JP6521477B2 - Data analysis apparatus and data analysis method, data analysis program - Google Patents

Description

  The present invention relates to a data analysis device, a data analysis method, and a data analysis program for visualizing and providing a motion state (motion state) at the time of motion of a human body.

  BACKGROUND ART In recent years, with the background of rising health awareness and the like, an increasing number of people regularly perform activities such as running, walking and cycling to maintain and improve their health status. In addition, more and more people are aiming to participate in competitions such as marathon events through daily exercise. Such people are very conscious and interested in measuring and recording various kinds of biological information and exercise information with numerical values and data in order to understand their own health and exercise status. . In addition, people aiming to participate in competitions and the like aim for good results in the competition, so their awareness and interest in efficient and effective training methods are also very high.

  Various indexes are known as indicators for grasping one's own health condition and exercise condition based on numerical values and data measured during exercise. For example, in the case of quantitatively evaluating the running condition and the form, information such as the traveling speed and the stride can be used as an important and basic index. Here, as a method of measuring running speed and stride during running and marathon, for example, a method using positioning data and a received signal by GPS (Global Positioning System) is known. For example, Patent Document 1 calculates the distance based on the velocity of the human body measured from the Doppler frequency of the carrier wave received by the GPS receiver attached to the human body and the vibration displacement detected by the acceleration sensor. It is described that the stride per one step is calculated based on the number of steps, and thereafter the movement distance and the movement speed are calculated based on the stride updated by receiving GPS radio waves periodically and the number of steps integrated. It is done. Further, for example, in Patent Document 2, the exercise mode of the user is determined based on the current position of the user acquired by the GPS signal, and if it is in the exercise area, the movement distance or movement speed of the user, calorie consumption It is described that the amount of exercise such as is calculated.

Japanese Patent Application Laid-Open No. 10-325735 JP 2002-306660 A

  The above-described Patent Documents 1 and 2 disclose a method of calculating the movement speed, movement distance, stride, etc. of a human body or correcting the movement based on positioning data and received signals by GPS. However, in such a method, in places where reception of GPS radio waves is difficult, such as indoors or in a valley of a building, GPS signals can not be acquired, and the movement speed, movement distance, stride, etc. calculated depending on the acquisition situation. There is a problem that the accuracy is largely changed, and it can not be sufficiently used for accurate grasping of the exercise state, its judgment, and improvement.

  Therefore, in the present invention, in view of the above-mentioned problems, the index related to the motion state of the human body is accurately estimated based on various data collected in time series without using GPS, and grasping the motion state And a data analysis apparatus, data analysis method, and data analysis program that can be used for the determination and improvement thereof.

The data analysis apparatus according to the present invention is
Based on angular velocity data collected chronologically from the motion sensor of a sensor having a motion sensor mounted on the trunk of the user's body during an exercise in which the user travels a course having a known distance A time-series angle data generation unit that generates time-series angle data indicating a change in an angle in a traveling direction of the user of the path moved during the movement by accumulating absolute values of angles from the start of the movement;
An input operation unit that defines a plurality of sections having different change tendencies of the angle in the time-series angle data based on an input instruction;
Based on the time required for moving each of the plurality of sections specified by the instruction by the input operation unit and the distance value of each section, time series velocity data indicating the moving speed of each section is generated Time series velocity data generation unit,
A display unit for displaying at least the time-series angle data and the time-series velocity data in a predetermined form;
Have
The input operation unit receives the switching point serving as the boundary of the continuous sections as the instruction, with respect to the time series angle data displayed in the predetermined form on the display unit . Each section is defined based on the switching point.

The data analysis method according to the present invention is
Based on angular velocity data collected chronologically from the motion sensor of a sensor having a motion sensor mounted on the trunk of the user's body during an exercise in which the user travels a course having a known distance By accumulating absolute values of angles from the start of exercise, time-series angle data indicating change in angle in the traveling direction of the user of the path moved during the exercise is generated.
Based on the input instruction, the change trend of the angle before Symbol time series angle data defining a plurality of different sections,
Time required for the movement of each of the defined plurality of sections, on the basis of the value of the distance of each segment, and generating a sequence velocity data when indicating the moving speed of each of the sections,
A data analysis method, wherein at least the time series angle data and the time series velocity data are displayed on a display unit in a predetermined form,
With respect to the time series angle data displayed in the predetermined form on the display unit, a switching point serving as a boundary of the successive sections is input as the instruction, and the switching point is input based on the input switching point Each of the sections is defined .

The data analysis program according to the present invention is
On the computer
Based on angular velocity data collected chronologically from the motion sensor of a sensor having a motion sensor mounted on the trunk of the user's body during an exercise in which the user travels a course having a known distance By accumulating absolute values of angles from the start of exercise, time-series angle data indicating change in angle in the traveling direction of the user of the path moved during the exercise is generated.
Based on the input instruction, the change trend of the angle before Symbol time series angle data to define a plurality of different sections,
Time required for the movement of each of the defined plurality of sections, on the basis of the value of the distance of each segment, and generating a sequence velocity data when indicating the moving speed of each of the sections,
It is a data analysis program which displays at least said time series angle data and said time series speed data on a display part in a predetermined form,
With respect to the time series angle data displayed in the predetermined form on the display unit, a switching point serving as a boundary of the successive sections is input as the instruction, and the switching point is input based on the input switching point Each of the sections is defined .

  According to the present invention, it is possible to accurately estimate an index related to the motion state of a human body based on various data collected chronologically without using GPS, and to help grasp, judge, and improve the motion state. be able to.

It is a schematic block diagram which shows one Embodiment of the exercise assistance apparatus to which the data analysis apparatus which concerns on this invention is applied. It is a flow chart which shows an example of the data analysis method concerning a 1st embodiment. It is a figure which shows the relationship of the integral value of angular velocity data produced | generated in the data-analysis method which concerns on 1st Embodiment, and time-sequential angle data. It is a figure which shows the example of a display of the time-sequential angle data produced | generated in the data-analysis method which concerns on 1st Embodiment, and time-sequential speed data. It is a figure which shows the example of a display for demonstrating input operation (editing operation) in the data analysis method which concerns on 1st Embodiment. It is a figure which shows the specific example of input operation (editing operation) in the data analysis method which concerns on 1st Embodiment. It is a flow chart which shows an example of the data analysis method concerning a 2nd embodiment. It is a flowchart which shows an example of the area estimation process in the data analysis method which concerns on 2nd Embodiment. It is a conceptual diagram for demonstrating the class selection process in the data analysis method which concerns on 2nd Embodiment. It is a conceptual diagram for demonstrating the area estimation process in the data analysis method which concerns on 2nd Embodiment. It is a conceptual diagram for demonstrating the area determination process in the data analysis method which concerns on 2nd Embodiment. It is a figure which shows the example of a display of the time-sequential angle data produced | generated in the data-analysis method which concerns on 2nd Embodiment, time-sequential speed data, and an angle histogram. It is a figure which shows the example of a display for demonstrating the area correction process in the data analysis method which concerns on 2nd Embodiment.

  Hereinafter, a data analysis apparatus, a data analysis method, and a data analysis program according to the present invention will be described in detail by showing embodiments. In the following embodiments, the data analysis apparatus according to the present invention is applied to an exercise support apparatus, and a user (user) runs a track such as an athletics stadium, a predetermined running course, a marathon course, etc. A case will be described in which the moving speed and the stride (step length) during traveling are estimated based on various data (sensor data) collected, and visualized and provided to the user.

First Embodiment
(Exercise support device)
FIG. 1 is a schematic configuration view showing a first embodiment of an exercise support apparatus to which a data analysis apparatus according to the present invention is applied. FIG. 1A is a conceptual diagram showing how a sensor device or the like applied to the exercise support apparatus according to this embodiment is attached to a human body, and FIG. 1B shows the configuration of the sensor apparatus and data analysis apparatus. It is a schematic block diagram.

  For example, as shown in FIG. 1A, the exercise support apparatus according to the embodiment of the present invention includes a sensor device 100 attached to the waist of the user US and a control device 300 attached to the wrist of the user US. And a data analysis device 200 that analyzes sensor data collected by the sensor device 100.

  The sensor device 100 is a motion sensor having a function of measuring and storing various sensor data related to the operation state of a human body during exercise accompanied by movement such as running or marathon. Here, in the present embodiment, a configuration in which the sensor device 100 is attached to the waist of the user US is shown, but the present invention is not limited to this. As long as it is worn on the body axis passing through the center of the human body, it may be worn at another position, for example, the chest, neck, abdomen or the like. Also, the method of attaching the sensor device 100 to the human body is not particularly limited, and various attachment methods, such as, for example, a form in which it is clipped in training wear, a form in which it is stuck with a tape member, a form in which it is wound around a body by a belt, etc. The method may be applied as appropriate.

  Specifically, for example, as illustrated in FIG. 1B, the sensor device 100 includes an acceleration measurement unit 110, an angular velocity measurement unit 120, a storage unit 130, a control unit 140, and an interface for wireless communication (hereinafter referred to as The wireless communication interface (abbreviated as “wireless communication I / F”) 150 and a wired communication interface (hereinafter, abbreviated as “wired communication I / F”) 160 are provided.

  The acceleration measurement unit 110 measures the rate of change (acceleration) of the movement speed of the user US during exercise. The acceleration measurement unit 110 has a three-axis acceleration sensor, detects an acceleration component (acceleration signal) along each of three mutually orthogonal three axis directions, and outputs it as acceleration data. Further, the angular velocity measurement unit 120 measures a change (angular velocity) of the movement direction of the user US during the movement. The angular velocity measurement unit 120 has a three-axis angular velocity sensor, and detects angular velocity components (angular velocity signals) generated in the rotational direction of rotational movement along each of the three axes orthogonal to one another defining the acceleration data. Output as angular velocity data. The sensor data (acceleration data, angular velocity data) acquired by the acceleration measurement unit 110 and the angular velocity measurement unit 120 is associated with time data generated by the control unit 140 described later, and a predetermined storage area of the storage unit 130 described later Stored in

  The storage unit 130 stores sensor data (acceleration data, angular velocity data) acquired by the acceleration measurement unit 110 and the angular velocity measurement unit 120 in a predetermined storage area in association with time data. Note that a part or all of the storage unit 130 may have a form as a removable storage medium such as a memory card, for example, and may be configured to be attachable to and detachable from the sensor device 100.

  The control unit 140 is an arithmetic processing unit such as a CPU (central processing unit) or an MPU (microprocessor) having a clocking function, and executes a predetermined control program based on a predetermined operation clock. Thus, the control unit 140 performs sensing operations in the acceleration measurement unit 110 and the angular velocity measurement unit 120, stores various data in the storage unit 130, and reads out data, and externals in the wireless communication I / F 150 and the wired communication I / F 160 described later. It controls various operations such as communication with devices and data transfer operations.

  The wireless communication I / F 150 receives at least a command signal for instructing log start or log end in the sensor device 100 transmitted from the control device 300 described later, and transfers the command signal to the control unit 140. Thereby, the start or the end of the sensing operation in the acceleration measurement unit 110 or the angular velocity measurement unit 120 is controlled, and the sensor data acquired during the sensing operation is chronologically stored in a predetermined storage area of the storage unit 130. Stored in Here, as a method of transmitting various signals between the sensor device 100 and the control device 300 in the wireless communication I / F 150, for example, Bluetooth (registered trademark) or WiFi (wireless fidelity (registered) Various wireless communication systems such as trademarks) can be applied.

  The wired communication I / F 160 has at least a function of transferring sensor data stored in the storage unit 130 to the data analysis device 200 described later. Thereby, in the data analysis device 200, predetermined data analysis processing for estimating the traveling speed and stride of the user US is executed. Here, as a method of transmitting sensor data from the sensor device 100 to the data analysis device 200 in the wired communication I / F 160, various wired methods via, for example, a communication cable (USB cable) of USB (Universal Serial Bus) standard, etc. A communication scheme can be applied.

  The data analysis device 200 uses the moving speed (traveling speed) and stride as an index (exercise index) related to the motion state of the human body based on various sensor data measured and accumulated by the sensor device 100 during the exercise of the user US. Has a function to estimate and provide Here, the data analysis device 200 may be a laptop or desktop personal computer, as long as it has a function capable of executing a data analysis program described later, a smart phone (high-performance mobile phone) or a tablet terminal May be a portable information terminal such as Further, when the data analysis program is executed using a cloud system on a network, the data analysis device 200 may be a communication terminal connected to the cloud system.

  Specifically, as shown in FIG. 1B, for example, the data analysis device 200 includes a display unit 210, a storage unit 230, and a control unit (time-series angle data generation unit, time-series velocity data generation unit, motion An index calculation unit) 240, an input operation unit 250, and a wired communication I / F 260 are provided.

  The display unit 210 includes, for example, a liquid crystal display capable of color display and a light emitting element display panel such as an organic EL element, and at least information related to an input operation using an input operation unit 250 described later Display in a predetermined form. Specifically, the display unit 210 may be sensor data (acceleration data, angular velocity data) stored in a storage unit 230 described later, a graph indicating traveling speed and stride calculated based on the sensor data, and various types of graphs. Display the setting menu etc.

  The storage unit 230 stores sensor data transferred from the sensor device 100 via a wired communication I / F 260 described later in a predetermined storage area. Here, the sensor data accumulated in the storage unit 230 is stored in time series, for example, in association with a traveling method (training menu or the like) or a course condition (course type, traveling distance, corner angle or the like). The sensor data accumulated in the storage unit 230 may be for a specific one user or may be for a plurality of users. In addition, the storage unit 230 executes predetermined control programs and algorithm programs in the control unit 240 described later to generate data and graphs indicating the traveling speed and stride, and various information is displayed on the display unit 210. Save the data to be used and the data generated. The storage unit 230 may store a control program or an algorithm program executed by the control unit 240. In addition, a part or all of the storage unit 230 may have a form as a removable storage medium such as a memory card, for example, and may be configured to be removable from the data analysis device 200.

  The control unit 240 is an arithmetic processing unit such as a CPU or an MPU, and displays a variety of information on the display unit 210 by executing a predetermined control program, or from the sensor device 100 in the wired communication I / F 260 described later. It controls various operations such as transfer of sensor data and storage and readout of sensor data in the storage unit 230. In addition, the control unit 240 executes a predetermined algorithm program stored in the storage unit 130 to extract corresponding sensor data from the storage unit 230 for training and attempts desired by the user US, by the user US While reflecting the contents of the input operation (editing operation), analysis processing is performed to generate data and graphs indicating the traveling speed and the stride. Here, the control program or algorithm program executed by the control unit 240 may be incorporated in advance in the control unit 240. The data analysis method according to the present embodiment will be described in detail later.

  The input operation unit 250 is an input unit such as a keyboard, a mouse, a touch pad, or a touch panel attached to the data analysis device 200. The input operation unit 250 corresponds to the item, the icon, or the position by the user US selecting an arbitrary item or icon displayed on the display unit 210 or designating an arbitrary position on the screen display. The function is performed. In particular, in the present embodiment, when the input operation unit 250 selects training or an attempt to perform analysis processing from sensor data stored in the storage unit 230, or travels from sensor data corresponding to the selected training or attempt. It is used for input operation when generating data and graphs indicating speed and stride. Here, the input unit applied to the input operation unit 250 may include, for example, any one of the various input units described above, or a plurality of input units. It may be

  The wired communication I / F 260 has a function of receiving at least sensor data transmitted from the above-described sensor device 100 and transferring the data to the storage unit 230. Here, as a method of receiving sensor data from the sensor device 100 in the wired communication I / F 260, the wired communication method via the above-described USB cable or the like can be applied.

  The control device 300 is connected to at least the sensor device 100 using a predetermined wireless communication method, and the user US operates the operation unit of the control device 300 to start logging from the control device 300 to the sensor device 100 or A command signal instructing end of log is sent. Thereby, in the sensor device 100, the start or end of the sensing operation in the acceleration measurement unit 110 or the angular velocity measurement unit 120 is controlled. Here, as a method for transmitting various signals between the control device 300 and the sensor device 100, various methods such as Bluetooth (registered trademark) or WiFi (wireless fidelity (registered trademark)) described above may be used. A wireless communication scheme can be applied. The control device 300 has a function of displaying (or notifying) the sensor data acquired in the sensor device 100, the operation state of the sensor device 100, time information, etc., in addition to the control of the sensing operation in the sensor device 100 described above. You may have.

  In the exercise support device according to the present embodiment, data transmission is performed between the sensor device 100 and the data analysis device 200 by wired communication, and data transmission is performed between the sensor device 100 and the control device 300 by wireless communication. Although the configuration to be performed is shown, the present invention is not limited to this. That is, data may be transmitted between the sensor device 100 and the data analysis device 200 by wireless communication, or data may be transmitted between the sensor device 100 and the control device 300 by wired communication. Good. Also, the method of transmitting sensor data from the sensor device 100 to the data analysis device 200 is applied by replacing a removable storage medium such as a memory card constituting the storage unit 130 of the sensor device 100 or the storage unit 230 of the data analysis device 200. It may be

  Further, in the present embodiment, as the control device 300, as shown in FIG. 1A, a device having a wristwatch type (or wristband type) worn on the wrist of the user US is shown. Is not limited to this. That is, the control device may be, for example, a portable information terminal such as a smartphone housed in a pocket or attached to an upper arm or a dedicated terminal, or a sensor without using a device separate from the sensor device 100. The device body may be provided with an operation switch for instructing log start or log end.

(Data analysis method)
Next, a control method (data analysis method) in the exercise support apparatus according to the present embodiment will be described with reference to the drawings. Here, from collection and accumulation of sensor data during exercise in the sensor device 100 according to the present embodiment to estimation of an index (traveling speed, stride) related to the exercise state in the data analysis device 200 and provision to the user A series of control processes will be described.

  FIG. 2 is a flowchart showing an example of a control method (data analysis method) of the exercise support apparatus according to the present embodiment, and FIG. 3 is an integral value of angular velocity data generated in the data analysis method according to the present embodiment It is a figure which shows a relationship with time-sequential angle data. FIG. 4 is a diagram showing a display example of time-series angle data and time-series velocity data generated in the data analysis method according to the present embodiment. FIG. 5 is a view showing a display example for explaining an input operation (editing operation) in the data analysis method according to the present embodiment. FIG. 6 is a diagram showing a specific example of the input operation (editing operation) in the data analysis method according to the present embodiment.

  The control method (data analysis method) in the exercise support apparatus according to the present embodiment is roughly divided into a procedure (sensor data collection procedure) of collecting and accumulating various sensor data relating to a motion state during running, and collected sensor data And a procedure (estimated estimation procedure) for estimating and displaying an indicator (traveling speed, stride) relating to the movement state based on. Here, the assistance of the user's input operation (editing operation) in the index estimation procedure, and the process of generating data or graphs indicating the index are based on a predetermined algorithm program executed in the control unit 240 of the data analysis device 200. To be realized.

  In the sensor data collection procedure, first, as shown in FIG. 1A, with the user US wearing the sensor device 100 on the waist, a track of a stadium, a predetermined running course, a marathon course, etc. There are a plurality of sections (for example, straight and curves of a track, etc.) in which the angle of the course in the traveling direction of the user is different from each other, and the entire distance and the distance of each section are known (known) Run by running etc. Here, when running is started, when the user US operates the control device 300 worn on the wrist or the like, a command signal instructing the start of a log to the sensor device 100 is transmitted from the control device 300. Thereby, the control unit 140 of the sensor device 100 starts measurement of sensor data (acceleration data, angular velocity data) in the acceleration measurement unit 110 and the angular velocity measurement unit 120, and sequentially stores the measurement data in the storage unit 130. Then, when the running is ended, the user US operates the control device 300 to transmit a command signal instructing the sensor device 100 to end the log, and the sensor data in the acceleration measuring unit 110 and the angular velocity measuring unit 120 Measurement ends. Thereby, sensor data indicating an operating state during running is acquired in association with time data.

  Next, by connecting the sensor device 100 and the data analysis device 200 with a USB cable, sensor data accumulated during traveling is transferred from the sensor device 100 to the data analysis device 200 and stored in the storage unit 230. Here, when the user US transfers sensor data from the sensor device 100 to the data analysis device 200 (or while referring to the sensor data stored in the storage unit 230 with the display unit 210), the user data has acquired the sensor data Various pieces of information regarding running (exercise state) are input using the input operation unit 250. Specifically, item information such as a running method (training menu etc.) during running, course conditions (course type, traveling distance, corner angle etc.), user name etc. is input.

  Next, the control unit 240 executes axis correction processing on acceleration data of the sensor data stored in the storage unit 230. In general, the sensor device 100 attached to the trunk of the human body is affected by the swing and tilt of the upper body during exercise such as running, so the axis in the direction of gravity and the vertical direction of the human body detected by the sensor device 100. There is a difference between it and the axis of acceleration. Therefore, based on the value of the angular velocity data acquired by the sensor device 100, it is necessary to perform correction to offset the difference component in the axial direction which differs at each time.

  Specifically, the axis correction process first estimates the direction of gravity at each time based on angular velocity data acquired by the sensor device 100 by the control unit 240. Then, the control unit 240 corrects the value of the acceleration data by rotating each axis of the acceleration data so that the estimated gravity direction matches the vertical direction of the acceleration data. The corrected acceleration data and angular velocity data are stored in a predetermined storage area of the storage unit 230 as sensor data after correction.

  Next, in the index estimation procedure, the control unit 240 analyzes the above-mentioned sensor data after correction, and calculates pitch, vertical movement, contact time, and the like for each step of the running operation as an index related to the exercise state during running. Furthermore, a process of calculating and estimating the traveling speed and the stride is executed. Here, in the present embodiment, the calculation process (estimation process) of the traveling speed and the stride is premised on the input operation (edit operation) by the user US, and the estimation process in which the edit content is reflected is executed. In the present embodiment, in order to simplify the description, the user US can use a track such as an athletics stadium having two sections of a typical straight section and a curve section, and two sections having different course angles. It is assumed that the vehicle travels. However, the course that is the object of the present embodiment is not limited to such a track, and if the travel distance is known, three or more sections (for example, straight sections, loose sections, etc.) with different course angles. The same concept can be applied to a course having a curve section, a steep curve section, etc.).

  Specifically, in the index estimation process, first, the user US uses the input operation unit 250 of the data analysis device 200 to select arbitrary post-correction sensor data stored in the storage unit 230. Thereby, the control unit 240 executes a predetermined algorithm program (data analysis program) and integrates the angular velocity around the vertical axis for the corrected sensor data as shown in the flowchart of FIG. 2 (step S102). . Here, the vertical axis is an axis indicating a gravity direction perpendicular to the ground surface, and the control unit 240 extracts angular velocity data about the vertical axis from the sensor data after correction and performs integration processing. The result of integration processing of the angular velocity data is shown, for example, by a broken line in FIG.

  Next, the control unit 240 calculates an average value for each period of the running operation for the integration result of the angular velocity data, and generates time-series angle data (step S104). This time series angle data is shown, for example, by a solid line in FIG. 3 (a). Here, the cycle is a period from when two steps have been taken and the front is turned again, starting from the time when the user is facing the front during running. Specifically, for example, as shown in FIG. 3B, in the time-series angle data repeating cyclical change, two steps of the running operation (period in which the angle swings once in the positive direction and in the negative direction) is One cycle is shown. Alternatively, the period of the running operation may be calculated as one period based on the landing timing of the left and right feet detected from the acceleration data in the vertical direction of the sensor data. Further, in the time-series angle data shown in FIG. 3A, a region R1 in which the angle increases with the passage of time indicates a curve section, and a region R2 in which the angle is substantially constant (substantially equal) regardless of the passage of time is Indicates a straight section. That is, when the user US travels a track such as an athletic field where a straight section and a curve section are alternately repeated, the time-series angle data shown in FIG. 3A is a continuous curve section (region R1) of the track And the straight section (region R2).

  Next, the control unit 240 displays the generated time series angle data in the form of a graph on the screen of the display unit 210, as shown in the upper part of FIG. 4, for example. Here, the graph shown in the upper part of FIG. 4 is a track (straight section: repeating a straight section (corresponding to a "straight" section in the figure) and a curve section (corresponding to a "curve" section in the figure) Time series angle data at the time of going round a plurality of times at two places and a curve section: two places is shown. The value of the angle on the vertical axis in the upper part of FIG. 4 indicates the accumulated value of the absolute value of the value of the angle from the start of running.

  Then, the user US visually recognizes the graph (upper part of FIG. 4) of the time-series angle data displayed on the display unit 210 of the data analysis device 200, and manually adjusts each straight section using the input operation unit 250 such as a mouse. And select (or define) each curve section (step S106). Here, as shown in FIG. 3A and the upper part of FIG. 4, in the time-series angle data, the tendency of increase and decrease in angle differs between the straight section and the curve section. For example, when the angular velocity does not include noise or a drift component, the angle during traveling on the straight section becomes constant. From this, in the graph of time series angle data (upper part of FIG. 4), the straight section and the curve section can be judged relatively easily by the user US visually. Therefore, in the present embodiment, the user US visually recognizes the graph of the time-series angle data displayed on the display unit 210 (upper part in FIG. 4), and indicates the switching that becomes the boundary between each straight section and the curve section The switching point CP) is designated (or selected) one by one using the input operation unit 250 such as a mouse. The upper part of FIG. 4 shows a state in which the switching point CP is sequentially clicked and designated (or selected) by the mouse pointer MP with respect to the graph of the time-series angle data D1 displayed on the display unit 210.

The straight section and the curve section are defined by the input operation of sequentially specifying the switching points CP, and the time required to travel each section is determined. Further, the distances of the straight section and the curve section of the course (track on which the running was performed) which is the target of the present embodiment are known in advance. Accordingly, the control unit 240 calculates the traveling speed during running according to the following equation (1) based on the required time of each section and the distance of each section which is known.
Traveling speed (m / s) = section distance (m) / required time (s) ... (1)

  Thereby, the control unit 240 generates time-series velocity data (step S108), and displays the generated time-series velocity data in the form of a graph on the screen of the display unit 210, for example, as shown in the lower part of FIG. .

  Next, the user US performs an editing operation to correct the time-series angle data in accordance with the degree of change of the time-series speed data. That is, the speed (traveling speed) in the traveling operation such as running does not change rapidly at least between the continuous straight section and the curved section, but generally changes gently. Therefore, based on such an assumption, the user US looks at the graph of the time-series velocity data displayed on the screen of the display unit 210 (the lower part of FIG. 4) visually, and continues (or In the straight section (straight line) and the curve section (curved line) which are adjacent), attention is paid to a portion where the speed change is large. For example, in the lower part of FIG. 4, the user US focuses on a point VP where the speed change is relatively large.

  Then, the user US uses the input operation unit 250 such as a mouse in the graph of time series angle data in the upper part of FIG. For example, as shown in the upper part of FIG. 5, the user drags with the mouse pointer MP to correct (move to any position in the vertical and horizontal directions as indicated by the arrow in the drawing). Thereby, as shown in the lower part of FIG. 5, the speed change at the focused point VP of the time-series speed data is adjusted to be small (reduced).

  Specifically, the correction operation of the position of the switching point in this time series angle data is, first, the user US looking at the graph of the time series speed data shown in the lower part of FIG. Focus on. Then, as shown in the upper part of FIG. 5, the user US clicks (or drags) the switching point CPa of the position corresponding to the focused position VP with the mouse pointer MP to select it. Thereby, as shown in FIG. 6A, the control unit 240 selects a switching point adjacent to the switching point CPa while the switching point CPa is selected (switching points located on both sides; for example, switching point CPb) And the auxiliary lines Lsx, Lcx connecting the switching points to each other. As shown in FIG. 6B, the user US operates the mouse to move the switching point CPa selected by the mouse pointer MP to any position vertically and horizontally (refer to the arrow in the figure) on the screen. In order to make each auxiliary line Lsx, Lcx match or be equal to each change tendency of the straight section and the curve section of the time-series angle data D 1 (see the straight lines Ls, Lc in the figure), the switching point CPa Correct (adjust) the position of. Since the straight section and the curve section are reselected (or redefined) by such a correction operation, the control unit 240 determines the required time in each section which is determined by the corrected position of the switching point CPa. Then, the traveling speed is recalculated by the above equation (1). As a result, the control unit 240 regenerates the time-series velocity data and re-displays the graph on the screen of the display unit 210, as shown in the lower part of FIG. The user US sees the graph of the re-displayed time-series velocity data, and determines the degree (large or small) of the velocity change in the adjacent straight section and the curve section.

  That is, by such a correction operation, the position of the switching point CP is corrected in the time-series angle data, and the straight section and the curve section are reselected (or redefined) as in step S106 described above. Furthermore, as in step S108 described above, each straight section and curve during running according to equation (1), based on the required time determined by the corrected position of the switching point CP and the distance of each section that is known. The traveling speed of the section is recalculated to regenerate time-series speed data.

  Then, the user US repeatedly executes the above-mentioned correction operation for each switching point CP until the speed change in the time-series speed data becomes a desired small (that is, the speed change is gentle). Then, when the user US determines that the change in time-series velocity data is gradual, for example, by selecting the "edit end" menu on the screen of the display unit 210 shown in FIG. 4 and FIG. , And end the series of editing operations (step S110).

  Next, the control unit 240 stores the values of each straight section and curve section in the time-series velocity data generated (recalculated) by the above editing operation as a traveling speed in a predetermined storage area of the storage unit 230 (step S112).

Further, the control unit 240 performs stride during running according to the following equation (2) based on the traveling speed recalculated as described above and the pitch calculated as needed for each straight section and curve section. It is calculated and stored in a predetermined storage area of the storage unit 230 (step S114). Here, since the pitch is the number of steps per minute, for example, the number of cycles per minute in the signal waveform of the component in the vertical direction of the acceleration data stored in the storage unit 230 is measured for each section. It is acquired by doing.
Stride (m / step) = traveling speed (m / s) / pitch (step / s) (2)

  Thus, in the present embodiment, a track having a plurality of sections whose traveling distances are known (known) and whose course angles are different from each other (for example, a track of an athletic field having straight and curves) Etc.) based on the angular velocity (or time series angle data) about the vertical axis among the sensor data collected in time series by the motion sensor without using GPS. It can be estimated. Here, in the present embodiment, an input operation by the user US (an editing operation for correcting the position of a switching point defining each straight section and curve section in time-series angle data) when calculating the traveling speed and the stride described above. The contents can be reflected.

  That is, in general, when the angular velocity acquired by the gyro sensor (angular velocity measuring unit) is integrated, the error becomes large due to the influence of noise and drift component included in the angular velocity data, for example, when traveling in a straight section Even if there is a problem, the angle may increase or decrease. On the other hand, in the present embodiment, when the noise or drift component included in the angular velocity data does not change significantly with time, the input operation by the user US (a switching point defining each straight section and curve section) Each straight section and curve section in the time-series angle data can be accurately estimated by the estimation process reflecting the content of the editing operation of correcting the position of.

  As a result, the index (traveling speed and stride) related to the exercise state of the user US while running is accurately estimated, and the result is displayed on the screen of the display unit 210 in the form of graph or numerical value. It is possible to grasp the motion state through vision and use it for judgment and improvement.

Second Embodiment
(Data analysis method)
Next, a control method (data analysis method) in the exercise support apparatus according to the second embodiment will be described with reference to the drawings. In addition, about the structure and method equivalent to 1st Embodiment mentioned above, an equivalent code | symbol is attached | subjected and description is simplified.

  FIG. 7 is a flowchart showing an example of a control method (data analysis method) of the exercise support apparatus according to the second embodiment, and FIG. 8 shows an example of a section estimation process in the data analysis method according to the present embodiment. It is a flowchart. FIG. 9 is a conceptual diagram for explaining class selection processing in the data analysis method according to the present embodiment. FIG. 10 is a conceptual diagram for explaining a section estimation process in the data analysis method according to the present embodiment. FIG. 11 is a conceptual diagram for explaining the section determination process in the data analysis method according to the present embodiment. FIG. 12 is a diagram showing a display example of time-series angle data, time-series velocity data, and an angle histogram generated in the data analysis method according to the present embodiment. FIG. 13 is a view showing a display example for explaining the section correction process in the data analysis method according to the present embodiment.

  In the data analysis method according to the first embodiment described above, the time series angle generated by the user US using the input operation unit to specify the switching points defining each straight section and curve section of the running course. Based on the data, we showed the method to calculate the traveling speed and stride in each section. In the data analysis method according to the second embodiment, when traveling on a track such as an athletics stadium several tens of times, the input operation of the user US who designates a switching point defining each straight section and a curve section. There is a way to reduce the burden. Also in the second embodiment, the estimation processing of the traveling speed and the stride executed in the index estimation procedure described later is realized by executing a predetermined algorithm program in the control unit 240 of the data analysis device 200.

  In the data analysis method according to the second embodiment, first, as in the first embodiment described above, a sensor data collection procedure is performed, and the sensor device 100 is a sensor that indicates the operating state of the user US while running. Data is acquired, and the sensor data is transferred to the data analysis device 200 and stored in the storage unit 230.

  Next, in the index estimation procedure, as shown in the flowchart of FIG. 7, first, as in the first embodiment described above, with regard to any post-correction sensor data stored in the storage unit 230, the angular velocity around the vertical axis Are integrated (step S202). Next, the control unit 240 calculates an average value for each period of the running operation for the integration result of the above-mentioned angular velocity, and generates time-series angle data (step S204), for example, as shown in the upper part of FIG. , In the form of a graph on the screen of the display unit 210.

  Next, the control unit 240 integrates the angular velocity about the vertical axis at predetermined intervals. For example, when the predetermined cycle is set to 5 cycles, a value obtained by integrating the angular velocity for 5 cycles from the start of running is stored as an angle in the storage unit 230, and then the angular velocity for the next 5 cycles shifted for 5 cycles is A process of storing the integrated value as an angle in the storage unit 230 is repeatedly executed at each predetermined period (step S206). Here, the predetermined cycle may be set to, for example, about 5 cycles in the case of a track such as an athletics stadium, but if the section changes at a relatively short distance, a shorter cycle is set. It is preferable to do.

  Next, the control unit 240 calculates a class width for generating a histogram of angle change, which will be described later, based on the above calculation result (step S208). Specifically, for example, the class width may be determined by applying the "Sturgest formula", which is a well-known method, or the other according to the number of data for which the class width is to be calculated. The method may be applied.

  Next, the control unit (angle histogram generation unit) 240 is an angle histogram indicating the frequency change (angle change) of the angle for each predetermined period (angle change) based on the class width calculated and determined in step S208 above. Is displayed on the screen of the display unit 210, as shown in the lower part of FIG. 12, for example. Here, when traveling a track having a straight section having a predetermined angle and a curve section as in this embodiment, the histogram of the angle change is, for example, a curve section as shown in the left diagram of FIG. In this case, the distribution shows that the frequency is maximal at a position where the angle is away from “0”, and the distribution where the frequency is maximal at a position where the angle is closer to “0” than the distribution of the curve section in the straight section have. From this, in the angle histogram as shown in the left diagram of FIG. 9A, the user US can visually judge the class corresponding to the straight section and the curve section relatively easily. Therefore, in the present embodiment, the user US visually recognizes the angle histogram displayed on the display unit 210, and using the input operation unit 250 such as a mouse, for example, as shown in FIG. b) A class corresponding to the straight section and the curve section is selected as shown in the left drawing of (c) (step S212).

  Then, when the user US completes the input operation of selecting the class corresponding to the straight section and the curve section in the above step S212, the user US clicks the button or icon indicating completion, for example, with the mouse cursor to execute the above class End the selection process. As a result, the control unit 240 causes the straight section and the curve section of the time-series angle data in the state before the class is selected as shown in the right diagram of FIG. As shown in the right figure, the result of the class selection operation by the user US is reflected and displayed on the display unit 210 (step S214). Here, the class selection process shown in FIG. 9 shows a concept in the case of using an angle histogram and time-series angle data in an ideal state in which noise and drift components are not included in sensor data.

  Next, the control unit 240 executes a process of estimating a straight section and a curve section based on time series angle data in which the class selected by the user US is reflected (step S216). Here, the estimation process of the straight section and the curve section described below is applied to time-series angle data in a state in which noise and drift components are included in sensor data to remove the influence of the noise and drift components or It is for reducing. That is, even if the sensor data does not include noise or drift components or is hardly influenced by the noise, even if the straight segment and curve segment estimation processing shown in step S216 and FIG. 8 is omitted. Good.

  In this embodiment, based on the result of the selection operation of the class of the angle histogram in the above steps S212 and S214, for example, as shown in FIG. 10A, the time series angle data is divided into two clusters of straight and curve. It is classified. Here, the time-series angle data shown in FIG. 10 differs from the time-series angle data in the ideal state as shown in the right figure of FIG. 9, and the sensor data includes noise and drift components. It is an example of time series angle data in a more realistic state.

  In the estimation process of the straight section and the curve section in the present embodiment, as shown in the flowchart of FIG. 8, the control unit 240 performs the straight and curve clusters classified on the time series angle data in the following procedure. The grouping is performed within a predetermined time (step S252).

  Specifically, the control unit 240 first groups points that are temporally continuous in the same cluster on the time-series angle data into the same group as illustrated in FIG. 10B, for example. In FIG.10 (b), the state grouped into the straight 1-3 and the curves 1-3 is shown. Here, the points included in the straight 2 and the curve 2 are abnormal values caused by noise and drift components included in the sensor data.

  Next, the control unit 240 compares the groups in the same cluster, and integrates the groups which are not separated by Ta or more for a predetermined time. For example, in FIG. 10B, focusing on each group of straights 1, 2 and 3 in the same cluster, if the groups are not separated from each other by, for example, 10 seconds or more, as shown in FIG. These groups are integrated into one group (straight 1). Here, it is preferable to set the predetermined time Ta for determining whether or not the groups are to be integrated, for example, to about 10 seconds when traveling the track as in the present embodiment.

  Next, after integration of the above-described groups, for example, as illustrated in FIG. 10D, the control unit 240 deletes a group having a predetermined time Tb or less existing in each group. FIG. 10D shows a state in which the group of curve 2 existing in the group of straight 1 is deleted. Here, it is preferable to set the predetermined time Tb for determining whether or not to delete a group to, for example, about 10 seconds when traveling a track as in the present embodiment.

  Next, the control unit 240 calculates a straight line using the least squares method for each of the grouped clusters (step S254). Specifically, for example, as shown in FIG. 11A, for each cluster including each of the groups G1 to G4 in the time series angle data, each cluster (groups G1 to G4 as shown in FIG. 11B, for example) Straight lines L1 to L4 indicating the tendency of the time change of the angle of) are calculated.

  Next, the control unit 240 calculates an intersection point of straight lines of different temporally adjacent clusters (step S256). Specifically, for example, as shown in FIG. 11 (b), straight lines L1 to L4 calculated for each cluster (groups G1 to G4) on the time series angle data, for example, as shown in FIG. 11 (c) The points of intersection of straight lines of different clusters adjacent in time are calculated. FIG. 11C shows a state in which the intersection CP1 of the straight lines L1 and L2, the intersection CP2 of the straight lines L2 and L3, and the intersection CP3 of the straight lines L3 and L4 are calculated.

Next, the control unit 240 determines a section between two temporally adjacent points as a straight section and a curve section based on the attribute of the cluster of the section, and stores the section in the storage unit 230 (step S258). Specifically, for example, as shown in FIG. 11C, the intersections CP1 to CP3 calculated based on the straight lines L1 to L4 for each cluster (groups G1 to G4) on the time series angle data are adjacent to each other A section between them is, for example, as shown in FIG. 11D, for example, based on the tendency of the time change of the angle in the groups G1 to G4 included in the cluster of the section (or the inclination of the straight lines L1 to L4) The section is determined as a straight section or a curve section. FIG. 11D shows a state in which the section between the intersections CP1 and CP2 is determined as the straight section, and the section between the intersections CP2 and CP3 is determined as the curve section.
By such a series of processing operations, estimation processing of the straight section and the curve section in the time series angle data is executed.

  Next, returning to the flowchart shown in FIG. 7, the control unit 240 determines the required durations of the straight section and the curve section determined (estimated) in step S216 above and the distance of each known section. The traveling speed during running is calculated by the equation (1) shown in the embodiment 1 to generate time-series speed data (step S218). The control unit 240 displays the generated time-series velocity data in the form of a graph on the screen of the display unit 210, for example, as shown in the middle part of FIG.

  Next, the user US looks at the graph of the time-series velocity data displayed on the screen of the display unit 210 (the middle part of FIG. 12), and continuously (or adjacent) straight segments and curve segments in the time-series velocity data. It is determined whether or not the speed change with the speed change is slow (step S220). The control unit 240 generates, for example, by determining that the change of the time-series velocity data is gradual, and the user US selects the "edit end" menu on the screen of the display unit 210 shown in FIG. 12, for example. The values of each straight section and curve section in the time series speed data are stored as a traveling speed in a predetermined storage area of the storage unit 230 (step S228).

  Further, the control unit 240 is configured by the equation (2) according to the first embodiment based on the traveling speed calculated as described above and the pitch calculated as needed for each straight section and curve section. The stride during running is calculated and stored in a predetermined storage area of the storage unit 230 (step S230).

  On the other hand, when the user US determines that the change in the time-series velocity data is not gradual in step S220 described above, a process of correcting each straight section and curve section in the time-series velocity data is executed. Here, in the present embodiment, a method (first method) of changing the class width in the angle histogram as correction processing of the straight section and the curve section in the time-series velocity data is shown in the first embodiment described above. A method (second method) of changing the position of the switching point in the time series angle data is applied.

  Specifically, first, the user US selects whether to use the first method for changing the class width in the angle histogram or to use the second method for changing the position of the switching point in the time series angle data ((1) Step S222). When the first method described above is selected, the user US uses the input operation unit 250 to input an arbitrary numerical value using the class width in the angle histogram shown in the lower part of FIG. The value is changed from the value (step S226). Thereby, the control unit 240 returns to step S210, and generates again an angle histogram based on the class width changed by the user US, for example, on the screen of the display unit 210 as shown in the lower part of FIG. indicate. The lower part of FIG. 13 shows an angle histogram when the class width in the angle histogram shown in the lower part of FIG. 12 is changed from “8.508 (degree)” to “4.5 (degree)”.

  Then, the above steps S212 to S218 are executed again, and the control unit 240 reflects the result of the class reselection operation corresponding to each section by the user US on the time series angle data, and makes the straight section and the curve section It reestimates and regenerates time series velocity data based on the estimation result and the interval distance.

  On the other hand, when the user US selects the second method described above in step S222, the same processes as steps S106 and S108 in the data analysis method described in the first embodiment described above are performed. . That is, the user US visually recognizes the graph of the time-series angle data displayed on the display unit 210 (upper part of FIG. 12), and manually uses the input operation unit 250 to manually define the straight section and the curve section. Is corrected (step S224), and the speed change between the straight section and the curve section in the time-series speed data is adjusted to be small (slowly). As a result, the control unit 240 recalculates the traveling speed based on the required time of each section determined by the corrected position of the switching point CP, and regenerates time-series speed data.

  In the present embodiment, the correction process of the straight section and the curve section in such time-series speed data is performed until the user US determines that the change of the time-series speed data is gradual in step S220 (or the user US For example, the process is repeatedly executed until the menu of “end of editing” on the screen of the display unit 210 shown in FIG. 12 is selected.

  As described above, also in the present embodiment, as in the first embodiment described above, a course having a plurality of sections in which the travel distance is known and the angles of the courses are different from each other (for example, a track of an athletics stadium) The traveling speed and stride when traveling on the vehicle can be estimated based on time series angle data without using GPS. Here, in the present embodiment, an input operation by the user US (an editing operation for setting and selecting a class corresponding to at least a straight section and a curve section in an angle histogram, or the like) in calculating the traveling speed and the stride described above , And may include an editing operation for correcting the position of the switching point that defines each section in the time-series angle data.

  That is, also in the present embodiment, when noise and drift components included in angular velocity data do not change significantly with time, input operation by the user US (a class corresponding to a straight section and a curve section in an angle histogram is set) Each straight section and curve section in the time-series angle data can be accurately estimated by the estimation processing reflecting the content of the editing operation to be selected.

  As a result, while reducing the load of the input operation by the user US, it is possible to accurately estimate the index (traveling speed and stride) related to the exercise state during running, and the result is displayed on the screen of the display unit 210 Since the display is made in the form of numerical values, the user US can grasp the motion state through vision and can use it for the judgment and improvement.

  In each of the above-described embodiments, in order to clarify the description, the case where the user US travels a track such as an athletics stadium is specifically described, but the present invention is not limited to this. Absent. The present invention is applicable to any running course or marathon event as long as it travels on a course having a plurality of sections (straight, curves having different angles, etc.) whose course angles are known. Even in the case of running on a course or the like, it is possible to accurately estimate the straight section and the curve section, and provide the user with indices (traveling speed and stride) related to the exercise state during running.

Although some embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, but includes the invention described in the claims and the equivalents thereof.
In the following, the invention described in the original claims of the present application is appended.

(Supplementary note)
[1]
During exercise of moving a course having a known distance by the user, the user of the user who traveled during the exercise is based on angular velocity data collected chronologically from sensors attached to the user. A time-series angle data generation unit that generates time-series angle data indicating a change in angle in the traveling direction;
An input operation unit that defines a plurality of sections having different change tendencies of the angle in the time-series angle data based on an input instruction;
Based on the time required for moving each of the plurality of sections specified by the instruction by the input operation unit and the distance value of each section, time series velocity data indicating the moving speed of each section is generated Time series velocity data generation unit,
A display unit for displaying at least the time-series angle data and the time-series velocity data in a predetermined form;
A data analysis apparatus characterized by having:

[2]
Furthermore, the data analysis device according to [1], further including a motion index calculation unit that provides an index based on the movement speed of each section as a motion index.

[3]
The input operation unit is configured to input a switching point serving as a boundary of the continuous sections as the instruction with respect to the time series angle data graphically displayed on the display unit. The data analysis device according to [1] or [2], wherein each of the sections is defined based on a point.

[4]
The data analysis device
The angle histogram generation unit generates an angle histogram indicating a frequency of change of the angle in the path moved during the movement based on the angular velocity data.
The data analysis device according to [1], wherein the input operation unit defines each section by specifying a class corresponding to each section in the angle histogram and a width of the class.

[5]
The time-series angle data generation unit and the time-series speed data generation unit are configured to generate the time-series angle data and the time-series speed data based on an instruction for changing and redefining each section by the input operation unit. The data analysis apparatus according to any one of [1] to [4], wherein

[6]
The sensor comprises a motion sensor mounted on the trunk of the user's body,
The data analysis apparatus according to any one of [1] to [5], wherein the sensor data includes the acceleration and the angular velocity during the movement collected chronologically by the motion sensor.

[7]
During exercise of moving a course having a known distance by the user, the user of the user who traveled during the exercise is based on angular velocity data collected chronologically from sensors attached to the user. Generate and display time series angle data indicating change in angle in the traveling direction,
Define a plurality of sections having different change tendency of the angle in the time-series angle data, based on an instruction inputted to the displayed time-series angle data,
Generating and displaying time-series velocity data indicating moving speeds of the respective sections based on the time required for moving each of the plurality of defined sections and the distance value of the respective sections;
Data analysis method characterized by

[8]
On the computer
During exercise of moving a course having a known distance by the user, the user of the user who traveled during the exercise is based on angular velocity data collected chronologically from sensors attached to the user. Generate and display time series angle data indicating a change in angle in the traveling direction,
Based on an instruction input for the displayed time series angle data, a plurality of sections having different change tendency of the angle in the time series angle data are defined,
Generating and displaying time-series velocity data indicating the moving velocity of each section based on the time required for moving each of the plurality of defined sections and the distance value of each section;
A data analysis program characterized by

100 sensor device 110 acceleration measurement unit 120 angular velocity measurement unit 130 storage unit 140 control unit 150 wireless communication I / F
160 Wired communication I / F
200 Data Analysis Device 210 Display Unit 230 Storage Unit 240 Control Unit 250 Input Operation Unit 260 Wired Communication I / F
300 Control Equipment US Users

Claims (7)

Based on angular velocity data collected chronologically from the motion sensor of a sensor having a motion sensor mounted on the trunk of the user's body during an exercise in which the user travels a course having a known distance A time-series angle data generation unit that generates time-series angle data indicating a change in an angle in a traveling direction of the user of the path moved during the movement by accumulating absolute values of angles from the start of the movement;
An input operation unit that defines a plurality of sections having different change tendencies of the angle in the time-series angle data based on an input instruction;
Based on the time required for moving each of the plurality of sections specified by the instruction by the input operation unit and the distance value of each section, time series velocity data indicating the moving speed of each section is generated Time series velocity data generation unit,
A display unit for displaying at least the time-series angle data and the time-series velocity data in a predetermined form;
Have
The input operation unit receives the switching point serving as the boundary of the continuous sections as the instruction, with respect to the time series angle data displayed in the predetermined form on the display unit . A data analyzer characterized in that each section is defined based on the switching point.   The data analysis device according to claim 1, further comprising a motion index calculation unit that provides an index based on the moving speed of each section as a motion index. Based on angular velocity data collected chronologically from the motion sensor of a sensor having a motion sensor mounted on the trunk of the user's body during an exercise in which the user travels a course having a known distance A time-series angle data generation unit that generates time-series angle data indicating a change in an angle in a traveling direction of the user of the path moved during the movement by accumulating absolute values of angles from the start of the movement;
An input operation unit that defines a plurality of sections having different change tendencies of the angle in the time-series angle data based on an input instruction;
Based on the time required for moving each of the plurality of sections specified by the instruction by the input operation unit and the distance value of each section, time series velocity data indicating the moving speed of each section is generated Time series velocity data generation unit,
An angle histogram generation unit configured to generate an angle histogram indicating a frequency of change of the angle in the path moved during the movement based on the angular velocity data;
At least a display unit for displaying the angle histogram in a predetermined form;
Have
The input operation unit defines the sections by specifying a class corresponding to each section in the angle histogram displayed in the predetermined form on the display section and a width of the class. Data analysis device. Based on angular velocity data collected chronologically from the motion sensor of a sensor having a motion sensor mounted on the trunk of the user's body during an exercise in which the user travels a course having a known distance A time-series angle data generation unit that generates time-series angle data indicating a change in an angle in a traveling direction of the user of the path moved during the movement by accumulating absolute values of angles from the start of the movement;
At least a display unit for displaying the time series angle data in a predetermined form;
An input operation unit that defines a plurality of sections having different change tendencies of the angle in the time-series angle data displayed in the predetermined form on the display unit based on an input instruction;
Based on the time required for moving each of the plurality of sections specified by the instruction by the input operation unit and the distance value of each section, time series velocity data indicating the moving speed of each section is generated Time series velocity data generation unit,
Have
The time-series angle data generation unit and the time-series speed data generation unit are configured to generate the time-series angle data and the time-series speed data based on an instruction for changing and redefining each section by the input operation unit. A data analyzer characterized by generating again.   The data analysis apparatus according to any one of claims 1 to 4, wherein the data of the sensor includes acceleration data and angular velocity data during movement collected in time series by the motion sensor. Based on angular velocity data collected chronologically from the motion sensor of a sensor having a motion sensor mounted on the trunk of the user's body during an exercise in which the user travels a course having a known distance By accumulating absolute values of angles from the start of exercise, time-series angle data indicating change in angle in the traveling direction of the user of the path moved during the exercise is generated.
Based on the input instruction, the change trend of the angle before Symbol time series angle data defining a plurality of different sections,
Time required for the movement of each of the defined plurality of sections, on the basis of the value of the distance of each segment, and generating a sequence velocity data when indicating the moving speed of each of the sections,
A data analysis method, wherein at least the time series angle data and the time series velocity data are displayed on a display unit in a predetermined form,
With respect to the time series angle data displayed in the predetermined form on the display unit, a switching point serving as a boundary of the successive sections is input as the instruction, and the switching point is input based on the input switching point A data analysis method characterized in that each section is defined . On the computer
Based on angular velocity data collected chronologically from the motion sensor of a sensor having a motion sensor mounted on the trunk of the user's body during an exercise in which the user travels a course having a known distance By accumulating absolute values of angles from the start of exercise, time-series angle data indicating change in angle in the traveling direction of the user of the path moved during the exercise is generated.
Based on the input instruction, the change trend of the angle before Symbol time series angle data to define a plurality of different sections,
Time required for the movement of each of the defined plurality of sections, on the basis of the value of the distance of each segment, and generating a sequence velocity data when indicating the moving speed of each of the sections,
It is a data analysis program which displays at least said time series angle data and said time series speed data on a display part in a predetermined form,
With respect to the time series angle data displayed in the predetermined form on the display unit, a switching point serving as a boundary of the successive sections is input as the instruction, and the switching point is input based on the input switching point A data analysis program that defines each of the sections .

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