Exercise ability difference estimation device and exercise ability difference estimation method

Abstract

The present invention estimates a variation in exercise ability without depending on coordinate axes of a device. This exercise ability difference estimation device 1 comprises an exercise data acquisition unit 10 and an exercise ability difference estimation unit 20. The exercise data acquisition unit 10 acquires first data and second data composed of data of each of three axes obtained by measuring periodic exercises of a first part and a second part of an experiment participant. The exercise ability difference estimation unit 20 normalizes each variation in the first data and the second data, quantifies the normalized variations by using principal component analysis to project the quantified result onto a prescribed plane, calculates a variation difference that is the difference between the degree of variation calculated on the basis of the projected first data and the degree of variation calculated on the basis of the projected second data, and assesses the variation difference of the experiment participant by using a prescribed scale.

Description

Athletic ability difference estimation device and athletic ability difference estimation method

[0001] The present disclosure relates to a technique for estimating athletic ability.

[0002] Conventionally, there have been methods for assessing the difference between left and right hand motor skills using an interval scale based on a question branch (see, for example, Non-Patent Document 1), and methods for assessing the difference between left and right foot motor skills using an interval scale based on a question branch (see, for example, Non-Patent Document 2).

[0003] RC Oldfield, "THE ASSESSMENT AND ANALYSIS OF THE EDINBURGH INVENTORY", 1971. Neuropsychologia, 9(1), pp.97-113. S. Coren, "The lateral preference inventory for measurement of handedness, footedness, eyedness, and earedness: Norms for young adults", 1993. Bulletin of the Psychonomic Society, 31(1), pp.1-3.

[0004] However, with methods using question branches such as those in Non-Patent Documents 1 and 2, the score indicating handedness tends to stick to either the left or right hand, making it difficult to understand individual differences. In other words, even if a person is evaluated as being right-handed, it is not possible to objectively determine how right-handed they are in terms of the difference in motor ability with their left hand.

[0005] Therefore, an object of the present disclosure is to provide a technique for estimating the degree of difference in athletic ability.

[0006] The athletic ability difference estimation device disclosed herein includes an athletic data acquisition unit and an athletic ability difference estimation unit. The athletic data acquisition unit acquires first data and second data consisting of data on three axes obtained by measuring periodic movements of a first body part and a second body part of an experimental participant. The athletic ability difference estimation unit normalizes the variability of each of the first data and second data, quantifies the normalized variability using principal component analysis, and projects the normalized variability onto a predetermined plane. It calculates a variability difference between a degree of variability calculated based on the projected first data and a degree of variability calculated based on the projected second data, and evaluates the variability difference of each experimental participant using a predetermined scale.

[0007] According to the athletic ability difference estimation device according to the embodiment of the present disclosure, it is possible to estimate the degree of difference in athletic ability.

[0008] FIG. 1 is a diagram showing an example of the functional configuration of an athletic ability difference estimation device according to this embodiment. FIG. 2 is a diagram showing an example of the processing flow of the athletic ability difference estimation device according to this embodiment. FIG. 3 is a diagram showing an example of athletic data acquisition using a smartphone. FIG. 4 is a diagram showing the degree of variation difference when evaluations were conducted by multiple experiment participants using the athletic ability difference estimation device according to this embodiment. FIG. 5 is a diagram showing an example of the output of evaluation results by the athletic ability difference estimation device according to this embodiment. FIG. 6 is a diagram showing an example of evaluation results by a left-handed experiment participant. FIG. 7 is a diagram showing an example of evaluation results by an experiment participant who was corrected to right-handedness. FIG. 8 is a diagram showing an example of evaluation results by a right-handed experiment participant. FIG. 9 is a diagram showing an example of the functional configuration of a computer.

[0009] <Character notation> In this disclosure, for example, the "~" at the top left of a character, such as "~x(t)", is normally positioned above the character, but in the text of the specification, the symbol that should be placed above the character is shifted to the left and written. On the other hand, in the mathematical formulas in the specification, it can be written, so it is written above the character. This point is － x" in the upper left corner of the character. － The same applies to ".

[0010] Embodiments Hereinafter, embodiments of the present disclosure will be described in detail. Note that components having the same functions are assigned the same numbers, and duplicated descriptions will be omitted.

[0011] FIG. 1 is a diagram showing an example of the functional configuration of an athletic ability difference estimation device according to this embodiment. As shown in FIG. 1, the athletic ability difference estimation device 1 of this embodiment includes an athletic data acquisition unit 10 and an athletic ability difference estimation unit 20. The athletic ability difference estimation unit 20 includes a normalization unit 21, a principal component analysis unit 22, a variance calculation unit 23, and an evaluation unit 24. The athletic ability difference estimation device 1 performs the athletic ability difference estimation method of this embodiment by implementing the processing flow shown in FIG. 2. Hereinafter, with reference to FIG. 2, an example of the processing flow of the athletic ability difference estimation method in the athletic ability difference estimation device 1 will be described in order of procedure.

[0012] The motion data acquisition unit 10 acquires first data and second data consisting of data for each of three axes obtained by measuring the periodic motion of the first and second parts of the experiment participant (step S10).

[0013] Here, a "body part" refers to a body part of a person (human) that exists on both the left and right sides, such as a hand or a foot. However, the estimation target of the athletic ability difference estimation device 1 of the present disclosure is not limited to this, and may be any other body part of a person that exists on both the left and right sides and whose periodic movement can be measured. Alternatively, the subject may be any animal other than a human, as long as its periodic movement can be measured.

[0014] For convenience of explanation, the following description will be centered on the example of a human hand, with the right hand being the first part and the left hand being the second part. In this case, the first data is data measuring the movement of the right hand. The second data is data measuring the movement of the left hand. Data including the first data and the second data may be referred to as "movement data."

[0015] The measuring device used for measurement may be, for example, a device that is equipped with an acceleration sensor, has the ability to record data, and can be held in the hand. Other examples of sensors that can be installed in the measuring device include a speed sensor, an angular velocity sensor, a magnetic sensor, and a GPS. When an acceleration sensor is used, a multi-axis acceleration sensor is preferable, and for example, a triaxial acceleration sensor can be used. An example of the measuring device is a smartphone. For example, the participant holds the measuring device in both their right and left hands and repeatedly performs the same movement, such as a rotational movement. Note that if the measurement target is a part of the body that cannot be held by holding the measuring device, such as the foot, it may be possible to, for example, place the measuring device in close contact with the part of the body to be measured, such as the foot, and then secure it with a band or the like.

[0016] In the example shown in Figure 3, the participant rotates a device equipped with a three-axis acceleration sensor for 15 seconds, rotating it at least 30 times (N times). The diameter of the rotation circle is approximately 10 cm (see Figure 3). For example, measurements are taken once for each of the right and left hands.

[0017] The exercise data acquisition unit 10 records (buffers) the acquired exercise data for a certain period of time. The buffered exercise data is transmitted to the normalization unit 21. At this time, for example, the time immediately after the start of exercise may be removed before transmission to the normalization unit 21. For example, it may be configured so that data from the above 15-second exercise session, excluding the first 1.5 seconds immediately after the start of exercise, is transmitted to the normalization unit 21. Note that the function of removing the time immediately after the start may be provided in the athletic ability difference estimation unit 20, rather than in the exercise data acquisition unit 10.

[0018] The movement data in this example includes at least the following data if the movement is properly measured. For example, when the rotational movement of the left and right hands shown in Figure 3 is measured, the movement data includes right-side data and left-side data. The right-side data is time-series data showing the relationship between time and acceleration in each of the three axes (x-axis, y-axis, z-axis) in the movement of the right hand. The right-side data is the first data described above. The left-side data is time-series data showing the relationship between time and acceleration in each of the three axes (x-axis, y-axis, z-axis) in the movement of the left hand. The left-side data is the second data described above. Therefore, the movement data in one evaluation will include at least six types of data.

[0019] The normalization unit 21 normalizes the variations of the first data (right-side data) and the second data (left-side data) (step S21). The normalization performed by the normalization unit 21 is performed, for example, by the following method.

[0020] First, if there are N data points in the x-axis, y-axis, and z-axis directions in the motion data, the average value, － x, － y, － Calculate z.

[0021] Next, the average norm D is calculated by the following formula.

[0022] Next, the normalized values ​​~x(t), ~y(t), and ~z(t) are calculated using the following equations.

[0023] Although the above example uses the mean norm, other norms such as the maximum norm may be used for the calculation. The processing of the above equations (1) to (7) is performed on both the right-side data and the left-side data, and the calculated normalized values ​​~x(t), ~y(t), and ~z(t) of the right-side data and the left-side data are both transmitted to the principal component analysis unit 22.

[0024] The principal component analysis unit 22 quantifies the normalized variations by principal component analysis and projects them onto a predetermined plane (step S22). The principal component analysis unit 22 performs principal component analysis on both the right-side data and the left-side data, for example, as follows.

[0025] First, the covariance C of [~x(t), ~y(t), ~z(t)] is subjected to singular value decomposition, and the eigenvectors V are rearranged according to the magnitude of the eigenvalues ​​D to obtain the rotation matrix R.

[0026] Next, the rotation matrix R is used to rotate [~x(t), ~y(t), ~z(t)], and the rotated data [X(t), Y(t), Z(t)] is calculated. As a result, three-dimensional data is generated in which the two axes with the largest variances among X, Y, and Z are regarded as the principal components. For example, if the principal components are X and Y, the three-dimensional data is generated as projected onto the XY plane. In other words, the above-mentioned predetermined plane is a plane formed by the two axes with the largest variances among the quantified variations.

[0027] [X(t), Y(t), Z(t)] calculated based on the right-side data and [X(t), Y(t), Z(t)] calculated based on the left-side data are sent to the variation calculation unit 23.

[0028] The variation calculation unit 23 calculates a variation difference d, which is the difference between the degree of variation calculated based on the projected first data (right-side data) and the degree of variation calculated based on the projected second data (left-side data) (step S23). The variation difference d is calculated by the following method.

[0029] First, the position where atan2 = 0 is considered to be the point where a new cycle will begin (hereinafter also referred to as the "extraction point"), and the cycle is extracted starting from this extraction point. For example, if the principal components mentioned above are X and Y, the extraction point is calculated using the following formula: The extracted period in the above example is generated as three-dimensional data projected onto the XY plane. This extraction is performed for the entire period M for both the right-side data and the left-side data. The extraction point may also be performed at other angles.

[0030] Next, as shown in the following equation, the distance d between the locus extracted from the nth period (the collective locus of the variation in the nth period) and the locus extracted from the n+1th period (the collective locus of the variation in the n+1th period) is ｎ is calculated for the entire period M, the degree of variation σ is calculated for each of the right-side data and the left-side data. Note that, hereinafter, in order to distinguish the degree of variation σ of the right-side data from the degree of variation σ of the left-side data, the degree of variation of the right-side data will be referred to as the "right degree of variation σ" Ｒ ", and the left data variability is "left variability σ Ｌ " shall sometimes be referred to as ". Here, the distance d ｎ may be calculated, for example, by the Hausdorff distance described in Reference 1 below.

[0031] (Reference Non-Patent Document 1: Huttenlocher, Daniel P., Gregory A. Klanderman, and William J. Rucklidge. "Comparing images using the Hausdorff distance." IEEE Transactions on Pattern Analysis and Machine Intelligence 15.9 (1993): 850-863. <URL: https: / / ieeexplore.ieee.org / abstract / document / 232073>)

[0032] Next, the right dispersion degree σ Ｒ and the left dispersion σ Ｌ Using these, the variation difference d is calculated using the following equation. The calculated variation difference d is transmitted to the evaluation unit 24 .

[0033] FIG. 4 shows the degree of the difference in variance d when multiple experimental participants were evaluated using the motor ability difference estimation device according to this embodiment. In FIG. 4, (a) shows the aggregated results for hand movements, and (b) shows the aggregated results for foot movements. (i) in this figure is the average result of the motor ability difference d for left-handed people, with 32 subjects in (a) and 17 subjects in (b). (ii) in this figure is the average result of the motor ability difference d for people whose hands had been corrected to right-handedness, with 49 subjects in (a) and 21 subjects in (b). (iii) in this figure is the average result of the motor ability difference d for right-handed people, with 357 subjects in (a) and 182 subjects in (b). The vertical axis of the figure indicates the degree of difference between left and right.

[0034] As shown in Figure 4(a), for example, the person (ii) who underwent right-handedness correction showed a lower degree of left-right difference than the right-handed person (iii). Furthermore, as shown in Figure 4(b), a change in the degree of left-right difference was also observed in the results for the foot that had not been corrected. That is, in Figure 4(b), the person (ii) who underwent right-handedness correction showed a higher degree of left-right difference than the left-handed person (i), and a lower degree of left-right difference than the right-handed person (iii). According to the motor ability difference estimation device disclosed herein, by evaluating motor ability based on the variability of periodic movements, the individual skill levels of the left and right can be quantified, thereby estimating the degree of motor ability difference.

[0035] The evaluation unit 24 evaluates the variation difference d of the experiment participants using a predetermined scale (step S24). Here, the "predetermined scale" refers to, for example, the average value and standard deviation of the calculated variation difference d when multiple people perform periodic exercise and the variation difference d is calculated, as shown in FIG. 4. The predetermined scale corresponds to the athletic ability difference data shown in FIG. 1. The "evaluation" is performed, for example, by calculating the Laterality Index (hereinafter also referred to as "LI") shown in the following formula. where A is the scale number and σ ＾ｄ is the standard deviation of the variation difference d.

[0036] LI is a value between -100 and +100. The maximum degree of left-handedness is -100, and the maximum degree of right-handedness is +100. For example, if A=30, σ ＾ｄ When d = 0.12, LI = 100 when d = 0.4, and LI = -100 when d = -0.4. LI = 100 indicates that the individual is in the top 99.7% of right-handed people. LI = -100 indicates that the individual is in the top 99.7% of left-handed people. LI may be calculated using Sigmoid so that it can be evaluated within the range of -1.0 to +1.0.

[0037] FIG. 5 shows an example of the evaluation results output by the motor ability difference estimation device according to this embodiment. In this figure, a horizontal evaluation bar is displayed between images of left and right hands, with a vertical line at LI = 0 indicating the median value. The average value of multiple participants who performed the same periodic movement is indicated by a black circle. The LI, which is the evaluation score of the target participant, is then displayed numerically, and an arrow indicates the participant's position on the evaluation bar, promoting intuitive visual understanding. As shown in FIG. 5, the LI may be displayed by prefixing the LI value with an "R" (indicating a high degree of right-handedness) or an "L" (indicating a high degree of left-handedness) instead of a plus or minus sign.

[0038] In Fig. 5, in addition to the above-mentioned display to enable intuitive understanding of the LI value, the trajectories of the periodic movements extracted by the athletic ability difference estimation device 1 are shown below it for both the left and right ((i) and (ii) in Fig. 5). This allows the participant to understand what the trajectories of the movements made by the experiment participant were like, and furthermore, that consistency between the calculated LI value and the actual trajectories of the movements is guaranteed.

[0039] 6 to 8 show examples of the evaluation results of three experiment participants evaluated using the motor ability difference estimation device 1. Fig. 6 is an example of the evaluation results of a left-handed experiment participant, Fig. 7 is an example of the evaluation results of an experiment participant who had been corrected to right-handedness, and Fig. 8 is an example of the evaluation results of a right-handed experiment participant. All of them show the evaluation results of the hands and feet.

[0040] 6 to 8, by using an athletic ability difference estimation device to evaluate athletic ability based on the variability of periodic movements, it is possible to easily calculate the difference in athletic ability between the first data and the second data. Furthermore, by calculating the LI for the hand (Hand Laterality Index: hLI) and the LI for the foot (Foot Laterality Index: fLI), it is possible to make the scale of the difference in athletic ability easier to understand.

[0041] As described above, the athletic ability difference estimation device 1 of the present disclosure quantitatively evaluates the variability in the periodic movements of, for example, the left and right hands or feet individually using a predetermined scale, and evaluates the difference in that variability, thereby making it possible to estimate the degree of difference in athletic ability of the hands, feet, etc.

[0042] The various processes described in the embodiments of the present disclosure may not only be performed in chronological order according to the order described, but may also be performed in parallel or individually depending on the processing capacity of the device performing the processes or as needed.

[0043] Furthermore, a device (terminal) for using the device of the present disclosure or the method of the present disclosure via a network (telecommunications line) may also be provided. The "device (terminal) for using" may be provided with functions (e.g., control function, decoding function, restoration function, input / output function, etc.) necessary to obtain the effects of implementing the device of the present disclosure or the method of the present disclosure.

[0044] [Processor, Program, Recording Medium] The functions performed by the components described herein may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), a CPU (a Central Processing Unit), conventional circuits, and / or combinations thereof, programmed to perform the described functions. A processor includes transistors and other circuits and is considered to be circuitry or processing circuitry. A processor may also be a programmed processor that executes a program stored in a memory.

[0045] In this specification, a circuitry, unit, or means is hardware that is programmed to realize or performs the described functions, which may be any hardware disclosed herein or any hardware known to be programmed to realize or perform the described functions.

[0046] If the hardware is a processor considered to be a type of circuitry, the circuitry, means, or unit is a combination of the hardware and software used to configure the hardware and / or processor.

[0047] The various processes described above can be implemented by loading a program that executes each step of the above method into the recording unit 2020 of the computer 2000 shown in Figure 9, and operating the control unit 2010, input unit 2030, output unit 2040, display unit 2050, etc.

[0048] The program describing the processing contents can be recorded on a computer-readable recording medium, which may be, for example, a magnetic recording device, an optical disk, a magneto-optical recording medium, a semiconductor memory, or any other suitable recording medium.

[0049] The program may be distributed by, for example, selling, transferring, lending, etc. portable recording media such as DVDs and CD-ROMs on which the program is recorded. Furthermore, the program may be stored in a storage device of a server computer, and then transferred from the server computer to other computers via a network, thereby distributing the program.

[0050] A computer that executes such a program may first temporarily store the program recorded on a portable recording medium or transferred from a server computer in its own storage device. Then, when executing a process, the computer reads the program stored on its own recording medium and executes the process in accordance with the read program. Alternatively, the computer may read the program directly from a portable recording medium and execute the process in accordance with the program. Furthermore, the computer may execute the process in accordance with the received program each time a program is transferred from a server computer to the computer. Alternatively, the server computer may not transfer the program to the computer, but may instead execute the process through a so-called ASP (Application Service Provider) service, which realizes the processing function by issuing an execution instruction and obtaining the results. Furthermore, the server computer may execute the process at the terminal using a so-called SaaS (Software as a Service) service, which allows users to use part of a server computer along with the program. In this embodiment, the program includes information used for processing by an electronic computer that is equivalent to a program (such as data that is not a direct instruction to a computer but has properties that dictate computer processing).

[0051] Furthermore, in this embodiment, the device is configured by executing a predetermined program on a computer, but at least a part of the processing contents may be realized by hardware.

Claims

1. An athletic ability difference estimation device including: an athletic data acquisition unit that acquires first data and second data consisting of data on each of three axes obtained by measuring the periodic movements of a first part and a second part of an experimental participant; and an athletic ability difference estimation unit that normalizes the variability of the first data and the second data, quantifies the normalized variability using principal component analysis, and projects the normalized variability onto a predetermined plane, and calculates a variability difference, which is the difference in variability, from a degree of variability calculated based on the projected first data and a degree of variability calculated based on the projected second data.

2. The athletic ability difference estimation device according to claim 1, wherein the athletic ability difference estimation unit evaluates the variation difference by calculating a Laterality Index.

3. The athletic ability difference estimation device according to claim 1, wherein the difference in variance is calculated using the Hausdorff distance.

4. A method for estimating differences in athletic ability, in which an athletic data acquisition unit of the athletic ability difference estimation device acquires first data and second data consisting of data on each of three axes that measure the periodic movements of a first part and a second part of an experimental participant, and an athletic ability difference estimation unit of the athletic ability difference estimation device normalizes the variability of the first data and the second data, quantifies the normalized variability using principal component analysis and projects each onto a predetermined plane, calculates a variability difference that is the difference between the degree of variability calculated based on the projected first data and the degree of variability calculated based on the projected second data, and evaluates the variability difference of the experimental participant using a predetermined scale.

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