Motion guidance information generation device, method, and program

Abstract

In one embodiment of the present invention, first, aspect-content information representing the correspondence relationship between a plurality of aspects that serve as guidance targets regarding a user's motion and a plurality of items of content that are associated with respective aspects is generated and a content graph reflecting co-occurrence relationships among the plurality of items of content is generated on the basis of the generated aspect-content information. Next, by analyzing the content graph according to a preprepared algorithm, graph analysis data in which the plurality of aspects are selected in descending order of the number of items of content associated with each aspect is generated, and coaching information defining the guidance order of the plurality of aspects and the plurality of items of content used for the guidance of each aspect is generated on the basis of the graph analysis data.

Description

Motion Guidance Information Generation Device, Method, and Program

[0001] One aspect of this invention relates to a motion guidance information generation device, method, and program used for training physical motions in daily life such as sports, art, and walking motions.

[0002] In recent years, technologies have been developed that utilize XR (Cross Reality) technology to generate information representing the movements of a model body and virtually present it to a user, and guide the user's physical movements according to the presented information.

[0003] For example, Non-Patent Document 1 describes a technology for examining the effects on walking motions, such as walking cycle, balance during walking, and physical exertion, by presenting the walker's own footstep sounds with a delay. By controlling the tempo and pace during walking using this technology, it becomes possible to bring the user's walking motion closer to an ideal state.

[0004] Also, Non-Patent Document 2 describes a technology for allowing a user to experience various motions by presenting motion support information to the user using a number of senses. When using this technology, for example, it becomes possible to use a combination of multiple senses to guide a user regarding items of a physical motion that are the target of guidance.

[0005] Yoshima Matsuo, Yoshaki Miyashita, "Intervention in the walking cycle using footstep delay feedback", Information Processing Society of Japan Research Report, Vol 2017-HCT-172 No.6, March 6, 2017 Ning Ikoi, Koichi Hirota, Koji Abe, Tomohiro Amemiya, Makoto Sato, Mitsuakira Kitazaki, "Proposal of the concept of physical follow-up experience and test implementation of some functions Sharing of walking and running experiences through multi-sensory and motion information presentation", TVRSJ Vol.24 No.2 pp.153-164, 2019

[0006] However, the technology described in Non-Patent Document 1 has the problem of low instruction efficiency because it only considers one aspect of the instruction, such as tempo or pace in the case of walking. On the other hand, the technology described in Non-Patent Document 2 can instruct the user's movements from multiple perspectives, but it has the problem of presenting multiple pieces of instructional information for multiple perspectives simultaneously, which can confuse the user and actually reduce the effectiveness of the instruction.

[0007] This invention was made in view of the above circumstances and aims to provide a technology that can improve the efficiency of instruction without causing confusion to the user.

[0008] To solve the above problems, one embodiment of the operation support information generation device or method according to the present invention first generates viewpoint-content information representing the correspondence between a plurality of viewpoints that are to be instructed regarding the user's actions and a plurality of contents associated with each of these viewpoints, and then generates a content graph that reflects the co-occurrence relationships of the plurality of contents based on the generated viewpoint-content information. Next, by analyzing the content graph according to a pre-prepared algorithm, graph analysis data is generated in which the plurality of viewpoints are selected in descending order of the number of contents associated with each viewpoint, and coaching information is generated based on the graph analysis data that defines the instruction order of the plurality of viewpoints and the plurality of contents to be used for instruction of each viewpoint.

[0009] According to one aspect of this invention, multiple viewpoints to be instructed are selected sequentially, and content corresponding to the selected viewpoints is presented to the user in chronological order. Therefore, compared to presenting the user with content corresponding to multiple viewpoints simultaneously, it is possible to accurately and effectively instruct the user's actions without causing confusion.

[0010] Furthermore, since content is selected sequentially from perspectives with a large number of associated contents and used for instruction, each perspective can be taught preferentially from the perspective with the most content, thereby increasing the efficiency of instruction.

[0011] In other words, according to one aspect of this invention, it is possible to provide a technology that makes it possible to improve the efficiency of instruction without causing confusion to the user.

[0012] Figure 1 is a block diagram showing an example of the hardware configuration of a motion guidance information generation device according to one embodiment of the present invention. Figure 2 is a block diagram showing an example of the software configuration of a motion guidance information generation device according to one embodiment of the present invention. Figure 3 is a flowchart showing an example of the overall processing procedure and processing content of the motion guidance information generation process executed by the control unit of the motion guidance information generation device shown in Figure 2. Figure 4 is a flowchart showing an example of the processing procedure and processing content of the graph generation process among the processing procedures shown in Figure 3. Figure 5 is a flowchart showing an example of the processing procedure and processing content of the graph analysis process among the processing procedures shown in Figure 3. Figure 6 is a flowchart showing an example of the processing procedure and processing content of the coaching information generation process among the processing procedures shown in Figure 3. Figure 7 shows an example of a combination (cross-modal) of multiple viewpoints and multiple contents used when instructing walking movements. Figure 8 shows an example of a processing matrix generated by the processing matrix generation processing unit among the processing procedures shown in Figure 3. Figure 9 shows an example of a content-content matrix generated by the graph generation process shown in Figure 4. Figure 10 shows an example of a content graph generated by the graph generation process shown in Figure 4. Figure 11 shows an example of a pruned content graph generated by the graph generation process shown in Figure 4. Figure 12 is a diagram illustrating the overview of the graph analysis process performed by the graph analysis processing unit in the processing procedure shown in Figure 3. Figure 13 is a diagram illustrating an example of the node selection process and content selection process in the graph analysis process shown in Figure 5. Figure 14 is a diagram illustrating an example of the node search process and content selection process during search in the graph analysis process shown in Figure 5. Figure 15 is a diagram illustrating an example of the node search process and content selection process during search in the graph analysis process shown in Figure 5. Figure 16 is a diagram illustrating an example of the node selection process and content selection process in the graph analysis process shown in Figure 5. Figure 17 is a diagram illustrating an example of the node search process and content selection process during search in the graph analysis process shown in Figure 5. Figure 18 is a diagram illustrating an example of the node selection process and content selection process in the graph analysis process shown in Figure 5.Figure 19 shows an example of graph analysis data finally obtained by the graph analysis process shown in Figure 5. Figure 20 shows an example of coaching content selection results generated by the coaching information generation process shown in Figure 6. Figure 21 shows an example of coaching information list data generated by the coaching information generation process shown in Figure 6.

[0013] Embodiments of this invention will be described below with reference to the drawings.

[0014] [One Embodiment] In one embodiment of this invention, the case in which a user's walking motion is instructed from multiple perspectives and using multiple instructional contents in combination will be described as an example.

[0015] (Example configuration) The motion instruction information generation device CS is composed of, for example, a personal computer installed in a training facility.

[0016] Figures 1 and 2 are block diagrams showing examples of the hardware and software configurations of the operation guidance information generation device CS, respectively.

[0017] The operation guidance information generation device CS includes a control unit 1 that uses a hardware processor such as a Central Processing Unit (CPU), and a storage unit having a program storage unit 2 and a data storage unit 3, and an input / output interface (hereinafter referred to as I / F) unit 4 are connected to this control unit 1 via a bus 5.

[0018] The input / output I / F section 4 is connected to the input device IN, the auditory device HD, the visual device VD, and the haptic device TD.

[0019] The input device IN consists of an operating device such as a keyboard or mouse, and is used by the instructor or manager to input information showing a list of instructional points or a request to start instruction. The input device IN may also include an external storage medium or a display device.

[0020] The auditory device (HD), visual device (VD), and haptic device (TD) are used to present stimuli to the user in accordance with coaching information. Of these, the auditory device (HD) and visual device (VD) are installed, for example, in a head-mounted display. The haptic device (TD) is installed in training shoes worn by the user, or on a walking board or floor surface on which the user walks.

[0021] The program storage unit 2 is configured, for example, as a storage medium, by combining a non-volatile memory that can be written to and read at any time, such as an HDD (Hard Disk Drive) or SSD (Solid State Drive), with a non-volatile memory such as ROM (Read Only Memory). In addition to middleware such as an OS (Operating System), it stores various programs necessary to execute various control processes according to one embodiment of this invention.

[0022] The data storage unit 3 is configured, for example, as a storage medium, by combining a non-volatile memory that can be written to and read at any time, such as an HDD or SSD, with a volatile memory such as RAM (Random Access Memory). The storage area of ​​this unit is provided with a viewpoint list information storage unit 31, a processing matrix storage unit 32, a graph information storage unit 33, a graph analysis data storage unit 34, and a coaching information storage unit 35, as the storage unit according to this invention.

[0023] The viewpoint list information storage unit 31 is used to store viewpoint list information entered by the instructor or administrator.

[0024] The processing matrix storage unit 32 is used to store processing matrix information generated by the control unit 1, which will be described later.

[0025] The graph information storage unit 33 is used to store the content graph generated by the control unit 1.

[0026] The graph analysis data storage unit 34 is used to store the graph analysis data generated by the control unit 1.

[0027] The coaching information storage unit 35 is used to store the coaching list information generated by the control unit 1.

[0028] The control unit 1 includes, as processing functions for realizing one embodiment of the present invention, a viewpoint list information acquisition processing unit 11, a processing matrix generation processing unit 12, a graph generation processing unit 13, a graph analysis processing unit 14, a coaching information generation processing unit 15, and a coaching information output processing unit 16.

[0029] Each of these processing units 11 to 16 is implemented by having the hardware processor of the control unit 1 execute the application program stored in the program storage unit 2. Note that some or all of the above processing units 11 to 16 may be implemented using hardware such as LSI (Large Scale Integration) or ASIC (Application Specific Integrated Circuit).

[0030] The viewpoint list information acquisition processing unit 11 acquires viewpoint list information necessary for walking instruction, which has been prepared in advance by the instructor or manager, from the input device IN via the input / output I / F unit 4, and stores the acquired viewpoint list information in the viewpoint list information storage unit 31.

[0031] The processing matrix generation unit 12 generates a content-viewpoint matrix based on the viewpoint list information stored in the viewpoint list information storage unit 31, with content in the rows and views in the columns. The processing matrix generation unit 12 then stores the generated content-viewpoint matrix in the processing matrix storage unit 32.

[0032] The graph generation processing unit 13 first generates a content co-occurrence matrix based on the content-perspective matrix stored in the processing matrix storage unit 32. Then, it generates a content graph based on the generated content co-occurrence matrix and stores the generated content graph in the graph information storage unit 33. An example of the content graph generation process will be explained in detail in the operation example.

[0033] The graph analysis processing unit 14 analyzes the content graph stored in the graph information storage unit 33 according to a pre-prepared algorithm and generates graph analysis data in which multiple perspectives are arranged in order of the frequency of content use. The graph analysis processing unit 14 then stores the generated graph analysis data in the graph analysis data storage unit 34. An example of the content graph analysis process will be explained in detail in the operation example.

[0034] The coaching information generation processing unit 15 selects a group of content to be used for instruction for each viewpoint based on the graph analysis data stored in the graph analysis data storage unit 34, and generates coaching list information in which the selected group of content is arranged in the order of instruction for each viewpoint. The coaching information generation processing unit 15 then stores the generated coaching list information in the coaching information storage unit 35.

[0035] When providing guidance on actions to a user, the coaching information output processing unit 16 generates presentation data for each content in the order of instruction based on the coaching list information stored in the coaching information storage unit 35, and selectively outputs each generated presentation data as a presentation signal from the input / output I / F unit 4 to the auditory device HD, the visual device VD, and the haptic device TD.

[0036] (Example of operation) Next, an example of the operation of the operation guidance information generation device CS configured as described above will be explained.

[0037] Figure 3 is a flowchart showing an example of the processing procedure and processing content of the motion guidance information generation process executed by the control unit 1 of the motion guidance information generation device CS.

[0038] (1) Acquisition of Perspective List Information The perspective list information is a list of combinations of perspectives and instructional content necessary to instruct exemplary walking movements according to the physical condition of the user being instructed, such as healthy individuals, elderly people, and people undergoing rehabilitation. This information is created in advance by the instructor or administrator.

[0039] In step S1, the control unit 1 of the motion instruction information generation device CS monitors the input of a request for instruction on walking motion. When a request for instruction on walking motion is input in this state, in step S2, under the control of the viewpoint list information acquisition processing unit 11, the viewpoint list information is acquired from the input device IN via the input / output I / F unit 4. The acquired viewpoint list information is then stored in the viewpoint list information storage unit 31.

[0040] Figure 7 shows an example of viewpoint list information stored in the viewpoint list information storage unit 31. In this example, seven items are set as viewpoints for teaching walking motion: "ground contact," "tempo (walking speed)," "stride length," "knee height," "gaze," "posture (trunk)," and "breathing." In addition, each of the above viewpoints is assigned a content selected from multiple instructional content "A" to "J." Of these, content "A" to "C" uses auditory information, "D" to "G" uses visual information, and "H" to "J" uses tactile information to present instruction to the user.

[0041] Furthermore, the types and number of viewpoints and corresponding instructional content in the viewpoint list information are not limited to the above example and can be arbitrarily set according to the type of walking motion. In addition, the viewpoint list information may be stored in advance in the motion instruction information generation device CS, rather than being acquired each time instruction is given.

[0042] (2) The control unit 1 of the processing matrix generation operation guidance information generation device CS then generates a processing matrix in step S3 under the control of the processing matrix generation processing unit 12 as follows.

[0043] In other words, the processing matrix generation unit 12 first checks which content is associated with each viewpoint based on the viewpoint list information stored in the viewpoint list information storage unit 31, and then generates a content-viewpoint matrix that represents the result.

[0044] Figure 8 shows an example of the content - perspective matrix. In this example, for six of the seven perspectives mentioned above, namely "grounding", "tempo", "stride", "knee height", "gaze", and "breathing", if the content "A" to "J" is associated, then "*" is set, and if not, "Null" is set.

[0045] The processing matrix generation processing unit 12 stores the generated content - perspective matrix in the processing matrix storage unit 32.

[0046] (3) Next, in step S4, the control unit 1 of the graph generation operation guidance information generation device CS generates a content graph as follows under the control of the graph generation processing unit 13.

[0047] Figure 4 is a flowchart showing an example of the processing procedure and content of the graph generation processing executed by the graph generation processing unit 13.

[0048] That is, the graph generation processing unit 13 reads the content - perspective matrix from the processing matrix storage unit 32 in step S40, and based on the read content - perspective matrix, first generates a content co - occurrence matrix in step S41.

[0049] Figure 9 shows an example of the content co - occurrence matrix. In this example, the content "A" to "J" are arranged in rows and columns respectively, and the number of times used simultaneously among these contents "A" to "J" is counted.

[0050] Next, in step S42, the graph generation processing unit 13 generates a content graph using a pre - prepared algorithm based on the content co - occurrence matrix. For example, a content graph is generated by connecting contents with a threshold value (E > 1) or more with edges and further visualizing it on a two - dimensional plane using a spring model. Then, in step S43, the graph generation processing unit 13 performs a pruning process on the content graph, for example, deleting contents with a large co - occurrence number, and stores the content graph after the pruning process in the graph information storage unit 33 in step S44.

[0051] Furthermore, the graph generation process using the spring model is described in detail in the following reference, for example: Reference; “Graph Visualization and Navigation in Information: A Survey”, IEEE TRANSACTION ON VISUALIZATION AND COMPUTER GRAPHICS, Vol 6, No.1 January-March 2000.

[0052] Figure 10 shows an example of a content graph before pruning, and Figure 11 shows an example of a content graph after pruning. In this example, content "G," in which cells with a co-occurrence count of "2" or higher account for more than 80% of the total, is deleted through pruning.

[0053] (4) The control unit 1 of the content graph analysis operation guidance information generation device CS then, in step S5, analyzes the content graph under the control of the graph analysis processing unit 14 as follows.

[0054] First, we will explain the overview of the graph analysis process using Figure 12.

[0055] As shown in Figure 12, the graph analysis processing unit 14 targets the pruned content graph shown in (1) and first selects the content located furthest from the center on the two-dimensional plane of the graph (hereinafter also referred to as a node) as shown in (2). Then, from among the viewpoints that possess the selected content, it selects the viewpoint with the largest number of associated content items (hereinafter also referred to as the number of content items) and adds it to the list.

[0056] Next, the graph analysis processing unit 14 selects the content that is furthest to the end on the two-dimensional plane from among the content that was selected immediately before, as shown in (3), and selects the viewpoint that has the largest number of contents among the viewpoints that have the selected content and adds it to the list.

[0057] Thereafter, the graph analysis processing unit 14 similarly selects content connected to the previously selected content, starting from the end as shown in (4), and adds the viewpoints with the most content among those that possess the selected content to the list.

[0058] Then, once the graph analysis processing unit 14 has finished traversing the content that co-occurs simultaneously, it returns to the content graph (1) after the pruning process described above, and this time selects another content located at the end, for example, as shown in (2)'. Then, it selects the viewpoint with the largest number of content possessions among the viewpoints that possess the selected content and adds it to the list.

[0059] The graph analysis processing unit 14 repeatedly performs the above process while sequentially traversing the content graph until all content has been selected. Then, as shown in (4)″, once all content on the content graph has been selected, the list of selected viewpoints is stored as graph analysis data in the graph analysis data storage unit 34.

[0060] Next, we will explain a specific example of the content graph analysis process described above.

[0061] Figure 5 is a flowchart showing an example of the processing procedure and processing content of the graph analysis process executed by the graph analysis processing unit 14.

[0062] In other words, in step S50, the graph analysis processing unit 14 reads the pruned content graph from the graph information storage unit 33. In this state, the graph analysis processing unit 14 first selects the content that is furthest from the center of the two-dimensional plane in the content graph in step S51. Then, in step S52, it selects the viewpoint that has the largest number of content items among the viewpoints that have the selected content.

[0063] Figure 13 shows an example of the above processing. In this example, "J" is selected as the content located at the very end of the content graph, and among the multiple viewpoints that have the selected content "J", [Tempo] is selected as the viewpoint that possesses the largest number of contents. This process of selecting the viewpoint that possesses the largest number of contents is performed by referring to the content-viewpoint matrix stored in the processing matrix storage unit 32.

[0064] If, in the perspective selection process in step S52, no perspective that meets the conditions can be selected, the graph analysis processing unit 14 returns from step S53 to step S51 to select another content located at the end. Furthermore, once all perspectives to be taught have been selected, the graph analysis processing unit 14 proceeds from step S54 to step S59.

[0065] If a viewpoint that meets the conditions is selected in step S52 and there are still unselected viewpoints, the graph analysis processing unit 14 proceeds to step S55. The graph analysis processing unit 14 then selects the content that is furthest from the center of the two-dimensional plane in the content graph from among the multiple content connected to the content that was selected immediately before in step S51. The graph analysis processing unit 14 then selects the viewpoint that has the largest number of content from among the viewpoints that possess the selected content in step S56.

[0066] Figure 14 shows an example of the above process. In this example, "I" is selected as the content, and among the viewpoints that have the selected content "I", [Grounding] is selected as the viewpoint that has the most content.

[0067] If the graph analysis processing unit 14 is unable to select a viewpoint that meets the conditions in step S56, it returns to step S51 from step S57. If all viewpoints to be taught have been selected, the graph analysis processing unit 14 proceeds to step S59 from step S58.

[0068] On the other hand, if a viewpoint that meets the conditions is selected in step S56 and there are still unselected viewpoints remaining, the graph analysis processing unit 14 returns to step S55. Then, in step S55, the graph analysis processing unit 14 selects the content that is furthest from the center of the two-dimensional plane in the content graph from among the multiple content connected to the previously selected content. Subsequently, in step S56, the graph analysis processing unit 14 selects the unselected viewpoint that has the largest number of content from among the viewpoints that possess the selected content.

[0069] Figure 15 shows an example of the processing operation. In this example, "H" is selected as the next content and "stride length" is selected as the viewpoint.

[0070] The graph analysis processing unit 14 then repeatedly performs a series of processes, selecting content in order of furthest from the center of the two-dimensional plane of the content graph, that is, from those closest to the ends of the content graph, and selecting the unselected viewpoint with the largest number of content possessions among the content that possesses the selected content.

[0071] Figures 16, 17, and 18 show the results of the series of processing operations described above. For example, Figure 16 shows the case where "E" is selected as another content located at the end, and "breathing" is selected as the viewpoint. Figure 17 shows the case where "B" is selected as content connected to content E, and "gaze" is selected as the viewpoint. Furthermore, Figure 18 shows the case where "C" is selected as another content located at the end, and "knee height" is selected as the viewpoint.

[0072] When the graph analysis processing unit 14 determines that it has finished selecting all the viewpoints to be taught by repeating the series of processes described above, it moves from step S54 or step S58 to step S59. The graph analysis processing unit 14 then stores the list information representing each viewpoint selected by the series of processes described above and the order in which they were selected as graph analysis data in the graph analysis data storage unit 34.

[0073] Figure 19 shows an example of graph analysis data. This example shows the case where the viewpoints were selected in the following order: "tempo," "ground contact," "stride length," "breathing," "eye gaze," and "knee height."

[0074] (5) Coaching Information Generation Operation Instruction Information Generation Device CS control unit 1 then, in step S6, executes the process of generating coaching information under the control of the coaching information generation processing unit 15 as follows.

[0075] Figure 6 is a flowchart showing an example of the processing procedure and processing content of the coaching information generation process executed by the coaching information generation processing unit 15.

[0076] In other words, the coaching information generation processing unit 15 first reads graph analysis data from the graph analysis data storage unit 34 in step S60. Next, in step S61, based on the graph analysis data, the coaching information generation processing unit 15 selects the content associated with each viewpoint in the selected order by referring to the content-viewpoint matrix stored in the processing matrix storage unit 32.

[0077] Figure 20 shows an example of the content selection results corresponding to each perspective: "tempo," "ground contact," "stride length," "breathing," "eye gaze," and "knee height."

[0078] The coaching information generation processing unit 15 then generates coaching list data based on the above-mentioned content selection data for each viewpoint in step S62, and stores the generated content list data in the coaching information storage unit 35 in step S63.

[0079] Figure 21 shows an example of content list data generated by the coaching information generation processing unit 15 described above. In this example, each perspective and its associated content group are arranged in the order of instruction. In this example, for perspectives with four or more content items, the content is selected so that the number of content items simultaneously presented to the user is limited to three or less.

[0080] (6) The control unit 1 of the coaching information output operation guidance information generation device CS executes the process of outputting coaching information to the user under the control of the coaching information output processing unit 16 when the user has completed preparation for walking training.

[0081] When undergoing gait training, the user wears a head-mounted display equipped with an auditory device (HD) and a visual device (VD) on their head, and shoes equipped with a haptic device (TD) on their feet.

[0082] In this state, the coaching information output processing unit 16 reads coaching list data from the coaching information storage unit 35. Based on the read coaching list data, the coaching information output processing unit 16 selects each perspective in the order of instruction at predetermined time intervals, and selects a group of content for each selected perspective. It then generates presentation data to present the selected group of content to the user. The presentation data is selectively output as presentation signals from the input / output I / F unit 4 to the auditory device HD, visual device VD, and haptic device TD corresponding to each of the above-mentioned contents.

[0083] For example, in the coaching list data shown in Figure 21, "Tempo," which is ranked first in instruction priority, is selected first, and a presentation signal is output to the haptic device TD to present the content "J," "I," and "H" associated with it to the user. As a result, the instruction content is presented to the user's feet via haptic feedback through the haptic device TD.

[0084] Then, once the "tempo" instruction is performed for a predetermined period, for example, one minute, the next instruction priority, "breathing," is selected, and presentation signals for the content "E," "B," and "H" associated with it are output to the visual device VD, auditory device HD, and haptic device TD, respectively. As a result, the user is presented with instructional content through the visual device VD, auditory device HD, and haptic device TD, using visual, auditory, and haptic senses.

[0085] Thereafter, similarly, according to the instruction ranking of the perspectives specified in the coaching list data, perspectives ranked 3rd to 6th are selected sequentially at predetermined time intervals, and multiple content items associated with each perspective are presented to the user through the visual device VD, auditory device HD, and haptic device TD.

[0086] Furthermore, the time interval for selecting each viewpoint, that is, the presentation time for content from each viewpoint, can be set arbitrarily.

[0087] (Effects) As described above, in one embodiment, based on the acquired list of viewpoints, a content-viewpoint matrix is ​​first generated that represents the correspondence between each viewpoint and a group of content. Then, a content co-occurrence matrix is ​​generated based on the above content-viewpoint matrix, and a content graph is generated by applying, for example, a spring model to the generated content co-occurrence matrix. Next, in the above content graph, content is selected sequentially by traversing from the content located at the end of the two-dimensional plane, and the viewpoint that possesses the content with the largest number of content is selected, thereby generating graph analysis data in which multiple viewpoints to be instructed are arranged in descending order of the number of content held. Then, based on the above graph analysis data, a group of content to be used for instructing each viewpoint is selected, thereby generating coaching list information in which the group of content is arranged in the order of instruction for each viewpoint. Based on this coaching list information, the content is presented to the user in chronological order according to the instruction order for each viewpoint using the auditory device HD, the visual device VD, and the haptic device TD.

[0088] Therefore, according to one embodiment, multiple perspectives to be taught are selected sequentially, and their content is presented to the user at regular intervals in chronological order. As a result, compared to presenting the user with content corresponding to multiple perspectives simultaneously, it becomes possible to accurately and effectively guide the user's actions without causing confusion.

[0089] Furthermore, since instruction is conducted sequentially based on the amount of content possessed, each aspect can be prioritized for instruction from the perspective of the amount of content possessed, thereby increasing the efficiency of instruction.

[0090] [Other Embodiments] (1) In one embodiment, each viewpoint is selected sequentially at predetermined time intervals according to the instruction order of viewpoints specified by the coaching list data, and each content associated with each viewpoint is presented to the user through the visual device VD, auditory device HD, and haptic device TD. However, the embodiment is not limited to this, and the time interval for selecting each viewpoint does not have to be constant, and the presentation time for each viewpoint may be arbitrarily set to be variable. For example, the presentation time can be set longer for viewpoints that are difficult for the user to understand. In this way, it becomes possible to provide the user with instruction on viewpoints that are difficult for the user to understand over a longer period of time.

[0091] (2) In one embodiment, the case of instructing a user on walking movements was described as an example, but this invention can also be applied to instructing body movements in other sports such as track and field, throwing, and breaking, as well as in gymnastics, yoga, rehabilitation, dance, and other forms of physical activity.

[0092] (3) In one embodiment, the description was given using a case where the motion instruction information generation device CS is configured using a personal computer installed in a training facility or the like. However, the motion instruction information generation device CS may be configured using a smartphone, tablet, or wearable device owned by the user being instructed. Furthermore, each processing function of the motion instruction information generation device CS may be provided on a server computer installed on the Web or in the cloud, or may be distributed and arranged across multiple personal computers or server computers.

[0093] (4) In addition, the functional configuration of the motion guidance information generation device CS, the processing procedures and content of the processing performed by each processing function, the types of viewpoints and the types of content associated with each viewpoint can also be modified in various ways without departing from the spirit of this invention.

[0094] Although embodiments of this invention have been described in detail above, the above description is merely illustrative in all respects. It goes without saying that various improvements and modifications can be made without departing from the scope of this invention. In other words, when implementing this invention, specific configurations may be adopted as appropriate depending on the embodiment.

[0095] In short, this invention is not limited to the embodiments described above, and in the implementation stage, the components can be modified and materialized without departing from the gist of the invention. Furthermore, various inventions can be formed by appropriately combining the multiple components disclosed in the embodiments. For example, some components may be deleted from all the components shown in the embodiments. Moreover, components from different embodiments may be appropriately combined.

[0096] CS: Motion guidance information generation device IN: Input device HD: Auditory device VD: Visual device TD: Tactile device 1: Control unit 2: Program storage unit 3: Data storage unit 4: Input / output I / F unit 5: Bus 11: Perspective list information acquisition processing unit 12: Processing matrix generation processing unit 13: Graph generation processing unit 14: Graph analysis processing unit 15: Coaching information generation processing unit 16: Coaching information output processing unit 31: Perspective list information storage unit 32: Processing matrix storage unit 33: Graph information storage unit 34: Graph analysis data storage unit 35: Coaching information storage unit

Claims

1. An action instruction information generating device comprising: a first processing unit that generates perspective-content information representing the correspondence between multiple perspectives to be instructed regarding user actions and multiple content associated with each of these perspectives; a second processing unit that generates a content graph based on the perspective-content information, reflecting the co-occurrence relationships of the multiple content; a third processing unit that generates graph analysis data by analyzing the content graph according to a pre-prepared algorithm, selecting the multiple perspectives in descending order of the number of content associated with each perspective; and a fourth processing unit that generates coaching information based on the graph analysis data, defining the instruction order of the multiple perspectives and the multiple content to be used for instruction of each perspective.

2. The operation guidance information generation device according to claim 1, wherein the third processing unit selects destination content in the content graph by sequentially traversing content that is connected to a terminal content located at the end of the two-dimensional plane, and each time a destination content is selected, it selects the viewpoint with the largest number of associated content from among a plurality of viewpoints to which the destination content is associated, thereby generating the graph analysis data.

3. A method for generating operation guidance information to be executed by an information processing device, comprising: a process of generating view-content information representing the correspondence between a plurality of views that are subject to instruction regarding the user's actions and a plurality of contents associated with each of these views; a process of generating a content graph that reflects the co-occurrence relationships of the plurality of contents based on the view-content information; a process of generating graph analysis data by analyzing the content graph according to a pre-prepared algorithm to select the plurality of views in descending order of the number of contents associated with each view; and a process of generating coaching information that defines the instruction order of the plurality of views and the plurality of contents to be used for instruction of each view based on the graph analysis data.

4. A program that causes a processor in the operation guidance information generating device to execute at least one of the processes performed by the first to fourth processing units in the operation guidance information generating device according to claim 1 or 2.

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