JP2018143681A - Exercise support device, exercise support method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To perform notification according to the type of problem that a user suffers in order to support a user who engages in exercise.SOLUTION: An exercise support device 1 includes a problem type identifying part 52 and a notification part 54. The problem type identifying part 52 identifies a type of psychosomatic problem occurring in a user based on a measurement value that is output by a sensor part 16 and varies depending on exercise of the user. The notification part 54 provides a notification corresponding to a type of psychosomatic problem occurring in the user that is identified by the problem type identifying part 52.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an exercise support apparatus, an exercise support method, and a program.

2. Description of the Related Art Conventionally, there is a technique for measuring user's biological information or the like with a sensor worn by the user during exercise such as walking or running, and performing exercise support based on the measured biological information.
An example of a technique related to such exercise support is disclosed in Patent Document 1. In the technique disclosed in Patent Literature 1, when it is determined that the pace of exercise should be reduced based on the user's biological information, a configuration for notifying the user of a caution or warning to prompt the user to reduce the pace of exercise is provided. Have been described. By performing such notification, it is possible to suppress the user's overpace.

Japanese Unexamined Patent Publication No. 2016-30093

By the way, during exercise, various types of abnormalities such as a user failure may occur in addition to overpace. However, in the general technique such as the technique disclosed in Patent Document 1 described above, it is not considered that various types of abnormality occur, and whether or not one type of abnormality called overpace has occurred. It is only determined.
Therefore, with such a general technique, it has not been possible to perform notification corresponding to various types of abnormalities.

  The present invention has been made in view of such a situation, and an object of the present invention is to be able to take an appropriate action according to the type of abnormality that has occurred in the user when assisting the user who performs the exercise.

In order to achieve the above object, an exercise support apparatus according to an aspect of the present invention includes:
An abnormality type specifying means for specifying the type of abnormality relating to the mind and body that has occurred in the user, based on the measurement value that changes with the user's movement output by the sensor;
Informing means for informing corresponding to the type of abnormality relating to the mind and body that has occurred to the user identified by the abnormality type identifying means;
It is characterized by providing.

  ADVANTAGE OF THE INVENTION According to this invention, in supporting the user who exercises, it can respond appropriately according to the kind of abnormality which generate | occur | produced in the user.

It is a block diagram which shows the structure of the hardware of the exercise assistance apparatus 1 which concerns on one Embodiment of this invention. It is a functional block diagram which shows the functional structure for performing an alerting | reporting process among the functional structures of the exercise assistance apparatus 1 of FIG. It is a figure explaining calculation of the abnormality level about each abnormality kind which the abnormality kind specific | specification part of FIG. 2 performs. It is a flowchart explaining the flow of the route load determination process which the route load determination part of FIG. 2 performs. It is a flowchart explaining the flow of the alerting | reporting process which the alerting | reporting part of FIG. 2 performs.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

FIG. 1 is a block diagram showing a hardware configuration of an exercise support apparatus 1 according to an embodiment of the present invention.
The exercise support apparatus 1 is configured as a wearable terminal having a clock shape, for example. In the following description, “user” refers to a user wearing the exercise support apparatus 1.

  As shown in FIG. 1, the exercise support device 1 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, The sensor part 16, the input part 17, the output part 18, the memory | storage part 19, the communication part 20, and the positioning part 21 are provided.

  The CPU 11 executes various processes according to a program recorded in the ROM 12 or a program loaded from the storage unit 19 to the RAM 13.

  The RAM 13 appropriately stores data necessary for the CPU 11 to execute various processes.

  The CPU 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output interface 15 is also connected to the bus 14. A sensor unit 16, an input unit 17, an output unit 18, a storage unit 19, a communication unit 20, and a positioning unit 21 are connected to the input / output interface 15.

  The sensor unit 16 includes various sensors (not shown). For example, the sensor unit 16 includes an acceleration sensor that measures acceleration in three axial directions orthogonal to each other, an angular velocity sensor that measures angular velocity in three axial directions orthogonal to each other, a pulse sensor that measures a user's pulse, A respiration sensor for measuring number, depth, and speed. The sensor unit 16 samples the measurement results of these sensors every preset sampling cycle (for example, 0.1 second).

  The measurement value data generated by sampling (hereinafter referred to as “measurement data”) is stored in the storage unit 19 in association with the measurement time data. These measurement data and measurement time data are not only stored in the storage unit 19 but also supplied to the CPU 11 and the like as appropriate. The sensor unit 16 may further include other sensors. For example, a triaxial geomagnetic sensor that measures the magnitude of geomagnetism in three axial directions orthogonal to each other, a barometric sensor that measures atmospheric pressure to obtain a height difference, and a biological body that measures biological information other than pulse and respiration A sensor or the like may be further provided.

  The input unit 17 includes various buttons and the like, and inputs various types of information according to user instruction operations.

  The output unit 18 includes a lamp, a display, a speaker, a vibration motor, and the like, and outputs an image, sound, or a vibration signal. In addition, you may comprise the function of the input part 17 and the output part 18 integrally with a touch panel.

  The storage unit 19 includes a semiconductor memory such as a DRAM (Dynamic Random Access Memory) and stores various data. For example, the storage unit 19 stores map information for use in a notification process described later.

  The communication unit 20 is connected to another device (not shown) by wireless communication conforming to a standard such as Bluetooth (registered trademark) or Wi-Fi, or wired communication using a cable conforming to the USB (Universal Serial Bus) standard. Controls communication with the.

  The positioning unit 21 includes a positioning module such as a GPS (Global Positioning System) module. The positioning unit 21 measures the current position of the exercise support device 1 and generates positioning position data indicating the position that has been measured. The generated positioning position data is stored in the storage unit 19 in association with positioning time data. Further, the positioning position data and positioning time data are not only stored in the storage unit 19 but also appropriately supplied to the CPU 11 and the like. Note that the current position of the exercise support apparatus 1 measured in this way corresponds to the current position of the user who uses the exercise support apparatus 1.

The configuration of the exercise support device 1 has been described above, but this configuration is merely an example. For example, the acceleration sensor, the angular velocity sensor, the pulse sensor, the respiration sensor, and other sensors described as being included in the sensor unit 16 may be realized by an external sensor provided outside the exercise support device 1. In this case, the exercise support apparatus 1 may acquire measurement data obtained by sampling the measurement result of the external sensor through communication by the communication unit 20.
Further, the functions of the input unit 17 and the output unit 18 may be realized by a smartphone, and input information and output information may be transmitted and received by communication between the smartphone and the communication unit 20.

[Functional configuration]
FIG. 2 is a functional block diagram showing a functional configuration for executing the notification process among the functional configurations of the exercise support apparatus 1 of FIG.
The notification process is a series of processes for specifying the type of psychosomatic abnormality that has occurred to the user based on the measurement results of the sensor unit 16 or an external sensor, and performing notification according to the specified type of abnormality. Say.

  When the notification process is executed, the sensor information acquisition unit 51, the abnormality type identification unit 52, the path load determination unit 53, and the notification unit 54 function as shown in FIG.

  The sensor information acquisition unit 51 acquires measurement data obtained by sampling measurement results measured by various sensors included in the sensor unit 16. At this time, the sensor information acquisition unit 51 also acquires measurement time data for each acquired measurement data. The sensor information acquisition unit 51 also acquires positioning position data and positioning time data measured by the positioning unit 21.

  The abnormality type identification unit 52 identifies the type of abnormality occurring in the user based on each data acquired in this way. A detailed method for specifying the type of abnormality occurring in the user by the abnormality type identification unit 52 will be described later.

  The route load determination unit 53 specifies a route including a position where the user's exercise load increases based on the map information. The route including the position where the user's exercise load increases is, for example, a mountain road or a staircase.

Based on the type of abnormality occurring in the user identified by the abnormality type identification unit 52 and the route including the position where the user's exercise load increases identified by the route load determination unit 53, the notification unit 54 Set a route suitable for moving during exercise such as walking and running. That is, a route corresponding to the type of abnormality that has occurred to the user is set. Then, the navigation information for guiding the set route is notified to the user. The notification of the navigation information is performed, for example, by outputting sound or an image. For example, it is performed by displaying a voice such as “Please go straight ahead” or “Turn right at the next intersection” or a route to be moved on the map image.
The user can perform an exercise according to the type of abnormality occurring in the user by moving the route based on the navigation information.
That is, according to this embodiment, in order to assist the user who performs the exercise, it is possible to notify the user according to the type of abnormality that has occurred.

[Identification of abnormality type]
Next, the detailed method of specifying the type of abnormality occurring in the user by the abnormality type specifying unit 52 will be described with reference to FIG.
FIG. 3 is a diagram for explaining the calculation of the abnormality level for each abnormality type performed by the abnormality type identification unit 52 in FIG.
An analysis information group 60 is shown in the upper part of FIG. Each piece of information included in the analysis information group 60 is information generated based on measurement data, positioning data, or the like for each sensor acquired by the sensor information acquisition unit 51. Also, the abnormality type information group 70 is shown in the lower part of FIG. The abnormality type information group 70 is information indicating the degree (level) of abnormality for each type of abnormality occurring in the user. The abnormality type identification unit 52 calculates each piece of information included in the abnormality type information group 70 based on each piece of information included in the analysis information group 60. Each piece of information included in the calculated abnormality type information group 70 is used in a notification process (described later) performed by the notification unit 54.

First, an example of the calculation of the abnormal heart rate level 71 (in the figure, the level is expressed as “LV”) will be described. The abnormality type identification unit 52 acquires a pulse rate 63 from the measurement data for a certain time based on the time 61 that is information representing time, and calculates a heartbeat abnormality level 71 based on the acquired pulse rate 63 for the certain time. To do. For example, it is assumed that the certain time is one minute. In the following calculation method, the pulse rate and the heart rate are considered to be the same number.
The abnormality type identification unit 52 calculates the maximum heart rate per minute corresponding to the user, using the formula “maximum heart rate = 220−user age (years)”. A value obtained by multiplying the maximum heart rate by 0.6 is set as a basic value. Next, the pulse rate 63 is compared with this basic value. If the pulse rate 63 is larger than the basic value as a result of the comparison, the abnormal heart rate level 71 is calculated as a high level. On the other hand, if the pulse rate 63 is smaller than the basic value, the abnormal heart rate level 71 is calculated as a low level.
For example, the fixed time may be 5 minutes, and the average value per minute of the pulse rate 63 for 5 minutes may be compared with the basic value.

Next, an example of the calculation of the respiratory abnormality level 72 will be described. The abnormality type identification unit 52 acquires the respiration information 64 from the measurement data for a predetermined time based on the time 61 which is information representing time, and calculates the respiratory abnormality level 72 based on the acquired respiration information 64 for the predetermined time. To do. For example, as the respiratory rate, respiratory rhythm, and respiratory depth included in the acquired respiratory information 64 for a certain period of time are farther from the normal respiratory rate, respiratory rhythm, and respiratory depth, the respiratory abnormality level 72 is calculated as a high level.
In this case, the past respiration information 64 is acquired from the past measurement data, and the average of the respiration rate, the respiration rhythm, and the respiration depth included in the respiration information 64 is calculated as the normal respiration rate and respiration rhythm. And the depth of breathing.

Next, an example of the calculation of the pitch abnormality level 73 will be described. The abnormality type identification unit 52 acquires the number of steps 65 from the measurement data for a certain time based on the time 61 that is information representing time, and calculates the pitch abnormality level 73 based on the acquired number of steps 65 for the certain time. Here, the number of steps 65 can be obtained by detecting vertical vibration from acceleration measured by an acceleration sensor, for example. Then, the user's pitch at the certain time is calculated by dividing the certain time by the value of the number of steps 65.
Then, as the calculated pitch is far from the normal pitch, the pitch abnormality level 73 is calculated as a higher level.
In this case, the past number of steps 65 may be acquired from the past measurement data, and the average of the pitches calculated from the average of the past number of steps 65 may be used as a normal pitch.

Next, an example of calculation of the stride abnormality level 74 will be described. The abnormality type identification unit 52 acquires the positioning position 62 and the number of steps 65 from the measurement data and the positioning data for a certain time based on the time 61 that is information representing time, and stride based on the acquired number of steps 65 for the certain time. An abnormal level 74 is calculated.
Here, the positioning position 62 indicates the position of the exercise support device 1 measured by the positioning unit 21 (that is, the position of the user). Based on the positioning position 62 for a certain period of time, the user performs a certain period of time. Calculate the distance traveled by. In addition, as described above when describing the pitch abnormality level 73, the user's pitch at a certain time is calculated. Further, the stride is calculated by dividing the distance traveled by the user during a certain time by the user's pitch during the certain time.
Then, as the calculated stride is far from the usual stride, the stride abnormality level 74 is calculated as a higher level.
In this case, the past positioning position 62 and the number of steps 65 may be acquired from the past measurement data, and the average of the stride calculated from the past positioning position 62 and the number of steps 65 may be used as a normal stride.

Next, an example of the calculation of the body axis shake abnormality level 75 will be described. A body axis shake 66 is obtained from the measurement data, and a body axis shake abnormality level 75 is calculated based on the obtained body axis shake 66.
Here, the blur 66 of the body axis is calculated based on the inclination angle with respect to the gravitational acceleration by specifying the direction of the gravitational acceleration based on, for example, the acceleration measured by the acceleration sensor.
Then, as the calculated body axis blur 66 is farther from the normal body axis blur, the body axis blur abnormality level 75 is calculated as a higher level.
In this case, the past body axis shake 66 may be obtained from the past measurement data, and the average of the past body axis shake 66 may be used as the normal body axis shake.

Next, an example of the calculation of the posture distortion abnormality level 76 will be described. An attitude distortion 67 is acquired from the measurement data, and an attitude distortion abnormality level 76 is calculated based on the acquired attitude distortion 67.
Here, the posture distortion 67 is calculated based on, for example, acceleration measured by an acceleration sensor and angular velocity data by an angular velocity sensor.
Then, the posture distortion abnormality level 76 is calculated as a higher level as the calculated posture distortion 67 is farther from the normal posture distortion.
In this case, the past posture distortion 67 may be acquired from the past measurement data, and the average of the past posture distortion 67 may be used as the normal posture distortion.

[Route load judgment processing]
Next, route load determination processing, which is processing for specifying a route including a position where the user's exercise load increases, will be described with reference to the flowchart of FIG.
FIG. 4 is a flowchart for explaining the flow of the route load determination process executed by the route load determination unit 53.
The route load determination process is started by an operation of starting a route load determination process to the input unit 17 by the user.

  In step S <b> 11, the route load determination unit 53 acquires map information stored in the storage unit 19 and positioning position data determined by the positioning unit 21. Then, the route load determination unit 53 specifies a route that the user can move around the current position of the user based on the acquired map information and positioning position data. There may be one route specified, but there may be a plurality of routes in the city. Then, the following steps are performed for each identified route. Each route specified in step S11 is referred to as a “target route” in the following description.

In step S12, the route load determining unit 53 determines whether the target route is a mountain road based on the map information.
If the target route is not a mountain road, NO is determined in step S12, and the process proceeds to step S14.
On the other hand, when the target route is a mountain road, YES is determined in step S12, and the process proceeds to step S13.

  In step S13, the mountain road is considered to increase the exercise load when the user exercises, so it is appropriate to increase the load factor. Therefore, the load factor of the target route determined to be a mountain road is set as a load factor of 5, and the process proceeds to step S25.

In step S25, it is determined whether or not determination has been made for all target routes specified in step S11.
If all the target routes have not been determined yet, NO is determined in step S25, and the process proceeds to step S12. Then, a determination is made for a target route that has not yet been determined.
On the other hand, when all the target routes have been determined, YES is determined in step S25, and the route load determination process ends.

In step S14, the route load determination unit 53 determines whether the target route is a slope or a staircase based on the map information.
If the target route is not a slope or a staircase, it is determined as NO in Step S14, and the process proceeds to Step S16.
On the other hand, when the target route is a slope or a staircase, YES is determined in step S14, and the process proceeds to step S15.

  In step S15, the slope and stairs are considered to increase the exercise load when the user exercises, so it is appropriate to increase the load factor slightly. Therefore, the load factor of the target route determined to be a slope or a staircase is set as a load factor of 4, and the process proceeds to step S25. The contents of the processing in step 25 are as described above.

  In step S15, the load factor set by the angle and distance of the slope or stairs included in the target route may be changed. For example, when the slope or stairs of the target route is steep, or when the slope or stairs occupies most of the target route, the load factor 5 may be set instead of the load factor 4. On the other hand, when the slope or stairs of the target route is gentle, or when the ratio of the slope or stairs in the target route is low, the load factor may be set to a load factor of 3 or less instead of the load factor of 4. . The angle and distance of a slope or stairs can be calculated based on, for example, contour line information included in map information. In addition, when the target route is a route that the user has ran in advance, the angle and distance of the slope or stairs may be calculated based on the measurement data when the user ran the route.

In step S16, the route load determination unit 53 determines whether the target route is a single road based on the map information.
If the target route is not a single road, NO is determined in step S16, and the process proceeds to step S18.
On the other hand, if the target route is a single road, YES is determined in step S16, and the process proceeds to step S17.

In step S17, since it is considered that the exercise load is normal when the user exercises on the straight road, it is appropriate that the load factor is medium. Therefore, the load factor of the target route determined to be a single road is set as load factor 3 or load factor 2, and the process proceeds to step S25. The contents of the processing in step 25 are as described above.
Here, based on the map information, it is confirmed what the target route is, for example, when the target route is a route that has a distance to the next branch, a route that includes a tunnel, It is possible to determine that the target route is a single road when it is a route that includes, or when it is a walking / running course.

  And if it is a route that has a distance to the next branch, or if it is a route that includes a tunnel or bridge, even if you are tired, it is difficult to go to a side road and take a break. Therefore, it is preferable to set the load factor to 3. On the other hand, in the case of a walking / running course, it is considered that if you are tired, you can take a break along the side road, so it is thought that the exercise load will not increase mentally, It is good to set. In other words, in this embodiment, it is preferable to set the load factor in consideration of not only whether it is physically difficult to actually run but also the mental influence.

In step S18, the route load determining unit 53 determines whether or not the target route is a long-distance course in which the target route is running normally based on the map information.
If the target route is not a long-distance course that is normally running, NO is determined in step S18, and the process proceeds to step S20.
On the other hand, if the target route is a long-distance course that is running normally, YES is determined in step S18, and the process proceeds to step S19.

  In step S19, the long-distance course that is normally running is considered to have a normal exercise load when the user exercises. Therefore, the load factor of the target route determined to be a long-distance course that is normally running is set as the load factor 3, and the process proceeds to step S25. The contents of the processing in step 25 are as described above. Whether or not the target route is a long-distance course that you normally run is assumed, for example, from the trajectory that the user normally runs, assuming the course that the user will run again this time, it will run this time When the distance of the course is long and the target route is included in the route, it may be determined that the target route is a long-distance course that is usually running.

In step S20, the route load determining unit 53 determines whether or not the target route is a short-distance course in which the target route is running normally based on the map information.
If the target route is not a short-distance course that is normally running, NO is determined in step S20, and the process proceeds to step S22.
On the other hand, if the target route is a short-distance course that is normally running, YES is determined in step S20, and the process proceeds to step S21.

  In step S21, it is considered that the load factor of the short-distance course that is normally running is somewhat low because it is considered that the exercise load does not increase so much when the user exercises. Therefore, the load factor of the target route determined to be a normal short-distance course is set as a load factor of 2, and the process proceeds to step S25. The contents of the processing in step 25 are as described above. Whether the target route is a short-distance course that you normally run is assumed, for example, from the trajectory that the user normally runs, assuming the course that the user will also run this time, and assuming that you will run this time When the distance of the course is short and the target route is included in the route, it may be determined that the target route is a short-distance course that is usually running.

In step S22, the route load determination unit 53 determines whether or not the target route is a road that may be unpaved based on the map information.
If the target route is not an unpaved road, NO is determined in step S22, and the process proceeds to step S24. Since it is considered that the exercise load does not increase when the user exercises on the road that is not paved, it is appropriate to reduce the load factor. Therefore, the load factor of the target route determined to be a road with no possibility of unpaving is set as a load factor of 1, and the process proceeds to step S25. The contents of the processing in step 25 are as described above.
On the other hand, if the target route is a road that may be unpaved, it is determined as YES in Step S22, and the process proceeds to Step S23.

In step S23, it is considered that the road load that is possibly unpaved does not increase the exercise load so much when the user exercises, so it is appropriate to lower the load factor slightly. Therefore, the load factor of the target route determined as a road that may be unpaved is set as a load factor of 2, and the process proceeds to step S25. The contents of the processing in step 25 are as described above.
Here, the route is confirmed based on the map information. For example, if the route is in a park / outdoor related facility, walking / running course, forest / forest・ If it is a riverbed, it can be determined that the target route may be unpaved.

  In this manner, when the above-described route load determination process is repeated and determination is made for all target routes, YES is determined in step S25, and the route load determination process ends. In this route load determination process, the value of the load factor set for each target route is used in a notification process performed by the notification unit 54 described below.

[Notification processing]
Next, a notification process that is a process for notifying the user of appropriate navigation information and the like will be described with reference to the flowchart of FIG.
FIG. 5 is a flowchart for explaining the flow of notification processing performed by the notification unit 54.
The notification process is started by an operation for starting the notification process to the input unit 17 by the user.

In step S <b> 31, the notification unit 54 confirms the abnormal heart rate level 41 calculated by the route load determination unit 53.
In step S32, the notification unit 54 determines whether or not the confirmed abnormal heart rate level 41 is equal to or higher than a predetermined level.
If the confirmed abnormal heart rate level 41 is not equal to or higher than the predetermined level, NO is determined in step S32, and the process proceeds to step S34.
On the other hand, if the confirmed abnormal heart rate level 41 is equal to or higher than the predetermined level, YES is determined in step S32, and the process proceeds to step S33.

  In step S33, an abnormality has occurred in the user's heart rate, which may cause a more serious abnormality. Therefore, assuming that the user is not in a state of running, the shortest route to the home is used as navigation information to the user. Inform. The user can determine a route based on the navigation information. After step S33, the notification process ends. Note that information indicating that a heartbeat abnormality has occurred in the user and how many levels of the confirmed heartbeat abnormality level 41 may be further notified to the user. From such information, the user can grasp that a heartbeat abnormality has occurred in the user and the degree (level) of the abnormality.

In step S <b> 34, the notification unit 54 confirms the abnormal breathing level 72 calculated by the route load determination unit 53.
In step S35, the notification unit 54 determines whether or not the confirmed abnormal breathing level 72 is equal to or higher than a predetermined level.
If the confirmed abnormal breathing level 72 is not equal to or higher than the predetermined level, NO is determined in step S35, and the process proceeds to step S37.
On the other hand, if the confirmed respiratory abnormality level 72 is equal to or higher than the predetermined level, YES is determined in step S35, and the process proceeds to step S36.

  In step S36, since an abnormality has occurred in the user's breathing and may cause an unexpected abnormality, the navigation recommending a route with a load factor of 2 or less, assuming that the route should not be over a load factor of 3 or more. Information is notified to the user. The user can determine a route based on the navigation information. After step S36, the notification process ends. Note that information indicating that the user has abnormal breathing and information regarding the level of the confirmed abnormal breathing level 72 may be further notified to the user. From such information, the user can grasp that the respiratory abnormality has occurred in itself and the degree (level) of the abnormality.

In step S <b> 37, the notification unit 54 checks the body shake abnormality level 75 calculated by the path load determination unit 53.
In step S <b> 38, the notification unit 54 determines whether or not the confirmed blur abnormality level 75 of the body axis is equal to or higher than a predetermined level.
If the confirmed body shaft shake abnormality level 75 is not equal to or higher than the predetermined level, NO is determined in step S38, and the process proceeds to step S40.
On the other hand, if the confirmed blur abnormality level 75 of the body axis is greater than or equal to the predetermined level, YES is determined in step S38, and the process proceeds to step S39.

  In step S39, an abnormality has occurred in the blurring of the user's body axis, and the user may be in a poor physical condition. Navigation information recommending the user is notified to the user. However, since there is a possibility of taking a break when the patient is really in poor health, do not recommend a straight road that is difficult to take a break even if the load factor is 3 or less. The user can determine a route based on the navigation information. After step S39, the notification process ends. It should be noted that the user is further informed of the information that the abnormality about the body axis shake has occurred and the information about the level of the confirmed body axis shake abnormality level 75. Also good. From such information, the user can grasp that the abnormality about the body axis blur has occurred in itself and the degree (level) of the abnormality.

In step S <b> 40, the notification unit 54 confirms the posture distortion abnormality level 76 calculated by the path load determination unit 53.
In step S41, the notification unit 54 determines whether or not the distortion abnormality level 76 of the confirmed posture is equal to or higher than a predetermined level.
If the confirmed distortion abnormality level 76 of the posture is not equal to or higher than the predetermined level, NO is determined in step S41, and the process proceeds to step S43.
On the other hand, when the confirmed distortion abnormality level 76 of the posture is equal to or higher than the predetermined level, YES is determined in step S41, and the process proceeds to step S42.

  In step S42, an abnormality has occurred in the distortion of the user's posture, and the user may feel uncomfortable or painful. The navigation information recommending the route is notified to the user. In such a case, there is a concern that it is not possible to run a long distance course, so if there is a short distance course instead of a long distance course, the navigation information recommending a route corresponding to the short distance course is notified to the user. To do. The user can determine a route based on the navigation information. After step S42, the notification process ends. It should be noted that the user may be further notified of information that an abnormality regarding posture distortion has occurred and information about the level of the confirmed posture distortion abnormality level 76. . From such information, the user can grasp that an abnormality regarding posture distortion has occurred in the user and the degree (level) of the abnormality.

In step S <b> 43, the notification unit 54 confirms the stride abnormality level 74 calculated by the route load determination unit 53.
In step S44, the notification unit 54 determines whether or not the confirmed stride abnormality level 74 is equal to or higher than a predetermined level.
If the confirmed stride abnormality level 74 is not equal to or higher than the predetermined level, NO is determined in step S44, and the process proceeds to step S46.
On the other hand, if the confirmed stride abnormality level 74 is equal to or higher than the predetermined level, YES is determined in step S44, and the process proceeds to step S45.

  In step S45, an abnormality has occurred in the user's stride, and there is a possibility that the user may accumulate fatigue or feel uncomfortable in the lower body. Navigation information recommending a route with a load factor of 3 or less is notified to the user. The user can determine a route based on the navigation information. After step S45, the notification process ends. Note that information indicating that an abnormality has occurred in the user's stride and information indicating the number of confirmed stride abnormality levels 74 may be further notified to the user. From such information, the user can grasp that an abnormality has occurred in his own stride and the degree (level) of the abnormality.

In step S <b> 46, the notification unit 54 confirms the pitch abnormality level 73 calculated by the route load determination unit 53.
In step S47, the notification unit 54 determines whether or not the confirmed abnormal pitch level 73 is equal to or higher than a predetermined level.
If the confirmed pitch abnormality level 73 is not equal to or higher than the predetermined level, NO is determined in step S47. In this case, since any determination in the notification process is NO, no abnormality has occurred in the user. Therefore, the notification process is terminated without performing notification.
On the other hand, if the confirmed pitch abnormality level 73 is greater than or equal to the predetermined level, YES is determined in step S47, and the process proceeds to step S48.

  In step S48, an abnormality has occurred in the user's pitch, and the user is not in full swing. Therefore, navigation information recommending a route with a load factor of 4 or less is given to the user, assuming that the route should not run on a route with a load factor of 5. Inform. In such a case, there is a concern that it is not possible to run a long distance course, so if there is a short distance course instead of a long distance course, the navigation information recommending a route corresponding to the short distance course is notified to the user. To do. The user can determine a route based on the navigation information. After step S48, the notification process ends. Note that the user may be further informed of information indicating that a pitch abnormality has occurred and the number of confirmed pitch abnormality levels 73. From such information, the user can grasp that the pitch is abnormal and the level (level) of the abnormality.

  As described above, the sensor information acquisition unit 51, the abnormality type identification unit 52, the route load determination unit 53, and the notification unit 54 perform the processing periodically during the exercise of the user. Notification according to the type of abnormality being performed can be performed. In addition, the notification process may be performed every time the user performs an operation for starting the notification process to the input unit 17, or input by the user after the user performs an operation for starting the notification process to the input unit 17. The operation may be continuously performed at a predetermined interval until the operation of notifying the notification processing to the unit 17 is performed. As described above, if the above notification process is continuously performed at a predetermined interval, when the type of abnormality occurring in the user changes, notification according to the type of abnormality newly occurring is performed. Can do.

The exercise support apparatus 1 configured as described above includes an abnormality type identification unit 52 and a notification unit 54.
The abnormality type identification unit 52 identifies the type of abnormality related to the mind and body that has occurred in the user, based on the measurement value that changes with the user's movement output from the sensor unit 16.
The notification unit 54 notifies the type of abnormality related to the mind and body that has occurred in the user identified by the abnormality type identification unit 52.
Thereby, it is possible to notify the user according to the type of abnormality that has occurred. Therefore, it is possible to support a user who performs exercise.

The exercise support device 1 further includes a path load determination unit 53.
The path load determination unit 53 identifies a path including a position where the user's exercise load increases for each type of anomalies related to the mind and body that may occur in the user.
The notification unit 54 refers to the route including the position where the exercise load increases specified by the route load determination unit 53, and corresponds to the type of abnormality related to the mind and body that has occurred in the user specified by the abnormality type specification unit 52. Is set, and the set route is notified.
Thereby, the route corresponding to the type of abnormality relating to the mind and body that has occurred to the user can be notified. Therefore, the user can select an appropriate route corresponding to the state of mind and body.

The abnormality type identification unit 52 identifies an abnormality related to at least one of pulse, breathing, pitch, stride, body axis blur, and posture during the user's exercise.
Thereby, the abnormality about the kind of abnormality of various users can be specified.

The notification unit 54 notifies the type of abnormality related to the mind and body that has occurred to the user specified by the abnormality type specification unit 52.
Thereby, the user can grasp | ascertain the kind of abnormality which has generate | occur | produced in self.

When the abnormality type specifying unit 52 newly specifies the type of abnormality related to the mind and body that has occurred in the user, the notification unit 54 corresponds to the newly specified type of abnormality. Switch to information.
This makes it possible to switch the information to be notified in real time even when the type of abnormality that has occurred is different during exercise.

The abnormality type identification unit 52 identifies the type of abnormality relating to the mind and body that has occurred in the user as well as the degree of abnormality relating to the mind and body that has occurred in the user, based on the measurement value that changes with the user's movement measured by the sensor unit 16. ,
The notification unit 54 notifies the combination of the type of abnormality and the degree of abnormality related to the mind and body that have occurred in the user identified by the abnormality type identification unit 52.
Thereby, in addition to the kind of abnormality which has generate | occur | produced in the user, the user can also grasp | ascertain the degree of the abnormality.

The exercise support apparatus 1 further includes a sensor unit 16.
The sensor unit 16 measures information related to the user's mind and body.
The abnormality type identification unit 52 identifies the type of abnormality related to the mind and body that has occurred in the user, based on the measurement value that changes with the user's exercise measured by the sensor unit 16 included in the exercise support apparatus 1.
Thereby, processing can be completed by the exercise support device 1 alone.

  In addition, this invention is not limited to the above-mentioned embodiment, The deformation | transformation in the range which can achieve the objective of this invention, improvement, etc. are included in this invention.

  In the above-described embodiment, the case where the user performs an exercise such as walking or running has been described. However, the present embodiment can also be applied when the user performs an exercise other than this. For example, the present embodiment can be applied to other exercises such as mountain climbing and cycling.

  In the above-described embodiment, it has been described that the navigation information for route guidance and the information on the type and level of abnormality occurring in the user are notified. In addition to this, information such as advice corresponding to the type of abnormality occurring in the user may be notified. For example, when abnormalities in heartbeat or breathing occur, notifications such as “Please take a break” and “Please breathe deeply” may be made. In addition, for example, when an abnormality occurs that the pitch is slow, “please speed up the pitch”, and when an abnormality that the pitch is fast, such as “Please slow down the pitch”, etc. You may make it alert | report. In addition, for example, when the stride is short, a notification such as “Please shake your arm greatly” may be given.

  Moreover, in the above-described embodiment, it has been described that measurement data is acquired and notified by a sensor during a user's exercise. In addition to this, measurement data may be acquired and notified before the start of exercise. For example, the user's state is measured by a sensor before the user's exercise is started, the user's state is grasped based on the measurement result, and the path of the exercise to be performed is determined based on the user's state. You may notify. Further, for example, data serving as an index about the user's emotion is measured by a sensor that measures the user's sweat. Then, it may be determined whether or not an abnormality has occurred based on the measured data. And a user's parameter | index may be acquired and you may make it alert | report according to a user's emotion. For example, you may make it alert | report, such as "Please feel calm because it feels stress." In addition, when using a sensor that measures the user's sweat, it can also be determined whether the user is dehydrated or not. Etc. "etc. may be performed. Moreover, you may alert | report the path | route which runs from now on according to a user's emotion before the exercise | movement start. For example, if a person feels stress, there is a possibility that the performance may be reduced during exercise. Therefore, a route where the load is not so high may be notified.

In the above-described embodiment, the exercise support apparatus 1 to which the present invention is applied has been described using a wristwatch-type wearable terminal as an example, but is not particularly limited thereto.
For example, the present invention can be applied to general electronic devices having a function of using sensor measurement data. Specifically, for example, the present invention can be applied to wearable terminals, smartphones, portable navigation devices, portable game machines, and the like of other shapes that can be worn on the user's arm or trunk other than the wristwatch type. .

The series of processes described above can be executed by hardware or can be executed by software.
In other words, the functional configuration of FIG. 2 is merely an example and is not particularly limited. That is, it is sufficient if the exercise support apparatus 1 has a function capable of executing the above-described series of processing as a whole, and what kind of functional block is used to realize this function is not particularly limited to the example of FIG. .
In addition, one functional block may be constituted by hardware alone, software alone, or a combination thereof.
The functional configuration in the present embodiment is realized by a processor that executes arithmetic processing, and the processor that can be used in the present embodiment is configured by various processing devices such as a single processor, a multiprocessor, and a multicore processor. In addition to the above, these various processing apparatuses and a combination of processing circuits such as ASIC (Application Specific Integrated Circuit) and FPGA (Field-Programmable Gate Array) are included.

When a series of processing is executed by software, a program constituting the software is installed on a computer or the like from a network or a recording medium.
The computer may be a computer incorporated in dedicated hardware. The computer may be a computer capable of executing various functions by installing various programs, for example, a general-purpose personal computer.

  The recording medium including such a program is not only constituted by the removable medium 31 of FIG. 1 distributed separately from the apparatus main body in order to provide the program to the user, but also in a state of being incorporated in the apparatus main body in advance. The recording medium etc. provided in The removable medium 31 is composed of, for example, a magnetic disk (including a floppy disk), an optical disk, a magneto-optical disk, or the like. The optical disc is composed of, for example, a CD-ROM (Compact Disk-Read Only Memory), a DVD (Digital Versatile Disc), a Blu-ray (registered trademark) Disc (Blu-ray Disc), and the like. The magneto-optical disk is configured by an MD (Mini-Disk) or the like. In addition, the recording medium provided to the user in a state of being preliminarily incorporated in the apparatus main body includes, for example, the ROM 12 in FIG. 1 in which a program is recorded, the hard disk included in the storage unit 19 in FIG.

  In the present specification, the step of describing the program recorded on the recording medium is not limited to the processing performed in time series along the order, but is not necessarily performed in time series, either in parallel or individually. The process to be executed is also included.

  As mentioned above, although several embodiment of this invention was described, these embodiment is only an illustration and does not limit the technical scope of this invention. The present invention can take other various embodiments, and various modifications such as omission and replacement can be made without departing from the gist of the present invention. These embodiments and modifications thereof are included in the scope and gist of the invention described in this specification and the like, and are included in the invention described in the claims and the equivalent scope thereof.

The invention described in the scope of claims at the beginning of the filing of the present application will be appended.
[Appendix 1]
An abnormality type specifying means for specifying the type of abnormality relating to the mind and body that has occurred in the user, based on the measurement value that changes with the user's movement output by the sensor;
Informing means for informing corresponding to the type of abnormality relating to the mind and body that has occurred to the user identified by the abnormality type identifying means;
An exercise support apparatus comprising:
[Appendix 2]
A path load determining means for specifying a path including a position where the user's exercise load increases for each type of anomalies relating to the mind and body that may occur in the user;
The notification means refers to a path including the position where the exercise load specified by the path load determination means increases, and corresponds to the type of abnormality related to the mind and body that has occurred in the user specified by the abnormality type specification means To notify the set route,
The exercise support apparatus according to Supplementary Note 1, wherein:
[Appendix 3]
The anomaly type specifying means specifies an anomaly related to at least one of pulse, respiration, pitch, stride, body axis blur, and posture during the user's exercise. Exercise support device.
[Appendix 4]
The exercise support apparatus according to any one of appendices 1 to 3, wherein the notification unit notifies a type of abnormality related to a mind and body that has occurred to the user specified by the abnormality type specification unit.
[Appendix 5]
When the abnormality type specifying unit newly specifies the type of abnormality relating to the mind and body that has occurred in the user, the notification unit is configured to update the currently notified information to information corresponding to the newly specified type of abnormality. The exercise support apparatus according to any one of appendices 1 to 4, wherein the exercise support apparatus is switched to
[Appendix 6]
The abnormality type specifying means also specifies the degree of abnormality relating to the mind and body that has occurred to the user along with the type of abnormality relating to the mind and body that has occurred to the user, based on the measurement value that changes with the user's movement measured by the sensor,
The exercise support apparatus according to any one of appendices 1 to 5, wherein the notification unit notifies the user in correspondence with a combination of a type and a degree of abnormality related to a mind and body that have occurred to the user specified by the abnormality type specification unit. .
[Appendix 7]
The exercise support apparatus further includes a sensor for measuring information related to the user's mind and body,
Addendum 1 to 6, wherein the abnormality type identification means identifies the type of abnormality relating to the mind and body that has occurred in the user, based on a measurement value that changes with the user's movement measured by a sensor included in the exercise support apparatus. The exercise support apparatus according to any one of the above.
[Appendix 8]
An exercise support method performed by an exercise support device,
An abnormality type identifying step for identifying the type of abnormality relating to the mind and body that has occurred to the user, based on the measurement value that changes with the user's movement output by the sensor;
An informing step for informing corresponding to the type of abnormality relating to the mind and body that has occurred in the user identified in the anomaly type identifying step;
An exercise support method comprising:
[Appendix 9]
On the computer,
An abnormality type identification function for identifying the type of abnormality relating to the mind and body that has occurred in the user, based on the measurement value that changes with the user's movement output by the sensor;
A notification function for informing corresponding to the type of abnormality relating to the mind and body that has occurred to the user identified by the abnormality type identification function;
A program characterized by realizing.

  DESCRIPTION OF SYMBOLS 1 ... Exercise support device, 11 ... CPU, 12 ... ROM, 13 ... RAM, 14 ... Bus, 15 ... Input / output interface, 16 ... Sensor part, 17 ... Input unit, 18 ... output unit, 19 ... storage unit, 20 ... communication unit, 21 ... positioning unit, 51 ... sensor information acquisition unit, 52 ... abnormality type identification unit, 53: Route load determination unit, 54: Notification unit, 61: Time, 62: Positioning position, 63: Pulse rate, 64 ... Respiration information, 65 ... Number of steps, 66 ... shaking of the body axis, 66 ... posture distortion, 71 ... heartbeat abnormality level, 72 ... respiratory abnormality level, 73 ... pitch abnormality level, 74 ... stride abnormality level, 75 ..Body axis shake abnormal level, 75 ... Posture distortion abnormal level

Claims (9)

An abnormality type specifying means for specifying the type of abnormality relating to the mind and body that has occurred in the user, based on the measurement value that changes with the user's movement output by the sensor;
Informing means for informing corresponding to the type of abnormality relating to the mind and body that has occurred to the user identified by the abnormality type identifying means;
An exercise support apparatus comprising: A path load determining means for specifying a path including a position where the user's exercise load increases for each type of anomalies relating to the mind and body that may occur in the user;
The notification means refers to a path including the position where the exercise load specified by the path load determination means increases, and corresponds to the type of abnormality related to the mind and body that has occurred in the user specified by the abnormality type specification means To notify the set route,
The exercise support apparatus according to claim 1.   The said abnormality kind specific | specification means identifies the abnormality regarding at least any one of a pulse, respiration, a pitch, a stride, a shake of a body axis, and a posture during the said user's exercise | movement. Exercise support device.   The exercise support apparatus according to claim 1, wherein the notification unit notifies a type of abnormality related to a mind and body that has occurred to the user specified by the abnormality type specification unit.   When the abnormality type specifying unit newly specifies the type of abnormality relating to the mind and body that has occurred in the user, the notification unit is configured to update the currently notified information to information corresponding to the newly specified type of abnormality. The exercise support device according to claim 1, wherein the exercise support device is switched to the exercise support device. The abnormality type specifying means also specifies the degree of abnormality relating to the mind and body that has occurred to the user along with the type of abnormality relating to the mind and body that has occurred to the user, based on the measurement value that changes with the user's movement measured by the sensor,
The exercise support according to any one of claims 1 to 5, wherein the notification means notifies the user in correspondence with a combination of a type of abnormality and a degree of abnormality related to a mind and body that have occurred to the user specified by the abnormality type specification unit. apparatus. The exercise support apparatus further includes a sensor for measuring information related to the user's mind and body,
The abnormality type identification unit identifies a type of abnormality relating to the mind and body that has occurred in the user based on a measurement value that changes with the user's movement measured by a sensor included in the exercise support apparatus. The exercise support apparatus according to any one of the above. An exercise support method performed by an exercise support device,
An abnormality type identifying step for identifying the type of abnormality relating to the mind and body that has occurred to the user, based on the measurement value that changes with the user's movement output by the sensor;
An informing step for informing corresponding to the type of abnormality relating to the mind and body that has occurred in the user identified in the anomaly type identifying step;
An exercise support method comprising: On the computer,
An abnormality type identification function for identifying the type of abnormality relating to the mind and body that has occurred in the user, based on the measurement value that changes with the user's movement output by the sensor;
A notification function for informing corresponding to the type of abnormality relating to the mind and body that has occurred to the user identified by the abnormality type identification function;
A program characterized by realizing.

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