JP2015190850A - Error estimation method, kinematic analysis method, error estimation device, and program - Google Patents

Abstract

An object of the present invention is to provide an error estimation method and the like that can reduce a processing load and can accurately estimate an error of an index representing a state of a moving object using a signal from a positioning satellite. A first coordinate system with a first sensor attached to the moving body as a reference and a second coordinate system with the moving body as a reference at a predetermined timing in a cycle of movement accompanying the movement of the moving body. When the detection result of the second sensor that receives the signal from the positioning satellite is obtained, the detection result of the first sensor is calculated using the coordinate conversion matrix. Converting one of the azimuth angle of the mobile body based on the detection result of the second sensor and the azimuth angle of the mobile body based on the detection result of the second sensor, and the predetermined time after the timing when the detection result of the second sensor is obtained An error estimation method comprising: estimating an error of an index representing the state of the moving object using a difference between the converted azimuth angle and the other azimuth angle at timing. [Selection] Figure 10

Description

  The present invention relates to an error estimation method, a motion analysis method, an error estimation device, and a program.

  A technique is known in which a sensor module incorporating a plurality of sensors is attached to a pedestrian, and the position and orientation of the pedestrian are estimated using the detection result of the sensor module. In estimating the position and direction using a sensor, GPS (Global Positioning System) is often used for correction of calculation results. For example, Patent Document 1 proposes a method of correcting a position using GPS by calculating a position by determining a moving direction with reference to the highest position arrival point or landing point of the body during walking.

JP 2000-97722 A

  However, for example, when the sensor module is attached to the waist, the hips often sway left and right during walking and running, so the measured azimuth is symmetrically distributed around the direction of travel according to the sway of the waist. . On the other hand, since the azimuth angle calculated using the GPS substantially coincides with the traveling direction of the pedestrian, the difference between these two azimuth angles changes every moment. Therefore, even if an azimuth angle calculated using GPS is used as a reference for the measured azimuth angle, an azimuth angle measurement error cannot always be correctly estimated. In order to correctly estimate the error, it is necessary to calculate information on the deviation between the azimuth angle of the waist and the direction of travel, that is, the transformation matrix between the coordinate system based on the sensor and the coordinate system based on the pedestrian There is. However, since the timing at which GPS positioning information is obtained is indefinite, the coordinate transformation matrix must be calculated every time based on the latest speed information at the timing at which GPS positioning information is obtained. As a result, the processing load increases, and the error in speed due to integration error etc. increases with time, and the coordinate transformation matrix may not be calculated correctly. there were.

  The present invention has been made in view of the above-described problems, and according to some aspects of the present invention, a mobile object can be obtained by reducing the processing load and using a signal from a positioning satellite. It is possible to provide an error estimation method, an error estimation device and a program capable of accurately estimating an error of an index representing the state of the above, and a motion analysis method capable of accurately analyzing a user's motion.

  SUMMARY An advantage of some aspects of the invention is to solve at least a part of the problems described above, and the invention can be implemented as the following aspects or application examples.

[Application Example 1]
The error estimation method according to this application example is based on a first coordinate system based on a first sensor attached to the moving body and the moving body at a predetermined timing in a cycle of movement accompanied by movement of the moving body. Calculating the coordinate transformation matrix between the second coordinate system and the second sensor receiving the signal from the positioning satellite, and using the coordinate transformation matrix when the detection result of the second sensor is obtained. One of the azimuth angle of the moving body based on the detection result of one sensor and the azimuth angle of the moving body based on the detection result of the second sensor is converted, and the detection result of the second sensor is obtained. Estimating an error of an index representing the state of the moving object using a difference between the converted azimuth angle and the other azimuth angle at the predetermined timing after the timing.

  In the error estimation method according to this application example, the difference between the azimuth angle based on the detection result of the first sensor and the azimuth angle based on the detection result of the second sensor at a predetermined timing for each cycle of movement accompanied by movement of the moving body. Is used to be almost constant. Using the coordinate transformation matrix calculated at the predetermined timing, the state of the moving body is expressed using the difference between the two azimuth angles calculated at a predetermined timing after the timing at which the detection result of the second sensor is obtained. Estimate the error of the indicator. Therefore, according to the error estimation method according to this application example, the error can be accurately estimated even when the timing at which the detection result of the second sensor is obtained is indefinite, and every time the detection result of the second sensor is obtained. Since it is not necessary to calculate a coordinate transformation matrix, the processing load can be reduced.

[Application Example 2]
In the error estimation method according to the application example, the speed of the moving body in the first coordinate system is calculated using the detection result of the first sensor, and the coordinate transformation matrix is calculated based on the calculated speed. May be.

  According to the error estimation method according to this application example, the coordinate transformation matrix can be calculated at a predetermined timing of the motion period regardless of the presence or absence of the detection result of the second sensor.

[Application Example 3]
In the error estimation method according to the application example, the predetermined timing may be a timing at which a detection result of the first sensor satisfies a predetermined condition.

  According to the error estimation method according to this application example, with the timing at which the detection result of the first sensor satisfies a predetermined condition as a reference, the timing corresponding to the timing at which the coordinate transformation matrix is calculated in the period of motion. The error can be estimated accurately.

[Application Example 4]
The error estimation method according to the application example further includes detecting the period using a detection result of the first sensor, and the predetermined timing may be a timing at which the period is detected.

  According to the error estimation method according to this application example, the coordinate transformation matrix can be calculated at the timing when the motion period is detected, and then the error can be accurately estimated at the timing when the motion cycle is detected.

[Application Example 5]
In the error estimation method according to the application example, the coordinate transformation matrix may be calculated using a detection result of the first sensor in a predetermined period after the moving body starts moving.

  According to the error estimation method according to this application example, since the coordinate transformation matrix is calculated as soon as the moving object starts moving, the error of the coordinate transformation matrix caused by an integration error or the like is reduced, and the error estimation accuracy is reduced. Can be improved.

[Application Example 6]
The error estimation method according to the application example further includes determining whether or not the moving body is traveling straight, and from the timing at which the detection result of the second sensor is obtained to the next predetermined timing. In the meantime, when it is determined that the moving body is not moving straight, the error need not be estimated at the next predetermined timing.

  According to the error estimation method according to this application example, when the moving body changes the traveling direction by the next predetermined timing after the detection result of the second sensor is obtained, at the predetermined timing, the first sensor Considering that the difference between the azimuth angle based on the detection result and the azimuth angle based on the detection result of the second sensor is different from the calculation time of the coordinate transformation matrix, in such a case, the error is not estimated. A decrease in error estimation accuracy can be suppressed.

[Application Example 7]
In the error estimation method according to the application example, the first sensor may include at least one of an acceleration sensor and an angular velocity sensor.

  According to the error estimation method according to this application example, using the difference between the azimuth angle based on the detection result of the acceleration sensor or the angular velocity sensor and the azimuth angle based on the detection result of the second sensor, The error can be estimated.

[Application Example 8]
In the error estimation method according to the application example, the first sensor may be a geomagnetic sensor.

  According to the error estimation method according to this application example, using the difference between the azimuth angle based on the detection result of the geomagnetic sensor and the azimuth angle based on the detection result of the second sensor, the error of the index representing the state of the moving object is estimated. can do.

[Application Example 9]
The motion analysis method according to this application example includes estimating the error using any one of the error estimation methods described above, correcting the index using the estimated error, and correcting the corrected index. And analyzing the movement of the moving body.

  According to the motion analysis method according to this application example, the motion of the mobile object using the index accurately corrected using the error of the index representing the state of the mobile object estimated using the error estimation method according to the application example described above. Can be analyzed with high accuracy.

[Application Example 10]
The error estimation apparatus according to this application example uses the first coordinate system with the first sensor attached to the moving body as a reference and the moving body as a reference at a predetermined timing in a cycle of movement accompanying the movement of the moving body. When the detection result of the coordinate conversion matrix calculation unit for calculating the coordinate conversion matrix between the second coordinate system and the second sensor for receiving the signal from the positioning satellite is obtained, the coordinate conversion matrix is used. An azimuth angle conversion unit that converts either the azimuth angle of the mobile body based on the detection result of the first sensor or the azimuth angle of the mobile body based on the detection result of the second sensor; and the second sensor An error estimator that estimates an error of an index representing the state of the moving object using a difference between the converted azimuth angle and the other azimuth angle at the predetermined timing after the timing when the detection result is obtained And including

The error estimation device according to this application example is configured such that the difference between the azimuth angle based on the detection result of the first sensor and the azimuth angle based on the detection result of the second sensor at a predetermined timing for each period of movement accompanied by movement of the moving body. Is used to be almost constant. Using the coordinate transformation matrix calculated at the predetermined timing, the difference between the two azimuth angles calculated at a predetermined timing after the timing when the detection result of the second sensor is obtained is used to determine the state of the moving body. Estimate the error of the index to represent. Therefore, according to the error estimation device according to this application example, the error can be accurately estimated even when the timing at which the detection result of the second sensor is obtained is indefinite, and every time the detection result of the second sensor is obtained. Since it is not necessary to calculate a coordinate transformation matrix, the processing load can be reduced.

[Application Example 11]
The program according to this application example has a first coordinate system based on the first sensor attached to the moving body and a first reference based on the moving body at a predetermined timing in a cycle of movement accompanied by movement of the moving body. When the coordinate transformation matrix between the two coordinate systems is calculated and the detection result of the second sensor that receives the signal from the positioning satellite is obtained, the first sensor is used by using the coordinate transformation matrix. After converting the azimuth angle of the moving body based on the detection result of the moving object and the azimuth angle of the moving body based on the detection result of the second sensor, and after the timing when the detection result of the second sensor is obtained The computer is caused to estimate an error of an index representing the state of the moving body using the difference between the converted azimuth angle and the other azimuth angle at the predetermined timing.

  In the program according to this application example, the difference between the azimuth angle based on the detection result of the first sensor and the azimuth angle based on the detection result of the second sensor is almost equal at a predetermined timing for each cycle of movement accompanied by movement of the moving body. Take advantage of being constant. Using the coordinate transformation matrix calculated at the predetermined timing, the difference between the two azimuth angles calculated at a predetermined timing after the timing when the detection result of the second sensor is obtained is used to determine the state of the moving body. Estimate the error of the index to represent. Therefore, according to the program according to this application example, the error can be accurately estimated even when the timing at which the detection result of the second sensor is obtained is indeterminate, and coordinate conversion is performed every time the detection result of the second sensor is obtained. Since it is not necessary to calculate a matrix, the processing load can be reduced.

Explanatory drawing about the outline | summary of the exercise | movement analysis system of this embodiment. The functional block diagram which shows the structural example of a motion analysis apparatus and a display apparatus. The figure which shows the structural example of a sensing data table. The figure which shows the structural example of a GPS data table. The figure which shows the structural example of a calculation data table. The functional block diagram which shows the structural example of the process part of a motion analysis apparatus. Explanatory drawing about the posture at the time of a user's walk. The figure which shows an example of the azimuth of an inertial measurement unit and a azimuth | direction of an advancing direction when a user walks (straightly). The figure which shows an example of the synthetic | combination acceleration of the triaxial acceleration which the inertial measurement unit detected at the time of a user's walk. The flowchart figure which shows an example of the procedure of an exercise | movement analysis process. The figure which shows an example of the relationship between the azimuth of an inertial measurement unit when a user goes straight ahead, and the timing of the process by a process part.

  DESCRIPTION OF EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings. The embodiments described below do not unduly limit the contents of the present invention described in the claims. Also, not all of the configurations described below are essential constituent requirements of the present invention.

1. Motion analysis system 1-1. Overview of System FIG. 1 is a diagram for describing an overview of a motion analysis system 1 of the present embodiment. As shown in FIG. 1, the motion analysis system 1 according to the present embodiment includes a motion analysis device 2 and a display device 3. The motion analysis device 2 is attached to a trunk portion (for example, right waist or left waist) of a user (an example of a moving body). The motion analysis apparatus 2 has an inertial measurement unit (IMU) 10 and captures movements of the user's walking (including running) to detect speed, position, and posture angle (roll angle, pitch angle, yaw angle). ) Etc., and further, the motion of the user is analyzed to generate motion analysis information. In the present embodiment, the motion is performed so that one detection axis (hereinafter referred to as z-axis) of the inertial measurement unit (IMU) 10 substantially coincides with the gravitational acceleration direction (vertically downward) while the user is stationary. The analysis device 2 is attached. The motion analysis device 2 transmits the generated motion analysis information to the display device 3.

  The display device 3 is a wrist-type (wristwatch-type) portable information device, and is attached to a user's wrist or the like. However, the display device 3 may be a portable information device such as a head mounted display (HMD) or a smartphone. The user can instruct the start and stop of measurement by the motion analysis device 2 by operating the display device 3. The display device 3 transmits a command for instructing measurement start or measurement stop to the motion analysis device 2. When the motion analysis device 2 receives a measurement start command, the motion analysis device 2 starts measurement by the inertial measurement unit (IMU) 10, analyzes the user's motion based on the measurement result, and generates motion analysis information. The motion analysis device 2 transmits the generated motion analysis information to the display device 3, the display device 3 receives the motion analysis information, and presents the received motion analysis information to the user in various forms such as letters, figures, and sounds. . The user can recognize the motion analysis information via the display device 3.

  Data communication between the motion analysis device 2 and the display device 3 may be wireless communication or wired communication.

  In the present embodiment, a case where the motion analysis apparatus 2 estimates a user's walking speed and generates motion analysis information including a travel route and a travel time will be described in detail below as an example. This motion analysis system 1 can be similarly applied to the case of generating motion analysis information in motion involving movement other than walking.

1-2. Coordinate system The coordinate system required in the following description is defined.
・ E Frame (Earth Center Earth Fixed Frame): 3D Cartesian coordinates of right-handed system with the center of the earth as the origin and the z axis parallel to the rotation axis ・ n Frame (Navigation Frame): Origin of the moving object (user) 3D Cartesian coordinate system with x-axis as north, y-axis as east, and z-axis as gravity direction ・ B frame (Body Frame): 3D Cartesian coordinates based on sensor (Inertial Measurement Unit (IMU) 10) System • m Frame (Moving Frame): A right-handed three-dimensional Cartesian coordinate system with the moving body (user) as the origin and the traveling direction of the moving body (user) as the x-axis

1-3. System Configuration FIG. 2 is a functional block diagram showing a configuration example of the motion analysis device 2 and the display device 3. As shown in FIG. 2, the motion analysis device 2 (an example of an error estimation device) includes an inertial measurement unit (IMU) 10, a processing unit 20, a storage unit 30, a communication unit 40, and a GPS unit 50. . However, the motion analysis apparatus 2 of the present embodiment may have a configuration in which some of these components are deleted or changed, or other components are added.

  The inertial measurement unit 10 (an example of a first sensor) includes an acceleration sensor 12, an angular velocity sensor 14, and a signal processing unit 16.

  The acceleration sensor 12 detects each acceleration in the three-axis directions that intersect (ideally orthogonal) with each other, and outputs a digital signal (acceleration data) corresponding to the magnitude and direction of the detected three-axis acceleration.

  The angular velocity sensor 14 detects angular velocities in the three axial directions that intersect (ideally orthogonal) with each other, and outputs a digital signal (angular velocity data) corresponding to the magnitude and direction of the measured three axial angular velocities.

  The signal processing unit 16 receives acceleration data and angular velocity data from the acceleration sensor 12 and the angular velocity sensor 14, respectively, attaches time information to the storage unit (not shown), and stores the time information in the stored acceleration data and angular velocity data. At the same time, sensing data matching a predetermined format is generated and output to the processing unit 20.

  The acceleration sensor 12 and the angular velocity sensor 14 are ideally attached so that each of the three axes coincides with the three axes of the sensor coordinate system (b frame) with the inertial measurement unit 10 as a reference. Error occurs. Therefore, the signal processing unit 16 performs a process of converting the acceleration data and the angular velocity data into data of the sensor coordinate system (b frame) using a correction parameter calculated in advance according to the attachment angle error. Note that the processing unit 20 described later may perform the conversion process instead of the signal processing unit 16.

  Further, the signal processing unit 16 may perform temperature correction processing of the acceleration sensor 12 and the angular velocity sensor 14. Note that the processing unit 20 to be described later may perform the temperature correction processing instead of the signal processing unit 16, and the acceleration sensor 12 and the angular velocity sensor 14 may incorporate a temperature correction function.

  The acceleration sensor 12 and the angular velocity sensor 14 may output analog signals. In this case, the signal processing unit 16 performs A / D conversion on the output signal of the acceleration sensor 12 and the output signal of the angular velocity sensor 14, respectively. Then, sensing data may be generated.

  The GPS unit 50 (an example of a second sensor) receives a GPS satellite signal transmitted from a GPS satellite that is a type of positioning satellite, performs a positioning calculation using the GPS satellite signal, and performs a positioning calculation of the user in n frames. The position and speed (vector including magnitude and direction) are calculated, and GPS data with time information and positioning accuracy information added thereto is output to the processing unit 20. In addition, since the method of calculating a position and speed and the method of generating time information using GPS are publicly known, detailed description is omitted.

  The processing unit 20 is configured by, for example, a CPU (Central Processing Unit), a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), and the like, and according to various programs stored in the storage unit 30, Perform control processing. In particular, the processing unit 20 receives sensing data from the inertial measurement unit 10, receives GPS data from the GPS unit 50, and calculates a user's speed, position, posture angle, and the like using the sensing data and GPS data. In addition, the processing unit 20 performs various arithmetic processes using the calculated information to analyze the user's motion, and motion analysis information (image data, text data sound data, etc.) including the travel route and travel time. Is generated. Then, the processing unit 20 transmits the generated motion analysis information to the display device 3 via the communication unit 40.

  The storage unit 30 includes various IC memories such as a ROM (Read Only Memory), a flash ROM, and a RAM (Random Access Memory), a recording medium such as a hard disk and a memory card, and the like.

  The storage unit 30 stores a motion analysis program 300 that is read by the processing unit 20 and used to execute a motion analysis process (see FIG. 10).

  The storage unit 30 also stores a sensing data table 310, a GPS data table 320, a calculation data table 330, a coordinate conversion matrix 340, motion analysis information 350, and the like.

  The sensing data table 310 is a data table that stores sensing data (detection results of the inertial measurement unit 10) received by the processing unit 20 from the inertial measurement unit 10 in time series. FIG. 3 is a diagram illustrating a configuration example of the sensing data table 310. As shown in FIG. 3, the sensing data table 310 includes the sensing data associated with the detection time 311 of the inertial measurement unit 10, the acceleration 312 detected by the acceleration sensor 12, and the angular velocity 313 detected by the angular velocity sensor 14. It is arranged in series. When the measurement starts, the processing unit 20 adds new sensing data to the sensing data table 310 every time a sampling period Δt (for example, 20 ms) elapses. Further, the processing unit 20 corrects the acceleration and the angular velocity using the acceleration bias and the angular velocity bias estimated by the error estimation using the extended Kalman filter (described later), and overwrites the corrected acceleration and the angular velocity, thereby sensing data table. 310 is updated.

  The GPS data table 320 is a data table that stores the GPS data (the detection result of the GPS unit (GPS sensor) 50) received by the processing unit 20 from the GPS unit 50 in time series. FIG. 4 is a diagram illustrating a configuration example of the GPS data table 320. As shown in FIG. 4, the GPS data table 320 includes a time 321 when the GPS unit 50 performs positioning calculation, a position 322 calculated by the positioning calculation, a speed 323 calculated by the positioning calculation, and a positioning accuracy (DOP (Dilution of Precision)). 324, GPS data associated with the signal strength 325 of the received GPS satellite signal is arranged in time series. When measurement is started, the processing unit 20 adds new GPS data and updates the GPS data table 320 every time GPS data is acquired (asynchronously with the acquisition timing of sensing data).

  The calculated data table 330 is a data table that stores the speed, position, and attitude angle calculated by the processing unit 20 using the sensing data in time series. FIG. 5 is a diagram illustrating a configuration example of the calculation data table 330. As shown in FIG. 5, the calculation data table 330 is configured by calculating data in which time 331, speed 332, position 333, and posture angle 334 calculated by the processing unit 20 are associated in time series. When measurement is started, the processing unit 20 calculates the speed, position, and attitude angle every time sensing data is acquired, that is, every time the sampling period Δt elapses, and new calculation data is stored in the calculation data table 330. Append. Further, the processing unit 20 corrects the speed, the position, and the attitude angle using the speed error, the position error, and the attitude angle error estimated by the error estimation using the extended Kalman filter, and the corrected speed, position, and attitude are corrected. The calculation data table 330 is updated by overwriting the corners.

  The coordinate transformation matrix 340 is a matrix for performing coordinate transformation between the b frame and the m frame. As will be described later, the processing unit 20 calculates at a predetermined timing in the cycle of the user's walking motion, and the storage unit 30 (storing).

  The exercise analysis information 350 is various information relating to the user's exercise. In the present embodiment, information relating to movement by walking, information relating to an evaluation index of walking exercise, advice relating to walking, guidance, warning, etc., calculated by the processing unit 20. Contains information.

The communication unit 40 performs data communication with the communication unit 140 of the display device 3. The communication unit 40 receives the motion analysis information generated by the processing unit 20 and transmits it to the display device 3. The received command (measurement start / stop command, etc.) is received and sent to the processing unit 20.

  The display device 3 includes a processing unit 120, a storage unit 130, a communication unit 140, an operation unit 150, a time measuring unit 160, a display unit 170, and a sound output unit 180. However, the display device 3 of the present embodiment may have a configuration in which some of these components are deleted or changed, or other components are added.

  The processing unit 120 performs various arithmetic processes and control processes according to programs stored in the storage unit 130. For example, the processing unit 120 performs various types of processing according to the operation data received from the operation unit 150 (processing for sending a measurement start / stop command to the communication unit 140, display processing according to the operation data, sound output processing, etc.), communication A process of receiving motion analysis information from the unit 140 and sending it to the display unit 170 and the sound output unit 180, a process of generating time image data corresponding to the time information received from the time measuring unit 160 and sending it to the display unit 170, and the like.

  The storage unit 130 includes various IC memories such as a ROM that stores programs and data for the processing unit 120 to perform various processes, and a RAM that is a work area of the processing unit 120, for example.

  The communication unit 140 performs data communication with the communication unit 40 of the motion analysis device 2 and receives a command (measurement start / stop command, etc.) corresponding to the operation data from the processing unit 120 to perform motion analysis. Processing to be transmitted to the device 2, processing for receiving motion analysis information (various data such as image data, text data, and sound data) transmitted from the motion analysis device 2 and processing to send to the processing unit 120 are performed.

  The operation unit 150 obtains operation data (operation data such as measurement start / stop and display content selection) from the user, and performs processing to send the operation data to the processing unit 120. The operation unit 150 may be, for example, a touch panel display, a button, a key, a microphone, or the like.

  The timer unit 160 performs processing for generating time information such as year, month, day, hour, minute, and second. The timer unit 160 is realized by a real time clock (RTC) IC, for example.

  The display unit 170 displays the image data and text data sent from the processing unit 120 as characters, graphs, tables, animations, and other images. The display unit 170 is realized by a display such as an LCD (Liquid Crystal Display), an organic EL (Electroluminescence) display, or an EPD (Electrophoretic Display), and may be a touch panel display. Note that the functions of the operation unit 150 and the display unit 170 may be realized by a single touch panel display.

  The sound output unit 180 outputs the sound data sent from the processing unit 120 as sound such as sound or buzzer sound. The sound output unit 180 is realized by, for example, a speaker or a buzzer.

  FIG. 6 is a functional block diagram illustrating a configuration example of the processing unit 20 of the motion analysis apparatus 2. In the present embodiment, the processing unit 20 executes the motion analysis program 300 stored in the storage unit 30 to thereby perform the bias removal unit 210, the integration processing unit 220, the error estimation unit 230, the walking detection unit 240, and the straight traveling determination. Functions as a unit 250, a coordinate conversion matrix calculation unit 260, an azimuth angle conversion unit 270, a coordinate conversion unit 280, and a motion analysis unit 290.

Bias removal unit 210, the newly acquired acceleration included in the sensing data (three-axis acceleration) and angular velocity, respectively, the acceleration bias b _a and angular velocity bias b _omega error estimation unit 230 has estimated by subtracting the acceleration and angular velocity The process which correct | amends is performed. Since the in the initial state immediately after the start of measurement no acceleration bias b _a and angular velocity bias b _omega, bias removal unit 210, the user in the initial state as a quiescent state, using the sensing data from the inertial measurement unit To calculate the initial bias.

Integration processing unit 220, the processing bias removal unit 210 calculates the speed ^{v e} of e frame from the acceleration corrected and the angular velocity, the position ^{p e} and orientation angle (roll angle phi _BE, pitch angle theta _BE, yaw angle [psi _BE) and I do. Specifically, the integration processing unit 220 first assumes that the initial state of the user is a stationary state, sets the initial speed to zero, or calculates the initial speed from the speed included in the GPS data. The initial position is calculated from the positions included in. Further, the integration processing unit 220 calculates the initial values of the roll angle φ _be and the pitch angle θ _be by specifying the direction of the gravitational acceleration from the triaxial acceleration of the b frame corrected by the bias removal unit 210, and _converts the initial value of the GPS into the GPS data. The initial value of the yaw angle ψ _be is calculated from the included velocity, and is set as the initial posture angle of the e frame. When GPS data cannot be obtained, the initial value of the yaw angle ψ _be is set to zero, for example. Then, the integration processing unit 220 calculates an initial value of a coordinate transformation matrix (rotation matrix) C _b ^e from the b frame to the e frame represented by Expression (1) from the calculated initial attitude angle.

Then, the integration processing section 220, the integrated three-axis angular velocity bias removal unit 210 is corrected (rotation operation) and to calculate the coordinate transformation matrix C _b ^e, calculates the posture angle from the equation (2).

Further, the integration processing unit 220 uses the coordinate transformation matrix C _b ^e, the 3-axis acceleration of b frames bias removal unit 210 is corrected by converting the 3-axis acceleration of the e frame, integrated to remove the gravitational acceleration component calculate the velocity v ^e of e frame by. Further, the integration processing unit 220 calculates the position p ^{e of the} e frame by integrating the speed v ^e of the e frame.

Performing addition, the integration processing section 220, speed error .delta.v ^e the error estimator 230 estimates, using the position error .delta.p ^e and attitude angle error epsilon ^e velocity ^{v e,} also processing for correcting the position ^{p e} and orientation angle .

Furthermore, the integration processing unit 220 also calculates a coordinate conversion matrix C _b ^m from the _b frame to the m frame and a coordinate conversion matrix C _e ^m from the e frame to the m frame. These coordinate transformation matrices are used as coordinate transformation information for coordinate transformation processing of the coordinate transformation unit 280 described later.

The error estimation unit 230 uses the velocity / position and posture angle calculated by the integration processing unit 220, the acceleration and angular velocity corrected by the bias removal unit 210, and the reference azimuth angle calculated by the azimuth angle conversion unit 270 described later. , To estimate the error of the index that represents the user's condition. In the present embodiment, speed, posture angle, acceleration, angular velocity, and position are used as indices representing the user's state, and the error estimation unit 230 estimates errors of these indices using an extended Kalman filter. That is, the error estimator 230, the error (velocity error) .delta.v ^e of the velocity ^{v e} of the integration processing unit 220 is calculated, an error of the posture angle integration processing unit 220 is calculated (posture angle error) epsilon ^e, acceleration bias _{b a} the angular velocity bias b _omega and integration processing unit 220 is an error of the position p ^e calculated (position error) .delta.p ^e and extended Kalman filter state variables defining the state vector X as in equation (3).

  The error estimation unit 230 predicts a state variable (an error of an index representing the user's state) included in the state vector X using a prediction formula of the extended Kalman filter. The prediction formula of the extended Kalman filter is expressed as in Equation (4). In Equation (4), the matrix Φ is a matrix that associates the previous state vector X with the current state vector X, and some of the elements are designed to change from moment to moment while reflecting the posture angle, position, and the like. The Q is a matrix representing process noise, and each element thereof is set to an appropriate value in advance. P is an error covariance matrix of state variables.

  Further, the error estimation unit 230 updates (corrects) the predicted state variable (the error of the index representing the user's state) using the extended Kalman filter update formula. The extended Kalman filter update formula is expressed as shown in Formula (5). Z and H are an observation vector and an observation matrix, respectively, and the update equation (5) uses the difference between the actual observation vector Z and the vector HX predicted from the state vector X to correct the state vector X. Represents. R is an observation error covariance matrix, which may be a predetermined constant value or may be dynamically changed. K is a Kalman gain, and the smaller R is, the larger K is. From equation (5), the larger the K (the smaller R), the larger the amount of correction of the state vector X, and the smaller P.

  In this embodiment, the error estimation unit 230 has the same azimuth angle calculated from the detection result of the inertial measurement unit and the azimuth angle of the inertial measurement unit 10 calculated from the GPS data, and the inertial measurement calculated from the GPS data. Under the condition that the azimuth angle of the unit 10 is a true azimuth angle (reference azimuth angle), the azimuth angle calculated from the detection result of the inertial measurement unit and the azimuth angle of the inertial measurement unit 10 calculated from GPS data The state vector X is estimated by applying an extended Kalman filter with the difference from the angle as an observation vector Z.

  By the way, when the user performs a walking motion (straight forward), the traveling direction is substantially constant, whereas the posture of the inertial measurement unit 10 periodically changes according to the movement of the user. FIG. 7 is an overview of the movement of the user when the user wearing the motion analysis apparatus 2 (IMU 10) on the right waist performs a walking motion (straight forward). As illustrated in FIG. 7, the posture of the inertial measurement unit 10 with respect to the user changes as the user walks. When the user steps on the right foot, as shown in (2) and (4) in FIG. 7, the inertial measurement unit 10 is inclined to the right with respect to the traveling direction (the x axis of the m frame). On the other hand, when the user steps on the left foot, the inertial measurement unit 10 is tilted to the left with respect to the traveling direction (the x axis of the m frame) as shown in (1) and (3) in FIG. Become posture.

  In other words, the azimuth angle of the inertial measurement unit 10 periodically changes every two steps left and right with the user's walking motion. Therefore, the azimuth calculated from the detection result of the inertial measurement unit 10 The angle also changes periodically. On the other hand, since the traveling direction of the user is substantially constant, the azimuth angle calculated from the GPS data is substantially constant. FIG. 8 is a diagram illustrating an example of the azimuth angle and the azimuth angle of the traveling direction of the inertial measurement unit 10 when the user performs a walking motion (straight forward), where the horizontal axis represents time and the vertical axis represents the azimuth angle. In FIG. 8, the solid line represents the azimuth angle of the inertial measurement unit 10, and the broken line represents the azimuth angle in the traveling direction. As shown in FIG. 8, the azimuth angle of the inertial measurement unit 10 periodically changes every two steps. For example, the azimuth angle is maximized when the right foot is stepped on (indicated by a circle), and is minimal when the left foot is stepped on. (Indicated by ●). On the other hand, since the azimuth angle in the traveling direction is almost constant and almost constant, the azimuth angle calculated from the GPS data is also almost constant.

Therefore, in order to establish the above condition for estimating the state vector X, information on the difference between the azimuth angle of the inertial measurement unit and the azimuth angle in the traveling direction calculated from the GPS data, that is, b frame and m frame it is necessary to calculate the coordinate transformation matrix C _m ^b between. However, as shown in FIG. 8, since the timing at which GPS data is obtained is indefinite, at the timing at which GPS data is obtained, the azimuth (indicated by Δ) of the inertial measurement unit 10 and the azimuth calculated from the GPS data. The difference from the corner (indicated by □) is not constant. Therefore, in order to perform error estimation by the extended Kalman filter at the timing when the GPS data is obtained, it is necessary to calculate the coordinate transformation matrix C _m ^b each time GPS data is obtained, a problem that the processing load increases Occurs.

Therefore, in this embodiment, as the user's walking motion, the azimuth angle of the inertial measurement unit 10 utilizes the fact that periodically changes every two steps, the coordinate transformation matrix C _m ^b at a predetermined timing in the walking cycle once calculated, then, each time the GPS data is obtained, at a next predetermined timing in walking period (same timing as when calculating the coordinate transformation matrix C _m ^b), using the coordinate transformation matrix C _m ^b The reference azimuth angle is calculated, and the state vector X is estimated. Thus, each time the GPS data is obtained, it is not necessary to recalculate the coordinate transformation matrix C _m ^b, it is possible to greatly reduce the processing load.

  Note that if the user changes the direction of travel between the timing at which GPS data is obtained and the next predetermined timing in the walking cycle, the difference between the GPS azimuth and the azimuth of the inertial measurement unit is coordinate transformed. This is different from the matrix calculation. Therefore, at the predetermined timing, if the reference azimuth calculated from the most recently obtained GPS data is used, the estimation accuracy of the state vector X may be greatly reduced. Therefore, in the present embodiment, it is determined whether or not the user is traveling straight, and when the user changes the traveling direction from the timing when the GPS data is obtained to the next predetermined timing in the walking cycle. Does not estimate the state vector X at the predetermined timing.

  Returning to FIG. 6, the walking detection unit 240 uses the detection result of the inertial measurement unit 10 (specifically, the sensing data corrected by the bias removing unit 210) as the user's walking cycle (walking timing) (2 steps). A process of detecting a cycle of one step on each side). FIG. 9 is a diagram illustrating an example of the combined acceleration of the three-axis accelerations detected by the inertial measurement unit 10 when the user walks. In FIG. 9, the horizontal axis represents time, and the vertical axis represents the value of the resultant acceleration. As shown in FIG. 9, the combined acceleration changes periodically, and the intermediate timing at which the combined acceleration becomes the minimum value to the maximum value coincides with the timing at which the azimuth angle becomes the maximum or minimum in FIG. Therefore, for example, when the difference (amplitude) between the maximum value and the minimum value of the combined acceleration is greater than or equal to the threshold value, the walking detection unit 240 walks every other intermediate timing when the combined acceleration becomes the minimum value. The period can be detected. For example, the gait detection unit 240 stores an intermediate value when the composite acceleration has recently changed from a minimum value to a maximum value (or from a maximum value to a minimum value), and when the intermediate acceleration exceeds the intermediate value (FIG. 9). You may detect a walk cycle every other time in (circle).

  The straight traveling determination unit 250 performs a process of determining whether or not the user is traveling straight ahead. For example, the straight traveling determination unit 250 may perform the straight traveling determination based on whether or not the change in the acceleration direction corrected by the bias removal unit 210 is within a certain range, or the speed direction calculated by the integration processing unit 220. The straight traveling determination may be performed based on the change or the change in the posture angle.

Coordinate transformation matrix calculation unit 260 at a predetermined timing in the gait cycle of the user, b frame coordinate transformation matrix C _m ^b between the m frame (an example of the first coordinate system) (an example of the second coordinate system) calculated performs processing for storing in the storage unit 30 the calculated coordinate conversion matrix C _m ^b were as coordinate transformation matrix 340 of FIG. Here, the predetermined timing is a timing at which the detection result of the inertial measurement unit 10 satisfies a predetermined condition, and specifically, a timing at which the walking detection unit 240 detects the walking cycle. In the present embodiment, the coordinate transformation matrix calculation unit 260 removes and integrates the gravitational acceleration from the detection result of the inertial measurement unit 10 (specifically, the acceleration corrected by the bias removal unit 210), and integrates the user in the b frame. Vb = (Vbx, Vby, Vbz) in the traveling direction is calculated. The coordinate transformation matrix calculation unit 260, based on the calculated velocity Vb, and calculates the coordinate transformation matrix C _m ^b as follows.

b frames at the rate Vb = (Vbx, Vby, Vbz ) speed Vm at m frames and = (Vmx, Vmy, Vmz) relationship with, as the equation (6) using the coordinate transformation matrix _C ^{m b} expressed. Since this is a straight line, Vmy = Vmz = 0 in equation (6).

From the equation (6), the roll angle φ _bm , the pitch angle θ _bm , and the yaw angle ψ _bm for the m frame of the inertial measurement unit 10 are expressed by the equation (7). In this embodiment, the roll angle φ _bm cannot be calculated, and the change in the roll angle φ _bm is sufficiently small with respect to the change in the yaw angle ψ _bm when the user walks. It approximates to φ _bm = 0.

Roll angle phi _bm of formula (7), the pitch angle theta _bm, using yaw angle [psi _bm, can be calculated coordinate transformation matrix _C ^{m b} by equation (8). In Expression (8), R _z (−ψ _bm ) is a rotation matrix that rotates about −z _bm around the z axis. R _y (−θ _bm ) is a rotation matrix that rotates about −y _bm around the y axis. R _x (−φ _bm ) is a rotation matrix that rotates about −x _bm around the x axis.

In the present embodiment, the coordinate transformation matrix calculation unit 260, the user calculates the coordinate transformation matrix C _m ^b using the detection result of the inertial measurement unit 10 in a predetermined time period from the start of walking. Here, the predetermined period is, for example, a period in which the user advances several steps, and the coordinate transformation matrix calculation unit 260 is one of the steps in which the walking detection unit 240 detects the walking cycle in several steps after the user starts walking. calculate the velocity Vb calculated coordinate transformation matrix C _m ^b at the timing, the storage unit 30. Thus, it is possible to by repeated integration of the acceleration is the accuracy of the speed Vb to suppress the decrease, it is possible to enhance the reliability of the coordinate transformation matrix C _m ^b.

Azimuth angle conversion unit 270, GPS data (detection result of the GPS unit 50) was obtained (updated) When using the coordinate transformation matrix _C ^{m b} in the storage unit 30 is stored as the coordinate transformation matrix 340 The azimuth angle of the user based on the GPS data (azimuth angle calculated from the GPS data) is converted to generate a reference azimuth angle. Specifically, the azimuth angle conversion unit 270 first calculates the azimuth angle in the traveling direction of the user using GPS data. For example, the azimuth angle conversion unit 270 may calculate the azimuth angle in the traveling direction from the direction of the speed included in the GPS data, or calculate the azimuth angle in the traveling direction from the two positions included in the two GPS data. Alternatively, when the GPS data includes azimuth information, the azimuth may be used as the azimuth of the traveling direction.

Assuming that the azimuth angle calculated from the GPS data is gpsYaw (ψ _nm ), the azimuth angle conversion unit 270 then calculates a coordinate conversion matrix C _n ^m from the n frame to the m frame by Expression (9).

Next, the azimuth angle conversion unit 270 uses the coordinate conversion matrix C _m ^b stored in the storage unit 30 and the coordinate conversion matrix C _n ^m calculated by the equation (9) to calculate the b frame from the n frame according to the equation (10). calculating the coordinate transformation matrix C _n ^b of the frame.

Then, the azimuth angle conversion unit 270, the transposed matrix _C ^{b n} coordinate transformation matrix _C ^{n b} calculated by the equation (10), the equation (11), and calculates the reference azimuth gpsYaw (ψ _nb).

The error estimation unit 230 then detects the timing (specifically, the timing when the walking detection unit 240 detects the walking cycle after the timing when the GPS data is obtained (the timing when the azimuth angle conversion unit 270 calculates the reference azimuth angle). Next, the state vector X is estimated using the extended Kalman filter at the timing when the walking cycle is detected next. However, when the error estimation unit 230 determines that the straight traveling determination unit 250 does not travel straight from the timing when the GPS data is obtained to the timing when the walking detection unit 240 detects the next walking cycle, The state vector X is not estimated using the extended Kalman filter at the timing when the walking cycle is detected. In this case, even if the reference azimuth is calculated, it is useless, so the azimuth conversion unit 270 is from the timing when the GPS data is obtained until the timing when the walking detection unit 240 detects the next walking cycle. In addition, only when the straight traveling determination unit 250 determines that the user is traveling straight, the reference azimuth may be calculated.

For example, the error estimating unit 230 calculates the observation vector Z and the observation matrix H as follows. First, the error estimation unit 230 calculates a coordinate transformation matrix from the b frame to the n frame from the coordinate transformation matrix (rotation matrix) C _b ^e from the b frame to the e frame calculated by the integration processing unit 220 using Equation (12). Calculate C _b ⁿ (rotation matrix). In the formula (12), C _e ⁿ is known because it is the coordinate transformation matrix to the n-frame from the e frame. E ^e is an alternating matrix of attitude errors of the e frame, and is expressed as in Expression (13).

Next, the error estimation unit 230 calculates the azimuth angle insYaw (ψ _nb ) of the n frames of the inertial measurement unit 10 from the coordinate transformation matrix (rotation matrix) C _b ⁿ calculated by the equation (12) according to the equation (14). calculate.

Expanding equation (14) and expressing C _b ⁿ (2,1) and C _b ⁿ (1,1) as misalignment errors (ε _x , ε _y , ε _z ), insYaw (ψ _nb ) is , Expressed as equation (15). _{N 1, n 2, n 3} , d 1, d 2, d 3 of the formula (15) is calculated by the equation (16).

Then, the error estimation unit 230 calculates the observation vector Z and the observation matrix H from the insYaw (ψ _nb ) and the reference azimuth angle gpsYaw (ψ _nb ) according to the equation (17). In Equation (17), O _1,3 is a zero matrix with 1 row and 3 columns, and O _1,9 is a zero matrix with 1 row and 9 columns. Each partial derivative of equation (17) is calculated as in equation (18).

The coordinate conversion unit 280 uses the b-frame to m-frame coordinate conversion information (coordinate conversion matrix C _b ^m ) calculated by the integration processing unit 220 to calculate the b-frame acceleration and angular velocity corrected by the bias removal unit 210, respectively. A coordinate conversion process is performed to convert the acceleration and angular velocity of m frames. Also, the coordinate conversion unit 280 uses the coordinate conversion information (coordinate conversion matrix C _e ^m ) from the e frame to the m frame calculated by the integration processing unit 220, and the speed and position of the e frame calculated by the integration processing unit 220. And a coordinate conversion process for converting the posture angle into the velocity, position, and posture angle of m frames, respectively.

The motion analysis unit 290 performs various calculations using the m-frame acceleration, angular velocity, speed, position, and posture angle after the coordinate conversion unit 280 performs coordinate conversion, analyzes the user's motion, and stores the motion analysis information 350. Generate the process. In the present embodiment, the motion analysis unit 290 includes information related to movement such as a movement path, movement speed, and movement time in a user's walk, a degree of forward tilt, a difference between left and right movements, propulsion efficiency, energy consumption, and energy efficiency. Information on evaluation index of walking movement such as, information on advice and guidance for better walking, warning information indicating that posture is bad (information for causing display device 3 to output warning display, warning sound, etc.), etc. The motion analysis information 350 including is generated.

  The processing unit 20 transmits the motion analysis information 350 to the display device 3, and the motion analysis information 350 is displayed on the display unit 170 of the display device 3 as text, an image, a figure, or the like, or from the sound output unit 180. Or buzzer sound. Basically, by displaying the motion analysis information 350 on the display unit 170, the user can check the display unit 170 when he / she wants to know the motion analysis information, and information (warning information) that the user wants to call attention. Etc.) at least as a sound, the user need not always walk while watching the display unit 170.

1-4. Processing Procedure FIG. 10 is a flowchart showing an example of a procedure of motion analysis processing by the processing unit 20 (an example of a motion analysis method). The processing unit 20 executes the motion analysis program 300 by executing the motion analysis program 300 stored in the storage unit 30, thereby executing the motion analysis process according to the procedure of the flowchart of FIG. 10.

  As shown in FIG. 10, when the processing unit 20 receives a measurement start command (Y in S1), first, the sensing data measured by the inertial measurement unit 10 and GPS Using the data, the initial posture, initial position, and initial bias are calculated (S2).

  Next, the processing unit 20 acquires sensing data from the inertial measurement unit 10, and adds the acquired sensing data to the sensing data table 310 (S3).

Next, the sensing processing section 20, (after estimating the acceleration bias b _a and angular velocity bias b _omega at S17, by using the acceleration bias b _a and angular velocity bias b _omega) by using the initial bias was acquired in S3 The correction is performed by removing the bias from the acceleration and angular velocity included in the data, and the sensing data table 310 is updated with the corrected acceleration and angular velocity (S4).

  Next, the processing unit 20 integrates the sensing data corrected in S4 to calculate the speed, position, and attitude angle, and adds calculation data including the calculated speed, position, and attitude angle to the calculation data table 330 (S5). ).

Next, when the GPS data is updated (Y in S6), the processing unit 20 calculates the azimuth angle gpsYaw (ψ _nm ) in the traveling direction from the GPS data (S7), and turns on the correction valid flag (S8). . On the other hand, when the GPS data is not updated (N in S6), the processing unit 20 does not perform the process of S7 and the process of S8.

Then, the processing unit 20 performs walking detecting (S9), when detecting a walking period (S10 of Y), if the coordinate transformation matrix _C ^{m b} prefabricated (already calculated) (S11 of Y), A straight traveling determination is performed (S12).

  If the processing unit 20 determines that the vehicle is not traveling straight (N of S13), the correction valid flag is turned off (S14).

Then, the processing unit 20, the correction effective flag is turned on (S15 of Y), using the coordinate transformation matrix _C ^{m b} calculates the reference azimuth _{gpsYaw (ψ nb) (S16)} .

Next, the processing unit 20 performs an error estimation process using the reference azimuth angle gpsYaw (ψ _nb ) calculated in S16 (S17), the velocity error δv ^e , the attitude angle error ε ^e , the acceleration bias b _a , and the angular velocity. estimating the bias b _omega and position error .delta.p ^e.

Then, the processing unit 20, the speed error .delta.v e estimated in ^S17, using the attitude angle error epsilon ^e and position error .delta.p ^e, speed, and corrects position and orientation angle of each corrected speed, position and attitude angle Thus, the calculation data table 330 is updated (S18).

  Moreover, the process part 20 does not perform the process of S11-S18, when a walk period is not detected (N of S10).

  Next, the processing unit 20 detects the sensing data (b frame acceleration and angular velocity) stored in the sensing data table 310 and the calculated data (e frame velocity, position, and attitude angle) stored in the calculation data table 330. Is converted to m frame acceleration, angular velocity, velocity, position and posture angle (S20). The processing unit 20 stores the acceleration, angular velocity, speed, position, and posture angle of the m frame in the storage unit 30 in time series.

If the correction valid flag is not on in S15 (N in S15), the processing unit 20 performs the process of S20 without performing the processes of S16 to S18. Further, in S11, if it is not already created a coordinate transformation matrix _C ^{m b} (already calculated) (S11 of N), the processing unit 20, creating a coordinate transformation matrix _C ^{m b} (calculated) and (S19), the processing step S20 I do.

  Next, the processing unit 20 analyzes the user's motion in real time using the acceleration, angular velocity, velocity, position, and posture angle of the m frame after the coordinate conversion in S20, and generates motion analysis information (S21). .

  Next, the processing unit 20 transmits the motion analysis information generated in S21 to the display device 3 (S22). The motion analysis information transmitted to the display device 3 is fed back in real time while the user is walking. In this specification, “real time” means that processing is started at the timing when information to be processed is acquired. Therefore, it includes the case where there is a certain time difference between the acquisition of information and the completion of processing.

  The processing unit 20 then receives a measurement stop command (N in S23 and N in S24) every time the sampling period Δt elapses after acquiring the previous sensing data (Y in S23), and after S3. Repeat the process. When a measurement stop command is received (Y in S24), the user performed using the m-frame acceleration, angular velocity, velocity, position, posture angle, and the analysis result of S21 that were coordinate-converted in S20 and stored in time series. The motion is analyzed, and motion analysis information is generated (S25). In S25, when the processing unit 20 receives the measurement stop command, the processing unit 20 may perform the motion analysis processing immediately, or may perform the motion analysis processing when the motion analysis command by the user's operation is received. Further, the processing unit 20 may transmit the motion analysis information generated in S25 to the display device 3, may transmit it to a device such as a personal computer or a smartphone, or may record it on a memory card.

  In FIG. 10, if the processing unit 20 does not receive a measurement start command (N of S1), the processing of S1 to S25 is not performed, but m frames of acceleration, angular velocity, speed, The process of S25 may be performed using the position and orientation angle and the analysis result of S21.

FIG. 11 is a diagram showing an example of the relationship between the azimuth angle of the inertial measurement unit 10 and the timing of processing by the processing unit 20 when the user goes straight ahead, with the horizontal axis representing time and the vertical axis representing azimuth. In FIG. 11, the azimuth angle insYaw (ψ _nb ) of the inertial measurement unit 10 when the walking cycle is detected is denoted by ○, and the azimuth angle gpsYaw (ψ _nm ) in the traveling direction calculated from the GPS data is denoted by □. doing.

11, after the user starts walking, at the timing when the walking detection unit 240 detects the first walking period (walking detection 1), the coordinate transformation matrix calculation unit 260 creates a coordinate transformation matrix C _m ^b (calculated) To do. In Figure 11, it is denoted azimuth insYaw the inertial measurement unit 10 when the coordinate transformation matrix _C ^{m b} is calculated ([psi _nb) at +. At the timing when the gait detection unit 240 next detects the gait cycle (gait detection 2), since the GPS data is not updated between the gait detection 1 and the gait detection 2, the error estimation unit 230 estimates the state vector X. No processing is performed (ie, speed, position, posture angle, acceleration bias and angular velocity bias are not corrected). At the timing when the walking detection unit 240 next detects the walking cycle (walking detection 3), the GPS data is updated between the walking detection 2 and the walking detection 3, and the update timing of the GPS data (GPS data update 1) Since the traveling direction does not change from walking to walking detection 3, the error estimation unit 230 performs the state vector X estimation process.

Similarly, at the timing when the walking detection unit 240 detects the next walking cycle (walking detection 4), the GPS data is updated between the walking detection 3 and the walking detection 4, and the update timing of the GPS data (GPS data) Since the traveling direction does not change from the update 2) to the walking detection 4, the error estimation unit 230 performs the state vector X estimation process. Difference between the azimuth angle insYaw (ψ _nb ) of the inertial measurement unit 10 in the walking detection 3 and the walking detection 4 and the azimuth angle gpsYaw (ψ _nm ) in the traveling direction in the GPS data update 1 (or GPS data update 2) They are both from substantially coincides with the difference between the azimuth angle of the walking detection 1 calculated coordinate transformation matrix C _m ^b, no recalculation of the coordinate transformation matrix C _m ^b is required.

If the user changes the direction of travel between GPS data update 1 and walk detection 3 (or between GPS data update 2 and walk detection 4), walk detection 3 (or walk detection 4) of the difference between the azimuth insYaw (ψ _nb) and GPS data update 1 (or GPS data update 2) traveling direction of the azimuth angle gpsYaw (ψ _nm) in the inertial measurement unit 10, calculates a coordinate transformation matrix _C ^{m b} The difference between these azimuths in the detected walking detection 1 does not coincide. Therefore, in such a case, the error estimation unit 230 does not perform the state vector X estimation process in the walking detection 3 (or the walking detection 4). However, since the azimuth angle of the inertial measurement unit 10 can swing substantially symmetrically about the traveling direction, even if the user changes the traveling direction, the azimuth angle and the traveling direction of the inertial measurement unit 10 before and after changing the traveling direction. The difference from the azimuth angle is not changed. Therefore, if the user changes the traveling direction and then goes straight again, the timing when the GPS cycle is first updated after the start of the straight traveling and the next walking cycle is detected is the same as before the user changes the traveling direction. it is possible to perform the estimation process of the state vector X without recalculating the transformation matrix C _m ^b.

1-5. Effect As described above, the present embodiment utilizes the fact that the difference between the azimuth angle of the inertial measurement unit 10 and the azimuth angle of the user's traveling direction is substantially constant at the timing of detecting the user's walking cycle. calculates a coordinate transformation matrix C _m ^b at the detection timing of the walking cycle, then at the detection timing of the next walking cycle obtained is GPS data, the detection result of the inertial measurement unit 10 using the coordinate transformation matrix C _m ^b The difference between the azimuth angle based on the azimuth angle and the azimuth angle based on the detection result of the GPS unit 50 is brought close to zero to estimate the error of the index representing the user state. Therefore, according to the present embodiment, even when the timing at which the detection result of the GPS unit 50 is obtained is indefinite, the error can be accurately estimated, and the coordinate transformation matrix C _m is obtained every time the detection result of the GPS unit 50 is obtained. Since it is not necessary to calculate ^b , the processing load can be reduced.

Further, according to this embodiment, the predetermined period after the user has started walking (e.g., a period of a few steps) so to calculate a coordinate transformation matrix C _m ^b using the detection result of the inertial measurement unit 10 in, It is possible to reduce the error of the coordinate transformation matrix caused by the integration error and improve the error estimation accuracy.

In addition, according to the present embodiment, when the user changes the traveling direction from the detection result of the GPS unit 50 to the detection timing of the next walking cycle, the inertial measurement unit 10 is detected at the detection timing of the walking cycle. the detected results to the azimuth angle based on the azimuth angle based on the detection result of the GPS unit 50, considering that becomes different from that during the calculation of the coordinate transformation matrix C _m ^b, estimate the error in this case Therefore, a decrease in error estimation accuracy can be suppressed.

  Further, according to the present embodiment, information such as the user's speed, position, posture angle, and the like can be accurately corrected using the accurately estimated error. Furthermore, according to the present embodiment, it is possible to analyze the user's walking motion with high accuracy using the information such as the user's speed, position, posture, etc. corrected with high accuracy.

2. The present invention is not limited to this embodiment, and various modifications can be made within the scope of the present invention. Hereinafter, modified examples will be described. In addition, about the structure same as the said embodiment, the same code | symbol is attached | subjected and description for the second time is abbreviate | omitted.

2-1. Sensor In the above embodiment, the acceleration sensor 12 and the angular velocity sensor 14 are built in the motion analysis apparatus 2 integrated as the inertial measurement unit 10, but the acceleration sensor 12 and the angular velocity sensor 14 are not integrated. Good. Alternatively, the acceleration sensor 12 and the angular velocity sensor 14 may be directly attached to the user without being built in the motion analysis apparatus 2. In any case, it is preferable to mount the two sensors so that their axes are parallel to each other. In addition, for example, any one of the sensor coordinate systems may be used as the b frame of the above embodiment, the other sensor coordinate system may be converted into the b frame, and the above embodiment may be applied.

  Further, in the above-described embodiment, the site where the sensor (the motion analysis apparatus 2 (IMU 10)) is attached to the user has been described as the waist, but it may be attached to a site other than the waist. A suitable wearing part is a user's trunk (parts other than limbs). However, it is not limited to the trunk, and may be worn on the user's head or feet other than the arms.

2-2. Walking detection In the above embodiment, the walking detection unit 240 detects the walking cycle using the combined acceleration of the triaxial acceleration detected by the inertial measurement unit 10, but the present invention is not limited to this. The walking cycle may be detected using the acceleration (z-axis (gravity direction axis) acceleration detected by the inertial measurement unit 10). In this case, for example, the walking detection unit 240 may detect the walking cycle once every two times when the z-axis acceleration is equal to or greater than the threshold value and reaches a maximum value, or the z-axis acceleration zero-crosses from positive to negative. The walking cycle may be detected once every two times of timing (or timing of zero crossing from negative to positive). Alternatively, the walking detection unit 240 calculates the vertical movement speed (z-axis speed) by integrating the vertical movement acceleration (z-axis acceleration), and uses the calculated vertical movement speed (z-axis speed). May be detected. In this case, for example, the walking detection unit 240 sets the walking cycle once every two times at a timing when the speed crosses the threshold value near the median value between the maximum value and the minimum value by increasing the value or by decreasing the value. It may be detected.

In the embodiment described above, the walking detection unit 240 detects the walking cycle every two steps (one step on the left and right), but detects the walking cycle for each step and sets a flag indicating whether the right foot or the left foot. It may be output. In this case, the coordinate transformation matrix calculation unit 260, during a few steps immediately after the start user's walking, the coordinate transformation matrix C _m ^b for the right foot and held by calculating only once at the timing of detecting a walking cycle of the right foot, the coordinate transformation matrix C _m ^b for the left foot at the timing of detecting a walking cycle of the left foot and holds the calculated only once. Then, every time a walking cycle is detected, the azimuth conversion unit 270 determines whether it is a right foot walking cycle or a left foot walking cycle according to the flag, and if it is a right foot walking cycle, a right foot coordinate conversion matrix C calculating the azimuth of the reference using _m ^b, the coordinate transformation matrix C _m ^b for the left foot, if the walking period of the left foot may calculate the azimuth angle of the reference used.

2-3. Reference Azimuth In the above-described embodiment, the azimuth conversion unit 270 calculates the reference azimuth using the detection result (GPS data) of the GPS unit 50. The reference azimuth angle may be calculated using signals from positioning satellites of the Global Navigation Satellite System (GNSS) or positioning satellites other than GNSS. For example, one or more satellite positioning systems such as WAAS (Wide Area Augmentation System), QZSS (Quasi Zenith Satellite System), GLONASS (GLObal NAvigation Satellite System), GALILEO, and BeiDou (BeiDou Navigation Satellite System) are used. May be. Further, an indoor positioning system (IMES: Indoor Messaging System) or the like may be used.

  In the above embodiment, the azimuth angle conversion unit 270 calculates the reference azimuth angle when the GPS data is acquired (updated), but when the GPS data is obtained, The reference azimuth angle may be calculated only when the positioning accuracy is equal to or higher than the reference value (when the DOP value is equal to or lower than the reference value).

Further, in the above embodiment, the coordinate transformation matrix calculation unit 260 calculates a coordinate transformation matrix C _m ^b to b frames from m frames, the azimuth angle conversion unit 270 references using the coordinate transformation matrix C _m ^b azimuth Although the angle gpsYaw (ψ _nb ) is calculated, the coordinate conversion matrix calculation unit 260 may calculate the coordinate conversion matrix C _b ^m (transposition matrix of C _m ^b ) from the b frame to the m frame. In this case, for example, the error estimation unit 230 uses the coordinate transformation matrix C _b ^m to change the azimuth angle insYaw (ψ _nb ) of the b frame based on the detection result of the inertial measurement unit 10 to the azimuth angle insYaw (ψ _nm ) of the m frame. ) (The error estimation unit 230 also functions as an azimuth angle conversion unit), and the azimuth angle gpsYaw (ψ _nm ) in the traveling direction calculated from the GPS data is used as a reference azimuth angle, insYaw (ψ _nm ) and gpsYaw ( What is necessary is just to make the difference with (ψ _nm ) the observation vector Z.

2-4. Error Estimation In the above-described embodiment, the error estimation unit 230 uses the velocity, posture angle, acceleration, angular velocity, and position as indices representing the user's state, and estimates an error of these indices using an extended Kalman filter. The error may be estimated using a part of the velocity, posture angle, acceleration, angular velocity, and position as an index representing the user's state. Alternatively, the error estimation unit 230 may estimate the error using an index (eg, movement distance) other than the speed, the posture angle, the acceleration, the angular velocity, and the position as an index representing the user state.

  In the above-described embodiment, the extended Kalman filter is used for error estimation by the error estimation unit 230, but other estimation means such as a particle filter or an H∞ (H infinity) filter may be used.

  In the above embodiment, when the GPS data is acquired (updated), the error estimation unit 230 performs the error estimation process at the timing when the next walking cycle is detected. However, the GPS data is obtained. In such a case, the error estimation process may be performed at the timing when the next walking cycle is detected only when the positioning accuracy is equal to or higher than the threshold.

Further, in the above embodiment, using an extended Kalman filter the difference between the azimuth angle insYaw based on the detection result of the inertial measurement unit 10 ([psi _nb) and azimuth gpsYaw (ψ _nb) based on GPS data as the observation vector Z While performing error estimation process, for example, instead of insYaw (ψ _nb), the difference between the geomagnetic sensor (first sensor for example) of the detection result based on azimuth and gpsYaw (ψ _nb) as an observation vector Z Also good. In this case, it may be calculated coordinate transformation matrix C _m ^b as b-frame coordinate system of the three-axis geomagnetic sensor. Similar to the above embodiments, it may be an acceleration sensor for calculating the coordinate transformation matrix C _m ^b, after calculating the coordinate transformation matrix C _m ^b, may be the acceleration sensor is stopped. In this way, it is possible to reduce power that is wasted in the acceleration sensor.

2-5. Others In the above embodiment, the integration processing unit 220 calculates the e-frame speed, position, and orientation angle, and the coordinate conversion unit 280 performs coordinate conversion to the m-frame speed, position, and orientation angle. The processing unit 220 may calculate the speed, position, and posture angle of m frames. In this case, the motion analysis unit 290 may perform the motion analysis process using the m-frame speed, position, and posture angle calculated by the integration processing unit 220. Therefore, the coordinate of the speed, position, and posture angle by the coordinate conversion unit 280 may be used. No conversion is required. Further, the error estimation unit 230 may perform error estimation using an extended Kalman filter using the speed, position, and attitude angle of m frames.

  In the above-described embodiment, the processing unit 20 generates motion analysis information such as image data, sound data, and text data. However, the present invention is not limited to this, and for example, the processing unit 20 includes propulsion efficiency and energy consumption. The processing unit 120 of the display device 3 that has received the calculation result may generate image data, audio data, text data (such as advice) according to the calculation result.

  Moreover, in said embodiment, although the process part 20 receives the measurement stop command, it analyzes the exercise | movement which the user performed and performs the process (S25 of FIG. 10) which produces | generates exercise | movement analysis information. This motion analysis process (post-processing) may not be performed by the processing unit 20. For example, the processing unit 20 may transmit various types of information stored in the storage unit 30 to devices such as a personal computer, a smartphone, and a network server, and these devices may perform motion analysis processing (post-processing).

  In the above embodiment, the display device 3 outputs the motion analysis information from the display unit 170 and the sound output unit 180. However, the display device 3 is not limited thereto. Various information may be output by vibrating the mechanism in various patterns.

  In the above embodiment, the GPS unit 50 is provided in the motion analysis device 2, but may be provided in the display device 3. In this case, the processing unit 120 of the display device 3 receives GPS data from the GPS unit 50 and transmits it to the motion analysis device 2 via the communication unit 140, and the processing unit 20 of the motion analysis device 2 performs GPS via the communication unit 40. Data may be received and the received GPS data may be added to the GPS data table 320.

  Further, in the above embodiment, the motion analysis device 2 and the display device 3 are separate, but a motion analysis device in which the motion analysis device 2 and the display device 3 are integrated may be used.

  In the above embodiment, the motion analysis device 2 is attached to the user. However, the present invention is not limited to this, and an inertial measurement unit (inertia sensor) or a GPS unit is attached to the user's torso, etc. The sensor) and the GPS unit may transmit the detection result to a portable information device such as a smartphone or a stationary information device such as a personal computer, and analyze the user's movement using the detection result received by these devices. . Alternatively, an inertial measurement unit (inertial sensor) or GPS unit mounted on the user's body or the like records the detection result on a recording medium such as a memory card, and the information medium such as a smartphone or a personal computer records the recording medium. The motion analysis processing may be performed by reading out the detection result from.

  Moreover, in said embodiment, although human walk was made into object, this invention is applicable not only to this but the walk of moving bodies, such as an animal and a walking robot, similarly. In addition, the present invention is not limited to walking, but includes various activities such as mountain climbing, trail running, skiing (including cross-country and ski jumping), snowboarding, swimming, cycling, skating, golf, tennis, baseball, rehabilitation, and the like. Can be applied to.

  Each embodiment and each modification mentioned above are examples, and are not limited to these. For example, it is possible to appropriately combine each embodiment and each modification.

  The present invention includes configurations that are substantially the same as the configurations described in the embodiments (for example, configurations that have the same functions, methods, and results, or configurations that have the same objects and effects). In addition, the invention includes a configuration in which a non-essential part of the configuration described in the embodiment is replaced. In addition, the present invention includes a configuration that exhibits the same operational effects as the configuration described in the embodiment or a configuration that can achieve the same object. Further, the invention includes a configuration in which a known technique is added to the configuration described in the embodiment.

DESCRIPTION OF SYMBOLS 1 Motion analysis system, 2 Motion analysis apparatus, 3 Display apparatus, 10 Inertial measurement unit (IMU), 12 Acceleration sensor, 14 Angular velocity sensor, 16 Signal processing part, 20 Processing part, 30 Storage part, 40 Communication part, 50 GPS unit , 120 processing unit, 130 storage unit, 140 communication unit, 150 operation unit, 160 timing unit, 170 display unit, 180 sound output unit, 210 bias removal unit, 220 integration processing unit, 230 error estimation unit, 240 walking detection unit, 250 rectilinear determination unit, 260 coordinate conversion matrix calculation unit, 270 azimuth angle conversion unit, 280 coordinate conversion unit, 290 motion analysis unit

Claims (11)

Coordinates between a first coordinate system based on the first sensor attached to the moving body and a second coordinate system based on the moving body at a predetermined timing in a cycle of movement involving movement of the moving body Calculating a transformation matrix;
When the detection result of the second sensor that receives the signal from the positioning satellite is obtained, the azimuth angle of the moving body based on the detection result of the first sensor and the second sensor using the coordinate transformation matrix Converting any one of the azimuth angles of the moving body based on the detection result of
At the predetermined timing after the timing when the detection result of the second sensor is obtained, the error of the index representing the state of the moving body is estimated using the difference between the converted azimuth angle and the other azimuth angle. And an error estimation method.   The error estimation according to claim 1, wherein a speed of the moving body in the first coordinate system is calculated using a detection result of the first sensor, and the coordinate transformation matrix is calculated based on the calculated speed. Method.   The error estimation method according to claim 1, wherein the predetermined timing is a timing at which a detection result of the first sensor satisfies a predetermined condition. Furthermore, using the detection result of the first sensor, detecting the cycle,
The error estimation method according to claim 1, wherein the predetermined timing is a timing at which the period is detected.   The error estimation method according to claim 1, wherein the coordinate transformation matrix is calculated using a detection result of the first sensor in a predetermined period after the moving body starts moving. And determining whether or not the moving object is traveling straight,
If it is determined that the moving body does not go straight between the timing at which the detection result of the second sensor is obtained and the next predetermined timing, the error is determined at the next predetermined timing. The error estimation method according to claim 1, wherein the error estimation method is not estimated.   The error estimation method according to claim 1, wherein the first sensor includes at least one of an acceleration sensor and an angular velocity sensor.   The error estimation method according to claim 1, wherein the first sensor is a geomagnetic sensor. Estimating the error using the error estimation method according to any one of claims 1 to 8,
Correcting the indicator using the estimated error;
Analyzing the motion of the moving body using the corrected index. Coordinates between a first coordinate system based on the first sensor attached to the moving body and a second coordinate system based on the moving body at a predetermined timing in a cycle of movement involving movement of the moving body A coordinate transformation matrix calculation unit for calculating a transformation matrix;
When the detection result of the second sensor that receives the signal from the positioning satellite is obtained, the azimuth angle of the moving body based on the detection result of the first sensor and the second sensor using the coordinate transformation matrix An azimuth angle conversion unit that converts any one of the azimuth angles of the moving body based on the detection result of
At the predetermined timing after the timing when the detection result of the second sensor is obtained, the error of the index representing the state of the moving body is estimated using the difference between the converted azimuth angle and the other azimuth angle. An error estimation unit. Coordinates between a first coordinate system based on the first sensor attached to the moving body and a second coordinate system based on the moving body at a predetermined timing in a cycle of movement involving movement of the moving body Calculating a transformation matrix;
When the detection result of the second sensor that receives the signal from the positioning satellite is obtained, the azimuth angle of the moving body based on the detection result of the first sensor and the second sensor using the coordinate transformation matrix Converting any one of the azimuth angles of the moving body based on the detection result of
At the predetermined timing after the timing when the detection result of the second sensor is obtained, the error of the index representing the state of the moving body is estimated using the difference between the converted azimuth angle and the other azimuth angle. A program that causes a computer to execute.

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