JP6870393B2 - Exercise support device, exercise support method and program - Google Patents

Description

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

Conventionally, there is a technique of measuring a 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 or the like.
An example of such a technique related to exercise support is disclosed in Patent Document 1. In the technique disclosed in Patent Document 1, when it is determined that the pace of exercise should be reduced based on the biometric information of the user, a configuration for notifying the user of caution or warning in order to promote the decrease in the pace of exercise is provided. Are listed. By performing such notification, it is possible to suppress the user's over-pacing.

Japanese Unexamined Patent Publication No. 2016-3093

By the way, during exercise, various kinds of abnormalities such as a user's failure may occur in addition to overpacing. However, in general techniques such as the technique disclosed in Patent Document 1 described above, it is not considered that various kinds of abnormalities occur, and whether or not one kind of abnormality called overpacing occurs. Is only determined.
Therefore, with such a general technique, it is not possible to perform notification corresponding to each of 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 appropriate measures according to the type of abnormality that has occurred in the user in supporting the user who exercises.

In order to achieve the above object, the exercise support device according to the present invention is an abnormality type identifying means for identifying the type of mental and physical abnormality generated in the user based on the measured value output by the sensor that changes with the movement of the user. And, when the type of abnormality is specified by the abnormality type identifying means, a notification means for narrowing down the candidates for the movement route to a predetermined recommended candidate and notifying the movement route candidate corresponding to the specified abnormality type is provided. When narrowing down the movement route candidates to the predetermined recommended candidates, the notification means excludes the movement routes in which the exercise load level when the user moves is higher than the predetermined threshold level from the movement route candidates. By doing so, the candidate is narrowed down to the predetermined recommended candidate, and the threshold level is set in advance for each type of abnormality, so that a plurality of threshold levels are registered in multiple stages .
Further, the exercise support method according to the present invention is an exercise support method performed by the exercise support device.
Based on the measured values output by the sensor that change with the movement of the user, the type of abnormality is specified in the abnormality type identification step for specifying the type of mental and physical abnormality generated in the user and the abnormality type identification step. In the case of the case, the notification step includes a notification step of narrowing down the candidates for the movement route to a predetermined recommended candidate and notifying the candidate according to the type of the identified abnormality, and the notification step sets the candidate for the movement route to the predetermined recommendation. When narrowing down to the candidates, the movement route in which the exercise load level when the user moves is higher than the predetermined threshold level is excluded from the candidates for the movement route to narrow down to the predetermined recommended candidates, and the threshold level is set to. It is characterized in that a plurality of threshold levels are registered in multiple stages by being preset for each type of abnormality.
Further, the program according to the present invention is an abnormality type identifying means for identifying the type of mental and physical abnormality generated in the user based on a measured value output by a sensor that changes with the movement of the user, the abnormality type. When the type of abnormality is specified by the specific means, the notification means is made to function as a notification means for narrowing down the candidates for the movement route to a predetermined recommended candidate and notifying the candidate according to the specified type of abnormality. When narrowing down the candidates for the movement route to the predetermined recommended candidates, the movement route in which the exercise load level when the user moves is higher than the predetermined threshold level is excluded from the candidates for the movement route. The threshold level is set in advance for each type of abnormality, so that a plurality of threshold levels are registered in multiple stages.

According to the present invention, in supporting a user who exercises, it is possible to take an appropriate response according to the type of abnormality that has occurred in the user.

It is a block diagram which shows the hardware structure of the exercise support device 1 which concerns on one Embodiment of this invention. Of the functional configurations of the exercise support device 1 of FIG. 1, it is a functional block diagram showing a functional configuration for executing a notification process. It is a figure explaining the calculation of the abnormality level for each abnormality type performed by the abnormality type identification part of FIG. It is a flowchart explaining the flow of the route load determination processing performed by the route load determination unit of FIG. It is a flowchart explaining the flow of the notification processing performed by the notification unit of FIG.

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 the exercise support device 1 according to the embodiment of the present invention.
The exercise support device 1 is configured as, for example, a watch-shaped wearable terminal. In the following description, the “user” refers to a user who wears the exercise support device 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. It includes 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.

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

Data and the like necessary for the CPU 11 to execute various processes are also appropriately stored in the RAM 13.

The CPU 11, ROM 12 and RAM 13 are connected to each other via the 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.

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

The measured value data (hereinafter, referred to as “measurement data”) generated by sampling is stored in the storage unit 19 in association with the measurement time data. Further, these measurement data and measurement time data are not only stored in the storage unit 19, but also appropriately supplied to the CPU 11 and the like. The sensor unit 16 may further include another sensor. For example, a 3-axis geomagnetic sensor that measures the magnitude of geomagnetism in the 3-axis directions that are orthogonal to each other, a barometric pressure sensor that measures barometric pressure to determine the height difference, and a living body for measuring biological information other than pulse and respiration. It may be further equipped with a sensor or the like.

The input unit 17 is composed of various buttons and the like, and inputs various information according to a user's instruction operation.

The output unit 18 is composed of a lamp, a display, a speaker, a vibration motor, or the like, and outputs an image, a sound, or a vibration signal. The functions of the input unit 17 and the output unit 18 may be integrally configured on the touch panel.

The storage unit 19 is composed of 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 the notification process described later.

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

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

The configuration of the exercise support device 1 has been described above, but this configuration is just an example. For example, the acceleration sensor, the angular velocity sensor, the pulse sensor, the breathing sensor, and other sensors described as 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 device 1 may acquire the measurement data obtained by sampling the measurement result of the external sensor by the 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 the input information and the 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 notification processing among the functional configurations of the exercise support device 1 of FIG. 1.
The notification process is a series of processes for identifying the type of mental and physical abnormality that has occurred to the user based on the measurement results of the sensor unit 16 and the 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 route 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 the measurement results measured by various sensors included in the sensor unit 16. At this time, the sensor information acquisition unit 51 also acquires the measurement time data for each of the acquired measurement data. In addition, the sensor information acquisition unit 51 also acquires the positioning position data and the 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 the abnormality type identification unit 52 to identify the type of abnormality occurring in the user will be described later.

The route load determination unit 53 identifies a route including a position where the exercise load of the user increases based on the map information. The route including the position where the exercise load of the user increases is, for example, a mountain road or stairs.

The notification unit 54 is based on the type of abnormality occurring in the user specified by the abnormality type identification unit 52 and the route including the position where the exercise load of the user specified by the route load determination unit 53 increases. Set a route suitable for moving during exercise such as walking and running. That is, a route is set according to the type of abnormality that has occurred in the user. Then, the user is notified of the navigation information for guiding to the set route. Notification of navigation information is performed, for example, by outputting voice 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 by superimposing the route to be moved on the map image.
By moving the route based on this navigation information, the user can perform exercise according to the type of abnormality occurring in himself / herself.
That is, according to the present embodiment, in order to support the user who exercises, it is possible to notify the user according to the type of abnormality that has occurred.

[Identification of type of abnormality]
Next, a detailed method for identifying the type of abnormality occurring in the user by the abnormality type identification 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 of FIG.
The 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 for each sensor acquired by the sensor information acquisition unit 51, positioning data, and the like. Further, 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 information included in the abnormality type information group 70 based on each information included in the analysis information group 60. Each of the calculated information included in the abnormality type information group 70 is used in the notification process (described later) performed by the notification unit 54.

First, an example will be described for calculating the abnormal heartbeat level 71 (in the figure, the level is referred to as “LV”). The abnormality type identification unit 52 acquires the pulse rate 63 for a certain period of time from the measurement data based on the time 61, which is information representing the time, and calculates the heart rate abnormality level 71 based on the acquired pulse rate 63 for the fixed time. To do. For example, assume that a fixed time is one minute. In the following calculation method, it is assumed that the pulse rate and the heart rate are the same number.
The abnormality type identification unit 52 calculates the maximum heart rate for one minute corresponding to the user by the mathematical formula "maximum heart rate = 220-age (year) of the user". Then, the value obtained by multiplying this maximum heart rate by 0.6 is used as the basic value. Next, the pulse rate 63 is compared with this basic value. As a result of comparison, when the pulse rate 63 is larger than the basic value, the heart rate abnormality level 71 is calculated as a high level. On the other hand, when the pulse rate 63 is smaller than the basic value, the heart rate abnormality level 71 is calculated as a low level.
The fixed time may be set to, for example, 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 respiratory information 64 for a certain period of time from the measurement data based on the time 61, which is information representing the time, and calculates the respiratory abnormality level 72 based on the acquired respiratory information 64 for the fixed time. To do. For example, the respiratory rate, the rhythm of breathing, and the depth of breathing included in the acquired breathing information 64 for a certain period of time are farther from the normal respiratory rate, the rhythm of breathing, and the depth of breathing. 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 past respiration information 64 is calculated as the normal respiration rate and the respiration rhythm. , And the depth of breathing.

Next, an example of calculating 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 period of time based on the time 61, which is information representing the time, and calculates the pitch abnormality level 73 based on the acquired number of steps 65 for the fixed time. Here, the number of steps 65 can be obtained by detecting the vibration in the vertical direction from the acceleration measured by the acceleration sensor, for example. Then, the pitch of the user in the fixed time is calculated by dividing the fixed time by the value of the number of steps 65.
Then, the farther the calculated pitch is from the usual pitch, the higher the pitch abnormality level 73 is calculated.
In this case, the past number of steps 65 may be acquired from the past measurement data, and the average pitch calculated from the average of the past number of steps 65 may be used as the normal pitch.

Next, an example of calculating 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 for a certain period of time from the measurement data and the positioning data based on the time 61, which is information representing the time, and strides based on the acquired number of steps 65 for the fixed time. The anomaly level 74 is calculated.
Here, the positioning position 62 indicates the position of the exercise support device 1 positioned by the positioning unit 21 (that is, the position of the user), and the user is in a certain period of time based on the positioning position 62 for a certain period of time. Calculates the distance traveled by. Further, the pitch of the user at a certain time is calculated as described above in the explanation of the pitch abnormality level 73. Further, the stride is calculated by dividing the distance traveled by the user in a fixed time by the pitch of the user in a fixed time.
Then, the farther the calculated stride is from the usual stride, the higher the stride abnormality level 74 is calculated.
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 strides calculated from the past positioning position 62 and the number of steps 65 may be used as the usual stride.

Next, an example will be described for calculating the blur abnormality level 75 of the body axis. The body axis blur 66 is acquired from the measurement data, and the body axis blur abnormality level 75 is calculated based on the acquired body axis blur 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, the farther the calculated body axis shake 66 is from the usual body axis shake, the higher the body axis shake abnormality level 75 is calculated.
In this case, the blur 66 of the past body axis may be acquired from the past measurement data, and the average of the blur 66 of the past body axis may be used as the normal blur of the body axis.

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

[Route load judgment processing]
Next, a route load determination process, which is a process for identifying 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 illustrating the flow of the route load determination process executed by the route load determination unit 53.
The route load determination process is started by the user's operation of starting the route load determination process to the input unit 17.

In step S11, the route load determination unit 53 acquires the map information stored in the storage unit 19 and the positioning position data positioned by the positioning unit 21. Then, the route load determination unit 53 identifies a route that the user can move around the user's current position based on the acquired map information and the positioning position data. There may be one route to be specified, but there may be multiple routes in the city. Then, the processing in each of the following steps is performed for each of the specified routes. Each route specified in step S11 is referred to as a "target route" in the following description.

In step S12, the route load determination unit 53 determines whether or not 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, it is considered that the exercise load increases when the user exercises on the mountain road, so it is appropriate to increase the load factor. Therefore, the load factor of the target route determined to be the mountain road is set to the load factor 5, and the process proceeds to step S25.

In step S25, it is determined whether or not all the target routes specified in step S11 have been determined.
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 are 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 or not the target route is a slope or stairs based on the map information.
If the target route is not a slope or stairs, NO is determined in step S14, and the process proceeds to step S16.
On the other hand, when the target route is a slope or stairs, YES is determined in step S14, and the process proceeds to step S15.

In step S15, it is considered that the exercise load increases when the user exercises on the slope or stairs, so it is appropriate to slightly increase the load factor. Therefore, the load factor of the target route determined to be a slope or stairs is set to the load factor 4, and the process proceeds to step S25. The content of the process in step 25 is 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 occupy most of the target route, the load factor 5 may be set instead of the load factor 4. On the other hand, if the slopes and stairs of the target route are gentle, or if the proportion of the slopes and 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 angles and distances of slopes and stairs can be calculated based on, for example, contour line information included in the map information. Further, when the target route is a route that the user has traveled in advance, the angle and distance of the slope or stairs may be calculated based on the measurement data when the user has traveled the route.

In step S16, the route load determination unit 53 determines whether or not the target route is a straight road based on the map information.
If the target route is not a straight road, NO is determined in step S16, and the process proceeds to step S18.
On the other hand, when the target route is a straight 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 in 1pondo, it is appropriate to set the load factor to a medium level. Therefore, the load factor of the target route determined to be a straight road is set to the load factor 3 or the load factor 2, and the process proceeds to step S25. The content of the process in step 25 is as described above.
Here, confirm what kind of route the target route is based on the map information, for example, when the target route is a route having a distance to the next branch, a route including a tunnel, or a bridge. It can be determined that the target route is a straight road when the route includes the above or when the route is a walking / running course.

If the route has a distance to the next branch, or if the route includes a tunnel or a bridge, it is difficult to take a break on a side road even if you are tired. It is advisable to set the load factor to 3 on the assumption that the load factor will increase slightly. On the other hand, in the case of a walking / running course, if you are tired, you can take a break by turning to a side road, so it is considered that the exercise load will not increase mentally, and the load factor is 2. It is good to set. That is, in the present embodiment, it is preferable to set the load factor not only in consideration of whether or not it is physically difficult to run, but also in consideration of the mental influence.

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

In step S19, in the long-distance course that is normally run, it is considered that the exercise load is normal when the user exercises, so it is appropriate to set the load factor to a medium level. Therefore, the load factor of the target route determined to be the long-distance course that is normally running is set to the load factor 3, and the process proceeds to step S25. The content of the process in step 25 is as described above. Whether or not the target route is a long-distance course that the user normally runs is determined by, for example, assuming a course that the user will run this time from the trajectory that the user usually runs, and assuming that the user will run this time. When the distance of the course is long and the target route is included in the route, it is preferable to determine that the target route is a long-distance course that is normally run.

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

In step S21, it is considered that the exercise load does not increase so much when the user exercises on the short-distance course that he or she normally runs, so it is appropriate to slightly lower the load factor. Therefore, the load factor of the target route determined to be the short-distance course that is normally running is set to the load factor 2, and the process proceeds to step S25. The content of the process in step 25 is as described above. Whether or not the target route is a sprint course that the user normally runs is determined by, for example, assuming a course that the user will run this time from the trajectory that the user usually runs, and assuming that the user will run this time. When the distance of the course is short and the target route is included in the route, it is preferable to determine that the target route is a short-distance course that is normally run.

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 a road that may be unpaved, NO is determined in step S22, and the process proceeds to step S24. On roads that are unlikely to be unpaved, it is considered that the exercise load does not increase when the user exercises, so it is appropriate to reduce the load factor. Therefore, the load factor of the target route determined to be a road that is not likely to be unpaved is set to the load factor 1, and the process proceeds to step S25. The content of the process in step 25 is as described above.
On the other hand, if the target route is a road that may be unpaved, YES is determined in step S22, and the process proceeds to step S23.

In step S23, it is considered appropriate that the load factor is slightly lower on the road that may be unpaved 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 road that may be unpaved is set to the load factor 2, and the process proceeds to step S25. The content of the process in step 25 is as described above.
Here, confirm what kind of route the target route is based on the map information, for example, when the target route is in a park / outdoor related facility, when it is a walking / running course, or when it is a forest / forest.・ If it is a riverbed, it can be determined that the target route may be unpaved.

In this way, when the above-mentioned route load determination process is repeated and determination is performed for all the 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 the notification process performed by the notification unit 54, which will be described next.

[Notification processing]
Next, the notification process, which 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 illustrating a flow of notification processing performed by the notification unit 54.
The notification process is started by the user's operation of starting the notification process to the input unit 17.

In step S31, the notification unit 54 confirms the heartbeat abnormality level 41 calculated by the path load determination unit 53.
In step S32, the notification unit 54 determines whether or not the confirmed heartbeat abnormality level 41 is equal to or higher than a predetermined level.
If the confirmed heart rate abnormality level 41 is not equal to or higher than a predetermined level, NO is determined in step S32, and the process proceeds to step S34.
On the other hand, when the confirmed heart rate abnormality level 41 is equal to or higher than a 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 heartbeat, which may cause a more serious abnormality. Therefore, the user is not in a state where he should run, and the shortest route to his / her home is used as navigation information for the user. Notify. The user can determine the route based on this navigation information. After step S33, the notification process ends. It should be noted that the information that the user has a heartbeat abnormality and the information that the confirmed heartbeat abnormality level 41 is at which level may be further notified to the user. From such information, the user can grasp that the heartbeat abnormality has occurred and the degree (level) of the abnormality.

In step S34, the notification unit 54 confirms the respiratory abnormality level 72 calculated by the path load determination unit 53.
In step S35, the notification unit 54 determines whether or not the confirmed respiratory abnormality level 72 is equal to or higher than a predetermined level.
If the confirmed respiratory abnormality level 72 is not equal to or higher than a predetermined level, NO is determined in step S35, and the process proceeds to step S37.
On the other hand, when the confirmed respiratory abnormality level 72 is equal to or higher than a 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, it is assumed that the route should not run on a route with a load factor of 3 or more, and a route with a load factor of 2 or less is recommended. Notify the user of the information. The user can determine the route based on this navigation information. After step S36, the notification process ends. It should be noted that the information that the user has a respiratory abnormality and the information that the confirmed respiratory abnormality level 72 is at which level may be further notified to the user. From such information, the user can grasp that the respiratory abnormality has occurred and the degree (level) of the abnormality.

In step S37, the notification unit 54 confirms the body axis shake abnormality level 75 calculated by the route load determination unit 53.
In step S38, the notification unit 54 determines whether or not the confirmed shake abnormality level 75 of the body axis is equal to or higher than a predetermined level.
If the confirmed body axis shake abnormality level 75 is not equal to or higher than a predetermined level, NO is determined in step S38, and the process proceeds to step S40.
On the other hand, when the confirmed abnormal blur level 75 of the body axis is equal to or higher than a predetermined level, YES is determined in step S38, and the process proceeds to step S39.

In step S39, there is a possibility that the user is in a state of poor physical condition due to an abnormality in the movement of the user's body axis. Notify the user of the navigation information recommended. However, if you are really in poor physical condition, you may take a break, so we do not recommend taking a break even if the load factor is 3 or less on a straight road where it is difficult to take a break. The user can determine the route based on this navigation information. After step S39, the notification process ends. It should be noted that the user is further notified of the information that an abnormality about the body axis shake has occurred and the information about the confirmed level of the body axis shake abnormality level 75. May be good. From such information, the user can grasp that an abnormality has occurred in the body axis shake and the degree (level) of the abnormality.

In step S40, the notification unit 54 confirms the posture distortion abnormality level 76 calculated by the route load determination unit 53.
In step S41, the notification unit 54 determines whether or not the strain abnormality level 76 of the confirmed posture is equal to or higher than a predetermined level.
If the strain abnormality level 76 of the confirmed posture is not equal to or higher than a predetermined level, NO is determined in step S41, and the process proceeds to step S43.
On the other hand, when the strain abnormality level 76 of the confirmed posture is equal to or higher than a 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 posture of the user, and the user may feel discomfort / pain. Therefore, it is assumed that the vehicle should not run on a route having a load factor of 4 or more, and the load factor is 3 or less. Notify the user of navigation information that recommends a route. In such a case, there is a concern that the long-distance course cannot be run, so if there is a short-distance course instead of the long-distance course, the user is notified of navigation information that recommends a route corresponding to the short-distance course. To do. The user can determine the route based on this navigation information. After step S42, the notification process ends. It should be noted that the user may be further notified of the information that an abnormality regarding the posture distortion has occurred and the information about the level of the confirmed posture distortion abnormality level 76. .. From such information, the user can grasp that an abnormality has occurred in the posture distortion and the degree (level) of the abnormality.

In step S43, 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 a predetermined level, NO is determined in step S44, and the process proceeds to step S46.
On the other hand, when the confirmed stride abnormality level 74 is equal to or higher than a predetermined level, YES is determined in step S44, and the process proceeds to step S45.

In step S45, an abnormality has occurred in the stride of the user, and the user may have accumulated fatigue or feel uncomfortable in the lower body. Therefore, it is assumed that the vehicle should not run on a route having a load factor of 4 or more. , Notifies the user of navigation information recommending a route having a load factor of 3 or less. The user can determine the route based on this navigation information. After step S45, the notification process ends. It should be noted that the information that an abnormality has occurred in the stride of the user and the information that the level of the confirmed stride abnormality level 74 is may be further notified to the user. From such information, the user can grasp that an abnormality has occurred in his / her stride and the degree (level) of the abnormality.

In step S46, 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 pitch abnormality 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 a predetermined level, NO is determined in step S47. In this case, since it is NO in any of the determinations of the notification process, no abnormality has occurred in the user. Therefore, the notification process is terminated without performing the notification.
On the other hand, if the confirmed pitch abnormality level 73 is equal to or higher than a predetermined level, YES is determined in step S47, and the process proceeds to step S48.

In step S48, since an abnormality has occurred in the pitch of the user and the user is not in a normal condition, it is assumed that the route should not run on the route with the load factor of 5, and navigation information for recommending the route with the load factor of 4 or less is provided to the user. Notify. In such a case, there is a concern that the long-distance course cannot be run, so if there is a short-distance course instead of the long-distance course, the user is notified of navigation information that recommends a route corresponding to the short-distance course. To do. The user can determine the route based on this navigation information. After step S48, the notification process ends. It should be noted that the information that the user has an abnormality about the pitch and the information that the confirmed pitch abnormality level 73 level is may be further notified to the user. From such information, the user can grasp that an abnormality has occurred in the pitch and the degree (level) of the abnormality.

In the present embodiment, the processing in the sensor information acquisition unit 51, the abnormality type identification unit 52, the route load determination unit 53, and the notification unit 54 described above is periodically performed by the user during exercise, so that the processing is generated in the user. It is possible to perform notification according to the type of abnormality that is occurring. Further, the above notification process may be performed each time the user performs an operation to start the notification process to the input unit 17, or the user inputs after the operation to start the notification process to the input unit 17 is performed. Until the operation of ending the notification process to the unit 17 is performed, the notification processing may be continuously performed at predetermined intervals. If the above notification process is continuously performed at predetermined intervals in this way, when the type of abnormality occurring in the user changes, notification is performed according to the type of newly occurring abnormality. Can be done.

The exercise support device 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 mental and physical abnormality generated in the user based on the measured value output by the sensor unit 16 that changes with the movement of the user.
The notification unit 54 notifies the user according to the type of abnormality related to the mind and body that has occurred to the user specified by the abnormality type identification unit 52.
As a result, it is possible to notify the user according to the type of abnormality that has occurred. Therefore, it is possible to support the user who exercises.

The exercise support device 1 further includes a route load determination unit 53.
The route load determination unit 53 identifies a route including a position where the exercise load of the user increases for each type of mental and physical abnormality 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 the route corresponding to the type of mental and physical abnormality generated in the user specified by the abnormality type identification unit 52. Is set, and the set route is notified.
As a result, it is possible to notify the user of the route corresponding to the type of mental and physical abnormality that has occurred. 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, respiration, pitch, stride, body axis shake, and posture during the user's exercise.
Thereby, it is possible to identify anomalies regarding various types of anomalies of users.

The notification unit 54 notifies the type of mental and physical abnormality that has occurred to the user specified by the abnormality type identification unit 52.
As a result, the user can grasp the type of abnormality occurring in himself / herself.

When the abnormality type identification unit 52 newly identifies the type of abnormality related to the mind and body that has occurred to the user, the notification unit 54 corresponds to the information currently notified to the newly specified type of abnormality. Switch to information.
As a result, it is possible to switch the information to be notified in real time even if the types of abnormalities occurring during exercise are different.

The abnormality type identification unit 52 identifies the type of mental and physical abnormality that occurred in the user and the degree of the mental and physical abnormality that occurred in the user based on the measured value measured by the sensor unit 16 that changes with the movement of the user. ,
The notification unit 54 notifies the user according to the combination of the type of abnormality related to the mind and body and the degree of abnormality that has occurred in the user specified by the abnormality type identification unit 52.
As a result, the user can grasp the degree of the abnormality in addition to the type of the abnormality occurring in himself / herself.

The exercise support device 1 further includes a sensor unit 16.
The sensor unit 16 measures information about the mind and body of the user.
The abnormality type identification unit 52 identifies the type of mental and physical abnormality that has occurred in the user based on the measured values measured by the sensor unit 16 included in the exercise support device 1 that changes with the movement of the user.
As a result, the process can be completed by the exercise support device 1 alone.

The present invention is not limited to the above-described embodiment, and modifications, improvements, and the like within the range in which the object of the present invention can be achieved are included in the present invention.

In the above-described embodiment, the description has been made assuming that the user performs exercises such as walking and running, but the present embodiment can also be applied to cases where the user performs other exercises. For example, the present embodiment can be applied to other exercises such as mountain climbing and cycling.

Further, 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 the abnormality occurring in the user are notified. In addition to this, information such as advice corresponding to the type of abnormality occurring may be notified to the user. For example, when an abnormality in heartbeat or breathing occurs, a notification such as "Please take a short break" or "Please take a deep breath" may be given. In addition, for example, "Please speed up the pitch" when the abnormality that the pitch is slow occurs, "Please slow down the pitch" when the abnormality that the pitch is fast occurs, etc. May be notified. Further, for example, when the stride is short, a notification such as "Shake your arm greatly" may be given.

Further, in the above-described embodiment, it has been described that measurement data is acquired by a sensor and notified while the user is exercising. In addition to this, measurement data may be acquired and notified before the start of exercise. For example, the state of the user is measured by a sensor before the start of the exercise of the user, the state of the user is grasped based on the measurement result, and the route of the exercise to be performed from now on is determined based on the state of the user. It may be notified. Further, for example, a sensor that measures the sweat of the user measures data that is an index of the emotion of the user. Then, the presence or absence of the occurrence of an abnormality may be determined based on the measured data. Then, the index of the user may be acquired and the notification may be performed according to the emotion of the user. For example, a notification such as "I feel stressed, so please calm down" may be given. In addition, when using a sensor that measures the user's sweat, it is possible to determine whether or not the user is dehydrated, so if there is an abnormality that the user is dehydrated, "Please rehydrate. You may make a notification such as "." Further, before the start of exercise, the route to be run may be notified according to the emotion of the user. For example, if you are feeling stressed, you may want to signal a route that is not very stressful, as performance may decline during exercise.

Further, in the above-described embodiment, the exercise support device 1 to which the present invention is applied has been described by taking a wristwatch-type wearable terminal as an example, but the present invention is not particularly limited thereto.
For example, the present invention can be generally applied to electronic devices having a function of utilizing measurement data of a sensor. Specifically, for example, the present invention can be applied to wearable terminals, smartphones, portable navigation devices, portable game machines, and the like other than the wristwatch type, which have other shapes to be worn on the user's arm, body, and the like. ..

The series of processes described above can be executed by hardware or software.
In other words, the functional configuration of FIG. 2 is merely an example and is not particularly limited. That is, it suffices if the exercise support device 1 is provided with a function capable of executing the above-mentioned series of processes as a whole, and what kind of functional block is used to realize this function is not particularly limited to the example of FIG. ..
Further, one functional block may be configured by a single piece of hardware, a single piece of software, 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 composed of various processing units such as a single processor, a multiprocessor, and a multicore processor. In addition to the above, the present invention includes a combination of these various processing units and processing circuits such as an ASIC (Application Specific Integrated Circuit) and an FPGA (Field-Programmable Gate Array).

When a series of processes are executed by software, the programs constituting the software are installed on a computer or the like from a network or a recording medium.
The computer may be a computer embedded in dedicated hardware. Further, 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 composed of the removable media 31 of FIG. 1 distributed separately from the device main body in order to provide the program to the user, but also is preliminarily incorporated in the device main body. Consists of recording media and the like provided in. The removable media 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 disk is composed of, for example, a CD-ROM (Compact Disk-Read Only Memory), a DVD (Digital Versaille Disk), a Blu-ray (registered trademark) Disc (Blu-ray Disc), or the like. The magneto-optical disk is composed of an MD (Mini-Disc) or the like. Further, the recording medium provided to the user in a state of being incorporated in the apparatus main body in advance includes, for example, the ROM 12 of FIG. 1 in which the program is recorded, the hard disk included in the storage unit 19 of FIG. 1, and the like.

In the present specification, the steps for describing a program recorded on a recording medium are not necessarily processed in chronological order, but also in parallel or individually, even if they are not necessarily processed in chronological order. It also includes the processing to be executed.

Although some embodiments of the present invention have been described above, these embodiments are merely examples and do not limit the technical scope of the present invention. The present invention can take various other embodiments, and various modifications such as omission and substitution 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 the present specification and the like, and are also included in the scope of the invention described in the claims and the equivalent scope thereof.

The inventions described in the claims at the time of filing the application of the present application are described below.
[Appendix 1]
An abnormality type identifying means for identifying the type of mental and physical abnormality that has occurred in the user based on the measured value output by the sensor that changes with the movement of the user.
A notification means that notifies the user of the type of abnormality related to the mind and body that has occurred to the user specified by the abnormality type identification means, and a notification means that notifies the user.
An exercise support device characterized by being equipped with.
[Appendix 2]
Further provided with a route load determining means for identifying a route including a position where the exercise load of the user increases for each type of mental and physical abnormality that may occur in the user.
The notification means refers to a path including a position where the exercise load increases specified by the path load determining means, and a route corresponding to the type of mental and physical abnormality generated in the user specified by the abnormality type identifying means. To notify the set route,
The exercise support device according to Appendix 1, wherein the exercise support device is characterized in that.
[Appendix 3]
The abnormality type identifying means according to Appendix 1 or 2, wherein the abnormality type identifying means identifies an abnormality relating to at least one of pulse, respiration, pitch, stride, body axis shake, and posture during the exercise of the user. Exercise support device.
[Appendix 4]
The exercise support device according to any one of Supplementary note 1 to 3, wherein the notification means notifies the type of an abnormality related to the mind and body that has occurred to the user specified by the abnormality type identification means.
[Appendix 5]
When the notification means newly identifies the type of abnormality related to the mind and body that has occurred to the user, the information currently being notified is the information corresponding to the newly specified type of abnormality. The exercise support device according to any one of Supplementary note 1 to 4, which is characterized by switching to.
[Appendix 6]
The abnormality type identifying means identifies the type of mental and physical abnormality that occurred in the user and the degree of the mental and physical abnormality that occurred in the user based on the measured value measured by the sensor that changes with the movement of the user.
The exercise support device according to any one of Supplementary note 1 to 5, wherein the notification means notifies according to a combination of a type of physical and mental abnormality and a degree of abnormality that have occurred to the user specified by the abnormality type identification means. ..
[Appendix 7]
The exercise support device further includes a sensor for measuring information about the user's mind and body.
The abnormality type identifying means identifies the type of mental and physical abnormality generated in the user based on the measured value measured by the sensor provided in the exercise support device and changes with the movement of the user. The exercise support device according to any one.
[Appendix 8]
It is an exercise support method performed by an exercise support device.
Based on the measured values output by the sensor that change with the movement of the user, the abnormality type identification step for identifying the type of mental and physical abnormality that occurred in the user, and the abnormality type identification step.
A notification step that notifies the user of the type of abnormality related to the mind and body that has occurred in the user specified in the abnormality type identification step, and a notification step.
An exercise support method characterized by including.
[Appendix 9]
On the computer
Based on the measured value output by the sensor that changes with the movement of the user, the abnormality type identification function that identifies the type of mental and physical abnormality that occurred in the user, and the abnormality type identification function.
A notification function that notifies the user of the type of abnormality related to the mind and body that has occurred to the user specified by the abnormality type identification function, and a notification function that notifies the user.
A program characterized by realizing.

1 ... Exercise support device, 11 ... CPU, 12 ... ROM, 13 ... RAM, 14 ... Bus, 15 ... Input / output interface, 16 ... Sensor unit, 17 ... -Input unit, 18 ... Output unit, 19 ... Storage unit, 20 ... Communication unit, 21 ... Positioning unit, 51 ... Sensor information acquisition unit, 52 ... Abnormal type identification unit, 53 ... Path load determination unit, 54 ... Notification unit, 61 ... Time, 62 ... Positioning position, 63 ... Pulse count, 64 ... Breath information, 65 ... Step count, 66・ ・ ・ Body axis shake, 66 ・ ・ ・ Postural distortion, 71 ・ ・ ・ Heart rate abnormality level, 72 ・ ・ ・ Breath abnormality level, 73 ・ ・ ・ Pitch abnormality level, 74 ・ ・ ・ Stride abnormality level, 75 ・・ ・ Abnormal level of body axis blur, 75 ・ ・ ・ Abnormal level of posture distortion

Claims (4)

An abnormality type identifying means for identifying the type of mental and physical abnormality that has occurred in the user based on the measured value output by the sensor that changes with the movement of the user.
When the type of abnormality is specified by the abnormality type identifying means, a notification means that narrows down the candidates for the movement route to a predetermined recommended candidate and notifies the notification according to the identified type of abnormality.
With
When the notification means narrows down the candidates for the movement route to the predetermined recommended candidates, the notification means excludes the movement routes in which the exercise load level when the user moves is higher than the predetermined threshold level from the candidates for the movement route. By narrowing down to the above-mentioned predetermined recommended candidates,
The threshold level is preset for each type of abnormality, so that a plurality of threshold levels are registered in multiple stages.
An exercise support device characterized by this. The abnormality type identifying means identifies an abnormality related to at least one of pulse, respiration, pitch, stride, body axis shake, and posture during the user's exercise .
The exercise support device according to claim 1. It is an exercise support method performed by an exercise support device.
Based on the measured values output by the sensor that change with the movement of the user, the abnormality type identification step for identifying the type of mental and physical abnormality that occurred in the user, and the abnormality type identification step.
When the type of abnormality is specified in the step for specifying the type of abnormality, a notification step for narrowing down the candidates for the movement route to a predetermined recommended candidate and notifying the candidate according to the type of the specified abnormality, and a notification step.
Including
In the notification step, when narrowing down the movement route candidates to the predetermined recommended candidates, the movement route in which the exercise load level when the user moves is higher than the predetermined threshold level is excluded from the movement route candidates. By narrowing down to the above-mentioned predetermined recommended candidates,
The threshold level is preset for each type of abnormality, so that a plurality of threshold levels are registered in multiple stages.
An exercise support method characterized by this. Computer ,
An abnormality type identifying means for identifying the type of mental and physical abnormality that has occurred in the user based on the measured value output by the sensor that changes with the movement of the user.
When the type of abnormality is specified by the abnormality type identifying means, a notification means that narrows down the candidates for the movement route to predetermined recommended candidates and notifies the notification according to the identified type of abnormality.
To function as
When the notification means narrows down the candidates for the movement route to the predetermined recommended candidates, the notification means excludes the movement routes in which the exercise load level when the user moves is higher than the predetermined threshold level from the candidates for the movement route. By narrowing down to the above-mentioned predetermined recommended candidates,
The threshold level is preset for each type of abnormality, so that a plurality of threshold levels are registered in multiple stages.
A program characterized by that.

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