JP2018143536A - Motion analysis device, motion analysis system, motion analysis method, motion analysis program and display method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To solve a problem of the conventional display method which cannot enable a user to grasp the improvement in his/her running ability although he/she wishes to know the improvement in running distance in his/her target index value so as to gain motivation in improving running ability.SOLUTION: A motion analysis device according to the present application includes: a motion analysis part which analyses a plurality of types of motion information in user's running or walking per running or walking sections using a detection result of a sensor; and an output part which outputs a graph in which the plurality of types of motion information are shown in different coordinate axes per the running or walking sections.SELECTED DRAWING: Figure 49

Description

  The present invention relates to a motion analysis device, a motion analysis system, a motion analysis method, a motion analysis program, and a display method.

  Conventionally, there has been a service that presents various indexes such as a user's heart rate, contact time, and vertical movement during running as information related to running ability. The user uses these index values as a guide for improving the running form and a guide for improving the training item. For example, the user sets his / her own target value for each indicator and practice. Incidentally, the user of the display method disclosed in the cited document 1 can practice to improve his / her running while referring to an arbitrary index analyzed during running.

JP2015-1119833 A

  In general, long-distance runners can identify the distance they can travel (running ability) by comparing a specific indicator that they have selected with their target values, thereby improving their training motivation. It is. However, there has been a problem that it is difficult for a runner to objectively and intuitively grasp the improvement of his / her running ability only by confirming the index presented by the display method of cited document 1.

  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 forms or application examples.

[Application Example 1]
The motion analysis apparatus according to this application example uses a sensor detection result to analyze a plurality of types of motion information in a user's running or walking for each section of the running or walking, and the plurality of types of And an output unit that outputs a graph in which exercise information is expressed with different coordinate axes for each section of running or walking.

  The graph for each section represents a change in balance during running or walking of a plurality of types of exercise information. Therefore, users (users may be managers such as coaches and directors as well as users who run or walk) specify the changing points (time or distance) where the balance of multiple types of exercise information is lost. It is easy to identify a change point (time or distance) at which a plurality of types of exercise information has deteriorated as a whole. For example, the user can also determine that the running ability is higher as the time or distance from the start of running or walking to the change point is longer. For example, the user can objectively grasp that his / her running ability is “5 km” when the change point is a section of 5 km to 10 km. Further, for example, when the change point is a section of 50 minutes to 60 minutes, the user can objectively grasp that his / her running ability is “50 minutes”. The output timing may be during running or walking, or after running or walking. In addition, the graphs for each section may be output as individual graphs or may be output as the same graph.

[Application Example 2]
In the exercise analysis apparatus according to the application example, the plurality of types of exercise information include at least one of exercise information related to energy efficiency, exercise information related to energy loss, and exercise information related to a burden on the body. And

  Therefore, the user can grasp his / her running ability based on at least one transition among energy efficiency, energy loss, and burden on the body.

[Application Example 3]
In the motion analysis apparatus according to the application example described above, the plurality of types of motion information include at least one of motion information related to true landing, motion information related to propulsion efficiency, motion information related to landing brake amount, and motion information related to contact time. Including one.

  Accordingly, the user can grasp his / her running ability based on at least one transition among true landing, propulsion efficiency, landing brake amount, and contact time.

[Application Example 4]
In the motion analysis apparatus according to the application example, the graph is a radar chart in which the different coordinate axes are radially arranged.

  Therefore, the user can recognize the balance and quality of the plurality of types of exercise information in each section as a radar chart.

[Application Example 5]
The motion analysis system according to this application example includes the motion analysis device according to any application example and the sensor.

  The graph for each section represents a change in balance during running or walking of a plurality of types of exercise information. Therefore, the user specifies a change point (time or distance) at which the balance of the plurality of types of exercise information is lost, or specifies a change point (time or distance) at which the plurality of types of exercise information has deteriorated as a whole. Is easy. For example, the user can also determine that the running ability is higher as the time or distance from the start of running or walking to the change point is longer. For example, the user can objectively grasp that his / her running ability is “5 km” when the change point is a section of 5 km to 10 km. Further, for example, when the change point is a section of 50 minutes to 60 minutes, the user can objectively grasp that his / her running ability is “50 minutes”. The output timing may be during running or walking, or after running or walking. In addition, the graphs for each section may be output as individual graphs or may be output as the same graph.

[Application Example 6]
The motion analysis method according to this application example includes a step of analyzing a plurality of types of motion information in a user's running or walking using the detection result of the sensor for each section of the running or walking, and the plurality of types of motion information. And a step of outputting graphs representing different coordinate axes from each other for each of the running or walking sections.

The graph for each section represents a change in balance during running or walking of a plurality of types of exercise information. Therefore, the user specifies a change point (time or distance) at which the balance of the plurality of types of exercise information is lost, or specifies a change point (time or distance) at which the plurality of types of exercise information has deteriorated as a whole. Is easy. For example, the user can also determine that the running ability is higher as the time or distance from the start of running or walking to the change point is longer. For example, the user can objectively grasp that his / her running ability is “5 km” when the change point is a section of 5 km to 10 km. Further, for example, when the change point is a section of 50 minutes to 60 minutes, the user can objectively grasp that his / her running ability is “50 minutes”. The output timing may be during running or walking, or after running or walking. In addition, the graphs for each section may be output as individual graphs or may be output as the same graph.

[Application Example 7]
The motion analysis program according to this application example uses the detection result of the sensor to analyze a plurality of types of motion information in a user's running or walking for each section of the running or walking, and the plurality of types of motion information. And a step of outputting, for each section of the running or walking, a computer that expresses the graph with different coordinate axes.

  The graph for each section represents a change in balance during running or walking of a plurality of types of exercise information. Therefore, the user specifies a change point (time or distance) at which the balance of the plurality of types of exercise information is lost, or specifies a change point (time or distance) at which the plurality of types of exercise information has deteriorated as a whole. Is easy. For example, the user can also determine that the running ability is higher as the time or distance from the start of running or walking to the change point is longer. For example, the user can objectively grasp that his / her running ability is “5 km” when the change point is a section of 5 km to 10 km. Further, for example, when the change point is a section of 50 minutes to 60 minutes, the user can objectively grasp that his / her running ability is “50 minutes”. The output timing may be during running or walking, or after running or walking. In addition, the graphs for each section may be output as individual graphs or may be output as the same graph.

[Application Example 8]
The display method according to this application example uses the detection result of the sensor to analyze a plurality of types of exercise information in a user's running or walking for each section of the running or walking, and the plurality of types of exercise information are different from each other. A graph represented by coordinate axes is displayed for each section of the running or walking.

  The graph for each section represents a change in balance during running or walking of a plurality of types of exercise information. Therefore, the user specifies a change point (time or distance) at which the balance of the plurality of types of exercise information is lost, or specifies a change point (time or distance) at which the plurality of types of exercise information has deteriorated as a whole. Is easy. For example, the user can also determine that the running ability is higher as the time or distance from the start of running or walking to the change point is longer. For example, the user can objectively grasp that his / her running ability is “5 km” when the change point is a section of 5 km to 10 km. Further, for example, when the change point is a section of 50 minutes to 60 minutes, the user can objectively grasp that his / her running ability is “50 minutes”. The output timing may be during running or walking, or after running or walking. In addition, the graphs for each section may be output as individual graphs or may be output as the same graph.

Explanatory drawing about the outline | summary of the exercise | movement analysis system which concerns on 1st Embodiment. The functional block diagram which shows the structural example of the motion-analysis apparatus and alerting | reporting apparatus which concern on 1st Embodiment. 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 geomagnetic 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 the motion analysis apparatus which concerns on 1st Embodiment. The functional block diagram which shows the structural example of the exercise | movement analysis part which concerns on 1st Embodiment. The graph which shows the relationship between the travel distance at the time of a user's driving | running | working, and contact time. Explanatory drawing of the determination method of the timing of landing and takeoff (kicking-out). Explanatory drawing of the determination method of the timing of depression. The figure which shows the relationship between input information and analysis information. The figure which shows an example of a traveling direction acceleration, a vertical direction acceleration, and a horizontal direction acceleration. The figure which shows an example of the advancing direction speed, the up-down direction speed, and the left-right direction speed. The figure which shows an example of a roll direction angular velocity, a pitch direction angular velocity, and a yaw direction angular velocity. The figure which shows an example of a roll angle, a pitch angle, and a yaw angle. The figure which shows an example of the advancing direction distance, the up-down direction distance, and the left-right direction distance. Explanatory drawing of the calculation method of impact time. Explanatory drawing of the calculation method of the brake amount 1 at the time of landing. Explanatory drawing of the calculation method of the brake amount 2 at the time of landing. Explanatory drawing of the calculation method of the true under landing rate 1. Explanatory drawing of the calculation method of the true under landing rate 2. FIG. Explanatory drawing of the calculation method of the true under landing rate 3. Explanatory drawing of the calculation method of the driving force 1. FIG. Explanatory drawing of the calculation method of the driving force 2. FIG. Explanatory drawing of the calculation method of propulsion efficiency 1. FIG. Explanatory drawing of the calculation method of the propulsion efficiency 2. FIG. Explanatory drawing of the calculation method of the propulsion efficiency 3. FIG. Explanatory drawing of a forward tilt angle. The figure which shows an example of the relationship between the rotation timing of a waist, and the timing of kicking. The figure which shows an example of the relationship between the rotation timing of a waist, and the timing of kicking. The figure which shows an example of the screen displayed during a user's driving | running | working. The figure which shows an example of the screen displayed during a user's driving | running | working. The figure which shows an example of the screen displayed during a user's driving | running | working. The figure which shows an example of the screen displayed during a user's driving | running | working. The figure which shows an example of a whole analysis screen. The figure which shows an example of a detailed analysis screen. The figure which shows an example of a detailed analysis screen. The figure which shows an example of a detailed analysis screen. The figure which shows an example of a comparative analysis screen. The figure which shows an example of a comparative analysis screen. The flowchart figure which shows an example of the procedure of an exercise | movement analysis process. The flowchart figure which shows an example of the procedure of an inertial navigation calculation process. The flowchart figure which shows an example of the procedure of a driving | running | working detection process. The flowchart figure which shows an example of the procedure of exercise | movement analysis information generation processing. The flowchart figure which shows an example of the procedure of a driving | running | working analysis process. An example of a radar chart according to the second embodiment. The other example of the radar chart which concerns on 2nd Embodiment. The flowchart figure which shows an example of the process which concerns on 2nd Embodiment. An example of a radar chart according to the third embodiment. An example of a radar chart according to the third embodiment. An example of the system concerning a 4th embodiment. An example of a radar chart according to the fourth embodiment.

  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. 1. First embodiment 1-1. Overview of Motion Analysis System FIG. 1 is an explanatory diagram of an overview of a motion analysis system 1 according to the first embodiment. As shown in FIG. 1, the motion analysis system 1 according to the first embodiment includes a motion analysis device 2 and a notification device 3 as a display device. The motion analysis apparatus 2 is attached to a user's torso (for example, right waist, left waist, or center of waist). The motion analysis apparatus 2 includes an inertial measurement unit (IMU) 10 and captures the motion of the user's running (including walking) to determine the speed, position, posture angle (roll angle, pitch angle, yaw angle). ), Etc., and further analyze the user's movement, calculate the user's driving state (various driving information), detect the changing point of the driving state, and analyze the movement that is the driving information including the changing point of the driving state Generate 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 to the user. The motion analysis device 2 transmits at least a part of the generated motion analysis information to the notification device 3. The traveling state in the present embodiment is various pieces of traveling information such as input information, basic information, first analysis information, second analysis information, left / right difference rate, and travel locus information, which will be described later.

  The notification device 3 is a wrist-type (wristwatch-type) portable information device and is worn on the wrist of a user. However, the notification 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 end of measurement (inertial navigation calculation processing and motion analysis processing described later) by the motion analysis device 2 by operating the notification device 3 before or during the travel start. In addition, the user can instruct the start and end of travel analysis processing (described later) by the motion analysis device 2 by operating the notification device 3 after the travel is completed. The notification device 3 transmits, to the motion analysis device 2, a command for instructing measurement start and measurement end, a command for instructing start and end of travel analysis processing, and the like.

  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 travel state based on the measurement result, and is travel information including a change point of the travel state. Generate motion analysis information. The motion analysis device 2 transmits the generated motion analysis information to the notification device 3. The notification device 3 receives the motion analysis information, and presents the received motion analysis information to the user in various forms such as characters, figures, sounds, and vibrations. The user can recognize the motion analysis information via the notification device 3 while traveling.

  Further, when the motion analysis device 2 receives a command for instructing the start of the travel analysis process, the motion analysis device 2 analyzes the past travel using the motion analysis information generated during the past travel, and notifies the analysis result information. 3 or an information device such as a personal computer or a smartphone (not shown). And the alerting | reporting apparatus 3 or the said information apparatus receives the information of an analysis result, and shows the received exercise | movement analysis information to a user with various forms, such as a character, a figure, a sound, and a vibration. The user can recognize the analysis result of the past traveling through the notification device 3 or the information device.

  Note that data communication between the motion analysis device 2 and the notification device 3 may be wireless communication or wired communication.

In the present embodiment, in the following, the motion analysis device 2 performs the user's running motion (running).
The motion analysis information in the present embodiment will be described in detail as an example, but the motion analysis system 1 of the present embodiment can be similarly applied to the case of generating motion analysis information in motion other than running. it can.

1-2. Coordinate system The coordinate system required in the following description is defined.

  ・ E-frame (Earth Centered Earth Fixed Frame): Right-handed 3D Cartesian coordinates with the center of the earth as the origin and the z-axis parallel to the rotation axis.

  N frame (Navigation Frame): A three-dimensional orthogonal coordinate system in which the moving body (user) is the origin, the x-axis is north, the y-axis is east, and the z-axis is the gravitational direction.

  B frame (Body Frame): a three-dimensional orthogonal coordinate system based on a sensor (inertial measurement unit (IMU) 10).

  M frame (Moving Frame): A right-handed three-dimensional orthogonal 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. Configuration of Motion Analysis System FIG. 2 is a functional block diagram showing a configuration example of the motion analysis device 2 and the notification device 3 according to the first embodiment. As shown in FIG. 2, the motion analysis apparatus 2 includes an inertial measurement unit (IMU) 10, a processing unit 20, a storage unit 30, a communication unit 40 as an output unit, a GPS (Global Positioning System) unit 50, and a geomagnetic sensor 60. It is comprised including. 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 an inertial 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 stored acceleration data, angular velocity data, and time information. Is generated in accordance with a predetermined format 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 receives a GPS satellite signal transmitted from a GPS satellite which is a kind of positioning satellite, performs a positioning calculation using the GPS satellite signal, and positions and speeds (size and direction) of the user in n frames. Vector) and GPS data with time information and positioning accuracy information added thereto are 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 geomagnetic sensor 60 detects each geomagnetism in the three-axis directions intersecting each other (ideally orthogonally), and outputs a digital signal (geomagnetic data) corresponding to the detected magnitude and direction of the three-axis geomagnetism. However, the geomagnetic sensor 60 may output an analog signal. In this case, the processing unit 20 may A / D convert the output signal of the geomagnetic sensor 60 to generate geomagnetic data.

  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, GPS data, and geomagnetic data from the inertial measurement unit 10, the GPS unit 50, and the geomagnetic sensor 60, respectively, and uses these data to determine the speed, position, posture angle, etc. of the user. calculate. In addition, the processing unit 20 performs various arithmetic processes using the calculated information to analyze the user's motion, and generates various motion analysis information described later. Then, the processing unit 20 transmits a part of the generated motion analysis information (output information during travel and output information after travel described later) to the notification device 3 via the communication unit 40, and the notification device 3 receives the received motion. The analysis information is output in the form of text, image, sound, vibration and the like.

  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 for executing a motion analysis process (see FIG. 42). The motion analysis program 300 includes an inertial navigation calculation program 302 for executing inertial navigation calculation processing (see FIG. 43), a motion analysis information generation program 304 for executing motion analysis information generation processing (see FIG. 45), and travel analysis. A travel analysis program 306 for executing the processing (see FIG. 46) is included as a subroutine.

  The storage unit 30 also stores a sensing data table 310, a GPS data table 320, a geomagnetic data table 330, a calculation data table 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 is started, the processing unit 20 adds new sensing data to the sensing data table 310 every time a sampling period Δt (for example, 20 ms or 10 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 (for example, every second, asynchronously with sensing data acquisition timing). .

  The geomagnetic data table 330 is a data table that stores the geomagnetic data (the detection result of the geomagnetic sensor) received by the processing unit 20 from the geomagnetic sensor 60 in time series. FIG. 5 is a diagram illustrating a configuration example of the geomagnetic data table 330. As shown in FIG. 5, the geomagnetic data table 330 is configured by arranging in time series geomagnetic data in which the detection time 331 of the geomagnetic sensor 60 and the geomagnetism 332 detected by the geomagnetic sensor 60 are associated with each other. When the measurement is started, the processing unit 20 adds new geomagnetic data to the geomagnetic data table 330 every time a sampling period Δt (for example, 10 ms) elapses.

  The calculated data table 340 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. 6 is a diagram illustrating a configuration example of the calculation data table 340. As shown in FIG. 6, the calculation data table 340 includes calculation data in which time 341, speed 342, position 343, and attitude angle 344 calculated by the processing unit 20 are associated with each other in time series. When measurement is started, the processing unit 20 calculates 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 340. Is added. 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 position are corrected. And the calculation data table 340 is updated by overwriting the posture angle.

  The exercise analysis information 350 is a running state related to the user's exercise, and each item of the input information 351, each item of the basic information 352, each item of the first analysis information 353, and second analysis information 354 generated by the processing unit 20. Each item, each item of the left / right difference rate 355, travel locus information 356, and the like. Details of these various types of information will be described later.

Returning to FIG. The communication unit 40 serving as an output unit performs data communication with the communication unit 140 of the notification device 3, and a part of the motion analysis information generated by the processing unit 20 (travel output information and travel information described later). (Post-output information) received and transmitted to the notification device 3, commands transmitted from the notification device 3 (measurement start / end commands, running analysis processing start / end commands, etc.)
Is received and sent to the processing unit 20.

  The notification device 3 as a display device 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, a sound output unit 180, and a vibration unit 190. However, the notification 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 is configured by, for example, a CPU, a DSP, an ASIC, and the like, and performs various arithmetic processes and control processes according to a program stored in the storage unit 130. For example, the processing unit 120 responds to various types of processing according to the operation data received from the operation unit 150 (processing for sending a measurement start / end command or start / end command for running analysis processing to the communication unit 140 and operation data. Display processing, sound output processing, etc.) and processing for receiving output information during traveling and output information after traveling from the communication unit 140 are performed. Moreover, the process which sends the text data and image data according to output information during driving | running | working and output information after driving | running | working to the display part 170 is performed. Moreover, the process which sends the sound data according to the output information during driving | running | working and the output information after driving | running | working to the sound output part 180, and the process which sends the vibration data according to output information during driving | running | working to the vibration part 190 are performed. Further, the processing unit 120 performs processing for generating time image data corresponding to the time information received from the time measuring unit 160 and sending the time image data to the display unit 170.

  The storage unit 130 is, for example, a recording medium such as a ROM or flash ROM, a hard disk, or a memory card that stores programs and data for the processing unit 120 to perform various processes, a RAM that is a work area of the processing unit 120, and the like ( For example, it is composed of various IC memories).

  The communication unit 140 performs data communication with the communication unit 40 of the motion analysis apparatus 2, and commands (measurement start / end commands and start / end of running analysis processing) corresponding to the operation data from the processing unit 120. Processing to receive and transmit to the communication unit 40 of the motion analysis device 2, the output information during traveling and the output information after traveling transmitted from the communication unit 40 of the motion analysis device 2 are received by the processing unit 120. Processing to send is performed.

  The operation unit 150 performs a process of acquiring operation data (operation data such as measurement start / end and display content selection) from the user and sending it 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.

The vibration unit 190 vibrates according to the vibration data sent from the processing unit 120. This vibration is transmitted to the notification device 3, and the user wearing the notification device 3 can feel the vibration.
The vibration unit 190 is realized by, for example, a vibration motor.

1-4. Functional Configuration of Processing Unit FIG. 7 is a functional block diagram illustrating a configuration example of the processing unit 20 of the motion analysis apparatus 2 according to the first embodiment. In the present embodiment, the processing unit 20 functions as the inertial navigation calculation unit 22 and the motion analysis unit 24 by executing the motion analysis program 300 stored in the storage unit 30.

  The inertial navigation calculation unit 22 performs inertial navigation calculation using sensing data (detection result of the inertial measurement unit 10), GPS data (detection result of the GPS unit 50), and geomagnetic data (detection result of the geomagnetic sensor 60), Acceleration, angular velocity, speed, position, posture angle, distance, stride, and running pitch are calculated, and calculation data including these calculation results is output. Calculation data output by the inertial navigation calculation unit 22 is stored in the storage unit 30.

  The motion analysis unit 24 analyzes the user's motion using the calculation data output from the inertial navigation calculation unit 22 (calculation data stored in the storage unit 30), and obtains the user's running results (an example of the exercise results). Each of a plurality of pieces of exercise information for improvement (each item of input information 351, each item of basic information 352, each item of first analysis information 353, each item of second analysis information 354, left-right difference ratio 355) Items, travel locus information 356, travel state change points, and the like). The running results may be, for example, running ability, a score (score) such as time, difficulty in injury, and the like. The motion analysis unit 24 further generates running output information that is output while the user is running using one or more items of the plurality of pieces of motion information. The motion analysis information including the plurality of motion information is stored in the storage unit 30, and the motion analysis unit 24 performs a travel analysis process using the motion analysis information after the end of the user's travel, and outputs after the travel is completed. Generate information. Details of the motion analysis unit 24 will be described later.

1-6. Functional Configuration of Motion Analysis Unit FIG. 8 is a functional block diagram illustrating a configuration example of the motion analysis unit 24 according to the first embodiment. In the present embodiment, the motion analysis unit 24 includes a feature point detection unit 260, a contact time / impact time calculation unit 262, a motion information generation unit 270 as a travel state calculation unit, a traveling output information generation unit 280, and a travel analysis unit. 290. However, the motion analysis unit 24 of the present embodiment may have a configuration in which some of these components are deleted or changed, or other components are added.

  The feature point detection unit 260 performs a process of detecting feature points in the user's running motion using the calculation data. The feature point in the user's running motion is a data portion corresponding to the feature portion of the user's running motion. For example, landing (when part of the sole touches the ground, when the entire sole of the foot touches the ground, when the heel arrives from the toe of the foot at any time while the heel of the foot leaves and the toe leaves) It may be set as appropriate at any time while leaving, while the entire sole of the foot is worn, etc., stepping on (the weight is the most on the foot), takeoff (also called kicking) When any part of the back is removed, when the entire sole of the foot is off the ground, any point in time when the toes are released from the heel of the foot, any point in time after the toes are released from the toes of the foot, etc. May be set as appropriate). Specifically, the feature point detection unit 260 separately detects the feature point in the right foot travel cycle and the feature point in the left foot travel cycle, using the left and right foot flags included in the calculation data. For example, the feature point detection unit 260 detects landing at the timing when the vertical acceleration (detected value of the z-axis of the acceleration sensor 12) changes from a positive value to a negative value, and after landing, the vertical acceleration is in a negative direction. It is possible to detect the depression when the traveling direction acceleration reaches a peak after the peak, and to detect the takeoff (kicking out) when the vertical acceleration changes from a negative value to a positive value.

The contact time / impact time calculation unit 262 performs a process of calculating each value of the contact time and the impact time using the calculation data with reference to the timing when the feature point detection unit 260 detects the feature point. Specifically, the contact time / impact time calculation unit 262 determines whether the current calculation data is the calculation data of the right foot travel cycle or the left foot travel cycle from the left and right foot flags included in the calculation data, Based on the timing at which the feature point detection unit 260 detects the feature point, each value of the contact time and the impact time is calculated separately for the right foot travel cycle and the left foot travel cycle. Details of the definition and calculation method of the contact time and impact time will be described later.

  The exercise information generation unit 270 as a running state calculation unit includes a travel locus calculation unit 271, a basic information generation unit 272, a first analysis information generation unit 273, a second analysis information generation unit 274, a left / right difference rate calculation unit 275, and a detection. Part 77 is included. The exercise information generation unit 270 analyzes a user's exercise using a part of the calculation data and the input information 351, calculates a user's running state, and generates a plurality of exercise information (running information); And a process for detecting a change point of the running state calculated in order to detect a change in the condition of the vehicle. Here, the input information 351 is information input to the first analysis information generation unit 273, and includes travel pitch, stride, m-frame acceleration in three axes, three-axis angular velocity, Each item includes an axial speed, a distance in the three-axis direction, a posture angle around the three axes, and the weight of the user. Specifically, the exercise information generation unit 270 analyzes the user's exercise using the input information 351, and as exercise information, each item of the basic information 352, each item of the first analysis information 353, and second analysis information. Each item of 354, each item of the information of the right / left difference ratio 355, the process of generating the travel locus information 356, and the like, and the process of detecting the change point of the generated exercise information are performed.

  The travel locus calculation unit 271 calculates the user's travel locus in the n frame using time series information of the position of the n frame included in the calculation data, and generates the travel locus information 356 that is one piece of exercise information. I do.

  The basic information generation unit 272 performs processing for generating basic information related to the user's movement using information on acceleration, speed, position, stride, and travel pitch included in the calculation data. Here, the basic information includes items of travel pitch, stride, travel speed, altitude, travel distance, and travel time (lap time). Each item of basic information is one piece of exercise information. Specifically, the basic information generation unit 272 outputs the traveling pitch and stride included in the calculation data as the traveling pitch and stride of the basic information, respectively. In addition, the basic information generation unit 272 uses the acceleration, speed, position, travel pitch, and part or all of the stride included in the calculation data to determine the current travel speed, altitude, travel distance, and travel time (lap time). Exercise information such as values and average values during running is calculated.

  The first analysis information generation unit 273 uses the input information 351 described above to perform a process of analyzing the user's motion based on the timing at which the feature point detection unit 260 detects the feature point, and generating first analysis information. . Here, the first analysis information includes the landing brake amount (landing brake amount 1, landing brake amount 2), true under landing rate (true under landing rate 1, true under landing rate 2, true under landing rate 3), propulsive force ( Propulsion 1, Propulsion 2), Propulsion efficiency (Propulsion efficiency 1, Propulsion efficiency 2, Propulsion efficiency 3, Propulsion efficiency 4), Energy consumption, Landing impact, Running ability, Forward tilt, and Timing agreement Including. Each item of the first analysis information is an item representing a user's running state (an example of an exercise state), and is one piece of running information. Details of each item of the first analysis information and details of the calculation method will be described later.

In the present embodiment, the first analysis information generation unit 273 calculates values of some items of the first analysis information 353 using the input information 351 at the timing when the feature point detection unit 260 detects the feature points. In addition, the first analysis information generation unit 273 detects a feature point from the time when the feature point detection unit 260 detects a feature point until the next feature point is detected (for example, between two same feature points (for example, from landing to the next landing). At least a part of the first analysis information 353 using the input information 351 at a timing between two different feature points (for example, between landing and takeoff). Is calculated.

  The first analysis information generation unit 273 calculates the value of each item of the first analysis information 353 separately on the left and right sides of the user's body. Specifically, the first analysis information generation unit 273 determines the first analysis information according to whether the feature point detection unit 260 detects a feature point in the right foot travel cycle or a feature point in the left foot travel cycle. Each item included in 353 is calculated separately for the right foot travel cycle and the left foot travel cycle. In addition, the first analysis information generation unit 273 also calculates left and right average values or total values for each item included in the first analysis information 353.

  The second analysis information generation unit 274 performs a process of generating the second analysis information 354 using the first analysis information 353 generated by the first analysis information generation unit 273. Here, the second analysis information 354 includes items of energy loss, energy efficiency, and burden on the body. Each item of the second analysis information 354 is one piece of exercise information. Details of each item of the second analysis information 354 and details of the calculation method will be described later. The second analysis information generation unit 274 calculates the value of each item of the second analysis information 354 separately for the right foot travel cycle and the left foot travel cycle. In addition, the second analysis information generation unit 274 also calculates the left and right average values or total values for each item included in the second analysis information 354.

  The left / right difference rate calculation unit 275 includes the right foot of the travel pitch, stride, contact time and impact time, all items of the first analysis information 353, and all items of the second analysis information 354 included in the input information 351. Using the value in the running cycle and the value in the running cycle of the left foot, a process of calculating a left / right difference rate that is an index indicating the left / right balance of the user's body is performed. The left / right difference rate 355 of each item is one piece of exercise information. Details of the right / left difference ratio and details of the calculation method will be described later.

  The detection unit 277 is calculated by the input information 351 such as the contact time and impact time calculated by the contact time / impact time calculation unit 262, the travel locus information 356 calculated by the travel locus calculation unit 271, and the basic information generation unit 272. Calculated by the basic information 352, the first analysis information 353 calculated by the first analysis information generation unit 273, the second analysis information 354 calculated by the second analysis information generation unit 274, and the right / left difference ratio calculation unit 275. The change point of the left / right difference rate 355 is detected. Here, the change point of the running state will be described by taking the contact time as an example. FIG. 9 is a graph showing the relationship between the travel distance and the contact time when the user travels. In FIG. 9, it is determined that the travel is not stable from the start of travel (0 km) to the travel distance of 2 km, and the point where the contact time changes from the travel distance of 2 km where the travel is stable is detected except for the measurement target. As a result, the average contact time was approximately 0.18 sec until the mileage near 5.3 km, while the contact time became longer than 0.18 sec after the mileage near 5.3 km and showed a tendency to increase. ing. Therefore, it can be determined that the travel distance of about 5.3 km is the changing point. An increase in the contact time during running means that the leg power for kicking the ground has deteriorated due to fatigue or the like, and the condition of the user has deteriorated. Therefore, detecting the change point of the running state including the contact time detects the change point at which the user's condition starts to deteriorate. In the embodiment, the change point of the travel state with respect to the travel distance is detected. However, the present invention is not limited to this, and the change point of the travel state with respect to the travel time may be detected.

  The traveling output information generation unit 280 includes items of travel locus information 356 and basic information 352, items of input information 351, items of first analysis information 353, items of second analysis information 354, and right / left difference ratio 355. Using the plurality of pieces of exercise information including each item, the change point of the running state, and the like, a process of generating running output information that is information output during the running of the user is performed.

  The running output information generation unit 280 may generate running output information based on at least one exercise information that satisfies a predetermined condition among the plurality of exercise information. The predetermined condition may be that the user's running state is better than the reference, or the user's running state is worse than the reference. For example, the traveling output information generation unit 280 may output only the best item as the traveling output information, or may output only the worst item. Alternatively, each item may be evaluated in stages, and only the item with the highest evaluation (for example, rank 1 among ranks 1 to 5) may be output as the running output information. Only items with a low evaluation (for example, rank 5 among ranks 1 to 5) may be output. Further, the traveling output information generation unit 280 includes, as traveling output information, evaluation information for evaluating the user's traveling state (evaluated in stages, etc.), advice for improving the user's traveling results, or the user's traveling state It may also contain advice information on advice to improve.

  For example, the traveling output information generation unit 280 performs the propulsion when the value of the propulsion efficiency included in the first analysis information satisfies a predetermined condition (being within the reference range or outside the reference range). In-travel output information including information for notifying that the numerical value of efficiency and propulsion efficiency are higher (or lower) than the reference value is generated. Alternatively, traveling output information including evaluation information indicating that the propulsion efficiency is high and advice information for improving (improving) the propulsion efficiency may be generated.

  Further, the traveling output information generation unit 280 may use a part or all of these various types of information as they are or may be processed to obtain the traveling output information, or a combination of these various types of information may be output during traveling. Information may be generated.

  The processing unit 20 transmits the output information during traveling to the notification device 3, and the notification device 3 receives the output information during traveling and generates corresponding data such as image, sound, vibration, etc. It is presented (transmitted) to the user via 180 and the vibration unit 190.

  The travel analysis unit 290 includes an overall analysis unit 291, a detailed analysis unit 292, a comparison analysis unit 293, and an output information selection unit 294. The travel analysis unit 290 includes a plurality of pieces of motion information stored in the storage unit 30 (running track information 356, items of basic information 352, items of input information 351, items of first analysis information 353, and second analysis. Processing for generating post-travel output information, which is information to be output after the user finishes traveling, based on at least one piece of motion information of each item of information 354, left-right difference ratio 355 of each item, change point of travel state, etc.) Do.

  The overall analysis unit 291 uses the various types of exercise information stored in the storage unit 30 to analyze the user's past travels as a whole (schematic analysis), and obtains the overall analysis information that is analysis result information. Generate the process. Specifically, the overall analysis unit 291 calculates an average value calculation process, a final value selection process at the end of the run, and the values for some or all of various exercise information in the run on the date selected by the user. Whether or not the reference value is better (or worse) or whether the improvement rate is higher (or lower) than the reference value is determined. Further, the overall analysis unit 291 calculates (or selects) an average value (or final value) for each travel date for a predetermined item determined in advance or an item selected by the user, and generates time-series data Etc. Further, the overall analysis unit 291 performs processing for selecting the travel locus information 356 in traveling on the date selected by the user.

The detailed analysis unit 292 uses the various types of exercise information stored in the storage unit 30 to perform a detailed analysis of the user's past travel and generate detailed analysis information that is analysis result information. Specifically, the detailed analysis unit 292 selects a value of some or all items of various types of exercise information at the time selected by the user for the date selected by the user or the item selected by the user. Performs processing to generate time-series data. Further, the detailed analysis unit 292 selects the travel locus information 356 in the travel on the date selected by the user, calculates the travel position at the time selected by the user, and calculates the left / right difference rate for a predetermined item or the item selected by the user. Processing for calculating time-series data is performed. In addition, the detailed analysis unit 292 (an example of an advice information generation unit) evaluates the driving performance in the driving on the date selected by the user, information on the evaluation result, how to improve the driving method, how to shorten the time, training guidance, etc. Processing to generate information related to the advice of

  The comparative analysis unit 293 compares and analyzes the user's past driving results using various types of exercise information stored in the storage unit 30, or the user's past driving results to other users. For example, a process of generating comparison analysis information that is analysis result information. Specifically, the comparison analysis unit 293 generates comparison analysis information similar to the detailed analysis information for each of a plurality of dates selected by the user, or the date selected by the user and other dates. A process of generating comparative analysis information similar to the detailed analysis information is performed for each past run of the user.

  The output information selection unit 294 performs a process of selecting any of the overall analysis information, detailed analysis information, and comparative analysis information according to the user's selection operation and outputting the selected information as post-travel output information.

  The post-running output information may include exercise information that was not output during the running of the user among the plurality of exercise information, that is, exercise information that was not included in the running output information. Alternatively, the post-running output information may include the exercise information output during the user's running among the plurality of pieces of exercise information, that is, the exercise information included in the running output information. The post-travel output information may include information related to advice for improving the user's travel performance or advice for improving the user's travel state. The post-travel output information may include information generated by the travel analysis unit 290 after the user's travel (information other than the motion information generated by the motion information generation unit 270 during the user's travel).

  The processing unit 20 transmits the post-travel output information to the notification device 3 or an information device such as a personal computer or a smartphone (not shown), and the notification device 3 or the information device receives the post-travel output information and receives the corresponding image and sound. Then, data such as vibration is generated and presented (transmitted) to the user via the display unit 170, the sound output unit 180, the vibration unit, and the like.

1-7. Detection of feature points When the user travels, the user repeats the operations of stepping on the right foot, landing, stepping on, taking off (kicking out), then stepping on the left foot, landing, stepping on, stepping off (kicking out). Therefore, landing, stepping on, and taking off (kicking out) can be regarded as characteristic points of travel. Based on the input information 351 at these feature points and the input information 351 from the feature point to the next feature point, the quality of the exercise can be evaluated. Therefore, in the present embodiment, the feature point detection unit 260 detects three feature points of landing, stepping on, and takeoff (kicking out) in the user's travel, and the contact time / impact time calculation unit 262 performs the landing and takeoff. The contact time and impact time are calculated based on the timing of the ground (kicking out). In addition, the first analysis information generation unit 273 calculates some items of the first analysis information using the input information 351 at the feature points and the input information from the feature point to the next feature point.

A method for determining the timing of landing and takeoff (kicking out) will be described with reference to FIG. FIG. 10 is a graph of acceleration data acquired when a subject with a floor reaction force meter installed on the ground and wearing a device with a built-in triaxial acceleration sensor 12 on his / her waist traveled. In FIG. 10, the horizontal axis represents time, and the vertical axis represents acceleration. In FIG. 10, the output data of the floor reaction force meter is also displayed side by side. Since the detection value of the floor reaction force meter changes only when the foot is in contact with the ground, from FIG. 10, when comparing the data of the floor reaction force meter and the acceleration data, the timing of landing is the vertical acceleration (
It can be seen that the z-axis detection value of the acceleration sensor 12 can be determined from a point at which it changes from a positive value to a negative value. Further, the timing of takeoff (kicking out) can be determined by the point that the vertical acceleration (detected value of the z-axis of the acceleration sensor 12) changes from a negative value to a positive value. As shown in FIG. 10, the contact time can be calculated from the difference between the takeoff time and the landing time.

  A method for determining the stepping-in timing will be described with reference to FIG. In FIG. 11, the horizontal axis represents time, and the vertical axis represents acceleration. As shown in FIG. 11, after landing (the point at which the vertical acceleration changes from a positive value to a negative value), the point where the acceleration in the traveling direction peaks after the peak in the negative direction is reached. The timing can be determined.

1-8. Details of input information and analysis information 1-8-1. Relationship between Input Information and Analysis Information FIG. 12 is a diagram illustrating a relationship between input information 351 and analysis information (first analysis information 353, second analysis information 354, and left / right difference rate 355).

  The input information 351 includes “traveling direction acceleration”, “traveling direction speed”, “traveling direction distance”, “vertical direction acceleration”, “vertical direction speed”, “vertical direction distance”, “left / right direction acceleration”, “left / right direction”. "Speed", "lateral distance", "posture angle (roll angle, pitch angle, yaw angle)", "angular velocity (roll direction, pitch direction, yaw direction)", "travel pitch", "stride", "contact time" "," Impact time ", and" weight ".

  The first analysis information 353 includes “landing brake amount 1”, “landing brake amount 2”, “true bottom landing rate 1”, “true bottom landing rate 2”, “true bottom landing rate 3”, “propulsion 1”, "Propulsion 2", "Propulsion efficiency 1", "Propulsion efficiency 2", "Propulsion efficiency 3", "Propulsion efficiency 4", "Energy consumption", "Landing impact", "Running ability", "Forward tilt" And “Timing coincidence”. Each item excluding “propulsion efficiency 4” included in the first analysis information 353 is calculated from at least one item of the input information 351. “Propulsion efficiency 4” is calculated from the energy consumption. In FIG. 12, which item of the input information 351 is used to indicate which item of the first analysis information 353 is calculated is indicated by an arrow. For example, the “true bottom landing rate 1” is calculated from the traveling direction acceleration and the vertical speed.

  The second analysis information 354 includes items of “energy loss”, “energy efficiency”, and “burden on the body”. Each item included in the second analysis information 354 is calculated from at least one item of the first analysis information 353. FIG. 12 shows which items of the second analysis information 354 are calculated using which items of the first analysis information 353. For example, “energy loss” is calculated from “true under landing rate (true under landing rate 1 to 3)” and “propulsion efficiency (propulsion efficiency 1 to 4)”.

  The left / right difference rate 355 is an index indicating the left / right balance of the user's body, and includes “running pitch”, “stride”, “contact time”, “impact time”, and first analysis information 353 included in the input information 351. It is calculated for all items and all items of the second analysis information 354.

1-8-2. Input Information Details of each item of the input information 351 will be described below.

[Advance direction acceleration, vertical acceleration, horizontal acceleration]
“Advancing direction” is the user's advancing direction (m-frame x-axis direction), “up-down direction” is the vertical direction (m-frame z-axis direction), and “left-right direction” is the advancing direction The direction is perpendicular to the direction (y-axis direction of m frame). The traveling direction acceleration, the vertical direction acceleration, and the horizontal direction acceleration are the acceleration in the x-axis direction, the acceleration in the z-axis direction, and the acceleration in the y-axis direction of m frames, respectively, and are calculated by the coordinate conversion unit 250. FIG. 13 shows an example of a graph in which the traveling direction acceleration, the vertical direction acceleration, and the horizontal direction acceleration while the user is traveling are calculated at a cycle of 10 ms.

[Speed in traveling direction, vertical speed, horizontal speed]
The traveling direction speed, the up-down direction speed, and the left-right speed are the speed in the x-axis direction, the speed in the z-axis direction, and the speed in the y-axis direction of the m frame, respectively, and are calculated by the coordinate conversion unit 250. Alternatively, the traveling direction speed, the up-down direction speed, and the left-right direction speed can be calculated by integrating the traveling direction acceleration, the up-down direction acceleration, and the left-right direction acceleration, respectively. FIG. 14 shows an example of a graph in which the traveling direction speed, the up-down direction speed, and the left-right speed while the user is traveling are calculated in a cycle of 10 ms.

[Angular velocity (roll direction, pitch direction, yaw direction)]
The angular velocity in the roll direction, the angular velocity in the pitch direction, and the angular velocity in the yaw direction are an angular velocity around the x axis, an angular velocity around the y axis, and an angular velocity around the z axis, respectively, and are calculated by the coordinate conversion unit 250. FIG. 15 shows an example of a graph in which the angular velocity in the roll direction, the angular velocity in the pitch direction, and the angular velocity in the yaw direction while the user is traveling are calculated at a cycle of 10 ms.

[Attitude angle (roll angle, pitch angle, yaw angle)]
The roll angle, the pitch angle, and the yaw angle are an attitude angle around the x-axis, an attitude angle around the y-axis, and an attitude angle around the z-axis of the m frame output from the coordinate conversion unit 250, respectively. Is calculated by Alternatively, the roll angle, the pitch angle, and the yaw angle can also be calculated by integrating (rotating calculation) the angular velocity in the roll direction, the angular velocity in the pitch direction, and the angular velocity in the yaw direction. FIG. 16 shows an example of a graph in which a roll angle, a pitch angle, and a yaw angle while the user is traveling are calculated at a cycle of 10 ms.

[Advance distance, vertical distance, horizontal distance]
The travel direction distance, the up-down direction distance, and the left-right direction distance are respectively the m frame moving distance in the x-axis direction, the moving distance in the z-axis direction, and the moving distance in the z-axis direction from a desired position (for example, a position immediately before the user starts traveling). The movement distance in the y-axis direction is calculated by the coordinate conversion unit 250. FIG. 17 shows an example of a graph in which the traveling direction distance, the vertical direction distance, and the horizontal direction distance while the user is traveling are calculated in a cycle of 10 ms.

[Running pitch]
The travel pitch is an exercise index defined as the number of steps per minute, and is calculated by the pitch calculation unit 246. Alternatively, the traveling pitch can be calculated by dividing the distance in the traveling direction for one minute by the stride.

[stride]
The stride is an exercise index defined as the step length of one step, and is calculated by the stride calculation unit 244. Alternatively, the stride can be calculated by dividing the traveling direction distance for 1 minute by the traveling pitch.

[Grounding time]
The contact time is a motion index defined as the time taken from landing to takeoff (kicking out) (see FIG. 10), and is calculated by the contact time / impact time calculation unit 262. Take off (kicking out) is when the toes leave the ground. Since the contact time is highly correlated with the traveling speed, it can be used as the running ability of the first analysis information.

[Shock time]
The impact time is a motion index defined as the time during which the impact generated by landing is applied to the body, and is calculated by the contact time / impact time calculation unit 262. A method for calculating the impact time will be described with reference to FIG. In FIG. 18, the horizontal axis represents time, and the vertical axis represents traveling direction acceleration. As shown in FIG. 18, it is possible to calculate by impact time = (time when traveling direction acceleration during one step is minimum−time of landing).

[body weight]
The body weight is the weight of the user, and the numerical value is input when the user operates the operation unit 150 before traveling.

1-8-3. First Analysis Information Hereinafter, details of each item of the first analysis information 353 calculated by the first analysis information generation unit 273 will be described.

[Brake amount at landing 1]
The landing brake amount 1 is an exercise index defined as a speed amount reduced by landing. A method of calculating the landing brake amount 1 will be described with reference to FIG. In FIG. 19, the horizontal axis represents time, and the vertical axis represents the traveling direction speed. As shown in FIG. 19, it is possible to calculate with the brake amount at landing 1 = (traveling direction speed before landing−lowest traveling direction speed after landing). The speed in the traveling direction decreases due to the landing, and the lowest point in the traveling direction speed after landing in one step is the lowest traveling direction speed.

[Brake amount at landing 2]
The landing brake amount 2 is an exercise index defined as the minimum acceleration amount minus the traveling direction generated by landing. A method of calculating the landing brake amount 2 will be described with reference to FIG. In FIG. 20, the horizontal axis represents time, and the vertical axis represents traveling direction acceleration. As shown in FIG. 20, the brake amount 2 at the time of landing coincides with the minimum acceleration in the traveling direction after landing at one step. The lowest point in the traveling direction acceleration after landing in one step is the traveling direction minimum acceleration.

[True underland landing rate 1]
The underground landing rate 1 is an exercise index that expresses whether or not the user can land directly under the body. If you can land right under your body, the amount of braking at the time of landing will decrease and you will be able to run efficiently. Since the normal brake amount increases according to the speed, the brake amount alone is not sufficient as an index. However, since the true underland landing rate 1 is an index that can be expressed as a rate, the true underland landing rate 1 is the same even if the speed changes. Can be evaluated. A method of calculating the true under landing rate 1 will be described with reference to FIG. As shown in FIG. 21, when α = arctan (traveling direction acceleration at landing / vertical acceleration at landing) using the traveling direction acceleration at the time of landing (negative acceleration) and the vertical direction acceleration, the true bottom landing rate 1 = cos α × 100 (%). Alternatively, an ideal angle α ′ is calculated using data of a plurality of people who travel fast, and the true under landing rate 1 = {1− | (α′−α) / α ′ |} × 100 (%). You can also

[True underland landing rate 2]
The underground landing rate 2 is an exercise index that expresses whether or not the vehicle has landed directly under the body by the speed reduction degree at the time of landing. A method for calculating the true under landing rate 2 will be described with reference to FIG. In FIG. 22, the horizontal axis represents time, and the vertical axis represents the traveling direction speed. As shown in FIG. 22, the true under landing rate 2 = (minimum traveling direction speed after landing / traveling direction speed immediately before landing) × 100 (%).

[True underland landing rate 3]
The direct landing rate 3 is an exercise index that expresses whether the landing has been performed directly under the body by the distance or time from the landing until the foot comes directly under the body. A method for calculating the true under landing rate 3 will be described with reference to FIG. As shown in FIG. 23, the true landing rate 3 = (traveling direction distance when the foot comes directly under the body−the traveling direction distance when landing) or the true under landing rate 3 = (when the foot comes directly under the body) Time-landing time). Here, as shown in FIG. 11, after landing (the point at which the vertical acceleration changes from a positive value to a negative value), there is a timing at which the vertical acceleration peaks in a negative direction. It can be determined as the timing (time) when the foot comes directly below.

  In addition to this, as shown in FIG. 23, it may be defined as a true under landing rate 3 = β = arctan (a distance from the landing to the foot just below the body / the height of the waist). Or, just below landing rate 3 = (1−distance from landing to just below the body / distance moved from landing to kicking up) × 100 (%) (distance moved while the foot was grounded) May be defined as the ratio of the distance from the landing to the foot just below the body). Or, just below landing rate 3 = (1—time from landing to just below the body / time to move from landing to kicking up) × 100 (%) (time to move while feet are grounded) It may be defined as the ratio of the time from landing to just below the body.

[Propulsion 1]
The driving force 1 is a motion index defined as a speed amount increased in the traveling direction by kicking the ground. A method for calculating the driving force 1 will be described with reference to FIG. In FIG. 24, the horizontal axis represents time, and the vertical axis represents traveling direction velocity. As shown in FIG. 24, it is possible to calculate with a propulsive force 1 = (maximum traveling direction speed after kicking-down traveling direction minimum speed before kicking out).

[Propulsion 2]
The driving force 2 is an exercise index defined as the maximum acceleration in the traveling direction plus generated by kicking. A method of calculating the thrust 2 will be described with reference to FIG. In FIG. 25, the horizontal axis represents time, and the vertical axis represents traveling direction acceleration. As shown in FIG. 25, the propulsive force 2 coincides with the maximum acceleration in the traveling direction after kicking out in one step.

[Propulsion efficiency 1]
The propulsion efficiency 1 is an exercise index indicating whether the kicking force is efficiently a propulsive force. Efficient driving will be possible if there is no useless vertical movement and useless horizontal movement. Normally, vertical movement and left-right movement increase according to speed, so vertical movement and left-right movement alone are not sufficient as an index, but propulsion efficiency 1 is an index that can be expressed as a rate. The same evaluation can be made even if it changes. The propulsion efficiency 1 is calculated for each of the vertical direction and the horizontal direction. A method of calculating the vertical propulsion efficiency 1 will be described with reference to FIG. As shown in FIG. 26, when γ = arctan (vertical acceleration at kicking / traveling direction acceleration at kicking) using the vertical acceleration and kicking acceleration at the time of kicking, the propulsion efficiency in the vertical direction 1 = cos γ × 100 (%). Alternatively, an ideal angle γ ′ is calculated using data of a plurality of people who travel fast, and the vertical propulsion efficiency 1 = {1- | (γ′−γ) / γ ′ |} × 100 (%) It can also be calculated. Similarly, if δ = arctan (lateral acceleration during kicking / traveling acceleration during kicking) using the lateral acceleration and the traveling acceleration during kicking, the propulsion efficiency in the lateral direction 1 = cos δ × 100 (%) can be calculated. Alternatively, an ideal angle δ ′ is calculated using data of a plurality of people who travel fast, and the propulsion efficiency in the left-right direction is 1 = {1− | (δ′−δ) / δ ′ |} × 100 (%). It can also be calculated.

  In addition, vertical propulsion efficiency 1 can be calculated by replacing γ with arctan (velocity in the vertical direction at the time of kicking / speed in the traveling direction at the time of kicking). Similarly, the propulsion efficiency 1 in the left-right direction can be calculated by replacing δ with arctan (the speed in the left-right direction at the time of kicking / the speed in the moving direction at the time of kicking).

[Propulsion efficiency 2]
The propulsion efficiency 2 is an exercise index that indicates whether the kicking force is efficiently a propulsive force by using an acceleration angle at the time of depression. A method of calculating the propulsion efficiency 2 will be described with reference to FIG. As shown in FIG. 27, the vertical propulsion efficiency 2 is expressed as follows: ξ = arctan (vertical acceleration during stepping / traveling direction acceleration during stepping) using the vertical acceleration and stepping direction acceleration during stepping. The propulsion efficiency in the vertical direction can be calculated as 2 = cosξ × 100 (%). Alternatively, an ideal angle ξ ′ is calculated using data of a plurality of people who travel fast, and the vertical propulsion efficiency 2 = {1− | (ξ′−ξ) / ξ ′ |} × 100 (%) It can also be calculated. Similarly, if η = arctan (left-right acceleration at the time of depression / travel-direction acceleration at the time of depression) using the left-right acceleration at the time of stepping and the traveling-direction acceleration, the propulsion efficiency in the left-right direction 2 = cos η × 100 (% ). Alternatively, an ideal angle η ′ is calculated using data of a plurality of people who travel fast, and the propulsion efficiency in the left-right direction 2 = {1− | (η′−η) / η ′ |} × 100 (%) It can also be calculated.

  In addition, the propulsion efficiency 2 in the vertical direction can also be calculated by replacing ξ with arctan (speed in the vertical direction during depression / speed in the traveling direction during depression). Similarly, η can be replaced with arctan (the speed in the left-right direction during depression / the speed in the traveling direction during depression) to calculate the left-right propulsion efficiency 2.

[Propulsion efficiency 3]
The propulsion efficiency 3 is an exercise index that indicates whether the kicking force is efficiently a propulsive force by using a jumping angle. A method of calculating the propulsion efficiency 3 will be described with reference to FIG. In FIG. 28, the horizontal axis represents the traveling direction distance, and the vertical axis represents the vertical direction distance. As shown in FIG. 28, if the highest point in the vertical direction at one step (1/2 of the amplitude of the vertical direction) is H and the distance in the traveling direction from kicking to landing is X, the propulsive efficiency 3 is It can be calculated in (6).

[Propulsion efficiency 4]
The propulsion efficiency 4 is an exercise index that indicates whether the kicking force is efficiently a propulsive force or not by the ratio of the energy used to advance in the traveling direction with respect to the total energy generated in one step. Propulsion efficiency 4 = (energy used for traveling in the traveling direction / energy used for one step) × 100 (%). This energy is the sum of potential energy and kinetic energy.

[Energy consumption]
The energy consumption is an exercise index defined as the amount of energy consumed to advance one step, and also represents an accumulation of the amount of energy consumed to advance one step during the travel period. Energy consumption = (vertical energy consumption + traveling energy consumption + horizontal energy consumption). Here, the energy consumption in the vertical direction = (weight × gravity × vertical distance) is calculated. Also, energy consumption in the traveling direction = [weight × {(maximum speed in the traveling direction after kicking out) −2− (minimum traveling direction speed after landing) 2} / 2]. Also, the energy consumption in the left-right direction = [body weight × {(maximum speed in the left-right direction after kicking) 2− (minimum speed in the left-right direction after landing) 2} / 2].

[Landing impact]
The landing impact is a motion index indicating how much impact is applied to the body due to landing. Landing impact = (Upper / lower impact force + traveling impact force + left / right impact force) Here, the vertical impact force = (weight × vertical speed at landing / impact time). Further, the impact force in the traveling direction = {body weight × (traveling direction speed before landing−traveling direction minimum speed after landing) / impact time}. Also, the impact force in the left-right direction = {body weight × (left-right speed before landing−left-right minimum speed after landing) / impact time}.

[Running ability]
The running ability is an exercise index that represents a user's running ability. For example, it is known that there is a correlation between the ratio between the stride and the contact time and the running record (time) (“About the contact time and takeoff time during a 100-m running race”, Journal of Research and Development for Future Athletics. 3 (1): 1-4, 2004.). Calculated by running ability = (stride / contact time).

[Forward tilt]
The forward tilt angle is an exercise index indicating how much the user's torso is tilted with respect to the ground. As shown in FIG. 29, the forward tilt angle when the user is standing vertically with respect to the ground is 0 degree (the leftmost figure), and the forward tilt angle when leaning forward is a positive value (the central figure). ), The forward tilt angle is a negative value (right end figure). The forward tilt angle can be obtained by converting the pitch angle of the m frame so as to have the above specifications. When the motion analyzer 2 (inertial measurement unit 10) is attached to the user, there is a possibility that there is already a tilt, so it is assumed that the stationary state is 0 degrees in the left figure, and the forward tilt angle is calculated from the amount of change from there. May be.

[Timing coincidence]
The timing coincidence is an exercise index that represents how close the timing of the feature point of the user is to the good timing. For example, an exercise index indicating how close the hip rotation timing is to the kicking timing is conceivable. In the running method with legs flowing, the reverse leg still remains behind the body when wearing one leg, so if the hip rotation timing comes after kicking out, it can be determined that the legs are flowing. In FIG. 30, the hip rotation timing almost coincides with the kicking timing, which is a good way to run. On the other hand, in FIG. 31, it can be said that the rotation timing of the hips is delayed from the timing of kicking out, and it can be said that the legs are flowing.

1-8-4. Second Analysis Information Hereinafter, details of each item of the second analysis information 354 calculated by the second analysis information generation unit 274 will be described.

[Energy loss]
Energy loss is an exercise index that represents the amount of energy that is wasted in the amount of energy consumed to advance one step, and the amount of energy that was wasted in the amount of energy consumed to advance one step. It also represents the accumulated travel period. Energy loss = {energy consumption × (100−true landing rate) × (100−propulsion efficiency)}. Here, the true under landing rate is one of the true under landing rates 1 to 3, and the propulsion efficiency is any of the propulsion efficiencies 1 to 4.

[Energy efficiency]
The energy efficiency is an exercise index that indicates whether the energy consumed to advance one step is efficiently used for the energy that advances in the traveling direction, and also represents an accumulated value of the traveling period. Energy efficiency = {(energy consumption-energy loss) / energy consumption}.

[Body burden]
The burden on the body is an exercise index that indicates how much impact is accumulated in the body by accumulating landing impacts. Since injuries occur due to the accumulation of shocks, the ease of injury can be determined by evaluating the burden on the body. Calculated by the burden on the body = (the burden on the right leg + the burden on the left leg). The load on the right leg can be calculated by integrating the landing impact on the right leg. The burden on the left leg can be calculated by integrating the landing impact on the left leg. Here, integration is performed both during running and from the past.

1-8-5. Left / right difference ratio (left / right balance)
The left / right difference rate is an exercise index that indicates how much difference is seen on the left and right sides of the body for each item of the running pitch, stride, contact time, impact time, first analysis information and second analysis information, Let us represent how much the left leg differs from the right leg. Left / right difference ratio = (Left leg value / Right leg value x 100) (%). The values are travel pitch, stride, contact time, impact time, brake amount, propulsive force, true landing rate, propulsion efficiency. , Speed, acceleration, travel distance, forward tilt angle, hip rotation angle, hip rotation angular velocity, left / right tilt amount, impact time, running ability, energy consumption, energy loss, energy efficiency, landing impact, burden on the body Each numerical value of Further, the left / right difference rate includes an average value and variance of each numerical value.

1-9. Feedback while driving 1-9-1. Information to be fed back The traveling output information generation unit 280 outputs basic information 352 such as traveling pitch, stride, traveling speed, altitude, traveling distance, traveling time and the like as traveling output information. Further, the running output information generation unit 280 includes, as running output information, a contact time, a braking amount at landing, a true landing rate, a propulsion efficiency, a forward tilt angle, a timing coincidence, a running ability, an energy efficiency, a left / right difference rate 355, and the like. Each numerical value of the current information or an average value (moving average value) of these several steps (for example, 10 steps) is output. Further, the running output information generation unit 280 outputs time series information such as information obtained by graphing these numerical values in time series, energy consumption, and burden on the body (accumulated damage) as running output information. . Further, the traveling output information generation unit 280 includes, as traveling output information, evaluation information on the user's traveling state, advice information for improving the user's traveling state, advice information for improving the user's traveling results, traveling The trajectory information 356 and the like are output. The running output information is presented (feedback) to the user while the user is running.

1-9-2. Timing of Feedback The traveling output information generation unit 280 may always output the traveling output information during traveling. Alternatively, the output information generation unit 280 during traveling displays information such as a state in which the numerical value of a predetermined item exceeds a set threshold (reference value), a state in which the numerical value has exceeded, an item that has been exceeded, and the worst item. It may be output. Or when the numerical value of a predetermined item does not exceed the set threshold value (reference value), the running output information generation unit 280 outputs information such as a state in which it does not exceed, an item that does not exceed, and the best item. May be. Alternatively, the traveling output information generation unit 280 may always output the information selected by the user while traveling. Alternatively, when the information selected by the user exceeds a threshold value (reference value), the traveling output information generation unit 280 may output a state that the information has been exceeded and its numerical value. Alternatively, when the information selected by the user does not exceed the threshold, the traveling output information generation unit 280 may output a state that the information is not exceeded and the numerical value thereof.

1-9-3. Feedback Method The running output information output by the running output information generation unit 280 may be displayed on the screen of the display unit 170 of the notification device 3 and fed back to the user. Or you may feed back with the sound from the sound output part 180 of the alerting | reporting apparatus 3. FIG. Alternatively, timing-related contents such as hip rotation timing, pitch, and kicking timing may be fed back from the sound output unit 180 of the notification device 3 with a short sound such as “beep”. Alternatively, the sound output unit 180 or the vibration unit 190 of the notification device 3 may be instructed to see the content displayed on the display unit 170 by sound or vibration.

1-9-4. Display method (display example)
FIGS. 32 to 35 show examples of screens displayed on the display unit 170 of the wristwatch-type notification device 3 while the user is traveling. In the example of FIG. 32, the display unit 170 displays numerical values of “forward tilt angle”, “true bottom landing rate”, and “propulsion efficiency”. In the example of FIG. 33, the current travel distance, the “contact time” at that time, and the numerical values of the travel distance and “contact time” when the change point of the “contact time” is detected are displayed. In the example of FIG. 34, a graph in which the horizontal axis is the travel distance and the vertical axis is “contact time” and an arrow indicating the detected change point are displayed. In the example of FIG. 35, the target value of “contact time”, the average value of “contact time” for each 5 km section which is a predetermined travel distance, and the average value of “contact time” at the travel distance are displayed as a table. Has been. The numerical value of each item and the graph of each item in FIGS. 32 to 35 are updated in real time while the user is traveling. Depending on the user's operation, numerical values of other items may be displayed, or the graph may be scrolled. The items displayed on the screens of FIGS. 32 to 35 may be items satisfying a predetermined condition (for example, items within the reference range or items outside the reference item), or notified by sound or the like. May be an item that has been designated, or may be an item that the user has designated in advance. Also, the screen for displaying the numerical values of the items as shown in FIGS. 32 and 33 and the screen for displaying the graphs and tables as shown in FIGS. 34 and 35 may be switched by a user input operation.

  The user checks the current driving state and condition by driving while looking at the screens as shown in FIGS. 32 to 35. For example, the driving method that improves the numerical value of each item and the item with the poor numerical value are improved. It is possible to continue running while being aware of how to run, or objectively recognizing fatigue. In addition, it is possible to recognize that the condition relating to the degree of fatigue, the traveling ability, or the traveling efficiency is lowered from the traveling time or the traveling distance in which the change point of each item is detected, and to travel so that the condition can be improved.

1-10. Feedback after running 1-10-1. Information to be fed back The travel analysis unit 290 outputs, as post-travel output information, part or all of various types of motion information generated by the motion information generation unit 270 while the user is traveling. That is, among the plurality of pieces of exercise information, exercise information that was not output during the user's travel or exercise information output during the user's travel is fed back after the user's travel is completed. In addition, the travel analysis unit 290 outputs information generated after the user travels using a plurality of pieces of exercise information. For example, information on advice for improving the user's driving performance or advice for improving the user's driving state is fed back after the user's driving. Specifically, in the present embodiment, any one of overall analysis information, detailed analysis information, and comparative analysis information is selected as post-travel output information by a user's selection operation.

1-10-2. Timing of Feedback The travel analysis unit 290 outputs post-travel output information according to a user input operation after the user travels. Specifically, when the user selects a travel to be analyzed from the past travel history, the travel analysis unit 290 shifts to the overall analysis mode, performs overall analysis of the travel selected by the user, and generates overall analysis information. Output as output information after running. When the user performs a detailed analysis selection operation, the traveling analysis unit 290 shifts to the detailed analysis mode, performs detailed analysis according to the subsequent user operation, generates detailed analysis information, and outputs output information after traveling. Output as. When the user performs a comparison analysis selection operation, the traveling analysis unit 290 shifts to the comparison analysis mode, performs comparison analysis according to the subsequent user operation, generates comparison analysis information, and outputs output information after traveling. Output as. In addition, when the user performs an overall analysis selection operation in the detailed analysis mode or the comparative analysis mode, the travel analysis unit 290 shifts to the overall analysis mode and outputs the overall analysis information as post-travel output information. In addition, the traveling analysis unit 290 uses, for example, FIFO (First-In First-Out), the entire analysis information, the detailed analysis information, and the comparison analysis information generated in the past.
) Method, when the entire analysis, detailed analysis, or comparative analysis is performed, if the analysis result information is stored in the storage unit 30, the storage unit 30 is not performed again without performing each analysis. The analysis information stored in may be read and output.

1-10-3. Feedback Method The post-travel output information output by the travel analysis unit 290 may be displayed on the display unit 170 of the notification device 3 and fed back to the user. Alternatively, user travel evaluation and advice may be fed back from the sound output unit 180 of the notification device 3 by voice.

1-10-4. Display method (display example)
[Overall analysis screen]
FIG. 36 shows an example of a screen (global analysis screen) of global analysis information displayed on the display unit 170 of the notification device 3. For example, FIG. 36 shows a screen for the first page. The user can select the screen shown in FIG. 36 and display it on the display unit 170 by scrolling the screen.

  In the example of FIG. 36, the overall analysis screen 410 (first page) includes a user image 411 and user name 412 registered in advance by the user, a summary image 413 that displays the analysis results of past driving selected by the user, and a start. A travel locus image 414 displaying a travel locus from to the goal, an item name 415 of the item selected by the user and its time series data 416, a detailed analysis button 417 and a comparative analysis button 418 are included.

  The summary image 413 includes the date on which the past travel selected by the user was performed, and each numerical value in this travel. The values are “travel distance”, “travel time”, “elevation difference between start and goal”, “average pitch (average value of travel pitch)”, “average stride (average value of stride)”, “ "Running ability", "Average true underland landing rate (average value of true underground landing rate)", "Average propulsion efficiency (average value of advancement efficiency)", "Timing coincidence", "Average contact time (average value of contact time)" , “Energy consumption”, “average energy loss (average value of energy loss)”, “average energy efficiency (average value of energy efficiency)”, “average left / right balance (average value of left / right difference ratio 355)” and “accumulated damage (Body burden) ”. It should be noted that at the start of post-travel analysis, an entire analysis screen of the latest travel data stored in the storage unit 30 may be displayed.

  In addition, a predetermined mark 419 is attached to the summary image 413 next to an item whose numerical value is better than the reference value. In the example of FIG. 36, “Running ability”, “Average true landing rate”, “Average energy loss”, and “Average left / right balance” are marked. It should be noted that a predetermined mark may be attached to an item whose numerical value is worse than the reference value or an item whose improvement rate is higher or lower than the reference value.

  The travel trajectory image 414 is an image that displays a travel trajectory from the start point to the goal point in the past travel (run corresponding to the summary image 413) selected by the user.

  The item name 415 indicates an item selected by the user from the items included in the summary image 413, and the time-series data 416 is a graph of the numerical values of the items indicated by the item name 415 in time series. In the example of FIG. 36, “average energy efficiency” is selected, and a time series graph is displayed in which the horizontal axis is the travel date and the vertical axis is the numerical value of the average energy efficiency. When the user selects any date on the horizontal axis of the time-series data 416, the summary image 413 displays the traveling analysis result on the selected date.

The detailed analysis button 417 is a button for shifting from the overall analysis mode to the detailed analysis mode. When the user performs the selection operation (pressing operation) of the detailed analysis button 417, the detailed analysis button is displayed and the detailed analysis screen is displayed. Is done.

  The comparison analysis button 418 is a button for shifting from the overall analysis mode to the comparison analysis mode. When the user performs the selection operation (pressing operation) of the comparison analysis button 418, the comparison analysis mode is displayed and the comparison analysis screen is displayed. Is done.

  The user sees the overall analysis screen shown in FIG. 36 and confirms the results of the past driving, thereby recognizing the strengths and weaknesses of his / her driving style and improving the driving performance in the next and subsequent driving. You can practice how to run and improve how you run.

Detailed analysis screen
37, 38, and 39 show an example of the detailed analysis information screen (detailed analysis screen) displayed on the display unit 170 of the notification device 3. FIG. It is preferable that the detailed analysis screen can present more detailed information than the overall analysis screen. For example, information on more items than the overall analysis screen may be presented. Alternatively, the number of items to be displayed on one page may be smaller than that of the entire analysis screen, and finer time intervals, finer numerical values, and the like may be displayed. For example, FIG. 37 shows the screen for the first page, FIG. 38 shows the screen for the second page, and FIG. 39 shows the screen for the third page. The user can perform a screen scroll operation or the like to select the screen of FIG. 37, the screen of FIG. 38, or the screen of FIG.

  In the example of FIG. 37, the detailed analysis screen 430 (first page) displays the user image 431 and the user name 432 registered in advance by the user, and the analysis result of the time selected by the user in the past driving selected by the user. A summary image 433 to be displayed and a travel locus image 434 to display a travel locus from the start to the goal are included. The detailed analysis screen 430 (first page) includes an item name 435 of the item selected by the user and its time-series data 436, an overall analysis button 437, and a comparative analysis button 438.

  The summary image 433 includes numerical values for the date selected by the user on the past run and the time selected by the user for this run (the time since the start). Each value is expressed as “travel distance (from start to selected time)”, “travel time (from start to selected time)”, “travel speed”, “(start point and travel position at selected time) ) Altitude difference "," traveling pitch "," stride "," running ability "," true landing rate "," propulsion efficiency "," timing coincidence "," brake amount at landing "," contact time "," consumption They are “energy”, “energy loss”, “energy efficiency”, “left / right balance (right / left difference rate 355)”, and “landing impact”.

  The travel locus image 434 is an image that displays a travel locus from the start point to the goal point in the past travel (travel corresponding to the summary image 433) selected by the user, and the travel position at the time selected by the user is predetermined. This is indicated by a mark 439b.

  The item name 435 indicates an item selected by the user from the items included in the summary image 433, and the time-series data 436 is a graph of the numerical value of the item indicated by the item name 435 in time series. In the example of FIG. 37, “traveling speed”, “braking amount at landing”, “traveling pitch”, and “stride” are selected, the horizontal axis is the time from the start of traveling, and the vertical axis is the numerical value of each of these items. A time series graph is displayed. The time series data 436 displays a slide bar 439a that can move in the left-right direction, and the user can select the time from the start of travel by moving the slide bar 439a. Then, in conjunction with the position of the slide bar 439a (the time selected by the user), the numerical value of each item of the summary image 433 and the position of the travel position mark 439b of the travel locus image 434 change.

  The overall analysis button 437 is a button for shifting from the detailed analysis mode to the overall analysis mode. When the user performs the selection operation (pressing operation) of the overall analysis button 437, the overall analysis mode is displayed and the overall analysis screen is displayed. Is done.

  The comparison analysis button 438 is a button for shifting from the detailed analysis mode to the comparison analysis mode. When the user performs the selection operation (pressing operation) of the comparison analysis button 438, the comparison analysis mode is displayed and the comparison analysis screen is displayed. Is done.

  In the example of FIG. 38, the detailed analysis screen 440 (second page) includes animated images 441 and 442 of the travel selected by the user, a message image 443, the item name 444 of the item selected by the user, and the right foot of the item name 444. A line graph 445 and a histogram 446 showing each numerical value of the left foot in time series are included.

  The animation image 441 is an animation image viewed from the side of the user, and the animation image 442 is an animation image viewed from the front of the user. The animation image 441 includes a comparison display between the user's propulsive force and kicking angle and the ideal propulsive force and kicking angle. Similarly, the animation image 442 includes a comparison display between the user's tilt angle and the ideal tilt angle.

  In the message image 443, evaluation information on a user's driving result, a message for improving driving performance, and the like are displayed. In the example of FIG. 38, “The propulsion efficiency is low. There is a possibility that the vertical movement and the horizontal movement are large. If the kick is lifted too much, it will form a form that springs up and the burden on the calf increases. Please run with an image that captures the ground with the entire back. "

  The item name 444 indicates an item selected by the user from the items included in the summary image 433 illustrated in FIG. 37, and the line graph 445 and the histogram 446 are obtained by arranging the values of the right foot and the left foot of the item indicated by the item name 444. It is graphed in series. In the example of FIG. 38, “Brake amount at landing” is selected, a line graph 445 in which the horizontal axis is the time from the start of running, the vertical axis is the value of the left and right feet of the landing brake amount, and the horizontal axis is when landing A histogram 446 showing the brake amount, the vertical axis as the frequency, and the left and right feet in different colors is displayed.

  In the example of FIG. 39, the detailed analysis screen 450 (third page) includes message images 451, 452 and 453 based on the analysis result of the travel selected by the user.

  In the example of FIG. 39, the message image 451 shows that “the efficiency has dropped by ○% due to landing. A wasteful jump occurred in the vertical movement direction at the time of kicking and the efficiency has dropped by ○%. The message of evaluation and advice that there is a left-right difference is ○% is displayed. In addition, the message image 452 is a message of advice for obtaining a time shortening effect of “It is about 3 cm slower by one step due to useless movement. By improving, it will be about 3 minutes faster in the full marathon.” it's shown. In addition, the message image 453 displays a guidance message that “the true landing rate tends to be worse in the second half of the run. Let's do LSD training to increase endurance”.

  The user sees the detailed analysis screens shown in FIGS. 37 to 39, and confirms the details and advice of the driving performed in the past, thereby recognizing the advantages and disadvantages of his own driving, and the driving after the next time. In the above, it is possible to practice how to run to improve running performance and how to run to improve running conditions.

[Comparison analysis screen]
40 and 41 show an example of a comparative analysis information screen (comparison analysis screen) displayed on the display unit 170 of the notification device 3.

  In the example of FIG. 40, the comparison analysis screen 460 includes a user image 461 and a user name 462 registered in advance by the user, a summary image 463 that displays the analysis result of the past travel selected by the user, and past travel of other people. A summary image 464 displaying the analysis result, an item name 465 and time-series data 466 of the item selected by the user, an overall analysis button 467 and a detailed analysis button 468 are included.

  The summary image 463 includes a date on which a past run selected by the user was performed and each numerical value in this run. The values are “travel distance”, “travel time”, “elevation difference between start and goal”, “average pitch (average value of travel pitch)”, “average stride (average value of stride)”, “ "Running ability", "Average true underland landing rate (average value of true underground landing rate)", "Average propulsion efficiency (average value of advancement efficiency)", "Timing coincidence", "Average contact time (average value of contact time)" , “Energy consumption”, “average energy loss (average value of energy loss)”, “average energy efficiency (average value of energy efficiency)”, “average left / right balance (average value of left / right difference ratio 355)” and “accumulated damage (Body burden) ”.

  In addition, a predetermined mark 469 is attached to the summary image 463 beside an item whose numerical value is better than the reference value. In the example of FIG. 40, a mark 469 is added to “average true underland landing rate”, “average energy loss”, and “average left / right balance”. It should be noted that a predetermined mark may be attached to an item whose numerical value is worse than the reference value or an item whose improvement rate is higher or lower than the reference value.

  The summary image 464 includes the date when another person's past run was performed and the numerical values of the same items as the items included in the summary image 463. In FIG. 40, the user name and user image of another person are displayed in the vicinity of the summary image 464.

  The item name 465 indicates an item selected by the user from the items included in the summary image 463, and the time-series data 466 is a graph of the numerical value of the item indicated by the item name 465 in time series. In the example of FIG. 40, “average energy efficiency” is selected, and a time series graph is displayed in which the horizontal axis represents the travel date and the vertical axis represents the numerical values of the average energy efficiency of the user and others. When the user selects one of the dates on the horizontal axis of the time-series data 466, the summary image 463 and the summary image 464 display the travel of the user and others on the selected date (if there is no travel on the selected date, for example, The analysis result of the most recent run) is displayed.

  The overall analysis button 467 is a button for shifting from the comparative analysis mode to the overall analysis mode. When the user performs the selection operation (pressing operation) of the overall analysis button 467, the overall analysis mode is displayed and the overall analysis screen is displayed. Is done.

  The detailed analysis button 468 is a button for shifting from the comparative analysis mode to the detailed analysis mode. When the user performs the selection operation (pressing operation) of the detailed analysis button 468, the detailed analysis button is displayed and the detailed analysis screen is displayed. Is done.

  In the example of FIG. 41, the comparison analysis screen 460 displays the item name 471 of the item selected by the user, the summary image 472 displaying the analysis result of the run of the runner selected by the user, and the travel result of the item selected by the user. A graph 473 is included.

In the example of FIG. 41, “contact time” is selected as the item name 471, and the summary image 472 shows the target value of the contact time during travel of the runners A and B selected by the user, and the contact time for each travel distance of 5 km. An average value and an average value of the contact time at a travel distance of 20 km are displayed.

  Also, in the graph 473, the horizontal axis represents the travel distance, the vertical axis represents the ground contact time, the target value of the contact time during the run of the runners A and B selected by the user, and the contact ground time measurement value during the run of the runner A and detection. The measured change point, the contact time measurement value while the runner B is traveling, and the detected change point are displayed.

  From the summary image 472 and the contents of the graph 473, in the case of training with a travel distance of 20 km, the runner A exceeds the target value of the index from the start to 5 km and gradually decreases after 5 km at the change point. However, the average value of the contact time up to the travel distance of 20 km is 0.22 sec which is the same as the target value. Runner B, on the other hand, maintained the target value of the index from the start to 15 km, and after 15 km, the run was gradually below the index value at the change point, and the average value of the contact time for the travel distance of 20 km Is 0.22 sec which is the same as the target value. Therefore, since the target value is achieved when the average values of the contact times of the runners A and B up to the travel distance of 20 km are compared, it can be said that both of them are in a good running state.

  However, the change point serving as an indicator of the running state of the runner A is about 5 km, whereas the change point of the runner B is about 15 km. That is, the runner A travels at an over pace from the start, and after the change point, the runner A travels in a state in which the degree of fatigue increases and the travel ability and the travel efficiency decrease, and the pace distribution needs to be improved. On the other hand, runner B maintains the pace distribution from the start to the target value, and it can be seen that he was running in good condition up to 15 km of the change point. Therefore, detecting the change point of the running state including the contact time can detect a change in the condition of the runner and can determine the quality of the runner.

  The user sees the comparative analysis screens shown in FIG. 40 and FIG. 41, and confirms the comparison results and conditions of the past driving results and conditions with other people's driving results, so that Recognizing the shortcomings, it is possible to practice how to run to improve the running performance and how to run to improve the running condition in the next and subsequent runs.

1-11. Processing Procedure FIG. 42 is a flowchart showing an example of a procedure of motion analysis processing performed by the processing unit 20 of the motion analysis device 2 while the user is traveling. The processing unit 20 of the motion analysis apparatus 2 (an example of a computer) executes the motion analysis process according to the procedure of the flowchart of FIG. 42 by executing the motion analysis program 300 stored in the storage unit 30.

  As shown in FIG. 42, the processing unit 20 waits until a measurement start command is received (N in S10). When a measurement start command is received (Y in S10), the user is first stationary. As an example, an initial posture, an initial position, and an initial bias are calculated using the sensing data and the GPS data measured by the inertial measurement unit 10 (S20).

  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 (S30).

  Next, the processing unit 20 performs inertial navigation calculation processing, and generates calculation data including various types of information (S40). An example of the procedure of the inertial navigation calculation process will be described later.

Next, the processing unit 20 performs exercise analysis information generation processing using the calculation data generated in S40, generates exercise analysis information and running output information, and transmits the running output information to the notification device 3 (S50). ). An example of the procedure of the motion analysis information generation process will be described later. The traveling output information transmitted to the notification device 3 is fed back in real time while the user is traveling. 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.

  Then, the processing unit 20 receives a sensing end command (N in S60 and N in S70) every time the sampling period Δt elapses after acquiring the previous sensing data (Y in S60), and after S30. Repeat the process. When receiving the measurement end command (Y in S70), the processing unit 20 stands by until a travel analysis start command for instructing the start of the travel analysis process is received (N in S80).

  When the processing unit 20 receives the travel analysis start command (Y in S80), the processing unit 20 uses the motion analysis information generated in S50 and the motion analysis information generated during the past travel and stored in the storage unit 30 to store the user's past. The travel analysis process is performed on the travel, and the information of the analysis result is transmitted to the notification device 3 or other information devices (S90). An example of the procedure of the travel analysis process will be described later. The processor 20 ends the motion analysis process when the travel analysis process ends.

  FIG. 43 is a flowchart showing an example of the procedure of the inertial navigation calculation process (the process of S40 of FIG. 42). The processing unit 20 (the inertial navigation calculation unit 22) executes the inertial navigation calculation program 302 stored in the storage unit 30, thereby executing the inertial navigation calculation process according to the procedure of the flowchart of FIG.

As shown in FIG. 43, first, the processing unit 20, after estimating the acceleration bias _{b a} and angular velocity bias b _omega in S150 by (described later using the initial bias calculated in S20 in FIG. 42, an acceleration bias _{b a} 42 and the angular velocity bias _bω ), the bias is removed from the acceleration and angular velocity included in the sensing data acquired in S30 of FIG. 42 for correction, and the sensing data table 310 is updated with the corrected acceleration and angular velocity (S100). .

  Next, the processing unit 20 integrates the sensing data corrected in S100 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 340 (S110). ).

  Next, the processing unit 20 performs a travel detection process (S120). An example of the procedure of the travel detection process will be described later.

  Next, when the traveling period is detected by the traveling detection process (S120) (Y in S130), the processing unit 20 calculates the traveling pitch and stride (S140). Moreover, the process part 20 does not perform the process of S140, when a driving cycle is not detected (N of S130).

Then, the processing unit 20 performs error estimation process, speed error .delta.v ^e, attitude angle error epsilon ^e, acceleration bias _{b a,} estimates the angular velocity bias b _omega and position error δp ^e (S150).

Then, the processing unit 20 uses the speed error .delta.v ^e, attitude angle error epsilon ^e, and position error .delta.p ^e estimated in S150, speed, position, and the posture angle is corrected respectively, corrected speed, position, Then, the calculation data table 340 is updated with the attitude angle (S160). Further, the processing unit 20 integrates the speed corrected in S160, and calculates the distance of the e frame (S170).

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 340. ) And the distance of the e frame calculated in S170 are converted into the m frame acceleration, angular velocity, velocity, position, posture angle, and distance, respectively (S180).

  Then, the processing unit 20 generates calculation data including the acceleration, angular velocity, speed, position, posture angle, and distance of the m frame after coordinate conversion in S180, and the stride and travel pitch calculated in S140 (S190). The processing unit 20 performs this inertial navigation calculation processing (processing of S100 to S190) every time sensing data is acquired in S30 of FIG.

  FIG. 44 is a flowchart showing an example of the procedure of the travel detection process (the process of S120 in FIG. 43). The processing unit 20 (running detection unit 242) executes the running detection process in the procedure of the flowchart of FIG.

  As shown in FIG. 44, the processing unit 20 performs low-pass filter processing on the z-axis acceleration included in the acceleration corrected in S100 of FIG. 43 (S200), and removes noise.

  Next, when the z-axis acceleration subjected to the low-pass filter processing in S200 is equal to or greater than the threshold value and the maximum value (Y in S210), the processing unit 20 detects the traveling cycle at this timing (S220).

  If the left and right foot flag is on (Y in S230), the processing unit 20 turns off the left and right foot flag (S240). If the left and right foot flag is not on (N in S230), the processing unit 20 turns on the left and right foot flag. (S250), and the travel detection process ends. If the z-axis acceleration is not less than the threshold value or the maximum value (N in S210), the processing unit 20 ends the traveling detection process without performing the processes after S220.

  FIG. 45 is a flowchart showing an example of the procedure of the motion analysis information generation process (the process of S50 of FIG. 42). The processing unit 20 (the motion analysis unit 24) executes the motion analysis information generation program 304 according to the procedure of the flowchart of FIG. 45 by executing the motion analysis information generation program 304 stored in the storage unit 30. Note that the motion analysis information generation processing corresponds to the motion analysis method of the present embodiment.

  As shown in FIG. 45, first, the processing unit 20 calculates each item of basic information using the calculation data generated by the inertial navigation calculation process of S40 of FIG. 42 (S300). In addition, the processing unit 20 calculates a travel locus using the calculation data, and generates travel locus information 356 (S310).

  Next, the processing unit 20 performs detection processing of feature points (landing, stepping, takeoff, etc.) in the user's running motion using the calculation data (S320).

  Next, as a process of calculating the user's running state, when the processing unit 20 detects a feature point in the process of S320 (Y in S330), the processing unit 20 calculates the contact time and the impact time based on the timing of detecting the feature point. (S340). Further, the processing unit 20 uses a part of the calculation data and the contact time and impact time generated in S340 as the input information 351, and based on the timing at which the feature points are detected, some items of the first analysis information (features of calculation) Items that require point information) are calculated (S350). When the feature point is not detected in the process of S320 (N of S330), the processing unit 20 does not perform the processes of S340 and S350.

  Next, the processing unit 20 uses the input information 351 to calculate other items (items that do not require feature point information for calculation) of the first analysis information 353 (S360).

Next, the processing unit 20 calculates each item of the second analysis information 354 using the first analysis information 353 (S370).

  Next, the processing unit 20 calculates the left / right difference ratio for each item of the input information 351, each item of the first analysis information 353, and each item of the second analysis information (S380). Thereafter, the processing unit 20 stores the input information 351, basic information 352, first analysis information 353, second analysis information 354, right / left difference ratio 355, travel locus information 356, and travel state change points as motion analysis information 350. Store in unit 30.

  Next, as a step of detecting a change point of the running state, the processing unit 20 sets a change point for each item of the calculated input information 351, each item of the first analysis information, and each item of the second analysis information. It is detected (S385). Thereafter, the change point of the running state is stored in the storage unit 30 as the motion analysis information 350.

  Next, as a process of generating travel information including a change point of the travel state, the processing unit 20 includes input information 351, basic information 352, first analysis information 353, second analysis information 354, left-right difference ratio 355, travel trajectory. The output information during traveling is generated using the information 356 and the change point of the traveling state, the generated output information during traveling is transmitted to the notification device 3 (S390), and the motion analysis information generation process is terminated.

  FIG. 46 is a flowchart showing an example of the procedure of the travel analysis process (the process of S90 in FIG. 42). The processing unit 20 (travel analysis unit 290) executes the travel analysis process according to the procedure of the flowchart of FIG. 46 by executing the travel analysis program 306 stored in the storage unit 30.

  As shown in FIG. 46, first, the processing unit 20 selects the overall analysis mode, and generates the motion analysis information generated in the motion analysis information generation processing in S50 of FIG. Using the stored motion analysis information, the user analyzes the entire past travel to generate the overall analysis information, and transmits the generated overall analysis information to the notification device 3 or other information device as post-travel output information ( S400).

  When the processing unit 20 receives a travel analysis end command instructing the end of the travel analysis process in the overall analysis mode (Y in S402), the processing unit 20 ends the travel analysis process and does not receive the travel analysis end command ( If neither the detailed analysis mode nor the comparative analysis mode is entered (N in S402 and N in S406), the entire analysis process (S400) is repeated according to the user's operation.

  When the processing unit 20 shifts from the overall analysis mode to the detailed analysis mode (Y in S404), the detailed analysis is performed to generate detailed analysis information, and the generated detailed analysis information is used as the post-travel output information for the notification device 3 or other The information is transmitted to the information device (S410). The transition from the overall analysis mode to the detailed analysis mode is performed, for example, when the user performs a selection operation (pressing operation) of the detailed analysis button 417 included in the overall analysis screen 410 illustrated in FIG.

  In the detailed analysis mode, when the travel analysis end command is received (Y in S412), the processing unit 20 ends the travel analysis process and does not receive the travel analysis end command (N in S412). If neither the mode nor the overall analysis mode is entered (N in S414 and N in S416), the detailed analysis process (S410) is repeated according to the user's operation.

When the processing unit 20 shifts from the overall analysis mode to the comparative analysis mode (Y in S406), or shifts from the detailed analysis mode to the comparative analysis mode (Y in S414), the processing unit 20 performs comparative analysis to generate comparative analysis information. The generated comparative analysis information is transmitted to the notification device 3 or other information device as post-travel output information (S420). The transition from the overall analysis mode to the comparison analysis mode is performed, for example, when the user performs a selection operation (pressing operation) of the comparison analysis button 418 included in the overall analysis screen 410 illustrated in FIG. Further, the transition from the detailed analysis mode to the comparative analysis mode is performed, for example, when the user performs a selection operation (pressing operation) of the comparison analysis button 438 included in the detailed analysis screen 430 illustrated in FIG.

  When the travel analysis end command is received in the comparative analysis mode (Y in S422), the processing unit 20 terminates the travel analysis process, and does not receive the travel analysis end command (N in S422). If neither the mode nor the detailed analysis mode is entered (N in S424 and N in S426), the comparative analysis process (S420) is repeated according to the user's operation.

  When the processing unit 20 shifts from the detailed analysis mode to the overall analysis mode (Y in S416) or shifts from the comparative analysis mode to the overall analysis mode (Y in S424), the processing unit 20 performs the overall analysis process of S400. The transition from the detailed analysis mode to the overall analysis mode is performed, for example, when the user performs a selection operation (pressing operation) of the overall analysis button 437 included in the detailed analysis screen 430 illustrated in FIG. Further, the transition from the comparative analysis mode to the overall analysis mode is performed, for example, when the user performs a selection operation (pressing operation) of the overall analysis button 467 included in the comparison analysis screen 460 illustrated in FIG.

  When the processing unit 20 shifts from the comparative analysis mode to the detailed analysis mode (Y of S426), the processing unit 20 performs the detailed analysis process of S410. The transition from the comparative analysis mode to the detailed analysis mode is performed, for example, when the user performs a selection operation (pressing operation) of the detailed analysis button 468 included in the comparison analysis screen 460 illustrated in FIG.

1-12. Effect In the first embodiment, the motion analysis system 1 detects the change point of the running state by analyzing the user's running motion from the output signal of the inertial measurement unit 10 and calculating the running state of the user. Motion analysis apparatus 2 capable of performing the above and a notification apparatus 3 as a display device that displays travel information including a change point of the travel state. Therefore, since the traveling information including the change point of the traveling state displayed on the notification device 3 can be viewed while the user is traveling, it is possible to check the conditions such as the user's own fatigue level and traveling ability. In particular, it can be confirmed that the user's condition is starting to deteriorate at the travel time and travel distance at the time when the change point of the travel state is detected.

  In addition, since the travel information includes information on energy loss, energy efficiency, or burden on the body in the travel of the user, it is possible to grasp the condition regarding the fatigue level of the user.

  In addition, since the travel information includes information related to the contact time, landing brake amount, true landing ratio, or propulsion efficiency in the user's travel, it is possible to grasp the conditions regarding the user's travel ability and travel efficiency. .

  In addition, as a display method, the user's own fatigue level, driving ability, etc. are displayed for each predetermined traveling distance by displaying a value obtained by averaging the traveling state for each predetermined traveling distance on the notification device 3 or other information devices. The condition of can be confirmed. Furthermore, by displaying the traveling information of a plurality of users, it is possible to check the conditions of each user.

2. Second Embodiment Next, a motion analysis system according to a second embodiment of the present invention will be described. In the present embodiment, differences from the first embodiment will be described, and description of similar parts will be omitted. In the present embodiment, the same elements as those in the first embodiment will be described with the same reference numerals. Since the main difference between the system of the present embodiment and the system of the first embodiment is the operation of the system, attention is paid to the operation of the system here. Note that the functions of the system of the present embodiment can be installed in the system as an additional function of the system of the first embodiment.

2-1. Processing According to Second Embodiment The motion analysis apparatus 2 according to the present embodiment includes detection results of sensors (including an acceleration sensor 12, an angular velocity sensor 14, a GPS unit 50, a geomagnetic sensor 60, a timer unit (not shown), and the like). And a motion analysis unit that analyzes a plurality of types of indices (an example of motion information) for each travel section (here, a distance section is assumed) in a user's travel or walk (here, travel is assumed). 24, and a communication unit 40 (an example of an output unit) that outputs a graph (a radar chart is assumed here) representing a plurality of types of indices on mutually different coordinate axes for each travel section. A radar chart (to be described later) for each section represents a change in balance during traveling of a plurality of types of indicators.

  In the motion analysis apparatus 2 according to the present embodiment, the plurality of types of indices are at least one of (i) an index related to energy efficiency, (ii) an index related to energy loss, and (iii) an index related to a burden on the body. Including one. Therefore, the traveling output information generation unit 280 of the motion analysis device 2 is based on the traveling state (such as the motion analysis information 350) generated by the motion information generation unit 270 during the user's traveling for each section such as an interval of 5 km. Indices (i), (ii), (iii) are calculated. Here, it is assumed that the average value within the section of the indices (i), (ii), and (iii) is calculated for each section. Hereinafter, the average value within the interval of the index is appropriately referred to as “index”.

  Therefore, the user can grasp his / her running ability based on at least one transition among energy efficiency, energy loss, and burden on the body.

  Further, in the motion analysis apparatus 2 according to the present embodiment, the plurality of types of indices are (iv) an index related to a direct landing, (v) an index related to propulsion efficiency, (vi) an index related to a braking amount at landing, (vii ) It includes at least one of indices related to the contact time. Therefore, the traveling output information generation unit 280 of the motion analysis device 2 is based on the traveling state (such as the motion analysis information 350) generated by the motion information generation unit 270 during the user's traveling for each section such as an interval of 5 km. Indices (iv), (v), (vi), (vii) are calculated. Here, it is assumed that the average value within the section of the indices (iv), (v), (vi), (vii) is calculated for each section. Hereinafter, the average value within the interval of the index is appropriately referred to as “index”.

  Accordingly, the user can grasp his / her running ability based on at least one transition among true landing, propulsion efficiency, landing brake amount, and contact time.

  In addition, the traveling output information generation unit 280 of the motion analysis apparatus 2 may calculate an average value in each section for each section such as an index related to a spring coefficient (propulsion force and the like) and an index related to a leg flow. .

  In addition, each index of each section calculated by the running output information generation unit 280 of the motion analysis device 2 is stored in the storage unit 30 of the motion analysis device 2.

Then, after the user travels, the detailed analysis unit 292 of the motion analysis device 2 generates image data of a radar chart (described later) as the travel results of the user based on each index stored in the storage unit 30. In addition, the communication unit 40 of the motion analysis device 2 transmits image data of a generated radar chart (described later) to the notification device 3 via post-travel output information.

  On the other hand, when the communication unit 140 of the notification device 3 receives image data of a radar chart (described later) from the motion analysis device 2, the communication unit 140 sends the image data to the processing unit 120 of the notification device 3, and the processing unit 120 of the notification device 3 The image data is displayed on the display unit 170 of the notification device 3.

  Here, it is assumed that the image data of the radar chart is output from the motion analysis apparatus 2 as post-travel output information. However, even if the image data of the radar chart is output from the motion analysis apparatus 2 as output information during travel. Good. In that case, the radar chart in the display unit 170 is updated every time the user travels in the section. In this case, for example, every time the processing unit 120 of the notification device 3 receives a radar chart, the processing unit 120 displays the received radar chart superimposed on the radar chart being displayed on the display unit 170.

2-2. Example of Radar Chart FIG. 47 is an example of a radar chart according to the present embodiment.

In the motion analysis apparatus 2 according to the present embodiment, the radar chart has different coordinate axes arranged radially as shown in FIG. In the example of FIG. 47, a radar chart in which five axes are arranged radially is displayed, the first axis of the radar chart is assigned to an index related to energy efficiency, and the second axis is a burden on the body. The third axis is assigned to an index related to leg flow, the fourth axis is assigned to an index related to a spring coefficient (such as propulsive force), and the fifth axis is energy Assigned to the loss indicator. The more polygonal shapes (here pentagons) that appear on this radar chart, the better the balance of these five indicators. In addition, the larger the size of the polygon (here, the pentagon) shown in the radar chart, the better the five indicators are as a whole.

  47, the radar chart according to the first distance section, the radar chart according to the second distance section, the radar chart according to the third distance section, and the radar chart according to the fourth distance section. Are superimposed and displayed.

  The first distance section is a section from the travel start point (0 km point) to the 5 km point, the second distance section is a section from the 5 km point to the 10 km point, and the third distance section is a 10 km point. The fourth distance section is a section from the 15 km point to the 25 km point.

  47, the radar chart according to the first distance section, the radar chart according to the second distance section, the radar chart according to the third distance section, and the radar chart according to the fourth distance section. Are distinguished from each other by different line types (color, density, pattern, etc.). Note that the legend shown on the right side of the figure shows the correspondence between sections and line types.

Therefore, the user can recognize the balance and quality of the plural types of indicators in each section as polygons (pentagons) of the radar chart. For example, the user can select a section where the polygonal (pentagonal) shape of the radar chart suddenly collapses (in the example of FIG. 47, a section of 10 km to 15 km), and a section to which the change point where the balance of the plural types of indicators is broken belongs. Or a section (in particular, none in the example of FIG. 47) in which the size of the polygon (pentagon) has suddenly decreased is specified as a section to which a change point in which a plurality of types of indicators are totally deteriorated belongs. Easy to do. For example, the user can also determine that the running ability is higher as the distance from the start of travel to the section to which the change point belongs (the section of 10 km to 15 km in the example of FIG. 47) is longer. For example, the user has 10 sections to which the change point belongs.
When it is a section of km to 15 km, it can be objectively grasped that its own running ability is “10 km”. Further, for example, when the section to which the change point belongs is a section of 5 km to 10 km, the user can objectively grasp that his / her running ability is “5 km”.

  FIG. 48 is another example of the radar chart according to the present embodiment.

  In the motion analysis apparatus 2 according to the present embodiment, the radar chart has different coordinate axes arranged radially as shown in FIG. In the example of FIG. 48, a radar chart in which five axes are arranged in a radial pattern is displayed. The first axis of the radar chart is assigned to an indicator relating to the true landing rate, and the second axis is related to propulsion efficiency. The third axis is assigned to an indicator relating to leg flow, the fourth axis is assigned to an indicator relating to a spring coefficient (propulsive force, etc.), and the fifth axis is assigned to a landing brake. It is assigned to the quantity indicator. The more polygonal shapes (here pentagons) that appear on this radar chart, the better the balance of these five indicators. In addition, the larger the size of the polygon (here, the pentagon) shown in the radar chart, the better the five indicators are as a whole.

  In the example of FIG. 48, the radar chart according to the first distance section, the radar chart according to the second distance section, the radar chart according to the third distance section, and the radar chart according to the fourth distance section. Are superimposed and displayed.

  The first distance section is a section from the travel start point (0 km point) to the 5 km point, the second distance section is a section from the 5 km point to the 10 km point, and the third distance section is a 10 km point. The fourth distance section is a section from the 15 km point to the 25 km point.

  In the example of FIG. 48, the radar chart according to the first distance section, the radar chart according to the second distance section, the radar chart according to the third distance section, and the radar chart according to the fourth distance section. Are distinguished from each other by different line types (color, density, pattern, etc.). Note that the legend shown on the right side of the figure shows the correspondence between sections and line types.

  Therefore, the user can recognize the balance and quality of the plural types of indicators in each section as polygons (pentagons) of the radar chart. For example, the user can select a section where the polygon (pentagon) shape of the radar chart suddenly collapses (in the example of FIG. 48, a section of 5 km to 10 km), and a section to which a change point where the balance of a plurality of types of indicators is lost belongs. Or a section (a section of 5 km to 10 km in the example of FIG. 48) in which the size of the polygon (pentagon) has suddenly decreased is defined as a section to which a change point in which a plurality of types of indicators are totally deteriorated belongs. It is easy to specify. For example, the user can also determine that the running ability is higher as the distance from the start of travel to the section to which the change point belongs (section of 5 km to 10 km in the example of FIG. 48) is longer. For example, when the section to which the change point belongs is a section of 5 km to 10 km, the user can objectively grasp that his / her running ability is “5 km”. Further, for example, when the section to which the change point belongs is a section of 10 km to 15 km, the user can objectively grasp that his / her running ability is “10 km”.

2-3. Flow FIG. 49 is an example of a processing flow according to the present embodiment. This flow is a processing flow of the motion analysis apparatus 2 related to transmission of image data of a radar chart. Since the example of the function sharing in the motion analysis device 2 has been described, the description here assumes that the subject is the motion analysis device 2. Here, it is assumed that the motion analysis device 2 outputs the image data of the radar chart as post-travel output information after the user travels.

  First, the motion analysis apparatus 2 sets the section number i to an initial value “1” (S301). The section number i is a number for identifying each section where the user travels (a section formed by dividing the travel distance by a predetermined interval such as 5 km), and is assigned 1, 2,. Hereinafter, the maximum value of the section number i is referred to as “end”. When there are four sections, section numbers i = 1, 2, 3, and 4 are allocated to the four sections, and the maximum value (i = 4) of the section numbers i is “end”.

  Next, the motion analysis apparatus 2 sets the index number j to “1” which is an initial value (S302). The index number j is a number for identifying a plurality of types of indices assigned to each axis of the radar chart. When the number of axes of the radar chart is “5”, index numbers j = 1, 2, 3, 4, and 5 are sequentially assigned to five types of indices. Hereinafter, the maximum value (j = 5) of the index number j is referred to as “end”.

  Next, the motion analysis device 2 calculates the average value of the j-th index in the i-th section (S303). This average value is treated as the j-th index related to the i-th interval.

  Next, the motion analysis apparatus 2 determines whether or not the index number j has reached the end (S304). If the index number j has not reached the end (S304N), the index number j is incremented by “1”. (S305), the process returns to step S303. When the index number j reaches the end (S302Y), the process proceeds to the next process (S306).

  Next, the motion analysis apparatus 2 determines whether or not the section number i has reached the end (S306). If it has not reached (S306N), the section number i is incremented by “1”. (S307), the process returns to step S302. When the section number i reaches the end (S306Y), the process proceeds to the next process (S308).

  Next, the motion analysis device 2 creates radar chart image data based on each index calculated for each section (S308).

  Next, the motion analysis device 2 transmits the created radar chart image data to the notification device 3 (S309).

  Here, it is assumed that the motion analysis device 2 outputs the image data of the radar chart as output information after traveling. However, when the motion analysis device 2 outputs the image data of the radar chart as output information during traveling. For example, a series of processes of steps S302, S303, S304, S305, S308, and S309 may be executed each time the travel of each section is completed.

3. Third Embodiment Next, a motion analysis system according to a third embodiment of the present invention will be described. In the present embodiment, differences from the first embodiment or the second embodiment will be described, and descriptions of similar parts will be omitted. In the present embodiment, the same elements as those in the first embodiment or the second embodiment will be described with the same reference numerals. Note that the main difference between the system of this embodiment and the system of the first embodiment or the second embodiment is the operation of the system, and therefore attention is paid to the operation of the system here. In addition, the function of the system of this embodiment can also be mounted in the said system as an additional function of the system of 1st Embodiment or 2nd Embodiment.

3-1. Processing According to Third Embodiment The motion analysis apparatus 2 according to this embodiment includes the detection results of sensors (including the acceleration sensor 12, the angular velocity sensor 14, the GPS unit 50, the geomagnetic sensor 60, a timer unit (not shown), etc.). And a motion analysis unit that analyzes a plurality of types of indices (an example of motion information) for each travel section (here, a time section is assumed) in a user's travel or walking (here, travel is assumed). 24, and a communication unit 40 (an example of an output unit) that outputs a graph (a radar chart is assumed here) representing a plurality of types of indices on mutually different coordinate axes for each travel section. A radar chart (to be described later) for each section represents a change in balance during traveling of a plurality of types of indicators.

  In the motion analysis apparatus 2 according to the present embodiment, the plurality of types of indices are at least one of (i) an index related to energy efficiency, (ii) an index related to energy loss, and (iii) an index related to a burden on the body. Including one. Therefore, the running output information generation unit 280 of the motion analysis device 2 is a section such as an interval of 10 minutes based on the running state (such as the motion analysis information 350) generated by the motion information generation unit 270 while the user is running. Index (i), (ii), (iii) is calculated for every. Here, it is assumed that the average value within the section of the indices (i), (ii), and (iii) is calculated for each section. Hereinafter, the average value within the interval of the index is appropriately referred to as “index”.

  Therefore, the user can grasp his / her running ability based on at least one transition among energy efficiency, energy loss, and burden on the body.

  Further, in the motion analysis apparatus 2 according to the present embodiment, the plurality of types of indices are (iv) an index related to a direct landing, (v) an index related to propulsion efficiency, (vi) an index related to a braking amount at landing, (vii ) It includes at least one of indices related to the contact time. Therefore, the running output information generation unit 280 of the motion analysis device 2 is a section such as an interval of 10 minutes based on the running state (such as the motion analysis information 350) generated by the motion information generation unit 270 while the user is running. Each index (iv), (v), (vi), (vii) is calculated. Here, it is assumed that the average value within the section of the indices (iv), (v), (vi), (vii) is calculated for each section. Hereinafter, the average value within the interval of the index is appropriately referred to as “index”.

  Accordingly, the user can grasp his / her running ability based on at least one transition among true landing, propulsion efficiency, landing brake amount, and contact time.

  In addition, the traveling output information generation unit 280 of the motion analysis apparatus 2 may calculate an average value in each section for each section such as an index related to a spring coefficient (propulsion force and the like) and an index related to a leg flow. .

  In addition, each index of each section calculated by the running output information generation unit 280 of the motion analysis device 2 is stored in the storage unit 30 of the motion analysis device 2.

  Then, after the user travels, the detailed analysis unit 292 of the motion analysis device 2 generates image data of a radar chart (described later) as the travel results of the user based on each index stored in the storage unit 30. In addition, the communication unit 40 of the motion analysis device 2 transmits image data of a generated radar chart (described later) to the notification device 3 via post-travel output information.

  On the other hand, when the communication unit 140 of the notification device 3 receives image data of a radar chart (described later) from the motion analysis device 2, the communication unit 140 sends the image data to the processing unit 120 of the notification device 3, and the processing unit 120 of the notification device 3 The image data is displayed on the display unit 170 of the notification device 3.

Here, it is assumed that the image data of the radar chart is output from the motion analysis apparatus 2 as post-travel output information. However, even if the image data of the radar chart is output from the motion analysis apparatus 2 as output information during travel. Good. In that case, the radar chart in the display unit 170 is updated every time the user travels in the section. In this case, for example, every time the processing unit 120 of the notification device 3 receives a radar chart, the processing unit 120 displays the received radar chart superimposed on the radar chart being displayed on the display unit 170.

3-2. Example of Radar Chart FIG. 50 is an example of a radar chart according to the present embodiment.

  In the motion analysis apparatus 2 according to the present embodiment, the radar chart has different coordinate axes arranged radially as shown in FIG. In the example of FIG. 50, a radar chart in which five axes are arranged radially is displayed. The first axis of the radar chart is assigned to an index relating to energy efficiency, and the second axis is a burden on the body. The third axis is assigned to an index related to leg flow, the fourth axis is assigned to an index related to a spring coefficient (such as propulsive force), and the fifth axis is energy Assigned to the loss indicator. The more polygonal shapes (here pentagons) that appear on this radar chart, the better the balance of these five indicators. In addition, the larger the size of the polygon (here, the pentagon) shown in the radar chart, the better the five indicators are as a whole.

  In the example of FIG. 50, the radar chart relating to the first time interval, the radar chart relating to the second time interval, the radar chart relating to the third time interval, and the radar chart relating to the fourth time interval. Are superimposed and displayed.

  The first time interval is the interval from the travel start time (0 minute time) to the 10 minute time point, the second time interval is the interval from the 10 minute time point to the 20 minute time point, and the third time interval Is a section from the 20-minute time point to the 30-minute time point, and the fourth time period is a section from the 30-minute time point to the 40-minute time point.

  In the example of FIG. 50, the radar chart relating to the first time interval, the radar chart relating to the second time interval, the radar chart relating to the third time interval, and the radar chart relating to the fourth time interval. Are distinguished from each other by different line types (color, density, pattern, etc.). Note that the legend shown on the right side of the figure shows the correspondence between sections and line types.

  Therefore, the user can recognize the balance and quality of the plural types of indicators in each section as polygons (pentagons) of the radar chart. For example, the user can use a section in which the shape of the polygon (pentagon) of the radar chart suddenly collapses (in the example of FIG. 50, a section of 20 min to 30 min) to which a change point where the balance of a plurality of types of indicators is lost belongs. Or a section (a section of 40 min to 40 min in the example of FIG. 50) in which the size of the polygon (pentagon) is suddenly reduced is set as a section to which a change point where a plurality of types of indicators are deteriorated as a whole belongs. It is easy to specify. For example, the user can also determine that the running ability is higher as the time from the start of travel to the section to which the change point belongs (for example, the section of 30 min to 40 min) is longer. For example, the user can objectively grasp that his / her running ability is “30 min” when the section to which the change point belongs is a section of 30 min to 40 min. Further, for example, when the section to which the change point belongs is a section of 20 min to 30 min, the user can objectively grasp that his / her running ability is “20 min”.

  FIG. 51 is another example of a radar chart according to the present embodiment.

In the motion analysis apparatus 2 according to the present embodiment, the radar chart has different coordinate axes arranged radially as shown in FIG. In the example of FIG. 51, a radar chart in which five axes are arranged in a radial pattern is displayed. The first axis of the radar chart is assigned to an indicator relating to the true landing rate, and the second axis is related to propulsion efficiency. The third axis is assigned to an indicator relating to leg flow, the fourth axis is assigned to an indicator relating to a spring coefficient (propulsive force, etc.), and the fifth axis is assigned to a landing brake. It is assigned to the quantity indicator. The more polygonal shapes (here pentagons) that appear on this radar chart, the better the balance of these five indicators. In addition, the larger the size of the polygon (here, the pentagon) shown in the radar chart, the better the five indicators are as a whole.

  In the example of FIG. 51, the radar chart relating to the first time interval, the radar chart relating to the second time interval, the radar chart relating to the third time interval, and the radar chart relating to the fourth time interval. Are superimposed and displayed.

  The first time interval is the interval from the travel start time (0 minute time) to the 10 minute time point, the second time interval is the interval from the 10 minute time point to the 20 minute time point, and the third time interval Is a section from the 20-minute time point to the 30-minute time point, and the fourth time period is a section from the 30-minute time point to the 40-minute time point.

  In the example of FIG. 51, the radar chart relating to the first time interval, the radar chart relating to the second time interval, the radar chart relating to the third time interval, and the radar chart relating to the fourth time interval. Are distinguished from each other by different line types (color, density, pattern, etc.). Note that the legend shown on the right side of the figure shows the correspondence between sections and line types.

  Therefore, the user can recognize the balance and quality of the plural types of indicators in each section as polygons (pentagons) of the radar chart. For example, the user can select a section where the polygonal (pentagonal) shape of the radar chart suddenly collapses (in the example of FIG. 51, a section between 10 min and 20 min), and a section to which a change point where the balance of a plurality of types of indicators is lost belongs. Or a section (a section of 10 min to 20 min in the example of FIG. 51) in which the size of the polygon (pentagon) suddenly decreases is set as a section to which a change point in which a plurality of types of indicators are totally deteriorated belongs. It is easy to specify. For example, the user can also determine that the running ability is higher as the time from the start of travel to the section to which the change point belongs (for example, a section of 10 min to 20 min) is longer. For example, when the section to which the change point belongs is a section of 10 min to 20 min, the user can objectively grasp that his / her running ability is “20 min”. Further, for example, when the section to which the change point belongs is a section of 20 min to 30 min, the user can objectively grasp that his / her running ability is “20 min”.

3-3. Flow The processing flow in the present embodiment is basically the same as the processing flow in the second embodiment (FIG. 49), and a description thereof will be omitted. The flow of this embodiment is equivalent to the flow of the second embodiment in which “distance section” is replaced with “time section”.

4). Fourth Embodiment Next, a motion analysis system according to a fourth embodiment of the present invention will be described. In the present embodiment, differences from the above-described embodiment will be described, and description of similar parts will be omitted. Further, in the present embodiment, the same elements as those in the above embodiment are described with the same reference numerals.

FIG. 52 is a block diagram of a motion analysis system 1 ′ according to this embodiment. As shown in FIG. 52, the motion analysis system 1 ′ according to the present embodiment includes a motion analysis device 2 (an example of a sensor, an example of a motion analysis device), a notification device 3, an information analysis device 4, and a server 5. ing. Among these, the structure of the motion analysis apparatus 2 and the alerting | reporting apparatus 3 is as having mentioned above. The information analysis device 4 can be connected to the server 5 via a network such as the Internet or a LAN (Local Area Network). The information analysis device 4 is an information device such as a tablet PC, a personal computer, or a smartphone, and can perform data communication with the server 5 via a network.

  This motion analysis system 1 'is effective, for example, in training a team (such as an Ekiden team) composed of a plurality of runners. Therefore, here, it is assumed that a team composed of a plurality of runners and a manager who has jurisdiction over the team use the motion analysis system 1 '. In this case, the plurality of runners carry the motion analysis device 2 and the notification device 3 respectively, and the supervisor carries the information analysis device 4 and uses them as host terminals. That is, although not clearly shown in FIG. 52, the motion analysis system 1 'according to the present embodiment provides the same number of motion analysis devices 2 and notification devices 3 as the number of runners.

  First, the plurality of motion analysis devices 2 transmit the running state (motion analysis information 350 or the like) of the runner that is the attachment destination to the information analysis device 4 for each section that is running, for example. Then, the information analysis device 4 transmits the running state (exercise analysis information 350 and the like) for each runner and for each section received from the motion analysis device 2 to the server 5 via the network. Further, the server 5 receives the running states (exercise analysis information 350 and the like) of a plurality of runners and stores them in a database constructed in a storage unit (not shown) of the server 5.

Next, the server 5 generates image data of the radar chart based on the database. The method for generating the radar chart is as described above. For example, the radar chart generated in this embodiment is
(I) Radar chart showing the running ability of each runner in the team,
(Ii) Radar chart showing the running ability of the entire team,
(Iii) A radar chart showing the running ability of the entire team and a radar chart showing the running ability of other teams,
(Iv) a radar chart showing the running ability of each runner in the team and a radar chart showing the running ability of a specific runner outside the team;
At least one of them. FIG. 53 shows an example in which the radar charts of six runners A, B, C, D, E, and F in the team are displayed side by side as an example of the radar chart of (i). In FIG. 53, the radar chart of runner A displays a plurality of types of indicators related to runner A for each section. The radar chart of runner B displays a plurality of types of indicators related to runner B for each section. The radar chart of the runner C displays a plurality of types of indicators related to the runner C for each section. The radar chart of the runner D displays a plurality of types of indicators related to the runner D for each section. The radar chart of the runner E displays a plurality of types of indicators related to the runner E for each section. The radar chart of the runner F displays a plurality of types of indicators related to the runner F for each section. The configuration of each person's radar chart is as described in the second or third embodiment (see FIGS. 47, 48, 50, and 51). Then, the server 5 transmits the generated image data of one or more radar charts to the information analysis device 4 via the network.

Next, the information analysis device 4 displays the image data of one or more radar charts received from the server 5 on a display unit (not shown) of the information analysis device 4. Therefore, the team manager
(I) grasp the running ability of each runner in the team;
(Ii) grasp the running ability of the entire team;
(Iii) comparing the running ability of the whole team with the running ability of other teams;
(Iv) comparing the run ability of each runner in the team with the run ability of a specific runner outside the team;
At least one of the following can be performed.

  In the motion analysis system 1 ′ of the present embodiment, one or a plurality of notification devices 3 can be omitted. Further, in the motion analysis system 1 ′ of the present embodiment, part or all of the functions of the server 5 may be mounted on the information analysis device 4 side, or may be mounted on the motion analysis device 2 side. . The data communication between the motion analysis device 2 and the information analysis device 4 may be direct communication such as short-range wireless communication, communication via a network, or the server 5 connected to the network. It may be via communication. Moreover, functions other than sensing among the functions of the motion analysis device 2 may be mounted on at least one side of the notification device 3, the information analysis device 4, and the server 5.

5. 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.

5-1. Regarding the section index In the above embodiment, the average value within the section of the index is used as the section index, but other statistical values such as the maximum value, the minimum value, and the median value within the section may be used as the section index. Good. In the above embodiment, various indices other than those described above can be used as indices assigned to the axes of the radar chart. For example, in the second embodiment, the travel time (lap time) in the distance section may be used as one of the indices of the section, and in the third embodiment, the travel distance (lap distance) in the time section is It may be used as one of the indices of the section. In addition, the start point (or end point) of the distance section in the second embodiment can be detected based on, for example, the output of the GPS unit 50 mounted on the motion analysis device 2, and the start period ( Alternatively, the start point (or end point) of the final stage can be detected based on, for example, the output of a timer unit (not shown) mounted in the motion analysis apparatus 2. In addition, the lap time can be detected based on the output of a clock unit (not shown) mounted on the motion analysis device 2, and the lap distance can be detected based on the output of the GPS unit 50 mounted on the motion analysis device 2. Can do.

5-2. About Radar Chart Criteria Each radar chart in the above-described embodiment is preferably created with reference to an index in the first interval (0 km to 5 k interval, 0 min to 10 min interval, etc.). That is, it is desirable that the polygon related to the first section is created so as to be a regular polygon. This is because the index balance of the other sections can be confirmed with the index balance of the first section as a reference.

5-3. 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 integrated. It does not have to be. 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 either case, 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.

5-4. In addition, in the above-described embodiment, the attachment part of the sensor (the motion analysis apparatus 2 (IMU 10)) to the user is “waist”, but it may be a part 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 the user's head or feet other than the arms. Further, the number of sensors is not limited to one, and an additional sensor may be attached to another part of the body. For example, sensors may be attached to the waist and legs and the waist and arms.

  Moreover, in said embodiment, although the motion-analysis apparatus 2 is mounted | worn with the user, it is not restricted to this. For example, an inertial measurement unit (inertial sensor) or a GPS unit 50 is mounted on a user's torso, etc., and the inertial measurement unit (inertial sensor) or GPS unit has a detection result obtained from a mobile information device such as a smartphone or a personal computer. The user's movement may be analyzed using the detection results transmitted to the server 5 via the information device or the network and received by these devices. Alternatively, an inertial measurement unit (inertial sensor) or GPS unit 50 mounted on the user's torso 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 personal computer records the recording medium. The motion analysis process may be performed by reading the detection result from the medium.

5-5. In the above-described embodiment, the inertial navigation calculation unit 22 performs a part of the inertial navigation calculation using a signal from a GPS satellite. However, a global navigation satellite system (GNSS: Global Navigation) other than GPS is used. Satellite System) positioning satellites or signals from positioning satellites other than GNSS may be used. 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) may be used. Good. Further, an indoor positioning system (IMES: Indoor Messaging System) or the like may be used.

5-6. Other Detection Methods In the above-described embodiment, the travel detection unit 242 detects the travel cycle at the timing when the acceleration of the user's vertical movement (z-axis acceleration) is equal to or greater than the threshold value. For example, the traveling cycle may be detected at a timing at which the vertical movement acceleration (z-axis acceleration) changes from positive to negative (or timing from negative to positive). Alternatively, the travel detection unit 242 calculates the vertical motion speed (z-axis speed) by integrating the vertical motion acceleration (z-axis acceleration), and uses the calculated vertical motion speed (z-axis speed) to determine the travel cycle. May be detected. In this case, for example, the travel detection unit 242 may detect the travel cycle 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. Further, for example, the travel detection unit 242 may calculate a combined acceleration of the x-axis, the y-axis, and the z-axis, and detect the travel cycle using the calculated combined acceleration. In this case, for example, the travel detection unit 242 may detect the travel cycle at a timing when the resultant acceleration 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. .

5-7. Utilization of Biological Information For example, the motion analysis apparatus 2 may generate the motion analysis information 350 (index) using the user's biological information. Moreover, it is good also considering biometric information as a parameter | index (an example of exercise information). Examples of biological information include skin temperature, center temperature, oxygen consumption, mutation between beats, heart rate, pulse rate, respiratory rate, skin temperature, center body temperature, heat flow, electric skin reaction, electromyogram (EMG) ), Electroencephalogram (EEG), electrooculogram (EOG), blood pressure, oxygen consumption, activity, beat-to-beat variation, electric skin reaction, and the like. The motion analysis device 2 may include a device that measures biological information, or the motion analysis device 2 may receive biological information measured by the measurement device. For example, the user wears a wristwatch-type pulse meter or runs with a Hartley sensor wrapped around the chest with a belt, and the motion analyzer 2 uses the measured value of the pulse meter or the Hartley sensor to run the user. You may calculate the inside heart rate.

5-8. Function sharing In the above embodiment, a part or all of the functions of the motion analysis device 2 may be mounted on at least one of the notification device 3 and the information analysis device 4. In the above embodiment, a part of the function of the motion analysis device 2 may be mounted on the server 5. In the above embodiment, a part or all of the functions of the notification device 3 may be mounted on at least one of the motion analysis device 2, the information analysis device 4, and the server 5. In the above embodiment, a part or all of the functions of the information analysis device 4 may be mounted on at least one of the motion analysis device 2, the notification device 3, and the server 5. In the above embodiment, some or all of the functions of the server 5 may be mounted on at least one of the motion analysis device 2, the notification device 3, and the server 5. In the above embodiment, the user's travel data (motion analysis information) is stored in the database of the server 5, but may be stored in a database constructed in the storage unit 430 of the information analysis device 4.

5-9. Other types of sports In addition, in the above-described embodiment, the subject is motion analysis in running of a person, but the present invention is not limited thereto, and may be subject to motion analysis in walking. Further, the present invention can be similarly applied to motion analysis in walking and running of a moving body such as an animal or a walking robot. In addition to running, apply to a wide variety of exercises such as mountain climbing, trail running, skiing (including cross country and ski jumping), snowboarding, swimming, cycling, skating, golf, tennis, baseball, rehabilitation, etc. Can do.

5-10. In the above embodiment, the notification device 3 is a wristwatch type device. However, the notification device 3 is not limited to this, and is not limited to a wristwatch type portable device (head mounted display (HMD) or user). A device (such as the motion analysis device 2) worn on the waist of the user or a portable device (such as a smartphone) that is not wearable. When the notification device 3 is a head-mounted display (HMD), the display unit 170 is sufficiently larger than the display unit of the wristwatch-type notification device 3 and has good visibility. So, for example, you may display information on the user's current running history, or a virtual runner created based on the time (time set by the user, self-record, celebrity record, world record, etc.) will run. An image may be displayed.

6). Other Each embodiment and each modification described above are examples, and the present invention is 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. In addition, 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 ... Notification apparatus as a display apparatus, 10 ... Inertial measurement unit (IMU), 12 ... Acceleration sensor, 14 ... Angular velocity sensor, 16 ... Signal processing part, 20 ... Processing part , 22 ... inertial navigation calculation unit, 24, 24a ... motion analysis unit, 30, 30a ... storage unit, 40 ... communication unit as output unit, 50 ... GPS unit, 60 ... geomagnetic sensor, 120 ... processing unit, 130 ... storage 140, communication unit, 150, operation unit, 160, timing unit, 170, display unit, 180, sound output unit, 190, vibration unit, 210, bias removal unit, 220, integration processing unit, 230, error estimation unit 232, a comparison unit, 234, a prediction unit, 240, a travel processing unit, 242, a travel detection unit, 244, a step length calculation unit, 246, a pitch calculation unit, 250, a coordinate conversion unit, 260, a feature inspection. Unit 262 ... contact time / impact time calculation unit 270 ... motion information generation unit as running state calculation unit 271 ... travel locus calculation unit 272 ... basic information generation unit 273 ... first analysis information generation unit 274 ... Second analysis information generation unit, 275 ... right / left difference rate calculation unit, 277 ... detection unit, 280, 280a ... traveling output information generation unit, 290 ... travel analysis unit, 291 ... overall analysis unit, 292 ... detailed analysis unit, 293 Reference analysis unit 294 Output information selection unit 300 Motion analysis program 302 Inertial navigation calculation program 304 Motion analysis information generation program 306 Travel analysis program 310 Sensing data table 320 GPS data table , 330 ... Geomagnetic data table, 340 ... Calculation data table, 350 ... Motion analysis information, 351 ... Input information, 352 Facts, 353 ... first analysis information, 354 ... second analysis information, 355 ... right difference ratio, 356 ... traveling locus information, 357 ... measured running state data, 358 ... change point distance data.

Claims (8)

Using the detection result of the sensor, a motion analysis unit that analyzes a plurality of types of motion information in the running or walking of the user for each section of the running or walking, and
An output unit that outputs a graph representing the plurality of types of motion information with mutually different coordinate axes, for each section of the running or walking,
A motion analysis apparatus comprising: In claim 1,
The plurality of types of exercise information are:
A motion analysis apparatus comprising at least one of exercise information related to energy efficiency, exercise information related to energy loss, and exercise information related to a burden on the body. In claim 1 or 2,
The plurality of types of exercise information are:
Including at least one of exercise information related to true landing, exercise information related to propulsion efficiency, exercise information related to landing brake amount, and exercise information related to contact time,
A motion analysis apparatus characterized by that. In any one of Claims 1-3,
The graph is
A radar chart in which different coordinate axes are arranged radially,
A motion analysis apparatus characterized by that. The motion analysis apparatus according to any one of claims 1 to 4,
The sensor;
A motion analysis system comprising: Using the detection result of the sensor, analyzing a plurality of types of motion information in the running or walking of the user for each section of the running or walking;
Outputting a graph representing the plurality of types of motion information with mutually different coordinate axes for each section of the running or walking;
A motion analysis method comprising: Using the detection result of the sensor, analyzing a plurality of types of motion information in the running or walking of the user for each section of the running or walking;
Outputting a graph representing the plurality of types of motion information with mutually different coordinate axes for each section of the running or walking;
A motion analysis program characterized by causing a computer to execute. Using the detection results of the sensor, analyze multiple types of motion information in the running or walking of the user for each section of the running or walking,
A graph representing the plurality of types of motion information with different coordinate axes is displayed for each section of the running or walking.
A display method characterized by that.