an apparatus for tracking the user's running mode by photographing the marker is not required.

As described with reference to FIG. 6, the coefficient of variation CV _AP is expressed as a function of the running speed v, thereby providing a landing pattern criterion (line PD) over a given range of running speeds. (In the example of FIG. 6, the running speed is in the range of 6 to 20 [km / h]). Further, as shown in FIG. 8, the coefficient of variation CV _VER is expressed as a function of the running speed v, so that the criterion (LD1 to LD4) of the impact level is provided over a given running speed range. (In the example of FIG. 8, the running speed is in the range of 6 to 20 [km / h]). Thereby, if the user runs at a speed within the range, the determination result is obtained, and the user is not required to run at a specific speed (the same running speed as the subject when the reference is generated).

  In the process of FIG. 9, the landing pattern is determined to determine the impact level (step S <b> 906). For the determination of the impact level, a reference corresponding to the determined landing pattern is used. It is known that the impact characteristics at the time of landing vary greatly depending on the landing pattern. According to the process of FIG. 9, the determination of the impact level according to the landing pattern of the user can be provided.

  In the determination of the landing pattern and the impact level, a logarithmically converted value of acceleration may be used instead of the acceleration shown as the vertical axis of the graph of FIG. 6 and / or FIG. Thereby, even if the difference in acceleration detected between subjects is relatively large, the data can be easily handled.

Figure 10 is a diagram showing another example of the regression line R _VER and level determination of a straight line LD1 to LD4. The graph of FIG. 10 represents data on a plurality of subjects classified into the landing pattern “Midfoot forefoot landing”. The vertical axis of the graph of FIG. 10 represents a value obtained by logarithmically converting the peak of the acceleration G _VER immediately after landing of each subject. The horizontal axis represents the running speed of each subject when the acceleration on the vertical axis is detected.

In the example shown in FIG. 10, the logarithm (LOG_G _VER ) of the average value of the peak of acceleration G _VER at the time of landing of each subject is calculated. Line R _VER (v) represents the regression line R _VER generated by a set of logarithmic (LOG_G _VER) and run rate of the average value. In the regression line R _{VER of} FIG. 10, the logarithm is expressed as a function of the running speed (v) as shown in the following equation (12).

LOG_G _VER (v) = a _VER · v + b _VER (12)
When the coefficient of variation (CV _VER ) is used, the standard deviation SD _VER is expressed as the following equation (13). As for the coefficient of variation CV _VER , the value calculated for an arbitrary running speed is used over the entire running speed in the same manner as described for the equation (6).

_{SD VER (v) = LOG_G VER} (v) × CV VER ... (13)
By substituting the equation (12) into the equation (13), the standard deviation SD _VER is expressed as a function of the running speed (v) as shown in the following equation (14).

SD _VER = (a _VER · v + b _VER ) x CV _VER
= A _VER・ v ・ CV _VER + b _VER・ CV _VER (14)
Using the equation (14), the line LD3 is expressed as a function of the running speed (v) as shown as the following equation (15).

LD3 = LOG_G _VER (v) + SD _VER (v)
= A _VER · v + b _VER + (a _VER · v · CV _VER + b _VER · CV _VER )
= (CV _VER +1) · a _VER · v + (CV _VER +1) · b _VER (15)
The line LD4 is expressed as a linear function of the running speed (v) as shown in the following equation (16).

LD4 = LOG_G _VER (v) +1.5 SD _VER (v)
= (1.5 CV _VER +1) · a _VER · v + (1.5 CV _VER +1) · b _VER (16)
The line LD2 is derived by subtracting SD _VER from the regression line R _VER . That is, the line LD2 is expressed as a linear function of the running speed (v) as shown in the following equation (17).

_{LD2 = LOG_G VER (v) -SD} VER (v)
= (1- _CVVER ) _.aVER.v + (1- _CVVER ) _.bVER (17)
The line LD1 is expressed as a linear function of the running speed (v) as shown in the following equation (18).

LD1 = LOG_G _VER (v) −1.5 SD _VER (v)
= (1-1.5 CV _VER ) · a _VER · v + (1−1.5 CV _VER ) · b _VER (18)
As described above, according to the example described with reference to FIG. 10, a value obtained by logarithm conversion of acceleration is used instead of acceleration in the generation of the impact level determination criteria (SD1 to SD4) at the time of landing. The graph of FIG. 10 is divided into five regions in the vertical direction by lines SD1 to SD4. Each of the five areas corresponds to each of levels 1 to 5. If this criterion Te user impact level is determined by combination of the logarithm of the acceleration G _VER of landing the user running speed belongs to any of the above five areas, users of the impact level is determined .

  The embodiment disclosed this time should be considered as illustrative in all points and not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.

  10 treadmill, 30 information processing device, 40 output device, 90 accelerometer, 100 landing determination system, 300 CPU, 318 hard disk, 400 determination result, 410, 420, 430 field, 421 landing pattern, 900 user.

Claims (12)

Obtaining a detection output of an acceleration sensor attached to a leg of a user during a movement exercise;
Using the detection output to generate a determination result relating to the landing of the user;
A landing determination method comprising: outputting the determination result.   The landing determination method according to claim 1, wherein the determination result includes a determination result regarding a landing mode of the user.   The landing determination method according to claim 2, wherein the step of generating the determination result includes generating a determination result for the landing mode using a detection output of acceleration in the traveling direction of the user. Further comprising obtaining a moving speed of the user;
Generating a determination result for the landing mode is to detect a landing determination criterion obtained based on each acceleration of two or more moving speeds and a landing mode, the moving speed, and acceleration in the traveling direction of the user. The landing determination method according to claim 3, comprising generating a determination result for the landing mode using an output.   The landing determination method according to claim 4, wherein the landing determination criterion includes a relational expression between a moving speed and an acceleration for distinguishing two or more landing modes.   The determination result for the landing mode may be determined according to whether the detection output of acceleration in the traveling direction of the user is included in the acceleration range associated with the given landing mode. The landing determination method according to claim 3, comprising determining whether or not the given landing mode.   The said determination result is the landing determination method of any one of Claims 1-6 containing the determination result about the impact at the time of the said user's landing.   The determination result for the impact includes determining an impact level at the time of landing of the user using an impact criterion based on an average value and a standard deviation of accelerations of a plurality of runners. The landing determination method according to 7.   Generating a determination result for the impact determines an impact level at the time of landing of the user by using an impact criterion based on an average value and a standard deviation of logarithmically converted values of a plurality of runners. The landing determination method according to claim 7. Further comprising obtaining a moving speed of the user;
The impact criterion includes a relational expression between moving speed and acceleration,
9. The determination result for the impact includes determining a level of impact at the time of landing of the user using a portion corresponding to the moving speed of the user in the impact determination criterion. Alternatively, the landing determination method according to claim 9.   It is a program run by a computer, Comprising: The program for making the said computer implement | achieve the landing determination method of any one of Claims 1-10. The acceleration sensor;
The computer;
A landing determination apparatus comprising: a memory storing the program according to claim 11.

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