Exercise improvement instruction device, exercise improvement instruction method, and exercise improvement instruction program

Abstract

This exercise improvement instruction device 100 comprises: a hierarchical index storage unit 110 that stores hierarchized indices for improving exercise motion; a challenge index specification unit 120 that evaluates the hierarchized indices on the basis of measurement data during exercise and specifies a challenge index; and an instruction information generation unit 130 that generates instruction information for improving the challenge index specified by the challenge index specification unit 120. Such an exercise improvement instruction device 100 can specify a challenge leading to effective improvement of exercise motion and generate instruction information.

Description

[Technical field]

[0001] The present invention relates to a technique for identifying problems for improving athletic performance. [Background technology]

[0002] In recent years, mobile communication terminals such as smartphones have become lighter, and the development of so-called wearable devices, such as smart watches, has been active. These devices can be worn while exercising, such as running, and can measure exercise movements using built-in acceleration sensors. There are also services and applications that provide guidance for improving exercise movements based on the measurement results. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2014-124448 A Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, as devices have become smarter, the number of built-in sensors has increased, and each sensor has become more sophisticated, allowing a large amount of measurement data to be obtained from a single exercise session. By analyzing such measurement data, it is possible to extract many exercise movement issues from various perspectives, but it is difficult to identify issues that can lead to effective exercise movement improvements.

[0005] The present invention has been made in consideration of the above circumstances, and has as its object to provide an exercise improvement instruction device that can identify issues that will lead to effective improvement of exercise movements. [Means for solving the problem]

[0006] In order to solve the above problems, an exercise improvement instruction device according to one embodiment of the present invention includes a hierarchical index storage unit that stores hierarchical indexes for improving exercise movements, and a problem index identification unit that evaluates the hierarchical indexes based on measurement data during exercise and identifies problem indexes.

[0007] In this embodiment, the hierarchical indices are evaluated based on the measurement data during exercise to identify problem indices. By referring to the relationship between the indices represented in the hierarchical structure, problem indices that lead to effective improvement of exercise movements can be identified.

[0008] Another aspect of the exercise improvement instruction method of the present invention includes a hierarchical index storage step of storing hierarchical indexes for improving exercise movements, and a problem index identification step of evaluating the hierarchical indexes based on measurement data during exercise and identifying problem indexes.

[0009] Another aspect of the exercise improvement instruction program of the present invention causes a computer to execute a hierarchical index storage step of storing hierarchical indexes for improving exercise movements, and a problem index identification step of evaluating the hierarchical indexes based on measurement data during exercise and identifying problem indexes.

[0010] Any combination of the above components, and any transformation of the present invention into a method, device, system, recording medium, computer program, etc., are also effective as aspects of the present invention. Effect of the Invention

[0011] According to the present invention, it is possible to identify problems that can lead to effective improvement of athletic movements. [Brief description of the drawings]

[0012] [Figure 1] 1 is an overall configuration diagram of a system including an exercise improvement instruction device according to an embodiment; [Diagram 2] 4 is a diagram illustrating an example of a hierarchical structure of indices stored in a hierarchical index storage unit; FIG. [Diagram 3]FIG. 11 is a diagram showing a process flow of the exercise improvement guidance device performing exercise improvement guidance using a hierarchical structure. [Figure 4] 13 is a diagram showing a process flow of the exercise improvement guidance device providing exercise improvement guidance when the user has performed another run. FIG. [Diagram 5] FIG. 13 illustrates another example of a hierarchical structure in the form of a directed graph. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] In this embodiment, as an example, guidance is given to improve athletic movements such as running form and use of force. Indicators for improving athletic movements are hierarchical, with the highest level of indicators consisting of "landing impact," "vertical movement," "braking force," etc. These indicators in the highest level are gradually subdivided toward lower levels, with the lowest level having indicators that can be improved individually, such as "ground contact position," "ground contact angle," and "trunk angle." By searching through this hierarchical structure of indicators from higher levels to lower levels while referring to the measurement data obtained during running, it is possible to identify problem indicators that should be improved with priority.

[0014] 1 is a diagram showing the overall configuration of a system including an exercise improvement instruction device 100 according to an embodiment. Exercise improvement instruction device 100 generates instruction information for improving a running exercise movement based on measurement data measured by measuring device 20 while a user is running. The generated instruction information is displayed on display device 30 used by the user.

[0015] Measuring device 20 is, for example, a wearable device such as a smart watch that the user can wear while running, or a smartphone, and measures the exercise movements while running using a built-in sensor. However, in this embodiment, measuring device 20 is not limited to these, and any device may be used as long as it has a function of measuring data while running and a minimum data transmission function for transmitting the measurement data to exercise improvement guidance device 100. For example, a camera (imaging device) that photographs the user while running may be used as measuring device 20. In this case, data photographed by the camera is supplied to exercise improvement guidance device 100 as measurement data.

[0016] In this way, various devices can be used as measuring device 20, and therefore exercise improvement guidance apparatus 100 can obtain various measurement data related to the exercise movements of the user while running.

[0017] For example, when a wearable device or a smartphone is used as the measuring device 20 as described above, basic physical quantities related to the user's position and movement can be obtained as measurement data from acceleration sensors, angular velocity sensors, position sensors (GPS, etc.), magnetic sensors, etc. built into these devices. By appropriately combining and analyzing these measurement data, not only basic information such as the user's position, speed, and acceleration while running can be obtained, but also information regarding the details of the user's exercise movements such as the user's posture while running, the strength and angle of the push-off, and the position and angle of the ground contact. Information regarding the running course such as the distance, altitude, and gradient can also be obtained.

[0018] Furthermore, when the measuring device 20 is equipped with sensors for measuring the external environment, such as an ambient light sensor for measuring brightness, a temperature sensor, and a humidity sensor, the exercise improvement guidance device 100 can provide appropriate exercise improvement guidance taking into account the external environment during running. Also, when a wearable device capable of measuring biological signals such as heart rate, which has been actively developed in recent years, is used as the measuring device 20, the exercise improvement guidance device 100 can provide appropriate exercise improvement guidance taking into account the user's physical condition.

[0019] The measuring device 20 does not need to be worn by the user during exercise, and may be used to measure the user from the surroundings while exercising. The above-mentioned camera photography is a typical example, but is not limited thereto. For example, when a user runs in a limited area indoors using a treadmill or the like, a measuring device 20 that is difficult to carry can be used, so the types of available measurement data are dramatically increased.

[0020] The various measuring devices 20 described above may be used alone or in combination. For example, a user may wear a wearable device as a first measuring device 20 to perform measurements, and a camera as a second measuring device 20 may photograph the user. The exercise improvement instruction apparatus 100 can analyze the user's exercise movements from multiple angles based on the measurement data from such multiple measuring devices 20, and can generate instruction information for effective improvement.

[0021] The display device 30 is a device that displays instruction information for improving exercise movements generated by the exercise improvement instruction device 100 based on the measurement data from the measuring device 20. For example, when a smartphone is used as the measuring device 20, it also functions as the display device 30 that displays the instruction information. When a measuring device 20 such as a camera that does not have a function for displaying instruction information is used, another device owned by the user, such as a smartphone, tablet, or personal computer, is used as the display device 30. Note that the means for displaying the instruction information for improving exercise movements generated by the exercise improvement instruction device 100 based on the measurement data from the measuring device 20 is not limited to display by the display device 30. For example, the above instruction information may be configured to be displayed to the user by outputting sound from an audio device.

[0022] The exercise improvement instruction device 100 is configured on a server capable of communicating with the measuring device 20 and the display device 30 via a wide area communication network such as the Internet. The exercise improvement instruction device 100 has a hierarchical index storage unit 110, a problem index identification unit 120, and an instruction information generation unit 130. Note that this embodiment is not limited to this, and the exercise improvement starting device 100 may be configured to be able to communicate with the measuring device 20 and the display device 30 via a local communication network such as a LAN. In addition, when the measuring device 20 or the display device 30 is an information processing device such as a smartphone, tablet, or personal computer, the function of the exercise improvement device 100 can also be implemented as application software that operates on these information processing devices. In addition, the data measured by the measuring device 20 may be stored in a storage medium such as a USB memory, and the exercise improvement instruction in this embodiment may be performed by reading it into a personal computer or the like having the function of the exercise improvement instruction device 100.

[0023] The hierarchical index storage unit 110 stores hierarchical indexes for improving exercise movements such as running form and how to use force. The hierarchical structure of the indexes stored by the hierarchical index storage unit 110 is configured during the initial setup of the system. The hierarchical structure configured therein can be used for multiple exercise sessions without updating for a certain period of time in order to provide consistent exercise improvement guidance. The hierarchical structure may be updated by adding new indexes or changing or deleting existing indexes. In addition, teacher data showing the correlation between the performance of the exercise movement and each index may be obtained, and a learning model may be generated by machine learning that inputs the performance of the exercise movement and outputs the corresponding index based on the teacher data. According to such a learning model, it is possible to preferentially set indexes that lead to effective improvement of the exercise movement based on the actual performance of the exercise movement. Furthermore, it is also possible to automatically generate a hierarchical structure of the indexes by autonomously analyzing the terms used in each index and their biomechanical meanings.

[0024] The problem index identifying unit 120 identifies problem indexes by referring to the measurement data during running by the measuring device 20, based on the hierarchical structure of indices stored by the hierarchical index storage unit 110. Specifically, the problem index identifying unit 120 evaluates the hierarchical indices based on the measurement data during running, and identifies the problem indexes.

[0025] The problem index identifying unit 120 searches for problem indexes from the higher levels to the lower levels of the hierarchical structure. At this time, for multiple indexes at the same level in the hierarchical structure, the index with the largest deviation between the predetermined reference data and the measured data is selected, and problem indexes are searched for from that level toward the lower levels. Then, the problem index identifying unit 120 identifies a problem index at the lowest level of the hierarchical structure. The embodiment is not limited to one in which the search is started from the top level of the hierarchical structure. For example, a user may specify an arbitrary index to be improved in advance, and problem indexes may be searched for from the level of that index toward the lower levels.

[0026] The training information generating unit 130 generates training information for improving the issue indicators identified by the issue indicator identifying unit 120. The generated training information is transmitted to the display device 30 and displayed on its display screen.

[0027] 2 shows an example of a hierarchical structure of indices stored by the hierarchical index storage unit 110. This hierarchical structure is composed of four layers, from the first layer L1, which is the top layer, to the fourth layer L4, which is the bottom layer. In the first layer L1, "landing impact," "vertical movement," and "braking force," which are main indices for evaluating running exercise movements, are exemplarily arranged.

[0028] In the second layer L2, which is immediately below the first layer L1, there are indices that are subdivisions of each of the indices in the first layer L1. For example, the "vertical movement" in the first layer L1 is subdivided into two indices in the second layer L2: "vertical movement at touchdown" and "vertical movement during flight". In other words, when there is a problem with the "vertical movement" in the first layer L1, it can be broadly divided into cases where there is a problem with the vertical movement at touchdown and cases where there is a problem with the vertical movement during flight, so that in the second layer L2, indices corresponding to each case are provided so that the cause of the problem can be specifically identified. In addition, the other indices in the first layer L1, "landing impact" and "braking force", are also similarly subdivided in the second layer L2 and below, but illustrations and explanations are omitted here. In addition, when subdividing an index in a higher hierarchy into an index in a lower hierarchy, strict logic is not required, and multiple subindices may overlap with each other, and a combination of multiple subindices may not completely reproduce the higher hierarchy.

[0029] In the third layer L3, which is directly below the second layer L2, there are indices that are subdivisions of each of the indices in the second layer L2. For example, the "vertical movement during flight" in the second layer L2 is subdivision into two indices, "take-off angle" and "take-off acceleration" in the third layer L3. In other words, when there is a problem with the "vertical movement during flight" in the second layer L2, it can be broadly divided into a problem with the take-off angle at take-off and a problem with the take-off acceleration at take-off, so that in the third layer L3, indices corresponding to each case are provided so that the cause of the problem can be specifically identified. In addition, the other indices in the second layer L2, "vertical movement at touchdown", are also similarly subdivisions in the third layer L3 and below, but illustration and explanation are omitted here.

[0030] In the fourth layer L4, which is directly below the third layer L3, there are indices that are subdivisions of each of the indices in the third layer L3. For example, the "kicking angle" in the third layer L3 is subdivision into three indices in the fourth layer L4: "ground contact position," "ground contact angle," and "trunk angle." In other words, when there is a problem with the "kicking angle" in the third layer L3, it can be broadly divided into cases where there is a problem with the ground contact position, where there is a problem with the ground contact angle, and where there is a problem with the trunk angle, so that in the fourth layer L4, indices corresponding to each case are provided so that the cause of the problem can be specifically identified. In addition, the "kicking acceleration," another indices in the third layer L3, is also subdivision in the fourth layer L4 in the same way, but illustration and explanation are omitted here.

[0031] In the above-mentioned hierarchical structure, by sequentially evaluating each index from the upper layer to the lower layer while referring to the measurement data obtained during running, it is possible to specifically identify the problem index to be improved. For example, even if there is a problem with the "vertical movement" in the first layer L1, which is the highest layer, it is not possible to provide appropriate exercise improvement guidance unless the specific cause is identified. According to the hierarchical structure of this embodiment, when the problem "vertical movement" in the upper layer is identified, it is possible to search for the specific cause from there toward the lower layers. And, for example, when the "landing position" in the fourth layer L4, which is the lowest layer, is identified as the cause, guidance information for improving it such as "land one step forward" can be generated.

[0032] 3 shows a process flow for exercise improvement guidance by exercise improvement guidance device 100 using the above-mentioned hierarchical structure. In the following explanation, "S" stands for step. In S10, measurements are taken by the measuring device 20 while the user is exercising. In S20, measuring device 20 transmits measurement data to exercise improvement guidance device 100. At this time, when the exercise measurement in S10 is performed using multiple measuring devices 20, the measurement data of the multiple measuring devices 20 may be transmitted to exercise improvement guidance device 100. Furthermore, when multiple pieces of data are measured using one measuring device 20, the measurement data of the multiple measuring devices 20 may be transmitted to exercise improvement guidance device 100.

[0033] In S30, the exercise improvement instruction device 100 processes the various measurement data received in S20 and converts each index in the hierarchical structure into evaluable measurement data. Many of the indexes in the hierarchical structure cannot be evaluated using the data measured by the measuring device 20 as is, so they need to be converted into evaluable data by performing appropriate arithmetic processing. Since various conversion methods are known in the technical field, detailed explanations will be omitted, but for the following indexes described in this flowchart, measurement data for evaluation can be obtained, for example, as follows.

[0034] "Vertical movement" of the first layer L1: This can be measured directly using an acceleration sensor, etc. Second layer L2: "Vertical movement while in the air": Using an acceleration sensor, etc., it is possible to measure vertical movement while in the air and when on the ground. "Kick-off angle" of the third layer L3: Can be calculated from measurement data from the acceleration sensor and angular velocity sensor when transitioning from a grounded state to an airborne state. "Touchdown position" of the fourth layer (L4): The relative position of the touchdown position to the takeoff position is calculated from measurement data from position sensors etc. at the time of takeoff (touchdown → airborne) and at the time of touchdown (airborne → touchdown).

[0035] In S40, the problem index identifying unit 120 searches for problem indexes based on the measurement data processed in S30. S40 is composed of S41 to S44 corresponding to the four levels L1 to L4 of the hierarchical structure. By performing the processes in the order of S41, S42, S43, and S44, a search for problem indexes is performed from the higher levels to the lower levels of the hierarchical structure. More specifically, multiple indexes at each level are evaluated based on the measurement data, the most problematic index is selected, and the cause is searched for from there, moving further down the levels.

[0036] The search step S41 of the first layer L1 is made up of S411 to S413. In S411, multiple indicators in the first layer L1 are evaluated based on the measurement data. Specifically, three indicators in the first layer L1, "landing impact," "vertical movement," and "braking force," are evaluated. Standard data representing normal values ​​is set in advance for each indicator, and the measurement data processed for evaluation of each indicator in S30 is compared with the standard data to determine the degree of deviation. Then, based on the magnitude of the deviation, it is evaluated whether or not the indicator has a problem. In the following, for the sake of simplicity, the standard for evaluating an indicator as having a problem is set to "a deviation of more than 10%" for all indicators, but in reality, different standards can be set for each indicator. In addition, the magnitude of the deviation represents the urgency of the indicator, that is, the priority in exercise improvement guidance. Therefore, between an indicator with a deviation of 20% and an indicator with a deviation of 15%, the former has a higher urgency and is a priority indicator in exercise improvement guidance.

[0037] In S412, it is determined whether or not there was a problematic index in S411. That is, it is determined whether or not there was any deviation of more than 10% for the three indexes "landing impact", "vertical movement", and "braking force" in the first layer L1. If the deviation of any index is less than 10%, it can be said that all indexes, including their respective sub-indices, are within the normal range, so the problem index identifying unit 120 does not identify a problem index, and the training information generating unit 130 ends the process without generating training information. In such a case, the training information generating unit 130 may generate a message to motivate the user, such as "Keep up the good work!"

[0038] On the other hand, if there is an index with a deviation of more than 10% in S412, the index with the largest deviation is selected in S413. In the hierarchical structure of Fig. 2, for example, if the deviation of "landing impact" is 15%, the deviation of "vertical movement" is 20%, and the deviation of "braking force" is 7%, there are problems with "landing impact" and "vertical movement" with deviations exceeding 10%, but "vertical movement" with the largest deviation has a high urgency and is selected as the priority index. In the subsequent steps, a search for problem indexes is performed from there toward lower hierarchical levels.

[0039] The search step S42 of the second layer L2 searches for lower level indicators of the priority indicator "vertical fluctuation" selected in S413, and is made up of S421 to S422. In S421, multiple indices of "vertical movement at touchdown" and "vertical movement during flight" in the second layer L2, which are sub-indices of the priority index "vertical movement" in the first layer L1, are evaluated based on the measurement data. Specifically, for each index, the measurement data is compared with predetermined standard data to determine the degree of deviation.

[0040] In S422, the index with the largest deviation is selected. For example, if the deviation of "vertical movement at touchdown" is 8% and the deviation of "vertical movement during flight" is 15%, the urgency of "vertical movement during flight" with the largest deviation is high and it is selected as the priority index. In the subsequent steps, a search for problem indexes is performed from there toward lower hierarchical levels.

[0041] S43, which is a search step in the third layer L3, searches for lower level indicators of the priority indicator "vertical movement during flight" selected in S422, and is made up of S431 to S432. In S431, multiple indicators of the third layer L3, "kick-off angle" and "kick-off acceleration," which are sub-indicators of the priority indicator of the second layer L2, "airborne vertical movement," are evaluated based on the measurement data. Specifically, for each indicator, the measurement data is compared with the predetermined standard data, and the degree of deviation is calculated.

[0042] In S432, the index with the largest deviation is selected. For example, if the deviation of "kick-off angle" is 17% and the deviation of "kick-off acceleration" is 8%, the "kick-off angle" with the largest deviation has a high urgency and is selected as the priority index. In the subsequent steps, a search for problem indexes is performed from there toward lower levels.

[0043] The search step S44 of the fourth layer L4 searches for lower level indicators of the priority indicator "kick angle" selected in S432, and is made up of S441 to S442. In S441, multiple indicators of the fourth layer L4, which are sub-indices of the priority indicator of the third layer L3, "touching position," "touching angle," and "trunk angle," are evaluated based on the measurement data. Specifically, for each indicator, the measurement data is compared with predetermined standard data, and the degree of deviation is calculated.

[0044] In S442, the index with the largest deviation is selected. For example, if the deviation of "ground contact position" is 20%, the deviation of "ground contact angle" is 13%, and the deviation of "trunk angle" is 5%, the "ground contact position" with the largest deviation has a high urgency and is selected as the priority index. Then, the problem index identifying unit 120 identifies the selected priority index "ground contact position" as the problem index.

[0045] In S50, the training information generating unit 130 generates training information such as "Land one step forward" for improving the problem indicator "ground contact position" identified by the problem indicator identifying unit 120. The training information generated here is transmitted to the display device 30 and displayed on its display screen.

[0046] According to the above processing flow, by referring to the relationships between the indices expressed in a hierarchical structure, it is possible to identify problem indices that lead to effective improvement of exercise movements and generate guidance information.

[0047] By searching for problem indicators from the higher levels to the lower levels in the hierarchical structure, the problem indicator identification unit 120 can identify major problems in the higher levels and their underlying causes in the lower levels, thereby enabling effective guidance for improving exercise.

[0048] This search for problem indicators is performed in order from the first layer L1, which is the top layer, to the fourth layer, which is the bottom layer, and in the search processes S41, S42, S43, and S44 in each layer, priority indicators are identified based on the deviation between the actual measured value and the reference value for each indicator. This makes it possible to identify the indicator with the highest urgency in each layer, and to identify the indicator that needs improvement with the highest priority as the problem indicator.

[0049] By the problem index identification unit 120 identifying problem indexes in the fourth layer L4, which is the lowest layer in the hierarchical structure, it is possible to identify problem indexes that represent the root cause of a problem and that are easy to generate specific guidance information for improvement.

[0050] FIG. 4 shows a process flow of the exercise improvement instruction device 100 performing exercise improvement instruction when the user performs another run after the exercise improvement instruction according to the process flow of FIG. 3 is performed. In this process, when the problem index identifying unit 120 identifies an index that has improved to a certain degree compared with the past run, it searches for the problem index among other indexes in the same hierarchy as the index or a higher hierarchy. In addition, when the problem index identifying unit 120 identifies the same problem index as the past run, the instruction information generating unit 130 generates instruction information different from the past run. In FIG. 4, the processes of S10, S20, S30, and S40 are performed as in FIG. 3, and in S442, which is the final step of S40, a priority index with a large deviation is selected. In the following, the previous run will be taken as an example of the past run, but the process of this embodiment can be applied to past runs before the previous run.

[0051] In S60, it is determined whether the priority index selected in S442 for the current exercise is the same as the priority index "ground contact position" selected in S442 for the previous exercise. If the priority index is different from that of the previous exercise, the problem index identifying unit 120 identifies the priority index as a problem index, and the training information generating unit 130 generates training information for improving the problem index in S50. For example, if the problem index for the current exercise is "trunk angle," training information such as "keep your trunk straight" is generated.

[0052] On the other hand, if the priority index is the same "ground contact position" as in the previous exercise, in S64, it is determined whether or not there has been a predetermined degree of improvement in that index compared to the previous exercise. The criteria for determining whether or not there has been improvement can be set arbitrarily for each index, but the deviation degree described above can be used. For example, it can be determined that there has been an improvement if the deviation degree is 5% or more smaller compared to the previous exercise. Since the deviation degree of the "ground contact position" in the previous exercise was 20%, if the deviation degree in the current exercise is smaller than 15%, it is determined that there has been a predetermined degree of improvement.

[0053] If it is determined in S64 that there has been no predetermined improvement in the "ground contact position," i.e., if the deviation is still high at 15% or more, the problem indicator identifying unit 120 identifies the same "ground contact position" as in the previous exercise as the problem indicator. In this case, the training information generating unit 130 generates training information again in S51 to improve the "ground contact position," but generates training information different from the previous time. This allows the user to improve the same problem indicator from different perspectives.

[0054] If it is determined in S64 that there has been a predetermined improvement in the "ground contact position", i.e., if the deviation is less than 15%, the search continues in the higher hierarchical level, the third layer L3. Specifically, in S63, it is determined whether there has been a predetermined improvement in the "take-off angle" (third layer L3), which is a higher-level indicator of the "ground contact position" (fourth layer L4), compared to the previous exercise.

[0055] If it is determined in S63 that the "push-off angle" has not improved to a predetermined degree, there is still a problem index that should be improved preferentially in the lower layer, so the process returns to S44 in FIG. 3, and the search continues in the lower layer (fourth layer L4). At this time, since the deviation of the "ground contact position" is the largest in the fourth layer L4, if S442 in S44 is executed as is, the "ground contact position" will end up being identified as the problem index. However, since the "ground contact position" has improved to a predetermined degree compared to the previous exercise (S64), it may be excluded from the candidates for the problem index for the current exercise. In that case, in S442, the index with the largest deviation among the other indices, "ground contact angle" and "trunk angle", excluding the "ground contact position", is identified as the problem index. On the other hand, S442 may be executed as is to identify the same "ground contact position" as in the previous exercise as the problem index. In that case, it is preferable to generate guidance information different from the previous one, as described with respect to S51.

[0056] If it is determined in S63 that there has been a predetermined improvement in the "take-off angle", the search continues by moving to the higher level, the second level L2. Specifically, in S62, it is determined whether there has been a predetermined improvement in the "airborne vertical movement" (second level L2), which is a higher level indicator of the "take-off angle" (third level L3), compared to the previous exercise.

[0057] If it is determined in S62 that there has not been a predetermined level of improvement in "vertical movement while airborne", there are still problem indicators in the lower hierarchical level that need to be improved as a priority, so the process returns to S43 in FIG. 3 and the search in the lower hierarchical level (third layer L3) continues.

[0058] If it is determined in S62 that there has been a predetermined improvement in "height-height movement", the search continues by moving to the higher hierarchical level, the first layer L1. Specifically, in S61, it is determined whether there has been a predetermined improvement in "height-height movement" (first layer L1), which is a higher-level indicator of "height-height movement" (second layer L2), compared to the previous exercise.

[0059] If it is determined in S61 that there has not been a predetermined level of improvement in "up and down fluctuations," there are still problem indicators in the lower hierarchical level that need to be improved as a priority, so the process returns to S42 in FIG. 3, and the search in the lower hierarchical level (second layer L2) continues.

[0060] If it is determined in S61 that there has been a predetermined improvement in "vertical movement", the process returns to S412 in Fig. 3, and it is determined whether there is a problematic index in the first layer L1. At this time, since "vertical movement" has improved to a predetermined degree compared to the previous exercise, it is preferable to exclude it from the judgment target of S412. In that case, in S412, judgment is made on the other indexes excluding "vertical movement", namely "landing impact" and "braking force".

[0061] According to the above process flow, it is possible to efficiently search for problem indicators for the current exercise, taking into account improvements from past exercises. At this time, it is possible to efficiently identify up to which level improvement has occurred by the improvement judgment processes S64, S63, S62, and S61, which are executed in sequence from the lower levels to the higher levels of the hierarchical structure.

[0062] The present invention has been described above based on the embodiments. The embodiments are merely examples, and it will be understood by those skilled in the art that various modifications are possible in the combination of each component and each treatment process, and that such modifications are also within the scope of the present invention.

[0063] In the above embodiment, the problem index identifying unit 120 searches for problem indexes from higher levels to lower levels in the hierarchical structure, but the search method in the hierarchical structure is not limited to this. For example, instead of starting the search for problem indexes from the highest level, the search may be started from a level specified in advance by the user or the system. Also, the lowest level may be searched from the beginning, and the index with the largest deviation may be identified as the problem index. In this search, indices in levels other than the lowest level are not taken into account, but when providing exercise improvement guidance taking into account improvements from past exercises as described in FIG. 4, indices in each level are taken into account as in S64, S63, S62, and S61.

[0064] In the above embodiment, all the indices in the top layer L1 are subdivided down to the indices in the bottom layer L4, and the depth of the hierarchical structure is uniformly four layers. However, the depth of the hierarchical structure may vary depending on the indices. That is, if the top layer is L1 as in the embodiment, there may be an index of depth 1 where there is no hierarchical structure below the first layer L1, an index of depth 2 that is subdivided down to the second layer L2, or an index of depth 3 that is subdivided down to the third layer L3. In this case, there may not be indices corresponding to all layers. For example, an index obtained by subdividing an index in the first layer L1 may not exist in the second layer L2 directly below, and may be further subdivided into an index in the third layer L3. In addition, the top layer does not need to be the first layer L1 for all indices, and the top index may be in the second layer L2, the third layer L3, or the fourth layer L4. When searching hierarchical structures with different depths as described above, it is not necessary to search down to the lowest level, and the user or system can specify the search depth for each indicator each time. For example, even when searching a hierarchical structure with a depth of 3, if it is specified to search up to a depth of 2, the lowest level of depth 3 will not be searched, and problem indicators will be identified in the level of depth 2 above.

[0065] In the above embodiment, the problem index is specified in the lowest level of the hierarchical structure, but the problem index may be specified in other levels. For example, the index with the largest deviation across all levels may be specified as the problem index. In this case, it is preferable to prepare guidance information corresponding to all indices regardless of the level. In addition, if guidance information is generated for an index of a certain level during a past exercise, and as a result, the index of that level has sufficiently improved during the current exercise, the problem index may be specified in a higher level. This allows guidance based on an appropriate level to be provided in accordance with the improvement of the user's exercise movements.

[0066] In the above embodiment, when the problem index identifying unit 120 searches for a problem index, the index with the largest deviation between the reference data and the measured data in each layer is selected, and the problem index is further searched for toward lower layers. However, multiple indexes with deviations larger than a preset value in each layer may be selected. In such a case, a search is performed in the lower layers for each selected index, and multiple problem indexes are identified. The training information generating unit 130 generates training information for each identified problem index and displays it on the display device 30. On the display device 30, the multiple pieces of training information may be displayed on one screen, or the user may switch between multiple screens to check the training information for each problem index.

[0067] In the process flow of FIG. 4, if an index in a certain layer has improved to a certain degree compared to a past exercise, the presence or absence of improvement was judged for the index in the upper layer, but the presence or absence of improvement may also be judged for other indexes in the same layer. For example, in FIG. 4, if the "ground contact position" in the fourth layer L4 has improved in S64, the presence or absence of improvement was judged for the "pull-off angle" in the third layer L3 in S63, but the presence or absence of improvement may also be judged for other indexes in the fourth layer L4, "ground contact angle" and "trunk angle". These indexes in the fourth layer L4 are subordinate to the "pull-off angle" in the third layer L3, so it makes no difference which layer the improvement is judged in.

[0068] In the above embodiment, a tree structure as shown in Fig. 2, that is, a structure branching from a higher level to a lower level, is exemplified as a hierarchical structure, but the hierarchical structure of the present invention is not limited to such a tree structure. Fig. 5 shows another example of a hierarchical structure in the form of a directed graph. In this figure, vertices A to K of the directed graph represent the indices of each of the levels L1 to L4, and the directed edges connecting the indices indicate the direction of search for problem indices. Indices A, B, and C exist in the first layer L1 of the top level.

[0069] Indicators D and E exist in the second layer L2. Here, indicator D is connected by an edge (arrow) starting from indicators A and B in the first layer L1, respectively, which means that indicator D is evaluated when the deviation of indicators A and B is greater than a predetermined value. Therefore, if the deviation of at least one of indicators A and B is equal to or less than a predetermined value, the search for problem indicators for indicators A and B ends in the first layer L1. At this time, if the deviations of indicators A and B are both equal to or less than a predetermined value, neither is a problem indicator, and if the deviation of either indicator is greater than a predetermined value, it is identified as a problem indicator. Although the same explanation will be omitted hereafter, a directed edge (arrow) means that if the deviation is greater than a predetermined value, the search for indicators in a lower hierarchy will proceed. Unlike the tree structure in FIG. 2, in this example, it can also be considered that upper indicators A and B are integrated into lower indicator D.

[0070] The third layer L3 has indices F, G, and H. Indicator F is specified by an arrow from indicator D in the second layer L2. Indicator G is specified by an arrow from indicator E in the second layer L2 and indicator C in the first layer L1. Indicator H is specified by an arrow from indicator E in the second layer L2.

[0071] The fourth layer L4 has indices I, J, and K. Indicator I is designated by an arrow from indicators F and G in the third layer L3. Indicator J is designated by an arrow from indicator G in the third layer L3. Indicator K is designated by an arrow from indicators F and H in the third layer L3.

[0072] According to the hierarchical structure of the directed graph as described above, it is possible to appropriately describe the relationships between indices that are more complicated than those in the tree structure shown in Fig. 2, and to effectively identify problem indices. Also, in Fig. 5, only arrows pointing from a higher level to a lower level are illustrated, but the process of searching for improvements from past exercise from a lower level to a higher level as described with reference to Fig. 4 can be described by arrows pointing from the lower level indices to the higher level in Fig. 5.

[0073] In this embodiment, running has been described as an example of exercise, but the present invention can be applied to other exercises, such as training and exercise in various track and field events, swimming, gyms, walking, road bikes, dancing, and ball games such as soccer.

[0074] The functional configuration of each device described in the embodiments can be realized by hardware resources, software resources, or by the cooperation of hardware and software resources. A processor, ROM, RAM, and other LSIs can be used as hardware resources. An operating system, application programs, and other programs can be used as software resources. [Explanation of symbols]

[0075] 100...exercise improvement instruction device, 110...hierarchical index storage unit, 120...problem index identification unit, 130...instruction information generation unit, 20...measuring device, 30...display device. [Industrial Applicability]

[0076] The present invention relates to an exercise improvement instruction device that identifies issues for improving exercise movements.

Claims

1. a hierarchical index storage unit for storing hierarchical indexes for improving exercise movements; a problem index identifying unit that evaluates the hierarchical indexes based on measurement data during exercise and identifies a problem index; Equipped with The hierarchical structure of the index includes at least three levels, and an index other than the lowest level is subdivided into a plurality of indexes in the level immediately below it; The problem indicator identification unit selects, from among multiple indices at the same level in the hierarchical structure of the indices, those in which the deviation between specified reference data and the measurement data is greater than a preset value, and then searches for the problem indicators in a stepwise manner, moving further down to at least two lower levels.

2. a guidance information generating unit that generates guidance information for improving the problem index identified by the problem index identifying unit; The exercise improvement instruction device according to claim 1.

3. The problem index identifying unit searches for the problem index from a pre-specified hierarchy of a hierarchical structure of the index.

3. The exercise improvement instruction device according to claim 1 or 2.

4. The problem index identifying unit identifies the problem index at the lowest level of a hierarchical structure of the indexes.

4. The exercise improvement instruction device according to claim 1.

5. When the problem index identifying unit identifies an index that has improved to a predetermined degree compared to a past exercise, the problem index identifying unit searches for the problem index among other indexes in the same hierarchy as the identified index or in a higher hierarchy.

5. The exercise improvement instruction device according to claim 1.

6. When the problem indicator identifying unit identifies the problem indicator that is the same as that in a past exercise, the instruction information generating unit generates the instruction information that is different from that in a past exercise. The exercise improvement instruction device according to claim 2.

7. A hierarchical index storage step of storing a hierarchical index for improving athletic movements; a problem index identifying step of evaluating the hierarchical indexes based on measurement data during exercise and identifying a problem index; Equipped with The hierarchical structure of the index includes at least three levels, and an index other than the lowest level is subdivided into a plurality of indexes in the level immediately below it; The problem indicator identification step is a method for providing exercise improvement guidance in which, for a plurality of indices at the same level in a hierarchical structure of the indices, those in which the degree of deviation between specified reference data and the measurement data is greater than a preset value are selected, and the problem indicator is then searched for in stages, moving further downward to at least two lower levels.

8. A hierarchical index storage step of storing a hierarchical index for improving athletic movements; a problem index identifying step of evaluating the hierarchical indexes based on measurement data during exercise and identifying a problem index; on the computer, The hierarchical structure of the index includes at least three levels, and an index other than the lowest level is subdivided into a plurality of indexes in the level immediately below it; The problem indicator identification step is an exercise improvement instruction program that selects, for multiple indicators at the same level in the hierarchical structure of the indicators, those in which the deviation between specified reference data and the measurement data is greater than a preset value, and then searches for the problem indicators in a stepwise manner, moving further down to at least two lower levels.

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