Information processing device, motion data acquisition device, setting method, and program

Abstract

To enable accurate calculation of motion information of a user.SOLUTION: A motion data acquisition device includes a control unit that determines the motion state of a user based on data obtained by a sensor, the data representing a series of actions including a plurality of motion states performed by the user, and sets, according to the user's motion state, a formula for deriving a Kalman gain, which is a parameter employed for vertical direction estimation during the data acquisition by the sensor.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates to an information processing device, an exercise data acquisition device, a setting method, and a program. [Background technology]

[0002] When sensing the user's motion, the sensor's attitude (axis direction) changes depending on the attitude of the user's wearing position. Therefore, in order to obtain a fixed reference for the attitude (direction), a technique is known that estimates the vertical direction from sensor data using a state estimation technique such as a Kalman filter (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-6034 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventionally, parameters used in a filter for estimating the vertical direction (for example, the Kalman gain of a Kalman filter) are calculated using a predetermined derivation formula. However, when the user's posture changes suddenly, such as at the start of a sprint, the parameter calculation using the conventional derivation formula was unable to keep up with the change in posture, and the user's motion information could not be calculated accurately.

[0005] An object of the present invention is to make it possible to calculate motion information of a user with high accuracy. [Means for solving the problem]

[0006] In order to solve the above problems, the information processing device of the present invention comprises: Determining a motion state of a user based on data regarding a series of motions including a plurality of motion states performed by the user, the data being acquired by a sensor; setting an equation corresponding to the determined motion state of the user as an equation for determining a parameter used in estimating the vertical direction at the time of acquiring the data in the sensor; It has a control unit. Effect of the Invention

[0007] According to the present invention, it is possible to accurately calculate user motion information even when the user's posture changes significantly during exercise. [Brief description of the drawings]

[0008] [Figure 1] 1 is an external view showing an embodiment of an exercise data acquisition device according to the present invention; [Diagram 2] 2 is a schematic diagram showing a state in which the exercise data acquisition device of FIG. 1 is attached to a human body. FIG. [Diagram 3] 2 is a block diagram showing a functional configuration of the exercise data acquisition device of FIG. 1. [Figure 4] 2 is a schematic diagram showing three axis directions in a motion sensor applied to the motion data acquisition device of FIG. 1. [Diagram 5] FIG. 13 is a diagram for explaining estimation of the vertical direction by a Kalman filter. [Figure 6] FIG. 1 is a diagram showing the sequence of a crouching start in sprinting. [Figure 7] 4 is a flowchart showing a flow of a sprint setting process executed by the control unit in FIG. 3. [Figure 8] FIG. 13 is a diagram showing the vertical direction in the "On your mark" position. [Figure 9] FIG. 13 is a diagram showing the vertical direction in the "Ready" position. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. However, the embodiment described below is merely an example of the present invention, and the technical scope of the present invention is not limited to the following embodiment and illustrated example.

[0010] (Movement data acquisition device) Fig. 1 is an external view showing one embodiment of the exercise data acquisition device according to the present invention, Fig. 2 is a schematic diagram showing the state in which the exercise data acquisition device according to this embodiment is attached to a human body, Fig. 3 is a functional block diagram showing an example of the configuration of the exercise data acquisition device according to this embodiment, and Fig. 4 is a schematic diagram showing three axial directions of a motion sensor (acceleration sensor 110, gyro sensor 120) applied to the exercise data acquisition device according to this embodiment.

[0011] As shown in Figures 1(a) to 1(c), the exercise data acquisition device 100 of the present invention has a device main body 102 incorporating various sensors described later, and a clip portion 104 rotatably attached to one side of the device main body 102 (for example, the back side shown in Figure 1(c)) via a hinge portion 106.

[0012] Such an exercise data acquisition device 100 is attached in close contact with a position close to the trunk of the human body, such as the center of the lower back on the back of the user US, as shown in Fig. 2. Here, the exercise data acquisition device 100 may be attached in close contact with any position of the human body, such as the center of the chest or abdomen, or the neck, as long as it can accurately detect the movement of the trunk including the trunk during exercise of the user US using a built-in motion sensor (described in detail later). In addition, the method of attaching the exercise data acquisition device 100 to the human body may be to clip it to the clothing or belt worn by the user US using a clip mechanism as shown in Figs. 1(a) to (c), or to attach it to the clothing or the like using adhesive tape or the like.

[0013] In this embodiment, the exercise data acquisition device 100 has the form of an independent sensor device that is removably attached to the body of the user US when starting exercise. However, the present invention is not limited to this, and all or part of the functions of the exercise data acquisition device 100 described below may be integrally woven or incorporated into the fabric of clothing, so that the user US wears the clothing to fit closely to the body.

[0014] As shown in FIG. 3, the exercise data acquisition device 100 has an acceleration sensor 110, a gyro sensor (angular velocity sensor) 120, a geomagnetic sensor 130, a timing unit 140, a control unit 150, an interface unit (hereinafter abbreviated as "I / F unit") 160, a memory unit 170, a display unit 180, a switch unit 190, and a power supply unit (not shown).

[0015] The exercise data acquisition device 100 according to the present embodiment has at least an acceleration sensor 110 and a gyro sensor 120 as motion sensors for measuring the body movement and motion state of the user US during exercise. The acceleration sensor 110 has a three-axis acceleration sensor, and measures the change in the motion speed of the user US during exercise by detecting the acceleration in three mutually orthogonal axial directions. The gyro sensor 120 has a three-axis angular velocity sensor, and measures the change in the motion direction of the user US during exercise by detecting the angular velocity in the rotation direction around each of the three axes that define the acceleration. The acceleration data detected by the acceleration sensor 110 and the angular velocity data detected by the gyro sensor 120 are associated with the time data of the elapsed time measured by the timer unit 140 described later, and are stored in a predetermined storage area of ​​the memory unit 170 described later.

[0016] Fig. 4(a) is a diagram showing the three axis directions of the acceleration sensor 110, and Fig. 4(b) is a diagram showing the three axis directions of the gyro sensor 120. As shown in Fig. 4(a) and (b), in the acceleration sensor 110 and the gyro sensor 120, when the user US is wearing the exercise data acquisition device 100 in an upright state, the direction that is the up and down direction is the z-axis direction (upward is the + direction, downward is the - direction), the directions of the left and right sides of the body of the user US are defined as the X-axis direction (the direction of the left hand of the user US is the + direction, the direction of the right hand is the - direction), and the front and rear direction of the user US is defined as the Y-axis direction (the direction opposite to the moving direction of the user US is the + direction, and the moving direction is the - direction).

[0017] The geomagnetic sensor 130 is a sensor that detects geomagnetism in two or three orthogonal axial directions, detects the earth's magnetic field (magnetic field), and outputs it as magnetic data (or three-dimensional directional data). The magnetic data acquired by the geomagnetic sensor 130 is stored in a predetermined storage area of ​​the memory unit 170 in association with time data of the elapsed time measured by the clock unit 140.

[0018] The geomagnetic sensor 130 is also used to estimate output errors in the sensing data of the acceleration sensor 110 and the gyro sensor 120 and to make adjustments to offset the output errors. The adjustment of the output errors may be made when the exercise data acquisition device 100 is shipped, or may be made immediately before the start of a section run, as described below.

[0019] The timekeeping unit 140 measures the elapsed time when acquiring various data (sensing data) by sensing operations in the acceleration sensor 110, the gyro sensor 120, and the geomagnetic sensor 130, and outputs the measured time as time data. Here, the timekeeping unit 140 has, for example, a function of a radio clock, and measures the elapsed time during exercise of the user US with high accuracy based on standard radio waves transmitted from a transmitting station or a function of a GPS (Global Positioning System), and based on time information transmitted from a GPS satellite. Note that the function of the timekeeping unit 140 may be to measure the elapsed time using a basic clock generated by a timekeeping function (such as a quartz device) built into the control unit 150.

[0020] The control unit 150 is an arithmetic processing device (computer) such as at least one CPU (Central Processing Unit) or MPU (Micro Processing Unit), and by executing a predetermined control program, it controls the operation of the entire exercise data acquisition device 100, such as the sensing operations of the acceleration sensor 110, gyro sensor 120, and geomagnetic sensor 130, the measurement of elapsed time in the timing unit 140, the saving and reading of various data in the memory unit 170, and the output operation of exercise data to the external device 200 in the I / F unit 160.

[0021] Furthermore, the control unit 150 calculates motion information (motion data and indicators) for grasping and analyzing the motion state of the user US, based on various data measured during the motion of the user US by the acceleration sensor 110, the gyro sensor 120, and the geomagnetic sensor 130. Here, examples of the motion data and indicators calculated by the control unit 150 include accurate acceleration and movement speed in the direction of travel during motion, motion posture (forward and backward leaning of the trunk), left and right tilt of the pelvis, up and down movement, landing timing, and the like.

[0022] Moreover, the control unit 150 functions as a control unit of the information processing device of the present invention by executing a setting process for sprinting (see FIG. 7) described later.

[0023] The memory unit 170 (storage unit) stores various data acquired by the acceleration sensor 110, the gyro sensor 120, and the geomagnetic sensor 130 in a predetermined storage area in association with time data of elapsed time. The memory unit 170 also stores motion data and indices for grasping and analyzing the motion state of the user US, which are calculated by the control unit 150, in a predetermined storage area. The memory unit 170 also stores programs executed by the control unit 150 and data required for executing the programs. Note that the programs may be pre-installed in the control unit 150. The memory unit 170 also stores data required for executing the programs in the control unit 150. For example, the memory unit 170 associates the motion states that the user US can take in a sprint (a stationary state such as a starting posture, a high-speed motion state which is a switching motion from a starting posture to a running state, and a cyclic motion state which is a running state) with the Kalman gain K corresponding to the motion state. t For example, the memory unit 170 stores a stationary gain derivation formula corresponding to a stationary state, a high-speed operation gain derivation formula corresponding to a high-speed operation state, and a periodic operation gain derivation formula corresponding to a periodic operation state. t More on this later. Memory unit 170 may be configured in part or in whole as a removable storage medium such as a memory card, and may be configured to be detachable from exercise data acquisition device 100.

[0024] The I / F unit 160 outputs various data acquired during exercise of the user US, and exercise data and indices for grasping and analyzing the exercise state of the user US, calculated based on the data, to a device (external device) 200 external to the exercise data acquisition device 100. Here, the I / F unit 160 can output data to the external device 200 by applying various wireless communication methods such as Bluetooth (registered trademark) and WiFi (registered trademark). The I / F unit 160 may also apply a method of outputting data to the external device 200 via a communication cable such as a USB (Universal Serial Bus) standard or a removable storage medium such as a memory card shown in the memory unit 170 described above.

[0025] Display unit 180 displays various data acquired by acceleration sensor 110, gyro sensor 120, and geomagnetic sensor 130, as well as information related to the exercise data and indicators calculated by control unit 150. Display unit 180 also displays information related to the state of exercise data acquisition device 100 (power on, power off, or error state) and the elapsed time measured by timing unit 140. Note that in the case where the data output via I / F unit 160 is displayed on a display unit (not shown) provided in external device 200, exercise data acquisition device 100 may not necessarily include display unit 180.

[0026] The switch unit 190 is an operating switch such as a slide switch or a push button, or a touch panel provided on the display unit 180, and is used by the user US to switch the state (power on, off, or various modes) of the exercise data acquisition device 100 or to specify the start and end timings of measurement. Note that the exercise data acquisition device 100 may not have the switch unit 190 if the acceleration sensor 110 and the gyro sensor 120 perform sensing operations constantly or at regular time intervals or at predetermined timings to monitor the exercise state of the user US and switch to an operation mode in which the exercise data acquisition device 100 automatically acquires various data during exercise.

[0027] (external device) The external device 200 receives and displays various data acquired during exercise by the user US, which is output from the above-mentioned exercise data acquisition device 100 via the I / F unit 160, as well as information regarding the exercise data and indicators calculated by the control unit 150.

[0028] Here, the external device 200 may be, for example, a personal computer, a smartphone, a tablet device, a wristwatch-type wrist terminal, a dedicated terminal, a server device on a network, etc. That is, the external device 200 may be a device carried or worn by the user US during exercise, or may be a device installed separately from the exercise data acquisition device 100 without being carried by the user US. When a device that the user US can carry or wear during exercise, such as a smartphone or a wrist terminal, is applied as the external device 200, a form capable of transmitting and receiving signals and data bidirectionally via the I / F unit 160 of the exercise data acquisition device 100 may be applied so that the external device 200 has the functions of the display unit 180 and the switch unit 190.

[0029] (Vertical direction estimation using Kalman filter) Next, estimation of the vertical direction using a Kalman filter will be described. In general, it is considered that the long-term average of the acceleration obtained from the acceleration sensor 110 is the average vertical direction. In addition, the vertical rotation direction can be detected based on the angular velocity obtained from the gyro sensor 120. However, there is a problem that estimation of the vertical direction based on the acceleration obtained by the acceleration sensor 110 cannot keep up with small movements. In addition, there is a problem that detection of the vertical direction based on the angular velocity obtained by the gyro sensor 120 accumulates errors. In order to solve these problems, in a conventional Kalman filter that estimates the vertical direction, a Kalman gain K is set so that, in estimating the vertical direction, the larger the variation (covariance) of the acceleration average is, the greater the contribution of the vertical direction calculated based on the angular velocity acquired by the gyro sensor 120, and the smaller the variation (covariance) of the acceleration average is, the greater the contribution of the vertical direction calculated based on the acceleration acquired by the acceleration sensor 110. t In this embodiment, the Kalman filter for estimating the suddenly changing vertical direction is a filter for estimating the vertical direction at the time of acquiring the acceleration data obtained from the acceleration sensor 110 and the angular velocity data obtained from the gyro sensor, based on at least one of these data.

[0030] Hereinafter, estimation of the vertical direction using a Kalman filter will be described with reference to FIG. In the Kalman filter, a reaction force against gravity (a vector of a reaction force against gravity, that is, a vertical vector) is estimated based on the system model shown in (Equation 1) and the observation model shown in (Equation 2).

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[0031] In the Kalman filter, as shown in Figure 5, the reaction force against gravity from the previous sample is estimated (X t-1 / t-1 ) and the rotation matrix (R t ) and the predicted value of the reaction force against gravity (X t / t-1 ) is calculated. In addition, the error covariance of the estimated value of the reaction force against gravity one sample before (P t-1 / t-1 ) and the rotation matrix (R t ) and the error covariance of the predicted value of the reaction force against gravity (P t / t-1 The calculation formulas are as follows:

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[0032] Once the prediction is complete, the Kalman filter performs an observation update. In the observation update, the Kalman gain K t Using this, we estimate the reaction force against gravity (X t / t ) and the error covariance of the estimated reaction force against gravity (P t / t ) is calculated. The Kalman gain K t The observation update formula using is as follows (Formula 7) and (Formula 8). Kalman gain K t is the reaction force against gravity (smoothed acceleration value (a)) calculated based on the acceleration data acquired by the acceleration sensor 110 when estimating the reaction force against gravity (vertical vector).t ; first vertical vector) and a predicted value (X t / t-1 ) (second vertical vector) contributes to the axial flow.

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[0033] In the observation update formula above, the estimated value of the reaction force against gravity (X t / t ) is K t The closer to 1, the greater the acceleration smoothing value (a t ) becomes larger, and K t When =1, the acceleration smoothing value (a t ) That is, K t When x = 1, the vertical direction is estimated using only the measurement value (smoothed value) of the acceleration sensor 110. In addition, the estimated value of the reaction force against gravity (X t / t ) is K t = 0, the predicted value (X t / t-1 ) becomes larger, and K t When = 0, the predicted value of the reaction force against gravity (X t / t-1 ) That is, K t When θ is 0, the vertical direction is estimated using only the measurement value of the gyro sensor 120. The vertical direction (vector) estimated by the Kalman filter is stored in a predetermined storage area in association with the data acquired by the acceleration sensor 110 and the gyro sensor 120 used in the estimation.

[0034] Here, the Kalman gain K t Conventionally, it is calculated by the following (Equation 9).

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[0035] As mentioned above, the Kalman gain K calculated by (Equation 9) t is the variation of the average acceleration (a t The larger the error covariance of the vertical direction, the greater the error in the predicted value (X t / t-1 ) contributes more, and the smaller the variation in the acceleration average, the greater the acceleration smoothing value (a t ) is becoming more prevalent. The Kalman gain K shown in (Equation 9) t The derivation formula of the Kalman gain K is accurate for stable and repeated periodic movements such as long-distance running. t can be calculated.

[0036] However, the speed of the high-speed movement involved in sprinting, where posture changes suddenly from the crouching start to the acceleration phase, is significantly different from the stable periodic movement after the acceleration phase ends. In the derivation of (Equation 9), t It takes time for the error covariance of to rise, and accurate vertical estimation is not possible.

[0037] Therefore, in this embodiment, the control unit 150 determines the motion state of the user US during a sprint including a crouching start, and sets a derivation formula for the Kalman gain Kt according to the motion state of the user US.

[0038] FIG. 6 is a diagram showing the sequence of a crouching start in sprint running. In a crouching start, the athlete (user US) slowly crouches down from a standing position when the starter gives the signal "On your mark," and stands still in the so-called "on mark" position. When the starter has confirmed that all the athletes are standing still in the "on mark" position, he gives the signal "Ready." In response to this signal, the athletes raise their hips, stand still, and wait for the sound of the pistol, which is the signal to start. The starter confirms that all the athletes are standing still, then, at the right timing, fires the pistol (Go). It is said that 2 to 3 seconds is appropriate from Ready to the pistol. The athletes start running when the signal to start is given.

[0039] In the exercise data acquisition device 100, when the start of measurement in the sprint mode is instructed by operating the switch unit 190, the control unit 150 starts measurement, and stores the various data acquired by the acceleration sensor 110, the gyro sensor 120, and the geomagnetic sensor 130 described above in a predetermined storage area in association with time data of the elapsed time. Also, a setting process for sprint running is executed, and a Kalman gain K according to the motion state of the user US in the sprint run is calculated. t The derivation formula of is set in the Kalman filter. The Kalman filter calculates the Kalman gain K t using the estimated vertical direction to estimate a vertical vector based on data acquired by the acceleration sensor 110 and / or the gyro sensor 120. The control unit 150 calculates motion data, index values, and the like of the user US in a sprint using the estimated vertical direction.

[0040] 7 is a flowchart showing the flow of the sprint setting process executed by the control unit 150. The sprint setting process is executed in cooperation with a program stored in the memory unit 170.

[0041] First, the control unit 150 judges whether or not the "On your mark" (position) attitude is detected (step S1). In the "On your mark" posture, as shown in FIG. 8, a vertical direction occurs between the Z-axis direction (positive direction) and the Y-axis direction (positive direction). That is, the acceleration sensor 110 measures the magnitude of the gravitational acceleration between the Z-axis direction (positive direction) and the Y-axis direction (positive direction). Then, the control unit 150 acquires acceleration data (measurement value) from the acceleration sensor 110, measures the magnitude of the gravitational acceleration between the Z-axis direction (positive direction) and the Y-axis direction (positive direction), and when the movement (change in the measurement value) becomes equal to or smaller than a predetermined threshold, it determines that the "On your mark" (position) posture has been detected. The control unit 150 repeats the process of step S1 until it is determined that "On your mark" has been detected. Since the "On your mark" position is a stationary state, the direction in which a force of the magnitude of gravitational acceleration is detected by acceleration sensor 110 is the vertical direction.

[0042] When it is determined that the "On your mark" posture has been detected (step S1; YES), the control unit 150 determines that the user US is in a stationary state (step S2), and activates the Kalman filter to calculate the Kalman gain K t A stationary gain derivation equation is set as the derivation equation for the gain, and the vertical direction is estimated (step S3). In step S3, the control unit 150 calculates the Kalman gain K t The derivation formula of the gain for stationary t = 1, and the acceleration smoothing value (a t ) is estimated as a reaction force against gravity (i.e., a vertical vector). The static gain derivation formula is K t For example, the static gain derivation formula is K t It may be an equation that derives another fixed value close to 1 as , or it may be an equation that multiplies a certain fixed value by a coefficient. In either case, in a stationary state, the acceleration smoothed value (a t ) the vertical direction can be estimated with high accuracy, so the Kalman gain derived from the stationary gain derivation formula is K t= 1. That is, when the motion state of the user US is stationary, the control unit 150 calculates the Kalman gain K t As a derivation formula for estimating the vertical direction, the smoothed acceleration value (a t The contribution ratio of the gyro sensor 120 is calculated based on the angular velocity data (X t / t-1 ) is higher than the contribution of the given Kalman gain K t Set the formula from which is derived.

[0043] Thereafter, the control unit 150 calculates the acceleration smoothed value (a t ) and its error covariance, and each time data is input from the gyro sensor 120, the rotation matrix R t These values ​​are input to the Kalman filter. As shown in Figure 5, the Kalman filter estimates the reaction force against gravity (vertical vector) based on the input values ​​and past estimated values.

[0044] Next, the control unit 150 determines whether or not a "Ready" attitude has been detected (step S4). In the "Ready" position, as shown in Fig. 9, a vertical direction occurs between the Z-axis direction (negative direction) and the Y-axis direction (positive direction). That is, the acceleration sensor 110 measures the magnitude of the gravitational acceleration between the Z-axis direction (negative direction) and the Y-axis direction (positive direction). Then, the control unit 150 acquires acceleration data from the acceleration sensor 110, measures the magnitude of the gravitational acceleration between the Z-axis direction (negative direction) and the Y-axis direction (positive direction), and determines that the "Ready" position has been detected when the movement (change in the measurement value of the acceleration sensor 110) is equal to or less than a predetermined threshold. Control unit 150 repeats the process of step S4 until it determines that "Ready" has been detected. Since the "Ready" position is a stationary state like the "On your mark" position, the control unit 150 calculates the Kalman gain K t The derivation formula will not be changed.

[0045] As an option, the offset error of the gyro sensor 120 may be acquired based on the change in attitude between "On your mark" and "Ready" and the data from the gyro sensor 120 may be corrected based on the offset error. This makes it possible to improve the accuracy of the subsequent estimation of the vertical direction. The error at this time may be treated as a constant or may be treated periodically as a function of time. It is known that the gyro sensor 120 has a periodic offset error, and the error may also change due to the environment such as temperature, so it is effective to measure it recently.

[0046] When it is determined that the "Ready" position has been detected (step S4; YES), the control unit 150 determines whether or not the "Start" has been detected (step S5). When the "Start" signal is given, the athlete takes a big leap from a stationary state (his posture changes suddenly), causing a sudden change in the measurement values ​​of both the acceleration sensor 110 and the gyro sensor 120. Therefore, the control unit 150 determines that a "Start" has been detected when the amount of change in both the acceleration data value acquired by the acceleration sensor 110 and the angular velocity data value acquired by the gyro sensor 120 exceeds a predetermined threshold value. Control unit 150 repeats the process of step S5 until it determines that "Start" has been detected.

[0047] When it is determined that "Start" has been detected (step S5; YES), the control unit 150 determines that the motion state of the user US is a high-speed motion state in which the posture changes rapidly (step S6), and calculates the Kalman gain K t A high-speed operation gain derivation formula is set as a derivation formula for the gain (step S7). In this embodiment, the control unit 150 calculates the Kalman gain K t The derivation formula of gain for high speed operation is K t = 0, and the predicted value of the reaction force against gravity (X t / t-1) is estimated as a reaction force against gravity (i.e., a vertical vector). During the acceleration phase from the start, the posture of the user US changes suddenly, so the acceleration smoothed value (a t ) cannot estimate the correct vertical direction. The gain derivation formula for high speed motion is K t = 0. For example, K t It may be a formula that derives another fixed value close to 0 as K, or it may be a formula that multiplies a certain fixed value by a coefficient. In either case, during high-speed movement after "Start", the estimation accuracy in the vertical direction based on the acceleration smoothed value (at) from the acceleration sensor 110 is low, so the Kalman gain calculated by the high-speed movement gain derivation formula is K t = a value close to 0. That is, when the motion state of the user US is a high-speed motion state, the control unit 150 calculates the Kalman gain K t The derivation formula for the vertical direction is a predicted value (X t / t-1 ) is higher than the acceleration smoothed value (at) t Set the formula from which is derived.

[0048] Next, the control unit 150 determines whether or not a stable periodic operation state has been achieved (step S8). The control unit 150 observes the acceleration data acquired by the acceleration sensor 110 and the angular velocity data acquired by the gyro sensor 120, and determines that a stable periodic operation state has been reached when the change in the measurement values ​​of the acceleration sensor 110 and the gyro sensor 120 falls below a predetermined threshold value and the measurement values ​​begin to change periodically at a constant pitch. The control unit 150 repeats the process of step S8 until it is determined that a stable periodic operation state has been achieved.

[0049] When it is determined that the periodic operation state is stable (step S8; YES), the control unit 150 adjusts the Kalman gain K t A periodic motion gain derivation formula is set as a derivation formula for the gain (step S9). The periodic motion gain is derived from the above formula (9). According to formula (9), the Kalman gain K t is the acceleration smoothing value a t The acceleration smoothing value a changes dynamically based on the variance (error covariance) of the acceleration smoothing value a. t The greater the variation in the vertical direction, the greater the predicted value (X t / t-1 ) becomes larger, and the acceleration smoothing value a t The smaller the variation of the acceleration smoothing value a t Increase the proportion of.

[0050] Next, the control unit 150 judges whether or not a condition for ending the measurement has occurred (step S10). For example, when the switch unit 190 issues an instruction to end the measurement, or when it is determined that the sprint has ended, such as when a stationary state is detected based on the measurement values ​​of the acceleration sensor 110 or the gyro sensor 120, the control unit 150 judges that a condition for ending the measurement has occurred.

[0051] When it is determined that the condition for ending the measurement has not been met (step S10; NO), the control unit 150 repeatedly executes the process of step S10. When it is determined that the condition for ending the measurement has been met (step S10; YES), the control unit 150 ends the setting process for sprinting. Also, the control unit 150 ends the estimation of the vertical direction by the Kalman filter. The control unit 150 calculates motion data and indices for grasping and analyzing the motion state of the user US during sprint running based on the measurement data stored in the memory unit 170, with the estimated vertical direction as a fixed axis.

[0052] In the above-mentioned sprint setting process, the motion state of the user US in the sprint is determined, and a different Kalman gain K tSince the derivation formula is set, it is possible to accurately estimate the vertical direction during sprinting, which is a combination of movements involving quick and large posture changes and constant, stable movements. By estimating the vertical direction with high accuracy, for example, it becomes possible to extract only the horizontal plane components of the measurement values ​​of the acceleration sensor 110 and the gyro sensor 120, and it becomes possible to estimate the running direction accurately. In addition, it becomes possible to convert the measurement values ​​of the acceleration sensor 110 and the gyro sensor 120 into values ​​in a fixed coordinate system seen from the outside. As a result, motion information such as the posture of the user US while running, the motion against the ground, and physical data acting on the ground and the running direction (for example, accurate acceleration and moving speed in the forward direction in a sprint, the motion posture (forward and backward leaning of the trunk), the left and right tilt of the pelvis, up and down movement, landing timing, etc.) can be obtained as objective numerical values, and by providing these numerical values ​​to the user US, it is possible to improve the form and the burden on the body.

[0053] As described above, the control unit 150 of the exercise data acquisition device 100 determines the motion state of the user US based on data related to a series of motions including a plurality of motion states performed by the user US, which is acquired from the acceleration sensor 110 and the gyro sensor 120, and calculates the Kalman gain K t As a formula for determining the above, a formula according to the operating state of the user US is set. Therefore, the Kalman gain K t Since an equation according to the motion state of the user US is set as the equation for calculating the vertical direction, even in an exercise in which the posture of the user US changes suddenly, the vertical direction can be accurately estimated according to the motion state of the user US, and the motion information of the user US can be accurately calculated.

[0054] In addition, when the motion state of the user US changes, the control unit 150 adjusts the Kalman gain K t Therefore, even if the motion state of the user US changes during exercise, the vertical direction can be estimated with high accuracy for each motion state, and motion information of the user US can be calculated with high accuracy.

[0055] Furthermore, the control unit 150 sets an equation corresponding to the motion state of the user US from among a plurality of types of equations stored in the memory unit 10. Therefore, since it is possible to set the type of equation corresponding to the motion state of the user US, it is possible to accurately estimate the vertical direction according to the motion state of the user US, and it becomes possible to accurately calculate the motion information of the user US.

[0056] For example, when the control unit 150 determines that the motion state of the user US is a high-speed motion state in which the posture of the user US changes rapidly, the control unit 150 t The formula for calculating the reaction force against gravity (X t / t-1 ) (the second vertical vector) contributes to the smoothed acceleration value a t The Kalman gain K becomes higher than the contribution of the first vertical vector. t Set the formula from which is derived. Therefore, in the case of a high speed operation state, the contribution rate of the second vertical vector can be increased, and the vertical direction can be estimated with high accuracy.

[0057] In addition, for example, when the motion state of the user US is a stationary state, the control unit 150 t The formula for determining the Kalman gain K is given so that the contribution of the first vertical vector is higher than the contribution of the second vertical vector. t Set the formula from which is derived. Therefore, when the vehicle is stationary, the contribution rate of the first vertical vector can be increased, enabling the vertical direction to be estimated with high accuracy.

[0058] In addition, for example, when the motion state of the user US is a periodic motion state in which a periodic motion is repeated, the control unit 150 calculates the Kalman gain K t The formula for determining the Kalman gain K t Set an equation that dynamically changes based on the error covariance of one vertical vector. Therefore, in a periodic motion state in which a periodic motion is repeated, the vertical direction can be estimated with high accuracy according to the current motion.

[0059] The contents described in the above embodiment are preferred examples of the information processing device, the exercise data acquisition device, the setting method, and the program according to the present invention, and are not limited to these.

[0060] For example, in the above embodiment, the information processing device of the present invention has been described as an example of the motion data acquisition device 100 in which the information processing device of the present invention is integrated with the acceleration sensor 110 and the gyro sensor 120, but the information processing device of the present invention may be separate from the acceleration sensor 110 and the gyro sensor 120. In this case, for example, a personal computer, smartphone, tablet device, wristwatch-type wrist terminal, dedicated terminal, server device on a network, etc. that can transmit and receive data to and from the acceleration sensor 110 and the gyro sensor 120 can be used as the information processing device of the present invention.

[0061] In the above embodiment, the Kalman gain K t The above description is given by taking as an example a case where the derivation formula is applied to a measurement in a sprint, but the present invention is not limited to this. t The setting of the derivation formula above is applicable to measurements during exercise in which the motion state switches from a stationary state to a plurality of motion states including a high-speed motion state, such as track and field events and swimming.

[0062] In the above embodiment, the filter of the present invention is described as a Kalman filter, but the filter is not limited to the Kalman filter as long as it is a filter for estimating the vertical direction when the sensor acquires data.

[0063] In addition, in the above embodiment, the motion state of the user is determined using the measurement values ​​of the acceleration sensor 110 and the gyro sensor 120, but the motion state of the user may be determined using only one of the measurement values, for example, the measurement value of the acceleration sensor 110.

[0064] In the above embodiment, the motion state of the user US is determined based on the data acquired by the acceleration sensor 110 and the gyro sensor 120 in real time during exercise, and the Kalman gain K t The derivation equation for is set in the Kalman filter, but these processes may be performed based on the data stored in the memory unit 170 after the exercise, by storing the data acquired by the acceleration sensor 110 and the gyro sensor 120 in real time during the exercise in the memory unit 170.

[0065] In the above embodiment, the control unit 150 determines the motion state of the user US based on data acquired by the acceleration sensor 110 and the gyro sensor 120, but the present invention is not limited to this, and may determine the motion state of the user US based on an image acquired by an image sensor of a camera. For example, when determining the motion state of the user US during a sprint run, the motion state of the user US may be determined based on a captured image of the user US during the sprint run acquired by an image sensor of a camera.

[0066] In the above embodiment, a semiconductor memory such as a ROM or a hard disk is used as a computer-readable medium for the program according to the present invention, but the present invention is not limited to this example. As other computer-readable media, a portable recording medium such as a CD-ROM can be applied. Furthermore, a carrier wave can be applied as a medium for providing data of the program according to the present invention via a communication line.

[0067] Furthermore, the detailed configuration and operation of exercise data acquisition device 100 may be modified as appropriate without departing from the spirit and scope of the invention.

[0068] Although the embodiment of the present invention has been described above, the technical scope of the present invention is not limited to the above embodiment, but is defined based on the claims. Furthermore, the technical scope of the present invention also includes equivalent ranges that are made by adding modifications that are not related to the essence of the present invention from the claims. [Explanation of symbols]

[0069] 100 Exercise data acquisition device 110 Acceleration Sensor 120 Gyro sensor 130 Geomagnetic Sensor 140 Timing section 150 Control section 160 I / F section 170 Memory section 180 Display section 190 Switch section 200 External equipment

Claims

1. Determining a motion state of a user based on data regarding a series of motions including a plurality of motion states performed by the user, the data being acquired by a sensor; setting an equation corresponding to the determined motion state of the user as an equation for determining a parameter used in estimating the vertical direction at the time of acquiring the data in the sensor; An information processing device having a control unit.

2. The control unit is changing the setting of the formula when the operating state of the user changes; The information processing device according to claim 1 .

3. A storage unit that stores a plurality of expressions corresponding to the user's motion state, The control unit is setting an equation corresponding to the determined motion state of the user from among a plurality of equations corresponding to the motion states of the user stored in the storage unit; The information processing device according to claim 1 .

4. The sensor includes an image sensor, an acceleration sensor, and a gyro sensor. The information processing device according to claim 1 .

5. The vertical estimation is performed using a Kalman filter; The parameter is a Kalman gain for determining a contribution ratio of a first vertical vector calculated based on acceleration data acquired by the acceleration sensor and a second vertical vector calculated based on angular velocity data acquired by the gyro sensor when estimating the vertical direction. The information processing device according to claim 4.

6. The control unit is When it is determined that the motion state of the user is a high-speed motion state in which the posture of the user changes rapidly, an equation is set as the equation for deriving a predetermined Kalman gain in which a contribution rate of the second vertical vector is higher than a contribution rate of the first vertical vector. The information processing device according to claim 5 .

7. The control unit is When it is determined that the motion state of the user is a stationary state, an equation is set as the equation for deriving a predetermined Kalman gain in which a contribution rate of the first vertical vector is higher than a contribution rate of the second vertical vector. The information processing device according to claim 5 .

8. The control unit is When it is determined that the motion state of the user is a periodic motion state, an equation in which a derived Kalman gain dynamically changes based on an error covariance of the first vertical vector is set as the equation. The information processing device according to claim 5 .

9. An information processing device according to any one of claims 1 to 8; The sensor; Equipped with Exercise data acquisition device.

10. The computer Determining a motion state of a user based on data regarding a series of motions including a plurality of motion states performed by the user, the data being acquired by a sensor; setting an equation corresponding to the determined motion state of the user as an equation for determining a parameter used in estimating the vertical direction at the time of acquiring the data in the sensor; How to set it up.

11. Computer, Determining a motion state of a user based on data regarding a series of motions including a plurality of motion states performed by the user, the data being acquired by a sensor; setting an equation corresponding to the determined motion state of the user as an equation for determining a parameter used in estimating the vertical direction at the time of acquiring the data in the sensor; A program to function as a control unit.

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