Method and system for providing digitally-based musculoskeletal rehabilitation therapy

Abstract

In order to solve the above problem, a method for providing digital-based musculoskeletal rehabilitation treatment provided by an application may include the steps of: selecting an exercise plan to be provided to the patient based on prescription information including an exercise plan for the patient being assigned from a doctor terminal; executing the application on a user terminal to which the patient account is logged in; providing an exercise list according to the exercise plan to the user terminal on which the application is executed; playing exercise videos corresponding to the multiple exercise items constituting the exercise list on the user terminal; providing an evaluation page for making an evaluation related to the exercise items based on the degree to which the exercise videos are played back meeting a preset criterion; and updating the exercise plan based on the evaluation information received via the evaluation page.

Description

[Technical field]

[0001] The present invention relates to a method and system capable of providing digitally based musculoskeletal rehabilitation therapy. [Background technology]

[0002] Musculoskeletal disorders refer to pain or injury that occurs in the musculoskeletal system, including muscles, nerves, tendons, ligaments, bones, and surrounding tissues. Musculoskeletal disorders can appear in various parts of the body, including the neck, lower back, arms, and legs.

[0003] According to a report by the World Health Organization (WHO), the economic loss due to musculoskeletal disorders is the fourth highest of all diseases, and musculoskeletal disorders are chronic pain conditions that affect not only daily life but also economic activity.

[0004] On the other hand, the treatment of musculoskeletal disorders should, in principle, be started with minimally invasive treatments, and non-pharmacological conservative treatments (e.g., exercise therapy and education, cognitive therapy, or relaxation therapy) should be performed first, followed by drug therapy and surgical treatment.

[0005] Treatment guidelines actively recommend non-pharmacological conservative treatment of musculoskeletal diseases, and research into methods of non-pharmacological conservative treatment of musculoskeletal diseases is being actively conducted, mainly in the United States and Europe.

[0006] On the other hand, with the development of technology, the use of electronic devices (e.g., smartphones, tablet PCs, etc.) has become widespread, which has led to a gradual increase in dependency on the Internet in many aspects of daily life.

[0007] In this way, with the development of various technologies including the Internet, consumption patterns that were previously highly dependent on offline consumption have gradually shifted to online consumption, and online-centered consumption is currently increasing dramatically.

[0008] In line with these changing trends, industries such as the medical industry, which are based on offline services due to the nature of their services, are increasingly moving to provide medical services online.

[0009] Therefore, nowadays, various medical services are provided online, allowing patients, or users, to consult with medical staff about their illnesses with just a few clicks via electronic devices connected to the Internet.

[0010] As an example of such technology, Korean Patent Registration No. 10-2195512 discloses technology relating to a server and system that provides an online medical platform, and provides patients with information regarding locations where medical services are provided online.

[0011] In line with these trends, there is a need for methods to deliver non-pharmacological conservative treatment of musculoskeletal disorders online. Summary of the Invention [Problem to be solved by the invention]

[0012] The present invention relates to a method and system for providing digital-based musculoskeletal rehabilitation therapy, which can provide digital-based rehabilitation therapy for musculoskeletal disorders.

[0013] Additionally, the present invention relates to a method and system for providing digital-based musculoskeletal rehabilitation therapy that can provide customized digital therapeutics suited to a patient's indications.

[0014] Furthermore, the present invention relates to a method and system for providing digital-based musculoskeletal rehabilitation therapy that can provide cognitive behavioral therapy in conjunction with rehabilitation therapy. [Means for solving the problem]

[0015] In order to solve the above problem, a method for providing digital-based musculoskeletal rehabilitation treatment provided by an application may include the steps of: selecting an exercise plan to be provided to the patient based on the assignment of prescription information including an exercise plan corresponding to the patient's indications from a doctor terminal; executing the application on a user terminal to which the patient account is logged in; providing an exercise list according to the exercise plan to the user terminal on which the application is executed; playing exercise videos corresponding to the multiple exercise items constituting the exercise list on the user terminal; providing an evaluation page for making an evaluation related to the exercise items based on the degree to which the exercise videos are played back meeting a preset criterion; and updating the exercise plan based on the evaluation information received via the evaluation page.

[0016] In one example, the exercise plan may include exercise items related to the patient's indications included in the patient's prescription information, with at least a portion of the exercise items assigned to each of a number of different days that make up a pre-set rehabilitation period.

[0017] In one example, in the step of providing the exercise list, the exercise list including the plurality of exercise items assigned to specific days on which the exercise video is played is provided based on a reference date on which the user terminal started counting the preset rehabilitation period, and the evaluation page may be provided to the user terminal for each specific day if the playback degree of the exercise video meets the preset criterion in order to perform an evaluation related to the plurality of exercise items provided to the patient for each specific day.

[0018] In one example, the evaluation page includes at least one of a first evaluation area for evaluating the exercise difficulty for the multiple exercise items assigned to the particular day, a second evaluation area for selecting a difficult exercise from the multiple exercise items, and a third evaluation area for evaluating exercise pain associated with the multiple exercise items, and in the step of updating the exercise plan, the difficulty of the exercise plan may be changed or at least a portion of the exercise items that constitute the exercise plan may be changed based on evaluation information received through at least one of the first evaluation area, the second evaluation area, and the third evaluation area.

[0019] In one example, exercise items according to the updated exercise plan may be provided to the user terminal from the day after the specific day has passed.

[0020] In one example, the second evaluation area displays the multiple exercise items assigned to the particular day, and in the step of updating the exercise plan, when at least one of the multiple exercise items is selected from the user terminal, the selected exercise item may be excluded from the exercise plan, and another exercise item that has an effect corresponding to the selected exercise item and induces less pain than the selected exercise item may be included in the exercise plan.

[0021] The method may further include a step of assigning a cognitive behavioral treatment plan to the patient account, the cognitive behavioral treatment plan being performed in conjunction with the exercise plan, and the step of assigning the cognitive behavioral treatment plan may include a step of receiving questionnaire response data for multiple questionnaire data via the user terminal, a step of detecting condition information of the pain patient related to the pain duration and degree of cognitive distortion of the pain patient based on the questionnaire response data, a step of identifying a user group corresponding to the condition information of the pain patient from a plurality of user groups classified according to the pain duration and the degree of cognitive distortion, a step of determining an initial treatment protocol corresponding to the user group from a plurality of treatment protocols, and a step of providing a plurality of specific treatment programs included in the initial treatment protocol in sequence during a predetermined rehabilitation period.

[0022] Further, the method further includes a step of providing an initial screen page in response to the application being executed on the user terminal, the initial screen page including at least one of a first menu item for accessing an exercise list according to the exercise plan, a second menu item for accessing the evaluation page, a third menu item for accessing the cognitive behavioral treatment plan assigned together with the exercise plan, and a fourth menu item for accessing a page for performing a functional evaluation for a specific movement of the patient, and if the playback degree of the exercise video does not satisfy the preset criterion, even if the second menu item is selected on the user terminal, the provision of the evaluation page to the user terminal may be restricted.

[0023] In one example, functional assessment for the specific movement is performed at preset day intervals during a preset rehabilitation period to which the exercise plan is assigned, and the fourth menu item is configured to be included on the initial screen page on a specific day corresponding to the preset day interval, and may not be included on the initial screen page if it is not the specific day during the rehabilitation period.

[0024] Furthermore, the digital-based musculoskeletal rehabilitation treatment provision system 100 according to the present invention includes a communication unit that receives prescription information including an exercise plan for the patient from a doctor terminal, and a control unit that provides an exercise list according to the exercise plan to the user terminal in response to the application being executed on the user terminal to which the patient account is logged in. The control unit plays exercise videos corresponding to the plurality of exercise items constituting the exercise list on the user terminal, respectively, and provides an evaluation page for making an evaluation related to the exercise items based on the degree to which the exercise videos are played back satisfying a preset criterion, and updates the exercise plan based on the evaluation information received via the evaluation page.

[0025] Furthermore, the program may be executed by one or more processes in an electronic device and stored on a computer-readable recording medium, and may include instructions to perform the following steps: determine an exercise plan to be provided to the patient based on prescription information including an exercise plan for the patient being assigned from a doctor terminal; execute the application on a user terminal to which the patient account is logged in; provide an exercise list according to the exercise plan to the user terminal on which the application is executed; play exercise videos corresponding to the exercise items, respectively, according to the exercise items constituting the exercise list on the user terminal; provide an evaluation page for evaluating the exercise items based on the degree to which the exercise videos are played back satisfying a preset criterion; and update the exercise plan based on the evaluation information received via the evaluation page.

[0026] Meanwhile, a method for providing exercise therapy according to the present invention may include the steps of receiving prescription information regarding exercise for a patient from a doctor terminal, assigning an exercise plan including at least one prescribed exercise to the patient account based on the prescription information, receiving an exercise video of the exercise corresponding to the prescribed exercise from the patient terminal, extracting key points corresponding to a plurality of pre-set joint points from the exercise video using an artificial intelligence posture estimation model, analyzing a relative positional relationship between the key points through an artificial intelligence motion analysis model, and analyzing the patient's exercise motion for the prescribed exercise based on the analysis of the positional relationship, and transmitting an analysis result of the patient's exercise motion to the patient terminal.

[0027] Furthermore, the exercise treatment provision estimation method according to the present invention may further include a step of outputting the exercise video to the patient terminal in real time in conjunction with the exercise video being captured by the patient terminal, and a step of providing a graphic object corresponding to the extracted key point by superimposing it on an area in the exercise video where a subject corresponding to the patient is located, so that the patient can recognize the joint points at which analysis is performed on the patient's exercise movement.

[0028] In one example, the step of extracting the key points may include identifying visible joint points of the subject that are visible in the motion image from among the plurality of pre-set joint points, and extracting the identified visible joint points as the key points.

[0029] In one example, the motion analysis model may predict invisible joint points of the subject that are not visible in the motion video among the plurality of pre-set joint points based on the learning data, and analyze the motion of the patient based on the visible joint points and the invisible joint points.

[0030] In this case, the key points may include key points corresponding to the visible joint points and the invisible joint points.

[0031] In one example, the learning data may include a first data group in which learning target visible joint points of a subject included in a learning target video and position information of learning target invisible joint points estimated based on the visible joint points are listed in order, and a second data group including data values ​​indicating whether each of the learning target visible joint points and the learning target invisible joint points is visible or not.

[0032] In this case, the enumeration order of the data values ​​included in the second data group may be the same as the enumeration order of the training target visible joint points and the training target invisible joint points.

[0033] In one example, the step of analyzing the patient's exercise motion may include analyzing the relative positional relationship between the key points based on rule information regarding the prescribed exercise, and determining whether the relative positional relationship between the key points satisfies the rule information, thereby analyzing the patient's exercise motion.

[0034] In one example, visual appearances of graphic objects overlaid on the motion image may differ depending on whether the relative positional relationship between the extracted key points satisfies the rule information.

[0035] In one example, the analysis results for the patient's exercise movements may include a first analysis result that provides the graphic objects corresponding to the key points superimposed on the exercise video in real time with different visual appearances based on the rule information while the exercise video is being shot on the patient terminal, and a second analysis result including the patient's evaluation score for the prescribed exercise based on key points extracted from each of a plurality of frames constituting the exercise video.

[0036] In this case, the first analysis result may be generated by a behavior analysis model of an application installed on the patient terminal, the second analysis result may be generated by a cloud server linked to the application, and both the first analysis result and the second analysis result may be transmitted to the doctor terminal. Furthermore, the exercise treatment providing system according to the present invention includes a communication unit that receives prescription information regarding exercise for a patient from a doctor terminal, and a control unit that assigns an exercise plan including at least one prescribed exercise to the patient account based on the prescription information, wherein the control unit receives an exercise video of the exercise corresponding to the prescribed exercise from the patient terminal, extracts key points corresponding to a plurality of pre-set joint points from the exercise video, analyzes the relative positional relationship between the key points through an artificial intelligence motion analysis model, analyzes the patient's exercise motion for the prescribed exercise based on the analysis of the positional relationship, and transmits the analysis result of the patient's exercise motion to the patient terminal.

[0037] Furthermore, the exercise treatment provision system of the present invention includes a communication unit that receives prescription information regarding exercise for a patient from a doctor terminal, and a control unit that assigns an exercise plan including at least one prescribed exercise to the patient account based on the prescription information, and the control unit receives exercise video of the exercise corresponding to the prescribed exercise from the patient terminal, analyzes the patient's exercise movements for the prescribed exercise from the exercise video using an artificial intelligence motion analysis model, and transmits the analysis results of the patient's exercise movements to the patient terminal.

[0038] Furthermore, a program executed by one or more processes in an electronic device and stored in a computer-readable recording medium may include instructions to perform the following steps: receiving prescription information regarding exercise for a patient from a doctor terminal; allocating an exercise plan including at least one prescribed exercise to the patient account based on the prescription information; receiving an exercise video of the exercise corresponding to the prescribed exercise from the patient terminal; extracting key points corresponding to a plurality of pre-set joint points from the exercise video; analyzing a relative positional relationship between the key points through an artificial intelligence motion analysis model, and analyzing the patient's exercise motion for the prescribed exercise based on the analysis of the positional relationship; and transmitting an analysis result of the patient's exercise motion to the patient terminal.

[0039] Meanwhile, the motion posture estimation method of the present invention includes the steps of receiving a motion video from a user terminal, analyzing a motion movement related to a specific motion movement of a user included in the motion video based on posture estimation information extracted from a posture estimation model trained using a learning dataset 400 (data set) including position information of joint points, and providing the user's motion movement analysis result related to the specific motion movement to the user terminal based on completion of the above analysis, wherein the position information of the joint points included in the learning dataset is position information of each of a plurality of pre-specified learning target joint points among the joint points of a subject included in the training target motion video, and the learning dataset may be composed of data extracted from the training target motion video.

[0040] In one example, the learning data set may be composed of a plurality of data groups each corresponding to a different information attribute, a first data group among the plurality of data groups includes position information of each of the plurality of training target joint points, and the position information included in the first data group may include coordinate information of each of the pre-specified plurality of training target joint points in the training target motion video in a form that is paired with each other.

[0041] In an example, the position information included in the first data group may be defined as different types of information depending on whether the plurality of training target joint points are visible in the training image, and the type of the position information may be defined according to a data value of data included in a second data group different from the first data group.

[0042] In one example, the pose estimation model may set different learning weights for each position information of the plurality of training target joint points included in the first data group based on the data values ​​included in the second data group.

[0043] In one example, the first data group may be arranged such that the position information of each of the plurality of training joint points is sequentially arranged in the first data group based on a predefined order among the plurality of training joint points, and the data values ​​included in the second data group may be arranged in the same order as the predefined order in which the position information of each of the plurality of training joint points is arranged, so as to indicate whether each of the plurality of training joint points is visible or not.

[0044] In one example, the learning data set may further include a third data group including data values ​​related to a shooting direction of the object, and the data values ​​included in the third data group may be configured to have different data values ​​depending on a shooting direction of the object relative to a camera that captured the object.

[0045] In this case, the posture estimation model is configured to be learned taking into account the shooting direction of the subject through the learning dataset having different data values ​​depending on the shooting direction of the subject, and the user's exercise motion analysis result may be a result of analyzing a specific exercise motion of the user based on posture estimation information extracted in the posture estimation model taking into account the shooting direction of the user included in the exercise video.

[0046] In one example, the exercise video received from the user terminal and the training target exercise video correspond to the specific exercise movement having the same exercise code, and the learning dataset may include a fourth data group including an exercise code matched to the specific exercise movement performed by the subject in the training target exercise video.

[0047] Furthermore, the training subject motion video may be a video, and the training data set may be composed of training data extracted from each of reference frames selected based on a preset criterion from among a plurality of frames constituting the training subject motion video, with a focus on the subject included in the training subject motion video.

[0048] The motion posture estimation system according to the present invention includes a posture estimation model that extracts posture estimation information using a learning data set including position information of joint points, a motion analysis model that analyzes a motion movement related to a specific motion movement of a user included in a motion video to be analyzed using the posture estimation information, and a service server that provides a motion movement analysis result of the user related to the specific motion movement to the user terminal based on completion of the analysis, wherein the position information of the joint points included in the learning data set is position information of each of a plurality of pre-specified learning target joint points among the joint points of a subject included in the learning target motion video, and the learning data set may be composed of data extracted from the learning target motion video.

[0049] Further, a program executed by one or more processes in an electronic device and stored in a computer-readable recording medium includes instructions for performing the steps of: receiving a motion video from a user terminal; analyzing a motion movement related to a specific motion movement of a user included in the motion video based on posture estimation information extracted from a posture estimation model trained using a learning dataset including position information of joint points; and providing a result of the motion movement analysis of the user related to the specific motion movement to the user terminal based on completion of the analysis. The position information of the joint points included in the learning dataset may be position information of each of a plurality of pre-specified learning target joint points among joint points of a subject included in a training target motion video, and the training dataset may be composed of data extracted from the training target motion video.

[0050] On the other hand, a cognitive behavioral therapy providing method according to the present invention may include the steps of providing a plurality of questionnaire data for diagnosing a condition of a pain patient on a user terminal, receiving questionnaire response data for the plurality of questionnaire data via the user terminal, detecting condition information of the pain patient related to the pain duration and degree of cognitive distortion of the pain patient based on the questionnaire response data, identifying a user group corresponding to the condition information of the pain patient from a plurality of user groups classified according to the pain duration and the degree of cognitive distortion, determining an initial treatment protocol corresponding to the user group from a plurality of treatment protocols, and sequentially providing a plurality of specific treatment programs included in the initial treatment protocol to the user terminal according to the treatment week set for each of the plurality of specific treatment programs.

[0051] The treatment protocol according to the present invention can be understood as a treatment process consisting of a plurality of treatment programs each matched to a different topic. A specific treatment program may be configured to include at least one treatment module related to a specific topic matched to the specific treatment program. Here, the treatment module can be understood as a detailed category for the cognitive behavioral treatment of a pain patient for a specific topic. In one example, a server stores a plurality of treatment programs each matched to a different topic, the plurality of treatment programs are configured to include at least one treatment module related to a topic matched to each of the plurality of treatment programs, and each of the plurality of treatment protocols may be composed of at least one treatment program or treatment module different from each other according to the characteristics of the pain duration and the degree of cognitive distortion of a user group matched to each of the plurality of treatment protocols.

[0052] In this case, the multiple user groups may include a first user group having acute pain and high cognitive distortion in association with the characteristics of pain duration and the degree of cognitive distortion, a second user group having acute pain and low cognitive distortion in association with the characteristics of pain duration and the degree of cognitive distortion, a third user group having chronic pain and high cognitive distortion in association with the characteristics of pain duration and the degree of cognitive distortion, and a fourth user group having chronic pain and low cognitive distortion in association with the characteristics of pain duration and the degree of cognitive distortion, and the criterion for classifying acute pain and chronic pain may be determined based on whether the pain duration exceeds a reference period, and the criterion for classifying high cognitive distortion and low cognitive distortion may be determined based on whether a score (SCORE) collected based on multiple questions related to cognitive distortion exceeds a reference score.

[0053] In one example, the step of sequentially providing to the user terminal includes a step of sequentially providing to the user terminal specific treatment modules constituting the plurality of specific treatment programs according to the treatment weeks set for each of the plurality of specific treatment programs, and a step of collecting treatment response data from the user terminal by the specific treatment modules, and may further include a step of updating the initial treatment protocol using the treatment response data.

[0054] In one example, the step of updating the initial treatment protocol is performed to update the initial treatment protocol using initial treatment response data obtained from treatment modules provided for each treatment week of a preset initial treatment period during a preset overall treatment period during which the multiple specific treatment programs are planned to be provided to the user terminal, and the updating of the initial treatment protocol may be performed by changing at least one of the remaining treatment programs assigned to the remaining treatment period excluding the initial treatment period of the overall treatment period and the treatment modules constituting the remaining treatment programs.

[0055] In one example, the step of updating the initial treatment protocol may include the steps of: using the initial treatment response data to analyze the pain patient's condition for each of a plurality of different analysis categories related to at least one of emotion, pain, insomnia, cognitive distortion, and stress; using the analysis results to identify one of the plurality of analysis categories in which the pain patient's problem symptoms meet predetermined criteria; and modifying the remaining treatment program assigned to the remaining treatment period and at least one of the treatment modules that constitute the remaining treatment program so as to be related to the identified category.

[0056] In one example, as a result of updating the initial treatment protocol, at least some of the example sentences provided from the treatment modules that make up the remaining treatment program may be modified to relate to the identified category.

[0057] In one example, the step of updating the initial treatment protocol includes a step of determining whether to maintain a preset total treatment period for which the plurality of specific treatment programs are to be provided to the user terminal based on the treatment response data, and a step of updating the preset total treatment period based on the determination result, and the updating of the preset total treatment period may include shortening the total treatment period to a period shorter than the total treatment period, or extending the total treatment period to a period longer than the total treatment period. In one example, each of the plurality of specific treatment programs includes a worksheet module for checking at least one of the pain level, pain duration, mental health condition, and physical health condition of the pain patient, and the step of sequentially providing the plurality of specific treatment programs to the user terminal may provide user's answer information to the worksheet module provided in a treatment week prior to the current treatment week, before providing the treatment program corresponding to the current treatment week among the plurality of specific treatment programs, in order to allow the pain patient to recognize the past condition of the pain patient.

[0058] Furthermore, the cognitive behavioral therapy provision system of the present invention includes a communication unit that communicates with a user terminal, a storage unit that stores a plurality of treatment programs that are each matched to different topics, and a control unit that provides a plurality of questionnaire data for diagnosing the condition of a pain patient on the user terminal, wherein the control unit receives response data to the plurality of questionnaire data from the user terminal via the communication unit, detects condition information of the pain patient related to the pain duration and degree of cognitive distortion of the pain patient based on the response data, identifies a user group corresponding to the condition information of the pain patient from a plurality of user groups classified according to the pain duration and the degree of cognitive distortion, determines an initial treatment protocol corresponding to the user group from a plurality of treatment protocols, and provides a plurality of specific treatment programs included in the initial treatment protocol to the user terminal in sequence according to the treatment week set for each of the plurality of specific treatment programs.

[0059] Furthermore, a program executed by one or more processes in an electronic device and stored on a computer-readable recording medium may include instructions to perform the following steps: providing a plurality of questionnaire data for diagnosing a condition of a pain patient on a user terminal; receiving answer data to the plurality of questionnaire data via the user terminal; detecting condition information of the pain patient related to the pain duration and degree of cognitive distortion of the pain patient based on the answer data; identifying a user group corresponding to the condition information of the pain patient from a plurality of user groups classified according to the pain duration and the degree of cognitive distortion; determining an initial treatment protocol corresponding to the user group from a plurality of treatment protocols; and sequentially providing a plurality of specific treatment programs included in the initial treatment protocol to the user terminal according to a treatment week set for each of the plurality of specific treatment programs. Effect of the Invention

[0060] As described above, the method and system for providing digital-based musculoskeletal rehabilitation therapy according to the present invention can provide a patient with an exercise plan for musculoskeletal rehabilitation therapy through an application based on prescription information including an exercise plan for a patient being assigned from a doctor terminal.

[0061] In particular, the method and system for providing digital-based musculoskeletal rehabilitation treatment according to the present invention can play back exercise videos corresponding to a plurality of exercise items constituting an exercise list on a user terminal on which an application is executed. As a result, even if a doctor and a patient do not meet in person for rehabilitation treatment for a musculoskeletal disease, the doctor can prescribe to the patient, and the patient can undergo rehabilitation through an exercise plan according to the doctor's prescription, thereby eliminating spatial, time, and economic constraints on musculoskeletal rehabilitation treatment and improving access to exercise treatment.

[0062] Furthermore, the method and system for providing digital-based musculoskeletal rehabilitation treatment according to the present invention may provide an evaluation page for evaluating exercise items based on whether the degree of playback of an exercise video satisfies a preset standard, and may update an exercise plan based on the evaluation information received through the evaluation page. This allows a patient to perform an exercise plan and provide appropriate feedback, thereby receiving customized rehabilitation treatment to which the feedback is applied. In particular, a patient may receive customized rehabilitation exercise treatment, such as adjusting the difficulty of exercise items according to the patient's own condition and excluding exercise items that are difficult for the patient.

[0063] Furthermore, the digital-based musculoskeletal rehabilitation therapy delivery method and system of the present invention can provide patients with treatment for not only their rehabilitation areas but also their mental health by providing a cognitive behavioral treatment plan in conjunction with a rehabilitation exercise plan.

[0064] In particular, the digital-based method and system for providing musculoskeletal rehabilitation therapy according to the present invention can receive questionnaire response data for a plurality of questionnaire data via a user terminal, and detect patient condition information related to the patient's pain duration and the degree of cognitive distortion based on the questionnaire response data. In addition, a treatment protocol for a cognitive behavioral treatment customized for the patient can be provided based on the patient's condition information. As a result, the method and system for providing rehabilitation therapy according to the present invention can provide a patient with a behavioral treatment program customized in consideration of the patient's pain duration and the degree of cognitive distortion even if the patient has the same musculoskeletal disease, rather than providing the patient with a uniform cognitive behavioral treatment due to a musculoskeletal disease. Furthermore, the patient can receive a customized cognitive behavioral treatment suited to his or her own condition.

[0065] Furthermore, the digital-based musculoskeletal rehabilitation therapy providing method and system according to the present invention can sequentially provide multiple treatment programs that are performed in conjunction with an exercise plan during the rehabilitation period, allowing patients to systematically receive cognitive behavioral therapy along with rehabilitation exercises and complete the rehabilitation therapy without interruption.

[0066] Furthermore, the method and system for providing digital-based musculoskeletal rehabilitation treatment according to the present invention updates a treatment program to be useful for the patient's cognitive behavioral treatment based on treatment response data collected during the progress of the cognitive behavioral treatment, thereby making it possible to provide an updated cognitive behavioral treatment taking into account the patient's improvement status, rather than continuously providing the patient with an initially determined cognitive behavioral treatment method. [Brief description of the drawings]

[0067] [Figure 1] FIG. 1 is a conceptual diagram for explaining a digital-based musculoskeletal rehabilitation treatment delivery system according to the present invention. [Figure 2a] FIG. 2 is a conceptual diagram for explaining a digital-based musculoskeletal rehabilitation treatment method according to the present invention. [Figure 2b]FIG. 2 is a conceptual diagram for explaining a digital-based musculoskeletal rehabilitation treatment method according to the present invention. [Figure 2c] FIG. 2 is a conceptual diagram for explaining a digital-based musculoskeletal rehabilitation treatment method according to the present invention. [Diagram 3] 1 is a flow chart illustrating a method for providing digital-based musculoskeletal rehabilitation therapy according to the present invention. [Figure 4] FIG. 1 is a conceptual diagram for explaining a method for providing musculoskeletal rehabilitation therapy and cognitive behavioral therapy in cooperation with each other in the present invention. [Figure 5a] In the present invention, a concept is provided to explain a method for providing a patient with a customized exercise plan for musculoskeletal rehabilitation therapy. [Figure 5b] In the present invention, a concept is provided to explain a method for providing a patient with a customized exercise plan for musculoskeletal rehabilitation therapy. [Figure 5c] In the present invention, a concept is provided to explain a method for providing a patient with a customized exercise plan for musculoskeletal rehabilitation therapy. [Figure 5d] In the present invention, a concept is provided to explain a method for providing a patient with a customized exercise plan for musculoskeletal rehabilitation therapy. [Figure 6] In the present invention, a concept is provided to explain a method for providing a patient with a customized exercise plan for musculoskeletal rehabilitation therapy. [Figure 7] In the present invention, a concept is provided to explain a method for providing a patient with a customized exercise plan for musculoskeletal rehabilitation therapy. [Figure 8a] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8b] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8c] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8d]1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8e] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8f] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8g] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8h] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8i] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8j] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8k] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8l] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 8m] 1 is a concept for illustrating a method of providing cognitive behavioral therapy in accordance with the present invention. [Figure 9a] This is a concept for explaining a method for providing an AI function evaluation service in the present invention. [Figure 9b] This is a concept for explaining a method for providing an AI function evaluation service in the present invention. [Figure 10] 1 is a concept for explaining a rehabilitation treatment report provided by the present invention. [Figure 11] 1 is a conceptual diagram for explaining an exercise therapy providing system according to the present invention. [Figure 12] 1 is a flowchart for explaining a method for providing exercise therapy according to the present invention. [Figure 13] 1 is a flowchart for explaining a method for providing exercise therapy according to the present invention. [Figure 14a] FIG. 1 is a conceptual diagram for explaining a doctor's prescription. [Figure 14b]FIG. 1 is a conceptual diagram for explaining a doctor's prescription. [Figure 15] 1 is a conceptual diagram for explaining a method for analyzing a patient's exercise motion from exercise video. [Figure 16] 1 is a conceptual diagram for explaining a method for analyzing a patient's exercise motion from exercise video. [Figure 17] FIG. 1 is a conceptual diagram for explaining an artificial intelligence posture estimation model. [Figure 18a] FIG. 1 is a conceptual diagram for explaining an artificial intelligence posture estimation model. [Figure 18b] FIG. 1 is a conceptual diagram for explaining an artificial intelligence posture estimation model. [Figure 18c] FIG. 1 is a conceptual diagram for explaining an artificial intelligence posture estimation model. [Figure 18d] FIG. 1 is a conceptual diagram for explaining an artificial intelligence posture estimation model. [Figure 18e] FIG. 1 is a conceptual diagram for explaining an artificial intelligence posture estimation model. [Fig. 18f] FIG. 1 is a conceptual diagram for explaining an artificial intelligence posture estimation model. [Figure 19] FIG. 13 is a conceptual diagram for explaining an example of use for providing a result of a user's behavior analysis. [Figure 20] FIG. 13 is a conceptual diagram for explaining an example of use for providing a result of a user's behavior analysis. [Figure 21a] 1 is a conceptual diagram for explaining a user environment in which the results of a patient's exercise motion analysis are provided. FIG. [Figure 21b] 1 is a conceptual diagram for explaining a user environment in which the results of a patient's exercise motion analysis are provided. FIG. [Figure 21c] 1 is a conceptual diagram for explaining a user environment in which the results of a patient's exercise motion analysis are provided. FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0068] Hereinafter, the embodiments disclosed in this specification will be described in detail with reference to the accompanying drawings. The same reference numbers are used for the same or similar components regardless of the drawing numbers, and the duplicated description thereof will be omitted. The suffixes "module" and "part" for components used in the following description are given or mixed for the sake of ease of specification writing only, and do not have any meaning or role that is different from each other. In addition, when describing the embodiments disclosed in this specification, if it is determined that a detailed description of related known technology may obscure the gist of the embodiments disclosed in this specification, the detailed description will be omitted. In addition, the accompanying drawings are provided for easy understanding of the embodiments disclosed in this specification, and the technical ideas disclosed in this specification are not limited by the accompanying drawings, and it should be understood that the accompanying drawings include all modifications, equivalents, and alternatives included in the idea and technical scope of the present invention.

[0069] Terms including ordinal numbers such as first, second, etc. may be used to describe various components, but the components are not limited by the terms. The terms are used only to distinguish one component from another.

[0070] When an element is described as being "coupled" or "connected" to another element, it should be understood that it may be directly coupled or connected to the other element, but that there may be other elements between them. On the other hand, when an element is described as being "directly coupled" or "directly connected" to another element, it should be understood that there are no other elements between them.

[0071] The singular expression includes the plural expression unless the context clearly dictates otherwise.

[0072] In this application, terms such as "comprise" or "have" are intended to specify the presence of features, numbers, steps, operations, components, parts, or combinations thereof set forth in the specification, but are to be understood as not precluding the possible presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0073] The present invention relates to a method and system for providing digital-based rehabilitation therapy and cognitive behavioral therapy linked to said rehabilitation therapy to patients with musculoskeletal disorders, and in particular to a method and system for providing a rehabilitation therapy customized according to the patient's indications. According to the present invention, a doctor (or medical staff) provides an exercise plan corresponding to the patient's indications, and a user can perform the exercise plan and provide feedback to the system according to the present invention. The system according to the present invention can appropriately update the exercise plan by reflecting the patient's feedback, thereby guiding the system to maximize the therapeutic effect according to the patient's indications by reflecting the patient's feedback.

[0074] Here, an indication is a symptom or clinical situation that requires a specific treatment or examination, and can be understood as a patient's disease or condition, etc. Furthermore, an indication can be understood as a patient's disease or condition that requires a medical or rehabilitative treatment.

[0075] The present invention can provide at least one of rehabilitation therapy and cognitive behavioral therapy for the rehabilitation treatment of musculoskeletal patients.

[0076] For the sake of convenience, the present invention will be described with a focus on "musculoskeletal disorders", but is not necessarily limited thereto. That is, the rehabilitation treatment described in the present invention can include treatment for patients with various diseases (e.g., cancer, diabetes, hypertension, etc.) as well as patients who need rehabilitation treatment due to musculoskeletal disorders.

[0077] Hereinafter, a method and system for providing a digital-based customized rehabilitation therapy method for musculoskeletal patients will be specifically discussed with reference to the accompanying drawings. Fig. 1 is a conceptual diagram for explaining a digital-based musculoskeletal rehabilitation therapy providing system according to the present invention. 2a, 2b and 2c are conceptual diagrams for explaining a digital-based musculoskeletal rehabilitation treatment method according to the present invention, FIG. 3 is a flowchart for explaining a digital-based musculoskeletal rehabilitation treatment providing method according to the present invention, FIG. 4 is a conceptual diagram for explaining a method of providing musculoskeletal rehabilitation treatment and cognitive behavioral treatment in collaboration in the present invention, FIGS. 5a, 5b, 5c, 5d, 6 and 7 are concepts for explaining a method of providing a patient with a customized exercise plan for musculoskeletal rehabilitation treatment in the present invention, FIGS. 8a, 8b, 8c, 8d, 8e, 8f, 8g, 8h, 8i, 8j, 8k, 8l and 8m are concepts for explaining a method of providing cognitive behavioral treatment in the present invention, FIGS. 9a and 9b are concepts for explaining a method of providing an AI functional assessment service in the present invention, and FIG. 10 is a concept for explaining a rehabilitation treatment report provided in the present invention.

[0078] As shown in FIG. 1, a digital-based musculoskeletal rehabilitation therapy delivery system (hereinafter, referred to as a "rehabilitation therapy delivery system") 100 according to the present invention can be embodied as an application or software.

[0079] In accordance with the software implementation of the rehabilitation therapy delivery system 100 of the present invention, the sequences, functions, and other aspects described herein may be implemented as separate software therapy modules, each of which may perform one or more of the functions and operations described herein.

[0080] The rehabilitation treatment providing system 100 embodied as software may be downloaded via a program (e.g., a Play Store) that allows applications to be downloaded onto the user terminal 10, or may be embodied by an initial installation program on the user terminal 10. In this case, the communication unit 110, storage unit 120, and control unit 130 according to the present invention may be utilized as components of the user terminal 10.

[0081] In the present invention, the terminal 10 is also named a "mobile terminal" or an "electronic device", and the terminals described in this specification may include mobile phones, smart phones, laptop computers, digital broadcasting terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), navigation systems, slate PCs, tablet PCs, ultrabooks, wearable devices such as smart watches, smart glass, and head mounted displays (HMDs).

[0082] Meanwhile, the rehabilitation treatment providing system 100 may exist inside a server (hereinafter referred to as a server) constructed for a specific purpose (e.g., a function related to providing rehabilitation treatment) separately from the user terminal 10, or may exist as a system separate from the server. When the rehabilitation treatment providing system 100 exists inside the server, the musculoskeletal rehabilitation treatment service can be provided by at least one of the components of the communication unit 110, the storage unit 120, and the control unit 130 located inside the server, or a configuration module performing a function similar to each of the components. In this case, the application can provide a service related to musculoskeletal rehabilitation treatment on the user terminal 10 in which the application is installed by communicating with the server. Furthermore, the rehabilitation treatment providing system 100 according to the present invention can provide the rehabilitation treatment providing method according to the present invention to the user terminal 10 in conjunction with an external server.

[0083] Meanwhile, the rehabilitation treatment delivery system 100 according to the present invention can provide rehabilitation treatment and cognitive behavioral treatment services to a patient based on a doctor's prescription for the patient, and can provide customized rehabilitation treatment to the patient based on the patient's feedback on the rehabilitation treatment.

[0084] As shown in FIG. 2(a), the present invention can receive prescription information prescribed by a doctor D for rehabilitation treatment of a musculoskeletal patient from a doctor terminal 20. In the present invention, rehabilitation treatment and cognitive behavioral treatment services can be provided via a user terminal 10 on which an application is installed, based on the prescription information received from the doctor terminal 20. A patient U can perform cognitive behavioral treatment by executing the application on the user terminal 10 and performing rehabilitation exercises while watching the provided exercise video (see FIG. 2(b)). In addition, the present invention can provide an evaluation service that allows a patient U who has completed rehabilitation treatment to evaluate the rehabilitation treatment and an analysis service of the patient's exercise motion (see FIG. 2(c)). In addition, the present invention can provide a customized rehabilitation treatment to a patient by updating the rehabilitation treatment provided to the patient based on the patient's feedback on the rehabilitation treatment (see FIG. 2(d)).

[0085] In this manner, the rehabilitation therapy delivery system 100 of the present invention can provide customized musculoskeletal rehabilitation therapy services to musculoskeletal patients by continually updating the rehabilitation therapy provided to the patients based on the patients' feedback.

[0086] Therefore, the rehabilitation treatment delivery system 100 of the present invention may be named a "digital rehabilitation treatment solution," "digital rehabilitation exercise solution," "non-face-to-face rehabilitation treatment solution," "non-face-to-face rehabilitation exercise solution," "mobile rehabilitation treatment program," "mobile rehabilitation exercise program," and "application for digital treatment purposes for skeletal system patients," etc.

[0087] Meanwhile, patient U can perform rehabilitation exercises according to the exercise plan prescribed by the doctor through an application or web page provided by the rehabilitation treatment provision system 100 according to the present invention, and can evaluate the exercise plan after the rehabilitation exercises.

[0088] At this time, the patient U may have a user account registered in the rehabilitation treatment delivery system 100 according to the present invention. For convenience of explanation, in this specification, the account of a patient user is called a "patient account (or user account)."

[0089] As described above, an "account" may be created via a page associated with the rehabilitation care delivery system 100. Alternatively, an "account" may be created in at least one other system associated with the rehabilitation care delivery system 100 of the present invention.

[0090] Therefore, in this specification, regardless of the system to which the account is issued, any account based on the rehabilitation treatment delivery system 100 of the present invention will be referred to as an "account pre-registered in the rehabilitation treatment delivery system 100 of the present invention."

[0091] Meanwhile, a doctor can prescribe rehabilitation treatment to a musculoskeletal patient U via the doctor terminal 20. At this time, the doctor D may have a user account registered in the rehabilitation treatment providing system 100 according to the present invention. For convenience of explanation, in this specification, the account of a doctor user is named a "doctor account." Also, the user terminal logged in with the doctor's account is named the doctor terminal 20 for explanation.

[0092] Hereinafter, a rehabilitation treatment providing system 100 according to the present invention will be described with reference to an embodiment embodied as an application on a user terminal 10.

[0093] 1, a digital-based musculoskeletal rehabilitation therapy delivery system 100 according to the present invention may be configured to include at least one of a communication unit 110, a storage unit 120, and a control unit 130. In this case, the rehabilitation therapy delivery system 100 according to the present invention is not limited to the above-mentioned components, and may further include components that perform the same functions as the devices described in this specification.

[0094] The communication unit 110 may include one or more communication modules that enable wireless or wired communication between the rehabilitation therapy delivery system 100 and the user terminal 10, or between the rehabilitation therapy delivery system 100 and an external server. The communication unit 110 may also include one or more communication modules that connect the rehabilitation therapy delivery system 100 to one or more networks.

[0095] Specifically, the communication unit 110 can receive an exercise plan assigned to a patient account so that the patient U performs rehabilitation exercise according to the exercise plan assigned to the patient. The communication unit 110 can also receive evaluation data of the patient regarding the exercise plan based on the patient's rehabilitation exercise according to the exercise plan. The communication unit 110 can also receive a customized exercise plan updated based on the evaluation data of the patient and provide the updated exercise plan to the user terminal 10.

[0096] The storage unit 120, also referred to as a database (DB), may be configured to store exercise plan history information, including exercise plans assigned to a patient, the patient's evaluation data for the exercise plans, and exercise plans updated with the evaluation data.

[0097] Furthermore, the storage unit 120 may be configured to store matching information 700 in which exercise movements, difficulty information of the exercise movements, and exercise images are matched for each rehabilitation part for treating an indication (e.g., shoulder, elbow, wrist & hand, hip & pelvis, knee, ankle & foot, neck, back, waist, abdomen) (see FIG. 7).

[0098] Furthermore, the rehabilitation treatment delivery system 100 according to the present invention can utilize data stored in external storage separate from the storage unit 120, and such external storage can also be expressed as a "database."

[0099] Meanwhile, the control unit 130 can control the overall operation of the rehabilitation therapy providing system 100 according to the present invention.

[0100] 1, the control unit 130 may control to output a page (or service page 300) for providing a musculoskeletal rehabilitation treatment service via a display unit (or touch screen) provided in the user terminal 10. Such a page 300 may be output on the user terminal 10 via an application or web page installed in the user terminal 10.

[0101] The page 300 is a page linked to the rehabilitation treatment delivery system 100 according to the present invention, and is configured to be controlled by the rehabilitation treatment delivery system 100 according to the present invention.

[0102] Furthermore, when the page 300 is provided in the form of an application, the page 300 can be controlled by a CPU (Central Processing Unit) of the user terminal 10 in which the application is installed. In this case, the CPU of the user terminal 10 can provide services related to musculoskeletal rehabilitation therapy to the patient based on information provided by the musculoskeletal rehabilitation therapy system 100 according to the present invention.

[0103] The components of the rehabilitation therapy delivery system 100 according to the present invention have been discussed above. Hereinafter, a method for providing customized musculoskeletal rehabilitation therapy services to a patient based on the above components will be described.

[0104] As shown in FIG. 3, the method of providing rehabilitation therapy according to the present invention may include a process of determining an exercise plan to be provided to a patient based on prescription information including an exercise plan for the patient being assigned from a doctor terminal (S310).

[0105] In the present invention, as shown in FIG. 2b, an exercise prescription page (or exercise assignment page) 21 including a prescription function for rehabilitation exercises for musculoskeletal patients can be provided on a doctor terminal 20 logged in with a doctor account.

[0106] The exercise prescription page 21 may be controlled by a server constructed for providing rehabilitation treatment services. The rehabilitation treatment providing system 100 according to the present invention may exist inside the server, or may exist as a system 100 separate from the server and be connected via communication. That is, in the present invention, the process for providing musculoskeletal rehabilitation treatment services will be described as being performed in the rehabilitation exercise treatment providing system 100 without separating the physical space in which the process is performed. In addition, the process for providing musculoskeletal rehabilitation treatment services will be described as being performed by the communication unit 110, storage unit 120, and control unit 130 according to the present invention.

[0107] Meanwhile, the control unit 130 can provide an exercise prescription page 21 for each patient account on the doctor terminal 20 so that a prescription can be made for a specific patient U account among the patient accounts matched with the doctor account.

[0108] For example, in the present invention, it is assumed that a first patient account (e.g., patient account of "Song Jin-Gyu") and a second patient account (e.g., patient account of "Kim So-Hee") are matched with a specific doctor D account. The control unit 130 can provide an exercise prescription page corresponding to the first patient account on the doctor terminal 20 based on receiving an exercise prescription request for the first patient account (e.g., patient account of "Song Jin-Gyu") from the doctor terminal 20.

[0109] The control unit 130 can assign prescription information for a particular patient from the doctor terminal 20 based on a user selection (or user input) made on an exercise prescription page corresponding to the particular patient.

[0110] The prescription information may include various information related to providing a rehabilitation exercise plan to a musculoskeletal patient. For example, the prescription information may include at least one of the following: i) information on indications (e.g., information on pain disorders), ii) information on rehabilitation sites of musculoskeletal patients (e.g., shoulders, elbows, wrists & hands, hips & pelvis, knees, ankles & feet, neck, back, waist, abdomen), iii) rehabilitation period information (e.g., 1 week, 4 weeks, or 8 weeks), iv) rehabilitation exercise items that musculoskeletal patients should exercise for rehabilitation (e.g., "stretch your calves by leaning your hands against the wall," "sit down and roll a ball to massage the soles of your feet"), vi) difficulty information of rehabilitation exercise items, vi) duration information of rehabilitation exercise items (e.g., 10 seconds), vii) number of repetitions of rehabilitation exercise items (e.g., 5 repetitions), viii) information on the repetition set of rehabilitation exercise items, ix) caution information (e.g., "please apply ice after exercise"), and x) exercise equipment information (see FIG. 4).

[0111] Furthermore, the prescription information may further include information about a digital therapeutic. As shown in FIG. 2b, the exercise prescription page 21 may include items 22, 23, and 24 representing digital therapeutics corresponding to different indications (e.g., patellofemoral pain syndrome, patellofemoral arthritis, chronic lower back pain, etc.). In the present invention, a specific digital therapeutic may be selected from the doctor terminal 20 according to the present invention in accordance with the patient's indication. In the present invention, a digital therapeutic may also be understood as an "exercise plan" for an indication corresponding to the digital therapeutic. The storage unit 120 according to the present invention stores a plurality of different exercise plans, and each exercise plan may correspond to a digital therapeutic matched to a different indication. That is, the digital therapeutic in the present invention may mean an exercise plan for treating an indication. Therefore, when a digital therapeutic item (e.g., a digital therapeutic item for treating patellofemoral pain syndrome) 22 for treating a specific indication is selected on the exercise prescription page 21, it may mean that an exercise plan for treating patellofemoral pain syndrome is selected as a prescription for the patient.

[0112] Here, the "exercise item" can be understood as an exercise action or an exercise type, and in the present invention, the terms "exercise item", "exercise action" and "exercise type" may be used interchangeably.

[0113] Meanwhile, the control unit 130 can select (or identify) an exercise plan to be provided to the patient based on the prescription information for the patient being assigned from the doctor terminal 20. That is, as described above, the control unit 130 can select an exercise plan corresponding to the digital therapeutic selected from the doctor terminal 20 as the exercise plan to be provided to the patient.

[0114] Meanwhile, in the exercise plan corresponding to each digital therapeutic, at least one exercise item may be assigned to each of a number of days or a number of weeks constituting the rehabilitation period. That is, in the exercise plan corresponding to the digital therapeutic, an exercise item for treating an indication by the corresponding digital therapeutic may be determined in advance. The exercise items constituting each exercise plan may be exercise items selected by an expert, an expert group, or an artificial intelligence algorithm as having an effect for treating an indication targeted by the digital therapeutic according to each exercise plan. The rehabilitation period may be preset and exist in the system 100, or may be included in the prescription information received from the doctor terminal 20 and exist. In the following, for convenience of explanation, an example in which a rehabilitation period of "8 weeks" is set will be described.

[0115] The exercise plan may be composed of rehabilitation exercise items, at least one of which is different from the other for each of a plurality of days constituting the rehabilitation period. For example, the first day of the exercise plan may be composed of a first exercise item and a second exercise item, and the second day may be composed of a first exercise item and a third exercise item.

[0116] Furthermore, the exercise plan may be composed of at least one different rehabilitation exercise item for each of the multiple weeks constituting the rehabilitation period. In this case, the exercise plan may include the same rehabilitation exercise item on multiple days (e.g., Monday to Sunday) constituting the same week. For example, the first week of the exercise plan (Monday to Sunday of the first week) may be composed of the first exercise item and the second exercise item, and the second week (Monday to Sunday of the second week) may be composed of the first exercise item and the third exercise item.

[0117] Meanwhile, the control unit 130 may assign a cognitive behavioral treatment plan 220 to the patient account during a pre-set rehabilitation period in conjunction with the exercise plan 210 so that cognitive behavioral treatment is provided in conjunction with the rehabilitation treatment for the musculoskeletal patient.

[0118] For example, as shown in Fig. 4, the control unit 130 can assign a weekly exercise plan 210 and a cognitive behavioral treatment plan 220 to each of a plurality of treatment weeks constituting a rehabilitation period. In the first week of the rehabilitation period, the control unit 130 can execute the exercise plan 211 and the cognitive behavioral treatment plan 221 assigned to the first week in conjunction with each other, and in the second week of the rehabilitation period, the control unit 130 can execute the exercise plan 212 and the cognitive behavioral treatment plan 222 assigned to the second week in conjunction with each other. The details of the cognitive behavioral treatment will be described later.

[0119] In this way, the control unit 130 can set the cognitive behavioral treatment plan 220 to correspond to the preset rehabilitation period corresponding to the exercise plan 210. For example, if the preset rehabilitation period corresponding to the exercise plan 210 is 8 weeks, the treatment period according to the cognitive behavioral treatment plan 220 may also be set to 8 weeks. In contrast, if the preset rehabilitation period corresponding to the exercise plan 210 is 6 weeks, the treatment period according to the cognitive behavioral treatment plan 220 may also be set to 6 weeks. Meanwhile, the control unit 130 can configure the preset rehabilitation period corresponding to the exercise plan 210 and the treatment period according to the cognitive behavioral treatment plan 220 independently of each other depending on the situation. For example, the rehabilitation period according to the exercise plan 210 may be set to 6 weeks, and the treatment period according to the cognitive behavioral treatment plan 220 may be set to 8 weeks.

[0120] Meanwhile, in the present invention, a process of executing an application can be performed on a user terminal in which a patient account is logged in (S320, see FIG. 3).

[0121] The control unit 130 may check whether the exercise plan assigned to the patient account is activated based on the execution of the application. The exercise plan assigned to the patient account may be an exercise plan corresponding to the digital therapeutic selected on the doctor terminal 20 described above.

[0122] Here, "exercise plan activation" can be understood as a state in which rehabilitation exercise services can be provided according to an exercise plan.

[0123] If the exercise plan assigned to the patient account is in an inactive state, the control unit 130 may control the exercise plan to be activated based on the application being executed.

[0124] For example, the control unit 130 may control the exercise plan assigned to the patient account to be activated based on the patient account being logged in through the executed application. As another example, the control unit 130 may provide an icon (e.g., "Kim Ji-seok, would you like to start rehabilitation exercise?") associated with the activation of the exercise plan assigned to the patient account on the screen of the user terminal 10 through the executed application. Also, the control unit 130 may control the exercise plan assigned to the patient account to be activated based on the icon being selected.

[0125] Meanwhile, as shown in FIG. 2c, the patient can check the digital therapeutic prescribed to the patient through the executed application. Information 31, 32 on the digital therapeutic prescribed to the patient may be provided on a page 30 of the application. Meanwhile, if multiple digital therapeutics are selected for the patient, the patient can select the digital therapeutic for which he or she wishes to perform rehabilitation treatment. In this case, the control unit 130 can provide the user terminal 10 with an exercise plan that matches the selected digital therapeutic.

[0126] Meanwhile, the control unit 130 may count a rehabilitation period matched to an exercise plan based on the activation of an exercise assigned to a patient account.

[0127] In the present invention, the day when the rehabilitation period starts to be counted may be named and described as the rehabilitation exercise "start date" or "reference date". For example, in the present invention, the reference date may be counted as the first day, and the day after the reference date may be counted as the second day. As another example, in the present invention, the seven days including the reference date may be named and described as the first week, and the seven days following the first week may be named and described as the second week.

[0128] Meanwhile, as shown in FIG. 5a (a), the control unit 130 can provide an initial screen page 300 on the user terminal 10 based on the execution of an application.

[0129] The initial screen page 300 may be configured to include at least one of a plurality of first to fourth menu items 310 to 340 associated with the provision of different services.

[0130] The first menu item 310 may be configured to interface with a rehabilitation exercise information page 600 that provides rehabilitation exercise services according to an exercise plan assigned to the patient account.

[0131] As shown in (b) of Fig. 5a, the control unit 130 can provide a rehabilitation exercise information page 600 including an exercise list 610 according to an exercise plan on the user terminal 10 based on the selection of the first menu item 310 on the user terminal 10. Therefore, in the present invention, the first menu item 310 can be understood as being associated with an access function to the exercise list according to the exercise plan.

[0132] The second menu item 320 may be configured to be linked to an access function to an evaluation page (see reference numerals 710 to 730 in FIG. 5b) that provides an evaluation service for an exercise plan assigned to a patient account. The control unit 130 may control so that the second menu item 320 is activated based on whether the patient's rehabilitation exercise execution level (or exercise video playback level) satisfies a preset criterion. Meanwhile, the control unit 130 may be configured so that when the patient's rehabilitation exercise execution level (or exercise video playback level) does not satisfy a preset criterion, provision of the evaluation pages 710 to 730 on the user terminal 10 is restricted (i.e., inactivated) even if the second menu item 320 is selected on the user terminal 10.

[0133] The third menu item 330 may be configured to link to a cognitive behavioral treatment page (see FIG. 8a, reference numeral 800) that provides the cognitive behavioral treatment plans assigned to the patient account. Cognitive behavioral treatment plans are described in more detail below.

[0134] Also, the fourth menu item 340 may be configured to cooperate with a functional evaluation page (or a motion analysis page, see reference numeral 900 in FIG. 9 ) for performing a functional evaluation for a specific motion of the patient. In the present invention, an artificial intelligence model trained from learning data may be used to analyze the posture and motion of the patient for a specific motion from the motion video of the patient. Therefore, in the present invention, the functional evaluation for evaluating the posture and motion of the patient for a motion item may be named "AI (Artificial Intelligence) functional evaluation."

[0135] Meanwhile, in the present invention, in order to confirm the patient's improvement in response to rehabilitation exercise (or the effect of rehabilitation exercise), the patient can be guided to undergo AI functional evaluation for a specific movement.

[0136] The control unit 130 can control the inclusion of a fourth menu item 340 on the initial screen page 300 on a specific day corresponding to a preset day interval, so that an AI function evaluation for a specific movement is performed at preset day intervals (e.g., "2 weeks") during a preset rehabilitation period (e.g., "8 weeks") to which an exercise plan is assigned.

[0137] In this way, in the present invention, various services provided by the present invention can be provided on the user terminal 10 based on the selection of any one of the multiple menu items 310 to 340 that make up the initial screen page 300.

[0138] Meanwhile, in the present invention, a process of providing an exercise list according to an exercise plan to a user terminal on which an application is executed can be performed (S330, see FIG. 3).

[0139] The control unit 130 can provide a rehabilitation exercise information page 600 including an exercise list 610 on the user terminal 10 based on the first menu item 310 being selected on the initial screen page 300 .

[0140] As shown in FIG. 5A(b), the exercise list 610 may include a number of rehabilitation exercise items 611-616 that are assigned to specific days on which the exercise list 610 is provided, based on a reference date on which counting of the rehabilitation period began. For example, if the patient's rehabilitation exercise execution date corresponds to "day 23" based on the reference date, the exercise list 610 may include exercise items 611-616 that are assigned to "day 23" among the days that make up the exercise plan matched to the patient account.

[0141] Furthermore, the control unit 130 can arrange a specific exercise item on the exercise list 610 repeatedly by the number of sets based on the exercise plan assigned to the patient account. For example, if three sets of the "straight leg raise 1" exercise item are assigned to the exercise plan, the control unit 130 can arrange three exercise items 611, 612, and 613 corresponding to "straight leg raise 1" on the exercise list 610.

[0142] Furthermore, the control unit 130 can arrange the multiple exercise items 611 to 616 in order on the exercise list 610 based on the exercise plan assigned to the patient account. The control unit 130 can arrange the first exercise items 611 to 613 corresponding to the highest priority exercise order in the upper row of the exercise list 610, and arrange the second exercise item 614 corresponding to the next exercise order below the first exercise items.

[0143] Meanwhile, in the present invention, a process of reproducing exercise videos corresponding to a plurality of exercise items, respectively, according to a plurality of exercise items constituting an exercise list, can be performed in the user terminal (S340, see FIG. 3).

[0144] Based on a patient's request to start rehabilitation exercise, the control unit 130 can sequentially play back on the user terminal 10 exercise videos corresponding to each of the exercise items 611 to 616 in accordance with the order of the exercise items 611 to 616 arranged in the exercise list 610. For example, as shown in (b) of FIG. 5a, based on a user's selection of a "Start Exercise" icon included in the rehabilitation exercise information page 600, the control unit 130 can play back on the user terminal 10 exercise videos corresponding to each of the multiple exercise items 611 to 616.

[0145] 7, exercise videos 710b, 720b, 730b, and 740b corresponding to each exercise item may be stored in the storage unit 120. The control unit 130 may load the exercise videos corresponding to the exercise items from the storage unit 120 and output them on the user terminal 10.

[0146] Meanwhile, in the present invention, a process of providing an evaluation page for evaluating the exercise item based on whether the playback level of the exercise video meets a preset criterion can be performed (S350, see FIG. 3).

[0147] As described above, in the present invention, feedback regarding the exercise plan can be received from the patient, and the exercise plan can be updated to reflect the feedback, so that a customized exercise plan can be provided for each patient.

[0148] The control unit 130 can monitor whether the patient has performed the rehabilitation exercise in order to receive feedback on the exercise plan from the patient who has actually performed the rehabilitation exercise according to the exercise plan assigned to the patient account. Furthermore, when the control unit 130 determines that the patient has actually performed the rehabilitation exercise as a result of the monitoring, it can provide evaluation pages 710 to 730 for receiving evaluation information on the rehabilitation exercise performed by the patient.

[0149] Specifically, the control unit 130 can monitor the extent to which the exercise video corresponding to the multiple exercise items 611 to 616 included in the exercise list 610 is played on the user terminal 10 in order to determine the rehabilitation exercise execution rate (or rehabilitation exercise execution degree) of the patient. In addition, the control unit 130 can provide evaluation pages 710 to 730 on the user terminal 10 based on the extent to which the exercise video is played meeting a preset criterion.

[0150] The control unit 130 may determine the playback level of the exercise video and whether to provide an evaluation page based on at least one of the playback time of the exercise video and the number of exercise items corresponding to the played exercise video.

[0151] The control unit 130 can determine whether to provide the evaluation pages 710 to 730 depending on whether the playback time of the exercise video exceeds a preset reference playback time.

[0152] The control unit 130 may decide to provide the evaluation pages 710-730 (or activate the evaluation pages) if the playback time of the exercise video exceeds a preset reference playback time, and may decide not to provide the evaluation pages 710-730 (or deactivate the evaluation pages) if the playback time of the exercise video does not exceed a preset reference playback time.

[0153] In this case, the control unit 130 may set the reference playback time, which is the basis for providing the evaluation pages 710, 720, and 730, to be different from each other based on the entire playback time (or total playback time) of the exercise videos corresponding to the multiple exercise items 611 to 616 included in the exercise list.

[0154] Specifically, the control unit 130 may set the reference playback time to a time corresponding to a certain range (or ratio) of the overall playback time so that the reference playback time is proportional to the overall playback time (or total playback time) of the exercise videos corresponding to the multiple exercise items 611 to 616 included in the exercise list.

[0155] Meanwhile, the control unit 130 may determine whether to provide evaluation pages 710-730 depending on whether the number of exercise items corresponding to the played exercise video (hereinafter, referred to as the "number of played exercise items") exceeds a preset reference number of plays.

[0156] The control unit 130 may decide to provide the evaluation pages 710 to 730 (or activate the evaluation pages) if the number of played exercise items exceeds a preset reference number of plays, and may decide not to provide the evaluation pages 710 to 730 (or deactivate the evaluation pages) if the number of played exercise items does not exceed a preset reference number of plays.

[0157] In this case, the control unit 130 may set the reference number of plays, which is the criterion for providing the evaluation pages 710 to 730, to be different from each other based on the total number (or total number) of the multiple exercise items 611 to 616 included in the exercise list.

[0158] Specifically, the control unit 130 may set the reference number of plays to a number corresponding to a certain range (or ratio) of the total number of the multiple exercise items 611 to 616 included in the exercise list, so that the reference number of plays is proportional to the total number (or total number) of the multiple exercise items 611 to 616 included in the exercise list.

[0159] That is, the control unit 130 may set different evaluation page provision criteria (standard playback time or standard number of playbacks) for each specific day corresponding to the day on which the patient performs rehabilitation exercise, based on the multiple exercise items assigned to the specific day.

[0160] Meanwhile, when it is determined that the evaluation pages 710 to 730 are provided, the control unit 130 may control the second area 320 associated with the evaluation pages on the initial screen page 300 to be activated (see (a) of FIG. 5a). For example, the control unit 130 may display the second area 320 on the initial screen page 300 based on the determination that the evaluation pages are provided.

[0161] Furthermore, as shown in (c) of FIG. 5a, a pop-up 602 associated with the evaluation page may be provided on the user terminal 10 based on the determination to provide the evaluation pages 710-730. For example, the control unit 130 may provide the pop-up 620 on the user terminal 10 based on the completion of playback of all of the videos corresponding to the multiple exercise items 611-616 included in the exercise list 610. Furthermore, the control unit 130 may provide the evaluation pages 710-730 on the user terminal 10 based on the selection of an icon (e.g., "Go to self-check") 620a included in the pop-up 620.

[0162] On the other hand, the evaluation pages 710 to 730 may be configured with a plurality of evaluation areas so that evaluations are made for a plurality of evaluation categories for a plurality of exercise items assigned to a specific day.

[0163] Here, the "evaluation category" refers to a category that is the subject of evaluation for an exercise plan so as to provide a customized rehabilitation exercise plan taking into account the condition of a musculoskeletal patient, and may include, for example, at least one of the exercise difficulty for an exercise item, high difficulty exercise items (difficult exercise items), and pain after rehabilitation exercise.

[0164] The control unit 130 can configure the evaluation page to include evaluation areas corresponding to each of a plurality of evaluation categories. In this case, the plurality of evaluation areas may be arranged on the same page, or may be arranged on different evaluation pages 710, 720, and 730 as shown in FIG. 5b. Therefore, the terms "evaluation page" and "evaluation area" in the present invention may be used interchangeably. In the present invention, the "evaluation area" is described by using the same reference numerals "710-730" as the "evaluation page."

[0165] The control unit 130 may configure the first evaluation area 710 so that the exercise difficulty levels for a plurality of exercise items assigned to a particular day are evaluated.

[0166] As shown in (a) of FIG. 5b, the control unit 130 may include, in the first evaluation area 710, question information asking about the difficulty of exercise (e.g., "How was the exercise intensity today?"), guidance information informing the user that the exercise plan will be updated based on the exercise difficulty evaluation information (e.g., "The exercise structure and difficulty will be adjusted based on the information you have entered"), and an evaluation information input area (hereinafter referred to as the "first input area") 710a for receiving the exercise difficulty evaluation information.

[0167] The control unit 130 may include a slider corresponding to a preset exercise difficulty scale on the first input area 710a. In addition, the control unit 130 may control to output an emotion graphic object and emotion information corresponding to the exercise difficulty specified by the slider on the first evaluation area 710 in conjunction with the slider.

[0168] For example, when the exercise difficulty level specified by the slider is the "lowest difficulty level (e.g., 0 points)", the control unit 130 may output a "smile" emotion graphic object and emotion information of "it feels like I didn't exercise" on the first evaluation area 710. As another example, when the exercise difficulty level specified by the slider is the "highest difficulty level (e.g., 9 points)", the control unit 130 may output a "frown" emotion graphic object and emotion information of "it was difficult to imitate the exercise" on the first evaluation area 710.

[0169] The patient can intuitively recognize the range of exercise difficulty scores from the lowest to the highest using the slider in the first evaluation area 710. In addition, the patient can input evaluation information while visually checking the difficulty of the rehabilitation exercise he or she feels through graphic objects and emotion information output in conjunction with the slider.

[0170] Furthermore, the control unit 130 can receive evaluation information of the exercise difficulty from the first evaluation area 710 based on the evaluation information being input to the first evaluation area 710.

[0171] On the other hand, as shown in (b) of FIG. 5b, the control unit 130 may include, in the second evaluation area 720, question information asking about exercise items of high difficulty exercise (e.g., "Please tell us which movements are painful or inconvenient"), guidance information informing the user that the exercise plan will be updated based on the evaluation information of the exercise items of high difficulty exercise (e.g., "The composition and difficulty of the exercise will be adjusted based on the content you enter"), and an input area for selecting high difficulty exercise items (hereinafter referred to as the "second input area").

[0172] Furthermore, the control unit 130 can control the second input area of ​​the second evaluation area 720 to include multiple check items 721a to 726a for inputting the user's selection for a high-difficulty exercise item (difficult exercise item) among the multiple exercise items 611 to 616 assigned to a specific day (e.g., "the 23rd day").

[0173] The control unit 130 may correspond one of the multiple check items (e.g., "There were no inconvenient movements") 721a to an option that does not select any of the multiple exercise items 611-616 assigned to a specific day (e.g., "23rd day") as a high-difficulty exercise item. In addition, the control unit 130 may correspond the multiple exercise items 611-616 assigned to a specific day (e.g., "23rd day") to each of the remaining check items 722a-726a, and display information of the multiple exercise items 611-616 (e.g., thumbnails of videos corresponding to the exercise items or names of the exercise items).

[0174] The control unit 130 may receive evaluation information on the difficult exercise item according to the user's selection of a check box included in the second evaluation area 720 .

[0175] On the other hand, as shown in (c) of FIG. 5b, the control unit 130 may include, in the third evaluation area 730, question information asking about exercise pain (e.g., "How much pain do you feel after rehabilitation exercise?"), guidance information informing that the exercise plan will be updated based on the evaluation information on exercise pain (e.g., "The composition and difficulty of the exercise will be adjusted based on the content you input"), and an evaluation information input area (hereinafter referred to as the "third input area") 730a for receiving evaluation information on exercise pain. The third evaluation area 730 may be configured in the same way as the first evaluation area 710, except for the question information. Therefore, detailed explanation will be omitted.

[0176] The control unit 130 may receive evaluation information regarding exercise pain through the third evaluation area 730.

[0177] On the other hand, in the present invention, an updated exercise plan can be provided to a patient by modifying the difficulty level of the exercise plan, excluding or adding at least a part of the exercise items constituting the exercise plan, etc., based on the patient's evaluation information on the exercise plan. A method for updating an exercise plan based on evaluation information will be specifically described below.

[0178] Based on the evaluation information for the difficult exercise items received from the second evaluation area 720 (hereinafter referred to as "second evaluation information"), the control unit 130 can update the exercise plan by changing (or replacing) the exercise items, such as excluding at least some of the exercise items included in the exercise plan assigned to the patient account or adding new exercise items.

[0179] In this case, the exercise items to be excluded or replaced may correspond to the exercise items assigned to the day after ("24th day or later") a specific day ("23rd day") among the multiple days (e.g., "8 weeks") that make up the rehabilitation period. That is, the control unit 130 can exclude or replace at least a part of the exercise items from the day after the specific day based on the evaluation information for the specific day (especially the second evaluation information).

[0180] The control unit 130 may exclude a specific item selected as a high-difficulty exercise from among the exercise items assigned to a specific day from the exercise plan from the day after the specific day. Also, the control unit 130 may add (include) another exercise item in place of the excluded exercise item. Differently, the control unit 130 may exclude a specific item selected as a high-difficulty exercise from among the exercise items assigned to a specific day from the exercise plan for another specific day (which may be the day after the specific day or another day) that is performed next after the specific day. In this case, too, the control unit 130 may add (include) another exercise in place of the excluded exercise item.

[0181] More specifically, the control unit 130 may replace a specific exercise item selected as a high difficulty level in the second evaluation area with another exercise item having the same difficulty level among the exercise items matched to the same rehabilitation part. Here, the other exercise item may be an exercise having the same or similar efficacy (e.g., rehabilitation efficacy) as the selected specific exercise item. Furthermore, the other exercise item may be an exercise that induces less pain than the selected specific exercise item. In this case, the efficacy and pain level of each exercise item may be determined based on information matched to each exercise item.

[0182] 7, a plurality of exercise items 710 to 750 may be stored in the storage unit 120, matched into the same group for each rehabilitation part (e.g., shoulder, elbow, wrist & hand, hip joint & pelvis, knee, ankle & foot, neck, back, waist, abdomen) or for each indication (e.g., patellofemoral pain syndrome, patellofemoral arthritis, chronic lower back pain, etc.). Exercise items included in the same group matched for each rehabilitation part may correspond to exercise items for the rehabilitation of a specific rehabilitation part.

[0183] Furthermore, the matching information 700 may include difficulty level information (e.g., "difficulty level 1 to difficulty level 3") 710a-750a of each exercise item matched to a specific rehabilitation part (e.g., "shoulder") 701, and exercise duration time information (or exercise repetition number information) that differs from each other for each exercise item. Also, the matching information 700 may include a plurality of exercise videos 711-714 that correspond to different exercise duration time information (or exercise repetition number information) for each specific exercise item (e.g., "stand up and raise your arms to the sides and flip over") 710, which are matched with each other.

[0184] For example, the exercise duration information may include one of "5 seconds duration", "10 seconds duration", "15 seconds duration", and "20 seconds duration", and the exercise repetition count information may include one of "5 repetitions", "10 repetitions", "15 repetitions", and "20 repetitions". For convenience of explanation, the exercise motion duration will be described below as an example.

[0185] As shown in (a) of FIG. 5c, assume that multiple exercise items 611-616 are assigned to a particular day ("Day 23"), and among these, the first exercise item 611 (e.g., "Stand up and flip over with arms raised to the sides") is selected as a high difficulty exercise.

[0186] 7, the matching information 700 may include a specific exercise item 710 selected as a high-difficulty exercise, and other exercise items 720 to 750 that are matched to the same rehabilitation part (e.g., "shoulder") 701. The control unit 130 may specify, among the other exercise items 720 to 750, another exercise item (e.g., "stand and raise both arms to the sides") 720 having the same difficulty level information (e.g., level 1) 720a as the difficulty level information (e.g., level 1) 710a of the specific exercise item 710, as an alternative exercise item.

[0187] The control unit 130 may update the exercise plan assigned to the patient account by removing the exercise item selected as the high difficulty exercise and adding (or including) an alternative exercise item, starting from the day after a certain date has passed (e.g., "day 24").

[0188] In addition, as shown in (b) of FIG. 5c, the control unit 130 can provide the user terminal 10 with an exercise list 620 including exercise items 621 to 626 according to the updated exercise plan from the day after a specific day has passed (e.g., “the 24th day”).

[0189] On the other hand, the control unit 130 may set the exercise intensity of the alternative exercise item to be the same as that of the specific exercise item selected as the high difficulty exercise.

[0190] Here, "exercise intensity" may refer to exercise duration information (or exercise repetition number information) and the number of sets.

[0191] For example, when the exercise intensity of the exercise item 611 selected as a high-difficulty exercise is "maintained for 10 seconds, one set," the control unit 130 may set the exercise intensity of the alternative exercise item 621 to "maintained for 10 seconds, one set."

[0192] Meanwhile, the control unit 130 can update the exercise plan by adjusting (or changing) the difficulty level of each exercise item included in the exercise plan assigned to the patient account based on the evaluation information received from either the first evaluation area 710 or the third evaluation area 730.

[0193] In this case, the exercise items to be subject to difficulty level adjustment may refer to the exercise items assigned to the day after ("day 24 or later") a specific day ("day 23") among the multiple days (e.g., "8 weeks") that make up the rehabilitation period. In other words, the control unit 130 can change the difficulty level of the exercise items from the day after the specific day based on the evaluation information for the specific day.

[0194] The control unit 130 can determine one of the following: downgrading the difficulty level (Down), maintaining the difficulty level, and upgrading the difficulty level (up) based on evaluation information received from either one of the exercise difficulty evaluation information received from the first evaluation area 710 (hereinafter referred to as the first evaluation information) and the exercise pain evaluation information received from the third evaluation area 730 (hereinafter referred to as the third evaluation information).

[0195] The control unit 130 may determine a change in the difficulty of the exercise items constituting the exercise plan based on either the first evaluation information or the third evaluation information. For example, the control unit 130 may determine a change in the difficulty of the exercise items using the first evaluation information. The control unit 130 may also determine a change in the difficulty of the exercise items using the third evaluation information. The control unit 130 may also determine a change in the difficulty of the exercise items using an evaluation score (SCORE) calculated by the sum or average of the first evaluation information and the third evaluation information. In the following, for convenience of explanation, an example of determining a change in the difficulty of the exercise items based on the exercise difficulty evaluation information will be described.

[0196] The control unit 130 can check whether the exercise difficulty evaluation information for the exercise item assigned to a specific day (e.g., "the 23rd") corresponds to any of the difficulty change criteria, such as lowering the difficulty level, maintaining the difficulty level, increasing the difficulty level, or updating the exercise, and can decide whether to change the difficulty level of the exercise item assigned to the day after the specific day.

[0197] Specifically, the control unit 130 can decide to lower the difficulty level (Down) when the exercise difficulty evaluation information corresponds to the first difficulty change criterion, decide to maintain the difficulty level when it corresponds to the second difficulty change criterion, and decide to increase the difficulty level (Up) when it corresponds to the third difficulty change criterion.

[0198] For example, it is assumed that the evaluation information of the exercise difficulty is a difficulty score (SCORE) consisting of a scale of "0 to 9 (natural numbers)". When the difficulty scores of the exercise items assigned to a specific day are "9", "8", "7", or "6", the control unit 130 can determine to downgrade the difficulty of the exercise items assigned to the specific day onward. When the difficulty scores of the exercise items assigned to a specific day are "5", "4", or "3", the control unit 130 can determine to maintain the difficulty of the exercise items assigned to the specific day onward. When the difficulty scores of the exercise items assigned to a specific day are "2", "1", or "0", the control unit 130 can determine to upgrade the difficulty of the exercise items assigned to the specific day onward.

[0199] On the other hand, when the control unit 130 determines that the difficulty level of the exercise items assigned on or after the day following a specific day is to be lowered or increased, the control unit 130 may change the exercise difficulty by changing at least one of the number of times the exercise action is maintained (or the number of times the exercise action is repeated) and the number of sets for each exercise item assigned on the specific day.

[0200] As shown in (a) of FIG. 5c, it is assumed that a plurality of exercise items 611-616 are assigned to a specific day ("23rd day"). When a difficulty level change is determined based on exercise difficulty evaluation information for the exercise items 611-616 assigned to a specific day ("23rd day"), the control unit 130 can change the difficulty levels of the exercise items 611-616. For convenience of explanation, the following describes a method of changing the difficulty level of a specific exercise item (e.g., "stand up and straighten your shoulders") 612 as an example. The method of changing the difficulty level of a specific exercise item 612 can be similarly applied to other exercise items.

[0201] The control unit 130 can change the difficulty level of a specific exercise item by referring to the matching information 700 stored in the storage unit 120 and changing at least one of the duration of the exercise action (or the number of times the exercise action is repeated) and the number of sets of the specific exercise item (standing up with shoulders straightened) 730.

[0202] Each exercise item may have a minimum exercise action duration (e.g., 5 seconds), a maximum exercise action duration (e.g., 20 seconds), a minimum number of sets (e.g., 1 set), and a maximum number of sets (e.g., 3 sets). Meanwhile, in the present invention, for convenience of explanation, the "exercise action duration" is used as an example, but it may be described as the "number of exercise action repetitions" depending on the type of exercise item.

[0203] Based on the determination of increasing the difficulty level of the exercise item, the control unit 130 may increase the exercise action duration of the specific exercise item 730. For example, if the current exercise duration of the specific exercise action 730 is "5 seconds (731)", the control unit 130 may increase the exercise duration of the specific exercise item 730 to "10 seconds (732)".

[0204] The control unit 130 may increase the current number of sets of a specific exercise item when the current exercise duration of the specific exercise action 730 is the maximum. For example, when the current exercise action duration of the specific exercise action 730 is "20 seconds" and the current number of sets is "1 set", the control unit 130 may increase the number of sets of the specific exercise item 730 to "2 sets".

[0205] On the other hand, when both the current exercise duration and the current number of sets of a specific exercise item are maximum, the control unit 130 may replace the specific exercise item with another exercise item having a difficulty level higher than the difficulty level of the specific exercise item. For example, when the difficulty level of the specific exercise item 730 is "2", the control unit 130 may replace (or substitute) the specific exercise item 730 with another exercise item 740 having a difficulty level "3". In this case, the control unit 130 may determine the exercise duration and the number of sets of the other exercise item 740 as the maximum exercise duration and the maximum number of sets matched to the other exercise item 740.

[0206] Meanwhile, the control unit 130 may reduce the exercise action duration of a specific exercise item 730 based on the determination of the difficulty level down of the exercise item. For example, if the current exercise duration of the specific exercise action 730 is "15 seconds (733)", the control unit 130 may reduce the exercise duration of the specific exercise item 730 to "10 seconds (732)".

[0207] The control unit 130 may reduce the current number of sets of a specific exercise item when the current exercise duration of the specific exercise action 730 is the minimum. For example, when the current exercise action duration of the specific exercise action 730 is "5 seconds" and the current number of sets is "2 sets", the control unit 130 may reduce the number of sets of the specific exercise item 730 to "1 set".

[0208] On the other hand, when both the current exercise duration and the current number of sets of a specific exercise item are the smallest, the control unit 130 may replace the specific exercise item with another exercise item having a difficulty level lower than the difficulty level of the specific exercise item. For example, when the difficulty level of the specific exercise item 730 is "2", the control unit 130 may replace (or substitute) the specific exercise item 730 with another exercise item 720 having a difficulty level of "1". In this case, the control unit 130 may determine the exercise duration and the number of sets of the other exercise item 730 as the maximum exercise duration and the maximum number of sets matched to the other exercise item 730.

[0209] On the other hand, the control unit 130 can update the exercise plan by changing at least one of the exercise type and difficulty level of the exercise item assigned on or after the day after the specific day (e.g., "the 23rd day") based on the evaluation information of the exercise item assigned on the specific day (e.g., "the 23rd day").

[0210] The control unit 130 can provide, on the user terminal 10, an exercise list including exercise items according to the updated exercise plan from the day after the specific day onwards.

[0211] Meanwhile, when the exercise plan is updated based on the evaluation information of the patient, the control unit 130 can provide the user terminal 10 with information informing the user of the exercise plan update.

[0212] The timing and method of notifying the user of the exercise plan update may vary. For example, the control unit 130 may notify the user of the exercise plan update when the application is executed on the day after a specific day. As another example, when the first menu item 310 is selected on the initial screen page 300 on the day after a specific day, the control unit 130 may provide an exercise plan update notification page 600' on the user terminal 10 before providing an exercise list according to the exercise items included in the updated exercise plan.

[0213] In this case, at least one of information regarding the reason for updating the exercise plan and information regarding the update items of the exercise plan may be displayed on the exercise plan update information page 600'.

[0214] For example, as shown in (a) of FIG. 6, based on the evaluation information of the exercise item assigned to a specific day, the difficulty level of the exercise item assigned to the day after the specific day has been changed, the control unit 130 can display information regarding the reason for the difficulty level change (e.g., “Hong Gil-dong, yesterday was hard” or “As your pain index has worsened, we will adjust the previously implemented program and proceed with the plan”) and information regarding the difficulty level change (e.g., “Difficulty level 2 program → difficulty level 1 program”) on the exercise plan update information page 600′.

[0215] As another example, as shown in (b) of FIG. 6, based on the evaluation information of the exercise items assigned to a specific day, at least some of the exercise items assigned to the day after the specific day are excluded, and based on that, the control unit 130 can display information regarding the reason for changing the type of exercise item (e.g., "It is determined that the difficult exercise will make it difficult to improve pain and progress with the plan, so from today onwards, the corresponding exercise will be excluded.") and information regarding the changes to the exercise item (e.g., "Passive ankle dorsiflexion 2 has been excluded") on the exercise plan update information page 600.

[0216] Meanwhile, the method and system 100 for providing musculoskeletal rehabilitation therapy according to the present invention can provide cognitive behavioral therapy in conjunction with rehabilitation exercise therapy to patients suffering from pain due to musculoskeletal disorders.

[0217] In particular, the present invention provides a number of different programs in sequence during a rehabilitation period set for rehabilitation exercise, thereby providing comprehensive cognitive behavioral therapy along with the progress of the rehabilitation exercise of a musculoskeletal patient.

[0218] As described above, the cognitive behavioral treatment plan 220 can be set to correspond to the preset rehabilitation period corresponding to the exercise plan 210. For example, if the preset rehabilitation period corresponding to the exercise plan 210 is 8 weeks, the treatment period according to the cognitive behavioral treatment plan 220 may also be set to 8 weeks. In contrast, if the preset rehabilitation period corresponding to the exercise plan 210 is 6 weeks, the treatment period according to the cognitive behavioral treatment plan 220 may also be set to 6 weeks. Meanwhile, the control unit 130 may configure the preset rehabilitation period corresponding to the exercise plan 210 and the treatment period according to the cognitive behavioral treatment plan 220 independently of each other depending on the situation. For example, the rehabilitation period according to the exercise plan 210 may be set to 6 weeks, and the treatment period according to the cognitive behavioral treatment plan 220 may be set to 8 weeks.

[0219] Meanwhile, the rehabilitation treatment delivery system 100 according to the present invention may be named a cognitive behavioral treatment delivery system 100.

[0220] The present invention relates to a method and system for providing a customized behavioral treatment protocol for each pain patient, taking into account the duration of pain and the degree of cognitive distortion of the pain patient, and in particular, to a method and system for providing cognitive behavioral treatment to patients with pain caused by musculoskeletal disorders.

[0221] In the present invention, for the sake of convenience, the description will be centered on "musculoskeletal disease", but the present invention is not necessarily limited thereto. That is, the cognitive behavioral therapy in the present invention can include not only patients with pain caused by musculoskeletal disease, but also patients with pain caused by various diseases (e.g., cancer, diabetes, hypertension, etc.).

[0222] In the present invention, the term "pain patient" refers to a patient who is experiencing pain due to a disease, and may be used interchangeably with "patient" or "user."

[0223] On the other hand, in the case of pain patients, even if they have the same disease (or illness), the duration of pain and the degree of cognitive distortion may differ from patient to patient. Therefore, in the present invention, a cognitive behavioral treatment protocol customized for each pain patient can be provided, reflecting the duration of pain and the degree of cognitive distortion of the patient.

[0224] Meanwhile, the rehabilitation treatment provision system 100 according to the present invention can use the user terminal 10 to diagnose the duration of pain and the degree of cognitive distortion of the "patient" U, and provide a customized behavioral treatment protocol taking this into consideration.

[0225] "Cognitive distortion" refers to cognitive errors that lead to erroneous assumptions or erroneous concepts about pain, surrounding circumstances, events, etc., and "degree of cognitive distortion" can be understood as a term that indicates the degree to which a patient's cognition is distorted. In the present invention, "degree of cognitive distortion" can be used interchangeably with "cognitive distortion state" and can be quantified as "cognitive distortion score" or "cognitive distortion score."

[0226] Meanwhile, as shown in (a) of Fig. 8a, the patient U can receive the cognitive behavioral treatment plan assigned to the patient account by selecting the third menu item 330 on the initial screen page 300. As shown in (b) and (c) of Fig. 8a, the patient can diagnose the duration of pain and the degree of cognitive distortion for the patient's own pain through the cognitive behavioral treatment page 800 provided by the rehabilitation treatment delivery system 100 according to the present invention, and manage the mental health condition, such as receiving counseling services according to a customized cognitive behavioral treatment protocol.

[0227] Below, a method for providing a customized cognitive behavioral treatment protocol to a pain patient is described.

[0228] Hereinafter, a cognitive behavioral therapy providing system 100 according to the present invention will be described with reference to an embodiment in which the system is embodied as an application on a user terminal 10.

[0229] The cognitive behavioral therapy providing system 100 for a pain patient according to the present invention may be configured to include at least one of a communication unit 110, a storage unit 120, and a control unit 130. In this case, the cognitive behavioral therapy providing system 100 according to the present invention is not limited to the above-mentioned components, and may further include components that perform the same functions as the devices described in this specification.

[0230] The communication unit 110 may include one or more treatment modules that enable wireless or wired communication between the cognitive behavioral therapy providing system 100 and the user terminal 10, or between the cognitive behavioral therapy providing system 100 and an external server. The communication unit 110 may also include one or more communication modules that connect the cognitive behavioral therapy providing system 100 to one or more networks.

[0231] Specifically, the communication unit 110 can collect, from at least one source, questionnaire response data to questionnaire data for diagnosing the patient's pain duration and the degree of cognitive distortion, via the user terminal 10. In addition, the communication unit 110 can provide, via the user terminal 10, a cognitive behavioral therapy service customized according to the pain duration and the degree of cognitive distortion of the pain patient.

[0232] The storage unit 120, also called a database (DB), is configured to store various information (or data) related to cognitive behavioral treatment of pain patients, such as collected questionnaire response data, diagnosis results based on the questionnaire response data, and cognitive behavioral treatment protocols based on the diagnosis results.

[0233] Furthermore, the cognitive behavioral therapy providing system 100 according to the present invention can utilize data stored in external storage separate from the storage unit 120, and such external storage can also be expressed as a "database."

[0234] Meanwhile, the control unit 130 can control the overall operation of the cognitive behavioral therapy providing system 100 according to the present invention.

[0235] 1, the control unit 130 can control to output a page (or a service page) for providing a cognitive behavioral therapy service via a display unit (or a touch screen) provided in the user terminal 10. Such a page can be output on the user terminal 10 via an application or a web page installed in the user terminal 10.

[0236] The page is a page linked to the cognitive behavioral therapy providing system 100 according to the present invention, and is configured to be controlled by the cognitive behavioral therapy providing system 100 according to the present invention.

[0237] Furthermore, when the page is provided in the form of an application, the page 300 can be controlled by a CPU (Central Processing Unit) of the user terminal 10 in which the application is installed. In this case, the CPU of the user terminal 10 can provide services related to cognitive behavioral therapy to the pain patient based on information provided by the cognitive behavioral therapy providing system 100 according to the present invention.

[0238] The components of the cognitive behavioral therapy delivery system 100 according to the present invention have been discussed above. Hereinafter, a method for providing a customized cognitive behavioral therapy protocol to a pain patient based on the above components will be described.

[0239] As shown in FIG. 8b, the method for providing cognitive behavioral therapy according to the present invention may include a step of providing a plurality of questionnaire data for diagnosing the condition of a pain patient on a user terminal (S210).

[0240] The control unit 130 can perform control so as to provide a page including multiple pieces of questionnaire data on the user terminal 10. In this case, the page including multiple pieces of questionnaire data can be output via a touch screen (or display) of the user terminal 10.

[0241] Here, the plurality of questionnaire (or question, or problem, or item, or test) data may include a questionnaire for assessing the status of factors related to pain.

[0242] In the present invention, the pain-related factors may be various. For example, the pain-related factors may mean various factors for evaluating the patient's condition related to pain, such as pain level, pain duration, mental health, and physical health. However, the above-mentioned factors are merely examples, and it is clear that the pain-related factors described in the present invention mean all factors for evaluating the patient's condition.

[0243] Such pain-related factors may include reliable questionnaire data that is actually used in psychiatric medicine to diagnose the patient's condition. In addition, the control unit 130 can update the questionnaire data as needed or periodically by downloading further questionnaires for diagnosing the patient's condition via an external server or website.

[0244] A user interface in which questionnaire data is provided will be described below as shown in Fig. 8c. As shown in Fig. 8c, a page including a questionnaire area 401 for displaying at least one of a plurality of questionnaires and an answer area 402 for receiving a patient's answer to the displayed questionnaire may be output on the user terminal 10. In this case, the answer area 402 may include answer graphic objects 402a, 402b corresponding to each of a plurality of different answers corresponding to the questionnaire displayed in the questionnaire area 401.

[0245] This allows the pain patient to select as the questionnaire response data the candidate answer that is considered to be most suitable (or appropriate, or accurate) for the user's condition from among the candidate answers output in answer area 402 as a response to any of the displayed questionnaires.

[0246] Meanwhile, in the present invention, a process of receiving survey response data for a plurality of survey data can be performed via a user terminal (S220, see FIG. 8b).

[0247] The control unit 130 can receive survey response data from the user terminal 10 based on the user's selection (or user's input) in the answer area 402 of the user terminal 10.

[0248] For example, as shown in (a) of FIG. 8c, based on the selection of any one of the multiple answer graphic objects 402a, 402b corresponding to a first questionnaire (e.g., "When did Jang Hye-ryeong's knee pain start?") for the pain duration of a pain patient, an answer (e.g., "one week ago") corresponding to the selected answer graphic object 402a can be received as questionnaire response data for the first questionnaire. Meanwhile, the first questionnaire may include multiple questionnaires. In this case, the user terminal 10 may be provided with multiple questionnaires necessary to detect condition information for the patient's pain duration. Also, the answers to the multiple questionnaires can be received from the user terminal as questionnaire response data.

[0249] As another example, as shown in (b) of Figure 8c, based on the selection of any one of multiple answer graphic objects corresponding to a second questionnaire related to the degree of the patient's cognitive distortion (How much have you felt negative emotions such as anxiety when you had pain in the past two weeks?), an answer corresponding to the selected answer graphic object (e.g., "3") can be received as questionnaire response data for the second questionnaire.

[0250] Meanwhile, the second questionnaire may include a plurality of questionnaires. In this case, the user terminal 10 may be provided with a plurality of questionnaires necessary for detecting state information on the degree of cognitive distortion of the pain patient. Also, responses to the plurality of questionnaires may be received as questionnaire response data from the user terminal. Examples of the second questionnaire include questionnaires related to at least one of the Pain Catastrophizing Scale and Fear (or Danger) Avoidance Beliefs, such as i) I always worry that the pain will not stop, ii) I feel like I can't bear it any more, iii) I think that the pain is too painful and will never get better, iv) my pain was caused by physical activity, v) physical activity makes my pain worse, and vi) my pain was caused by work or an accident at work. Furthermore, unlike those discussed above, the plurality of questionnaire data for diagnosing the condition of the pain patient may further include questionnaires for determining depression, insomnia, and the like.

[0251] Meanwhile, in the present invention, a process of detecting condition information of a pain patient related to the duration of pain and the degree of cognitive distortion of the pain patient based on the questionnaire response data can be performed (S230, see FIG. 8b).

[0252] As shown in FIG. 8d, the control unit 130 can compare the pain duration 450 extracted (obtained or detected) from the pain patient's questionnaire response data with a first criterion (e.g., "3 months") 450a to detect the condition information of the pain patient.

[0253] Here, the first criterion 450a can be understood as a predefined reference period (e.g., "3 months") for classifying (or dividing) the pain duration (or the nature of the pain according to the pain duration) into either "a first type of pain duration (e.g., acute pain) 451" or "a second type of pain duration (e.g., chronic pain) 452."

[0254] If the pain duration 450 of the pain patient is shorter than the predefined reference period 450a (i.e., equal to or shorter than the predefined reference period), the control unit 130 can classify (categorize or identify or determine) the pain duration of the pain patient as a "first type of pain duration (e.g., acute pain) 451". On the other hand, if the pain duration 450 of the pain patient is longer than the predefined reference period 450a (i.e., exceeds the predefined reference period), the control unit 130 can classify the pain duration of the pain patient as a "second type of pain duration (e.g., chronic pain) 452".

[0255] The control unit 130 can classify the pain duration of the pain patient into either acute pain or chronic pain depending on whether the pain duration 450 exceeds the reference period 450a.

[0256] Furthermore, the control unit 130 can compare the degree of cognitive distortion 460 extracted (acquired or detected) from the questionnaire response data of the pain patient with a second criterion 460a.

[0257] Here, the degree of cognitive distortion in a pain patient can be related to either the Pain Catastrophizing Scale and Fear (or Danger) Avoidance Beliefs, as described above.

[0258] Here, the second criterion 460a can be understood as a predefined standard score (e.g., "24 points") for classifying (or categorizing) the degree of cognitive distortion of a pain patient into either "degree of first type of cognitive distortion (e.g., high cognitive distortion) 461" or "degree of second type of cognitive distortion (e.g., low cognitive distortion) 462."

[0259] The control unit 130 can calculate (derive or calculate) a cognitive distortion score (SCORE) of the pain patient based on the questionnaire response data to compare the degree of cognitive distortion 460 of the pain patient with a predefined standard score 460a.

[0260] A plurality of selection items for a questionnaire for determining the degree of cognitive distortion (e.g., I always worry that the pain will not go away) may have different scores (e.g., not at all: 0 points, slightly agree: 1 point, often agree: 2 points, very much agree: 3 points, always agree: 4 points) that are matched to each other. The answer data is a selection signal for any one of the plurality of selection items, and the control unit 130 can calculate (derive or calculate) the cognitive distortion score (SCORE) of the pain patient using the score matched to the selection item according to the received selection signal.

[0261] Meanwhile, the control unit 130 can calculate the cognitive distortion score of the pain patient from the questionnaire response data using various methods. For example, the control unit 130 can calculate the cognitive distortion score of the pain patient using the sum or average of the questionnaire response data corresponding to a plurality of questionnaire data. In this case, the control unit 130 can calculate the cognitive distortion score of the pain patient by assigning (giving) a different weight to each questionnaire response data.

[0262] The control unit 130 can compare the calculated cognitive distortion score with a predefined reference score 460a.

[0263] If the comparison result shows that the pain patient's cognitive distortion score is higher than the predefined reference score 460a (i.e., exceeds the predefined score), the control unit 130 can classify (categorize or identify or determine) the degree of cognitive distortion of the pain patient as "degree of first type of cognitive distortion (high cognitive distortion) 461". On the other hand, if the pain patient's cognitive distortion score is lower than the predefined reference score 460a (i.e., is equal to or lower than the predefined cognitive distortion score), the control unit 130 can classify the degree of cognitive distortion of the pain patient as "degree of second type of cognitive distortion (low cognitive distortion) 462".

[0264] That is, the control unit 130 can determine the degree of cognitive distortion of the pain patient as either high cognitive distortion or low cognitive distortion depending on whether the cognitive distortion score exceeds a reference score.

[0265] Meanwhile, in the present invention, a process of identifying a user group corresponding to condition information of a pain patient from among a plurality of user groups classified according to pain duration and degree of cognitive distortion can be performed (S240, see FIG. 2).

[0266] As shown in FIG. 8d, in the present invention, a plurality of user groups 410, 420, 430, 440 can be classified according to pain duration 450 and cognitive distortion degree 460. For convenience of explanation, in the present invention, an example in which there are "four" user groups different from each other according to pain duration 450 and cognitive distortion degree 460 will be described. The user groups in the present invention may be named a first user group 410, a second user group 420, a third user group 430, and a fourth user group 440. However, it is clear that in the present invention, the condition items and number for classifying the user groups can be set in various ways.

[0267] Meanwhile, classification criterion information for classifying the plurality of user groups may be matched and exist for each of the plurality of user groups 410, 420, 430, and 440. Such classification criterion information may be predefined and existed in the storage unit 120 or an external server.

[0268] Specifically, in the first user group 410, a first type of pain duration (e.g., acute pain) 451 and a first type of cognitive distortion degree (e.g., high cognitive distortion) 461 may be matched and present as classification criteria information.

[0269] In the second user group 420, a first type of pain duration (e.g., acute pain) 451 and a second type of cognitive distortion degree (e.g., low cognitive distortion) 462 may be matched and present as classification criterion information.

[0270] In the third user group 430, the second type of pain duration (e.g., chronic pain) 452 and the first type of cognitive distortion degree (e.g., high cognitive distortion) 461 may be matched and present as classification criterion information.

[0271] In addition, in the fourth user group 440, a second type of pain duration (e.g., chronic pain) 452 and a second type of cognitive distortion degree (e.g., low cognitive distortion) 462 may be matched and present as classification criteria information.

[0272] The control unit 130 may determine a specific user group among the plurality of user groups 410, 420, 430, and 440, to which classification criterion information corresponding to the condition information of the pain patient is matched.

[0273] Specifically, if the condition information of a first pain patient (e.g., “Patient Kim Cheol-soo”) includes a first type of pain duration (e.g., acute pain) 451 and a first type of cognitive distortion degree (e.g., high cognitive distortion) 461, the control unit 130 can identify (or classify) the first pain patient as a pain patient belonging to a first user group 410 among multiple user groups.

[0274] Furthermore, if the condition information detected from the questionnaire response data of a second pain patient (e.g., “Patient Lee Yeong-suk”) includes a first type of pain duration (e.g., acute pain) 451 and a second type of cognitive distortion degree (e.g., low cognitive distortion) 462, the control unit 130 can identify (or classify) the second pain patient as a pain patient belonging to a second user group 420 among the multiple user groups.

[0275] Furthermore, if the condition information of a third pain patient (e.g., “Patient Choi Min-chul”) includes the duration of the second type of pain (e.g., chronic pain) 452 and the degree of the first type of cognitive distortion (e.g., high cognitive distortion) 461, the control unit 130 can identify (or classify) the third pain patient as a pain patient belonging to a third user group 430 among the multiple user groups.

[0276] Furthermore, if the condition information of a fourth pain patient (e.g., “Patient Bang Min-ji”) includes a second type of pain duration (e.g., chronic pain) 452 and a second type of cognitive distortion degree (e.g., low cognitive distortion) 462, the control unit 130 can identify (or classify) the fourth pain patient as a pain patient belonging to a fourth user group 440 among the multiple user groups.

[0277] Meanwhile, in the present invention, a process of determining an initial treatment protocol corresponding to a user group from among a plurality of treatment protocols can be performed (S250, see FIG. 8b).

[0278] As shown in Figures 8e and 8f, the treatment protocol according to the present invention may be composed of multiple treatment programs 510-580 that are matched to different topics 510a-580a, respectively. That is, each treatment program means a program for performing treatment according to a topic that is matched to each treatment program.

[0279] Here, the "topics 510a to 580a" correspond to a plurality of programs 510 to 580 for cognitive behavioral treatment of pain patients, respectively. For example, as shown in FIG. 8g and FIG. 8h, i) motivation enhancement (hereinafter, first topic) 510a corresponds to the first program (or first treatment program) 510, ii) emotion confirmation (hereinafter, second topic) 520a corresponds to the second program (or second treatment program) 520, iii) behavioral strategy (hereinafter, third topic) 530a corresponds to the third program (or third treatment program) 530, and iv) attention shifting (hereinafter, fourth topic) 540 corresponds to the fourth program (or fourth treatment program) 540. v) shifting one's way of thinking (hereinafter, fifth topic) 550a can correspond to the fifth program (or fifth treatment program) 550; vi) thought journaling (hereinafter, sixth topic) 560a can correspond to the sixth program (or sixth treatment program) 560; vii) management strategies (hereinafter, seventh topic) 570a can correspond to the seventh program (or seventh treatment program) 570; and viii) future self (hereinafter, eighth topic) 580a can correspond to the eighth program (or eighth treatment program) 580.

[0280] The first topic (enhancing motivation) is a process for understanding cognitive behavioral therapy and cognitive schemas regarding pain, and for pain patients to set their own goals and increase their motivation for treatment, and may consist of treatment modules related to this.

[0281] The second topic (emotion identification) is intended to explore pain, emotions, and the pain patient's own coping strategies and may consist of a treatment module to explore the pain patient's negative emotions, physical reactions, and behaviors related to pain and to discover the pain patient's own coping strategies.

[0282] The third topic (behavioral strategies) is treatment for pain patients to set activity goals, establish new coping behavior methods, and use breathing techniques and progressive relaxation techniques to cope with pain, and may be composed of treatment modules for this purpose.

[0283] The fourth topic (attention shifting) is a treatment for dealing with pain using attention shifting techniques that utilize activities, emotions, the five senses, etc., and can be composed of treatment modules for this purpose.

[0284] The fifth topic (changing thinking) involves exploring negative automatic thoughts related to pain and identifying the emotions and behaviors that result from these automatic thoughts, and may consist of a treatment module for this purpose, which involves the process of finding evidence to disprove the automatic thoughts.

[0285] The sixth topic (thought recording) is the process of exploring the pain patient's own irrational thinking patterns and finding cognitive flexibility, and can consist of a treatment module for this purpose.

[0286] The seventh topic (Management strategies) is using positive self-talk and practicing pain management strategies and may consist of a treatment module to use positive self-talk to deal with pain and understand the "Stop-Think-Assess-Act method."

[0287] The eighth topic (future me) could consist of a therapeutic module to organize the coping strategies learned so far in relation to jumping over stones (treatment resistance) and developing a positive self-image, to create the pain patient's own recipe for dealing with pain (pain coping recipe) and to identify ways of dealing with anticipated difficulties.

[0288] Meanwhile, in the present invention, the topics 510a to 580a may be predefined and stored in the storage unit 120. Meanwhile, in the present invention, it goes without saying that the number and contents (or types) of topics are not limited to the above examples and may be variously defined.

[0289] A specific treatment program may be configured to include at least one treatment module related to a specific topic that is matched to the specific treatment program. For example, a first treatment program 510 may be configured with multiple treatment modules 511-515 related to a first topic (e.g., "enhancing motivation") 510a.

[0290] Here, the term "treatment module" may refer to contents related to a detailed category (or subtopic) for cognitive behavioral treatment of a pain patient for a specific topic. For example, the treatment modules 511-515 related to the first topic (e.g., "enhancing motivation") 510a may include a "treatment module 511 for what pain means to me," a "treatment module 512 for pain cognitive schema," a "treatment module 513 for pain questionnaire," a "treatment module 514 for goal setting," and a "treatment module 515 for pain record exercise" (see FIG. 8e). In the present invention, a treatment module included in a treatment program related to a specific topic may be named a "treatment module corresponding to a specific topic." In addition, in the present invention, the term "treatment module" may be used interchangeably with "chapter."

[0291] Meanwhile, as shown in FIG. 8d, a plurality of user groups 410, 420, 430, 440 may each have a different treatment protocol 410a, 420a, 430a, 440a that is matched to each other.

[0292] The multiple treatment protocols 410a, 420a, 430a, 440a matched to each of the multiple user groups 410, 420, 430, 440 may be composed of at least one different treatment program or treatment module depending on the characteristics of the pain duration 450 and the degree of cognitive distortion 460 of the user groups 410, 420, 430, 440.

[0293] For example, the first treatment protocol 410a matched to the first user group 410 and the third treatment protocol 430a matched to the third user group 430 may be configured to include all treatment modules (full modules) stored in the server. In this case, all treatment modules may include all of the treatment modules matched to each of the first to eighth topics, and cognitive behavioral therapy may be performed on pain patients belonging to the first user group using all of the treatment modules included in each of the first to eighth topics.

[0294] As another example, the treatment protocol matched to the second user group 420 and the fourth user group 440 may be configured to include some of the treatment modules instead of all the treatment modules (full modules) stored in the server. The treatment protocol matched to the second user group 420 and the fourth user group 440 may be configured to include only some of the treatment modules of the multiple treatment modules matched to the first to eighth topics, respectively, and cognitive behavioral therapy may be performed on pain patients belonging to the second user group 420 and the fourth user group 440 using some of the treatment modules constituting the first to eighth topics.

[0295] As one example, the second treatment protocol 420a matched to the second user group 420 may be configured to emphasize modules related to coping strategies, breathing techniques, and disease education. As another example, the fourth treatment protocol 440a matched to the fourth user group 440 may be configured to emphasize modules related to acceptance commitment.

[0296] On the other hand, as shown in Figures 8e and 8f, each of the multiple treatment programs 510-580 may include worksheet modules 515, 522, 531, 541, 551, 561, 571, 581 for checking at least one of the pain level, pain duration, mental health status, and physical health status of a pain patient.

[0297] In this case, each worksheet module may be input with user response information for checking at least one of the pain level, pain duration, mental health condition, and physical health condition of the pain patient in relation to a specific topic of the treatment program in which the worksheet module is included. Furthermore, each worksheet module may be input with various user response information for checking at least one of the pain level, pain duration, mental health condition, and physical health condition of the pain patient in addition to the content related to the specific topic of the treatment program.

[0298] For example, a worksheet module 515 included in the first treatment program 510 may receive input of user response information to check at least one of a pain patient's pain level, pain duration, mental health condition, and physical health condition in relation to a first topic ("Enhancing Motivation") 510a.

[0299] As another example, a worksheet module 522 included in the second treatment program 520 may receive input of user response information to check at least one of the pain patient's pain level, pain duration, mental health status, and physical health status in relation to a second topic ("Checking Emotions") 520a.

[0300] The worksheet module may be provided at various points in each treatment program. For example, the worksheet module may be located at the end or middle of each treatment program. The location at which the worksheet module is included in each treatment program may vary depending on the condition of the pain patient.

[0301] On the other hand, the worksheet module may be provided as the last module in each treatment program. The worksheet module may be arranged as the last module among the treatment modules constituting each treatment program, and may be configured so that the pain patient proceeds to the worksheet module after completing all the treatment modules of the treatment program. This may include the purpose of receiving the pain patient's treatment effect, objectification, or feedback of the pain patient on the treatment program.

[0302] Furthermore, the worksheet module included in each treatment program may be configured so that the pain patient scores (by selecting, inputting, etc.) the severity of pain, the mood when experiencing pain, the degree of negative feelings about pain, the degree of stress about pain, etc. The control unit 130 can use the score received in the worksheet module to determine whether the treatment program including the corresponding worksheet module was helpful or effective for the pain patient.

[0303] For example, if there is a treatment program that has obtained a low pain score, the control unit 130 can determine that the treatment program is useful to the user. In this case, the treatment program can be reflected in updating the treatment protocol described later.

[0304] Meanwhile, in the present invention, a process can be performed in which a plurality of specific treatment programs included in an initial treatment protocol are sequentially provided to the user terminal 10 according to the treatment week set for each of the plurality of specific treatment programs (S260, see FIG. 8b).

[0305] Here, the "treatment week" can be understood as the order (or period) in which the treatment program is provided (or activated) via the user terminal 10 so that the patient performs cognitive behavioral therapy according to the treatment program. In the present invention, the "total number" of treatment weeks and the "weekly treatment period" corresponding to each treatment week may be preset and exist. Also, in the present invention, the "total treatment period" can be understood as being predefined. The total treatment period may be determined as the product of the preset total number and the preset weekly treatment period. For example, if the total number is "8" and the weekly treatment period is 1 week (7 days), the preset total treatment period is "8 weeks."

[0306] For the sake of convenience, an example will be described below in which different treatment weeks occur "eight times" for each one-week treatment period. That is, in the present invention, the first treatment week occurs in the first treatment session, and the second treatment week occurs in the second treatment session. Therefore, in the present invention, the "treatment week" may be used interchangeably with the "treatment session," "treatment number," "treatment period," and "treatment sequence."

[0307] The control unit 130 may set at least one treatment week for each of a plurality of treatment programs included in the initial treatment protocol. In addition, the control unit 130 can provide a treatment program in which a specific treatment week is set for a specific treatment week.

[0308] As mentioned above, in the present invention, the total number of treatment weeks (eg, "8") can be predefined.

[0309] When the multiple treatment programs included in the initial treatment protocol correspond to a predefined number of treatment weeks, the control unit 130 may set different treatment weeks for each of the multiple treatment programs. For example, when the first treatment program to the eighth treatment program are selected in the initial treatment protocol, the control unit 130 may set any one of the first treatment week to the eighth treatment week for each of the first treatment program to the eighth treatment program.

[0310] On the other hand, the first to eighth therapeutic programs may correspond to the first to eighth topics described above, respectively. That is, i) the first therapeutic program may be for training or treatment related to the first topic (motivation enhancement), ii) the second therapeutic program may be for training or treatment related to the second topic (emotion confirmation), iii) the third therapeutic program may be for training or treatment related to the third topic (behavioral strategy), iv) the fourth therapeutic program may be for training or treatment related to the fourth topic (attention shift), v) the fifth therapeutic program may be for training or treatment related to the fifth topic (changing the way of thinking), vi) the sixth therapeutic program may be for training or treatment related to the sixth topic (thought recording), vii) the seventh therapeutic program may be for training or treatment related to the seventh topic (management strategy), and viii) the eighth therapeutic program may be for training or treatment related to the eighth topic (future me).

[0311] On the other hand, when the multiple treatment programs included in the initial treatment protocol do not meet the number of predefined treatment weeks (shortfall), the control unit 130 may set multiple treatment weeks for at least some of the multiple treatment programs. The control unit 130 may repeatedly assign the same topic to different weeks so that treatment is performed according to the predefined treatment weeks. For example, when the predefined treatment week is 8 weeks and the number of treatment programs selected in the initial treatment protocol is 7, the control unit may repeatedly assign one of the 7 treatment programs to a specific number of weeks. For example, the control unit 130 may repeatedly set the same topic (e.g., thought recording) 560a in the 6th and 7th weeks.

[0312] Meanwhile, the control unit 130 can sequentially provide the multiple specific treatment programs to the user terminal 10 according to the treatment weeks set for each of the multiple specific treatment programs based on the initial treatment protocol.

[0313] The control unit 130 can activate the state of the specific treatment program based on the initial treatment protocol so that the specific treatment program set for the specific treatment week is provided to the user terminal 10 during the treatment period corresponding to the specific treatment week. In the following, in order to avoid confusion in terms, the state of the specific treatment program is named and described as the "mode" of the specific treatment program.

[0314] In the present invention, the "treatment program activation mode" can be understood as a mode in which at least a part of the treatment modules included in the treatment program can be viewed (or used), whereas the "treatment program inactivation mode" can be understood as a mode in which none of the treatment modules included in the treatment program can be viewed (or used).

[0315] For example, as shown in Fig. 8e, the control unit 130 can activate a mode of the first treatment program 510 in which the first treatment week is set based on the arrival of the first treatment week (first week). Also, the control unit 130 can activate a mode of the second treatment program 520 in which the second treatment week is set based on the arrival of the second treatment week (second week).

[0316] Thus, patients can receive cognitive behavioral treatment systematically, using multiple treatment programs in sequence according to the treatment weeks set out in the initial treatment protocol.

[0317] Meanwhile, the control unit 130 can sequentially provide the specific treatment modules constituting the multiple specific treatment programs to the user terminal 10 according to the treatment weeks set for each of the multiple specific treatment programs.

[0318] The control unit 130 can sequentially provide the multiple treatment modules 511 to 515 constituting the first treatment program 510 to the user terminal 10 during the first treatment week (see FIG. 8e).

[0319] Here, the term "treatment module sequence" may be understood as the order in which multiple treatment modules included in a particular treatment program are delivered.

[0320] In addition, the "activation mode of the treatment module" can be understood as a mode in which the treatment module can be viewed (or used). On the other hand, the "deactivation mode of the treatment module" can be understood as a mode in which the treatment module cannot be viewed (or used).

[0321] The control unit 130 can change (or switch) the mode of the treatment module corresponding to the next treatment module in a particular order from an inactive mode to an active mode based on the completion of cognitive behavioral treatment by the treatment module corresponding to a particular order in accordance with the treatment module order.

[0322] For example, as shown in Fig. 8e, the first treatment program 510 is composed of the first to fifth treatment modules 511 to 515. The control unit 130 can switch the mode of the second treatment module 512 from the inactive mode to the active mode based on the completion of the cognitive behavioral treatment of the first treatment module 511.

[0323] On the other hand, based on the activation of the mode of a particular treatment program, the control unit 130 can switch the mode of the treatment module that is the earliest among the multiple treatment modules that make up the particular treatment program from an inactive mode to an active mode.

[0324] For example, based on the activation of the first treatment program 510, the control unit 130 can switch the mode of the first treatment module 511, which corresponds to the highest priority (first order) of the multiple treatment modules 511-515 included in the first treatment program 510, from a deactivated mode to an activated mode.

[0325] In this case, the highest priority treatment module in the second week's treatment program to the eighth week's treatment program 520-580, excluding the first week's treatment program 510, may be the first treatment module corresponding to the highest priority order, which is a previous worksheet check module containing the user's answer information to the worksheet module provided in the previous treatment week.

[0326] In order to allow the pain patient to recognize the pain patient's past condition, the control unit 130 may preferentially provide the user's response information to the worksheet module provided in a treatment week prior to the current treatment week before providing a treatment program corresponding to the current treatment week among multiple specific treatment programs.

[0327] For example, before providing the second week of the treatment program, the control unit 130 may preferentially provide the user's answer information for the worksheet module provided in the first week of the treatment program to the user terminal 10. Furthermore, the user's answer information for the worksheet module provided in the previous treatment week may be provided at various times.

[0328] On the other hand, in the present invention, "a specific treatment program is activated" can be understood as switching the mode of the previous worksheet module corresponding to the highest priority (e.g., first order) among the multiple treatment modules that make up the specific treatment program from a deactivated mode to an activated mode.

[0329] Meanwhile, the control unit 130 can collect treatment response data from the user terminal 10 by a specific treatment module.

[0330] The control unit 130 may provide the user terminal 10 with a page corresponding to a particular treatment module based on the selection of a particular treatment module activated via the user terminal 10.

[0331] A page corresponding to a specific treatment module may include cognitive behavioral treatment content related to a specific topic. For example, a page corresponding to a treatment module 511 included in a first treatment program 510 may include cognitive behavioral treatment content (e.g., "What does pain mean to me?") related to a first topic (e.g., "Motivation Enhancement") 510a. In the following, for convenience of explanation, a page corresponding to a specific treatment module is named a "page related to a specific topic."

[0332] The control unit 130 may collect treatment response data based on the cognitive behavioral performance of a pain patient related to a specific treatment module via a page related to a specific topic provided to the user terminal 10.

[0333] In addition, the control unit 130 may match the treatment response data collected through a page corresponding to a specific treatment module with a specific topic and store it in the storage unit 120. In this case, the specific topic matched with the treatment response data may mean a topic related to a specific treatment module.

[0334] For example, assume that a page (e.g., a pain record exercise page) corresponding to a specific treatment module is provided on the user terminal 10 based on the selection of a specific module (one of 511 to 515) included in the first program (or first treatment program) 510. The control unit 130 can match the treatment response data input via the page with a first topic (e.g., "Enhance motivation") 510a and store it in the storage unit 120 (see FIG. 8e).

[0335] As another example, assume that a page corresponding to a particular module is provided on the user terminal 10 based on the selection of a particular module 521 or 522 included in the second program (or second treatment program) 520. The control unit 130 can match the treatment response data input via the page with a second topic (e.g., "affirming emotions") 520a and store it in the storage unit 120 (see FIG. 8e).

[0336] Alternatively, the controller 130 may configure pages such that the pain patient inputs treatment response data required from a particular treatment module on a page corresponding to the particular treatment module.

[0337] For example, the control unit 130 may display question data asking about pain intensity (e.g., "How is your pain today?") in one area of ​​the page, and display graphic objects corresponding to each of a number of pain intensities (e.g., "Pain Intensity 1 to Pain Intensity 5") in another area, in order to collect treatment response data related to a pain patient's perception of pain intensity. The control unit 130 may also collect treatment response data for the patient's pain intensity based on a particular graphic object being selected on the page.

[0338] As another example, the control unit 130 may provide an input field (Text Input Field) in which the pain patient can input the emotion switching method to be attempted on one region of the page in order to collect treatment response data for the emotion switching method of the pain patient. The control unit 130 may collect treatment response data related to the emotion switching method input in the input field.

[0339] Meanwhile, the control unit 130 can provide a page including various cognitive behavioral therapy contents other than the above-mentioned examples for the cognitive behavioral therapy of the pain patient on the user terminal 10. In addition, the control unit 130 can collect various treatment response data necessary for the cognitive behavioral therapy through the page.

[0340] On the other hand, in the present invention, the corresponding treatment program (or treatment modules constituting the treatment program) can be changed after a specific week of treatment based on the results (especially, initial treatment response data) of cognitive behavioral treatment of a pain patient conducted based on an initial treatment protocol.

[0341] Therefore, the "initial treatment protocol" described in this invention means a treatment protocol determined based on questionnaire response data, and the "updated treatment protocol" can be understood as a protocol in which at least one of the initial treatment program and initial treatment module included in the initial treatment protocol has been changed based on the initial treatment response data.

[0342] Meanwhile, various information other than information collected from the treatment module can be used for the information used to update the initial treatment protocol. For example, in the present invention, after starting treatment according to the initial treatment protocol, a survey (information collection) may be conducted on at least one of i) the patient's pain level, ii) the degree of negative emotions (depression), iii) the presence or absence of insomnia, iv) the degree of cognitive distortion (catastrophizing, risk avoidance), v) the degree of stress caused by pain, vi) the most inconvenient thing in daily life (work, interpersonal relationships, etc.), and vii) a sense of competence regarding pain management, through questions once a day during a preset initial treatment period (e.g., the first 4 weeks).

[0343] For example, in the present invention, after starting treatment according to the initial treatment protocol, viii) a survey (information collection) may be conducted once a week during a preset initial treatment period regarding the treatment module or method that the patient subjectively considers to be most useful for managing pain. The control unit 130 may update the initial treatment protocol based on an analysis of the surveyed information.

[0344] Furthermore, in order to collect the information, the control unit 130 may provide the user with an information collection alarm in various ways (e.g., an application push message, etc.), and may provide an additional alarm if a response to the information collection is not made within a preset time. In this case, the time required for the information collection may be limited, and the control unit 130 may provide a preset evaluation time, and may allow evaluation of at least one of the above items i) to viii) to be made within the evaluation time.

[0345] The information input by the user for the above items i) to viii) can be utilized as the "treatment response data" described in the present invention. Meanwhile, the frequency and intervals for collecting the treatment response data can be changed in various ways, as a matter of course.

[0346] The following describes in detail how to update a treatment protocol.

[0347] For the sake of convenience, the following description will be given assuming the total treatment period to be "8 weeks." However, this is merely an example for the sake of convenience, and it goes without saying that the treatment period can be set and changed in various ways by the patient and the system administrator.

[0348] Meanwhile, the control unit 130 can collect treatment response data related to cognitive behavioral treatment according to the initial treatment protocol, and update the initial treatment protocol using the collected initial treatment response data based on the passage of a preset initial treatment period (e.g., “4 weeks”) within a preset overall treatment period (e.g., “8 weeks”).

[0349] The controller 130 can analyze the pain patient's condition for at least one analysis category based on the initial treatment response data to update the initial treatment protocol.

[0350] As used herein, an "analysis category" is a category into which treatment response data is analyzed for cognitive behavioral treatment of a pain patient, and may include, for example, at least one of emotion, pain, insomnia, cognitive distortion, stress, competence, and inconvenience.

[0351] The control unit 130 can use the initial treatment response data to obtain (or derive or calculate or compute) a condition analysis result for at least one of: i) the pain level of the pain patient (associated with the pain analysis category), ii) the degree of negative emotions (associated with the emotion analysis category), iii) the degree of insomnia (associated with the insomnia category), iv) the degree of stress related to pain (associated with the stress analysis category), v) the degree of sense of competence in dealing with pain (sense of competence category), vi) the degree of inconvenience in daily life, and vii) the degree of cognitive distortion.

[0352] The control unit 130 can derive the analysis result of the condition of the pain patient for each analysis category by various methods.

[0353] For example, the control unit 130 can acquire a condition analysis result of the pain patient for each analysis category using an artificial intelligence model that has performed machine learning for analyzing the condition of the pain patient. In this case, the condition analysis result acquired using the artificial intelligence model may include information that identifies a specific analysis category (e.g., "insomnia") in which the problem symptom of the pain patient satisfies a preset criterion among multiple analysis categories. Alternatively, the condition analysis result acquired using the artificial intelligence model may include an analysis score for each of multiple analysis categories.

[0354] As another example, the control unit 130 can compare whether the analysis scores for each of the multiple analysis categories meet a preset criterion, and identify an analysis category in which the problem symptom of the pain patient meets the preset criterion among the multiple analysis categories. The control unit 130 can calculate (or compute) the analysis score for each analysis category based on the treatment response data. For example, the control unit 130 can calculate the analysis score by using (or computing) the initial treatment response data corresponding to the objective formula. In addition, the control unit 130 can calculate the analysis score for the initial response data consisting of text by performing an artificial intelligence analysis on the initial response data consisting of text. The control unit 130 can compare each of the calculated analysis scores for each category with a problem score preset for each category, and identify an analysis category in which the analysis score exceeds the preset problem score as a category in which the problem symptom of the pain patient exists.

[0355] Meanwhile, the control unit 130 can update the initial treatment program using the results of the pain patient's condition analysis based on the initial treatment response data so that at least one of the treatment programs and treatment modules related to the identified category is included in the treatment program.

[0356] As shown in FIG. 8i, the control unit 130 can change at least some of the programs 550, 560 of the fifth to eighth treatment programs 550-580 set in the fifth to eighth treatment weeks, respectively, to new programs 550', 560' based on treatment response data from the first to fourth treatment programs 510-540 set in the first to fourth treatment weeks, respectively.

[0357] In this case, the control unit 130 may modify the remaining treatment programs assigned to the remaining treatment periods (5 weeks to 8 weeks) excluding the pre-set initial treatment period (e.g., 1 week to 4 weeks) and at least one of the treatment modules constituting the remaining treatment programs so that they are associated with the identified analysis category (e.g., “insomnia”).

[0358] Assume that an initial treatment protocol 810 exists as shown in FIG. 8j. The initial treatment protocol 810 may be composed of a first initial treatment program to an eighth initial treatment program 811-818 related to different topics for each of the first to eighth weeks. Each of the initial treatment programs 811-818 may include a treatment module corresponding to each topic. For example, the fifth initial treatment program 815 may include a plurality of initial treatment modules 815a-815f related to the fifth topic, and the sixth initial treatment program 816 may include a plurality of initial treatment modules 816a-816f related to the sixth topic.

[0359] As shown in Fig. 8k, the control unit 130 may exclude at least some 815d-815f and 816d-816f of the treatment modules 815a-815f and 816a-816f included in the remaining treatment programs 815-816 based on the result of the pain patient's condition analysis of the initial treatment response data. The remaining treatment programs 815-816 of the updated treatment protocol 820 may include at least some of the treatment modules 815a-815c and 816a-816c that are not excluded. In this case, the treatment modules 815a-815c and 816a-816c that are not excluded may be related to the identified analysis category.

[0360] Furthermore, the control unit 130 may reset a specific treatment program set in a preset initial treatment period (or initial treatment week) to at least a part of the remaining treatment period (remaining treatment week) based on the result of the pain patient's condition analysis of the initial treatment response data. That is, the control unit 130 can reallocate (or reset or relocate) a treatment program related to a specific topic matched to the preset initial treatment period (or initial treatment week) to the remaining treatment period (or remaining treatment week) to update the initial treatment protocol.

[0361] For example, as shown in Fig. 8l, the control unit 130 may reset the third treatment program 813 set in the third week to the fifth week, and reset the fourth treatment program 814 set in the fourth week to the sixth week. The updated treatment program 830 may include the already performed treatment programs 813 and 814 in a duplicated (or repeated) manner in different weeks. That is, in the present invention, the initial treatment program can be updated based on the initial treatment response data so that the already performed treatment program is performed again.

[0362] The overlapping or repeated treatment programs may be modules that are determined to be most useful to the pain patient. Such a determination may be made through an analysis of evaluation information on the treatment programs received from the pain patient, changes in values ​​for pain received through the worksheet module (e.g., severity of pain, mood when experiencing pain, degree of negative feelings about pain, and numerical values ​​(scores) for degree of stress about pain), frequency of use of pain management methods created in a particular treatment program, duration of use, etc. Such an analysis may be made using various artificial intelligence algorithms.

[0363] In this case, the reassigned treatment program 813, 814 may be one that is related to the identified analysis category.

[0364] Furthermore, the control unit 130 may update the initial treatment protocol 810 so that a treatment program made up of treatment modules corresponding to different topics is set for the remaining treatment period (remaining treatment week).

[0365] For example, as shown in Fig. 8m, the updated protocol 830 may include a treatment program 841 in which treatment modules corresponding to multiple topics are combined. The treatment program 841 may include treatment modules 813a-813c related to a third topic and treatment modules 814a-814c related to a fourth topic, and may be set in a fifth treatment week. That is, at least a part of the treatment programs 841 set in the remaining treatment period (remaining treatment week) may include a part of the treatment modules provided in the preset initial treatment period (or initial treatment week).

[0366] Furthermore, although not shown, the control unit 130 can update the initial treatment program by adding new modules for the remaining treatment period (remaining treatment weeks) that are different from the modules set in the initial protocol.

[0367] For example, assume that the fifth treatment program 815 constituting the initial treatment protocol 810 includes six fifth topic treatment modules 815a to 815f (see FIG. 8j). The control unit 130 can update the initial treatment protocol 815 by adding a new fifth topic treatment module that was not included in the already included fifth topic-based treatment modules 815a to 815f to the fifth treatment program 815.

[0368] In this case, the control unit 130 can remove at least a part of the already included fifth topic treatment modules 815a-815f and add a new treatment module. Also, the control unit 130 may include a new treatment module while leaving the already included fifth topic treatment modules 815a-815f as they are. That is, the control unit 130 may include a completely new treatment module different from the already included treatment modules, and in this case, the already included treatment modules may be removed, maintained, or variously changed.

[0369] Meanwhile, the control unit 130 may modify at least some of the example sentences provided by the treatment modules constituting the remaining treatment program based on the results of updating the initial treatment protocol so that they are associated with the identified category.

[0370] As described above, the control unit 130 can identify, from among a plurality of analysis categories, a category in which the problem symptoms of the pain patient meet a preset criterion.

[0371] In this case, the control unit 130 can specify only a preset number (e.g., two) of the multiple analysis categories. For example, the control unit 130 can specify the "depression" and "analysis" categories with the highest analysis scores from the categories of emotion, pain, insomnia, cognitive distortion, stress, competence, and inconvenience.

[0372] The storage unit 120 may have example sentence information corresponding to at least one category that satisfies a preset criterion. The control unit 130 may match the patient account with at least one of information on the category that satisfies the preset criterion and example sentence information included in the category that satisfies the preset criterion, and store the matched information. Furthermore, the matching information may store information on selected example sentences selected by the pain patient from example sentences provided by a treatment module that the pain patient has already performed, in addition to the category that satisfies the preset criterion. Furthermore, the example sentences stored as the matching information for the category that satisfies the preset criterion may also be composed of selected example sentences selected by the pain patient.

[0373] The control unit 130 can control the treatment programs 550 to 580 matched to the remaining treatment week to include example sentences related to the identified analysis category based on the example sentence information present in the storage unit 120. The control unit 130 can change (replace) at least a part of the example sentences preset for the treatment modules constituting the treatment programs matched to the remaining treatment week to the selected example sentences stored in the example sentence information corresponding to the user history.

[0374] For example, assuming that the category of depression is identified, the control unit 130 may include an example sentence such as "Jang Hye-ryeong, do you think that 'my depressed mood will never get better'" in a specific treatment module constituting the fifth treatment program set for the fifth week.

[0375] As another example, assume that the category of insomnia is specified. The control unit 130 may include an example sentence such as "Jang Hye-ryeong, do you think that 'I can never sleep deeply if I have pain'" in a specific treatment module constituting the sixth treatment program set for the sixth week. In this way, the control unit 130 can objectify the user's recognition of the example sentence by continuously exposing the example sentence selected by the pain patient in the past or the example sentence of a specific category to the pain patient.

[0376] On the other hand, in the present invention, it is possible to determine whether to maintain or change the initial treatment protocol based on the results (particularly, initial treatment response data) of cognitive behavioral treatment of a pain patient performed based on the initial treatment protocol.

[0377] The control unit 130 can determine whether to maintain the total treatment period preset according to the initial treatment protocol based on the result of the cognitive behavioral treatment of the pain patient. Also, based on the determination result, the control unit 130 can update the total treatment period. Here, the update of the total treatment period may include extending or shortening the total treatment period. The control unit 130 can determine whether to shorten (or interrupt or shorten) the total treatment period, maintain the total treatment period, or extend the treatment period to a period longer than the total treatment period.

[0378] The control unit 130 can make the above-mentioned judgment based on various criteria, for example, it can make a judgment as to whether to interrupt, maintain, or extend treatment for a pain patient depending on whether the results of the cognitive behavioral treatment of the pain patient satisfy predetermined criteria (e.g., criteria respectively set in relation to interrupting, maintaining, or extending treatment).

[0379] As an example, the control unit 130 can digitize the result of the cognitive behavioral treatment of the pain patient into a score or the like, and can determine whether to interrupt, maintain, or extend the treatment for the pain patient depending on which of a plurality of intervals the corresponding score falls within. For example, when the cognitive behavioral treatment result score of the pain patient falls within the first interval (interruption interval), the control unit 130 can interrupt (or reduce) the cognitive behavioral treatment for the pain patient.

[0380] For example, if the result of the cognitive behavioral treatment of the pain patient is positive, the result of the cognitive behavioral treatment may belong to the score of the first section. In this case, the control unit 130 may shorten the entire treatment period, and in this case, the control unit 130 may determine the period to be shortened. The control unit 130 may update the initial treatment protocol so that only the preceding treatment is performed up to the time when monitoring is performed, and no subsequent treatment is performed. In this case, the cognitive behavioral treatment for the pain patient may be discontinued.

[0381] As another example, if the cognitive behavioral treatment result score of the pain patient is included in the second interval (maintenance interval), the control unit 130 may maintain the cognitive behavioral treatment for the pain patient. In this case, the treatment period (e.g., 8 weeks) set in the initial protocol may be maintained as is. If the result of the cognitive behavioral treatment of the pain patient is normal, the result of the cognitive behavioral treatment may belong to the score of the second interval. In this case, the control unit 130 may maintain the entire treatment period as is. Meanwhile, an update to the initial protocol may also be performed in this case, and the update to the initial protocol may be performed as described above in Figures 8j to 8m, so the specific description will be replaced with the above description.

[0382] As another example, if the cognitive behavioral treatment result score of the pain patient is included in the third section (maintenance section), the control unit 130 may extend the treatment period of the cognitive behavioral treatment for the pain patient. In this case, the treatment period (e.g., 8 weeks) set in the initial protocol may be further extended (e.g., 12 weeks, etc.). If the result of the cognitive behavioral treatment of the pain patient is negative, the result of the cognitive behavioral treatment may belong to the score of the third section. In this case, the control unit 130 may further extend the entire treatment period. The control unit 130 may determine the extension period. The control unit 130 may determine the range of the extension period according to the condition of the pain patient. The worse the condition of the patient, the longer the extension period may be. If the treatment period is extended, the control unit 130 may determine a treatment program to be performed during the extended treatment period and update the initial treatment program so that the determined treatment program is further assigned to the extended treatment period. Of course, the update of the initial treatment protocol may further include not only the determination of a program for the extended treatment period, but also the update of the already assigned treatment program.

[0383] Meanwhile, the control unit 130 may determine which treatment program to further allocate to the extended treatment period by using the pain patient's feedback on the treatment program. Furthermore, the control unit 130 may utilize the state information of the pain patient for each analysis category analyzed based on the initial treatment response data for the already-implemented treatment program in order to allocate the treatment program to the extended treatment period. The control unit 130 may determine a category in which treatment should be further performed for the pain patient based on state information of the pain patient for each analysis category for at least one of emotion, pain, insomnia, cognitive distortion, stress, competence, and inconvenience, and further allocate a treatment program for the corresponding category to the extended treatment period. Meanwhile, the method of determining the treatment program to be allocated to the extended treatment period may be very diverse. For example, the control unit 130 may receive the patient's intention regarding the extension of the patient's treatment program. The time point at which the patient's intention is received may be diverse, and may receive the patient's intention of whether or not to extend the treatment period at the time when the preset treatment period is completed or the preset treatment program is started, for example. The control unit 130 may provide an interface for receiving information from the patient in a pop-up page or various other ways. When the control unit 130 receives the patient's intention to extend the treatment period, the control unit 130 may extend the treatment program. In this case, the patient may also select the extension period. Furthermore, the user may select at least one of the topics or treatment modules of the extended treatment program. In this way, the patient can receive more effective treatment by selecting a treatment program or treatment module that is useful to him or her. As described above, the method and system for providing cognitive behavioral treatment for a pain patient according to the present invention may receive questionnaire response data for a plurality of questionnaire data via a user terminal, and detect condition information of the pain patient related to the pain duration and the degree of cognitive distortion of the pain patient based on the questionnaire response data.In addition, a treatment protocol for cognitive behavioral treatment customized for the pain patient can be provided based on the condition information of the pain patient. As a result, the cognitive behavioral treatment providing method and system according to the present invention can provide a behavioral treatment program customized for the pain patient, taking into consideration the duration of pain and the degree of cognitive distortion of the pain patient, even if the pain patient has the same disease, rather than providing a uniform cognitive behavioral treatment depending on the pain patient's disease. Furthermore, the pain patient can receive cognitive behavioral treatment customized to his or her own condition.

[0384] Furthermore, the method and system for providing cognitive behavioral therapy for pain patients according to the present invention can sequentially provide a plurality of specific treatment programs according to the treatment week, so that the pain patient can receive cognitive behavioral therapy systematically and complete the treatment without interruption.

[0385] Furthermore, the method and system for providing cognitive behavioral therapy for pain patients according to the present invention updates the treatment program to be useful for treating the pain patient based on treatment data collected during the progress of the cognitive behavioral treatment, thereby making it possible to provide updated cognitive behavioral therapy taking into account the improvement status of the pain patient without having to continuously provide the initial treatment program.

[0386] Meanwhile, the present invention described above can be embodied as a program that is executed by one or more processes in a computer and can be stored in a medium (or recording medium) that can be read by such a computer.

[0387] Meanwhile, as shown in FIG. 9a(a), the patient can select the fourth menu item 340 on the initial screen page 300 to receive an AI function evaluation service based on an artificial intelligence model for his / her exercise movement.

[0388] 9a(b), the control unit 130 can provide a function evaluation page 900 including a plurality of pre-set exercise items (e.g., "stretch your arms out to the side," "raise your arms forward," "sit down and straighten your knees and bend them") on the user terminal 10 based on the selection of the fourth menu item 340. The exercise items that are the subject of the AI ​​function evaluation service can be set and changed by the system 100 administrator.

[0389] Meanwhile, when any one of a plurality of pre-set exercise items is selected, the control unit 130 may control the camera provided in the user terminal 10 to capture an exercise video of the patient U in order to evaluate the exercise action of the patient for the selected exercise item. Hereinafter, the exercise video of the patient will be described as a "functional evaluation analysis target video."

[0390] As shown in (a) of Figure 9b, in order to detect a subject U corresponding to the patient from the functional evaluation analysis target image captured by the camera, the control unit 130 can output a guidance message (e.g., "Please stand within the screen") on the user terminal 10 so that the patient's entire body is entirely contained within a specific area of ​​the functional evaluation analysis target image (or the user terminal display).

[0391] The control unit 130 can detect the object U from the functional evaluation analysis target image using an object detection algorithm based on the fact that the object U corresponding to the patient's entire body is included in a specific region.

[0392] The control unit 130 can use various object detection algorithms. For example, the exercise therapy application 100 can use an algorithm (Weighted Box Fusion, WBF) that ensembles multiple bounding boxes. However, it goes without saying that the control unit 130 is not limited to the above-mentioned object detection algorithm, and can use various object detection algorithms that can detect an object corresponding to the subject U from the functional evaluation analysis target video.

[0393] Furthermore, based on detection of a subject U corresponding to the patient's entire body within a specific area, the control unit 130 can use a camera to capture a functional evaluation analysis subject image including the patient performing exercise movements according to pre-set exercise items.

[0394] In addition, the control unit 130 can extract key points P1 and P2 corresponding to pre-set joint points from the functional evaluation analysis target image in real time in conjunction with the functional evaluation analysis target image being captured by the user terminal 10.

[0395] In this case, in order to extract key points P1 and P2 corresponding to predetermined joint points from the video, the control unit 130 can extract key points P1 and P2 based on an artificial intelligence model (artificial intelligence posture estimation model) that has learned learning data including position information of the joint points.

[0396] In addition, the control unit 130 can provide the extracted key points P1 and P2 on the user terminal 10 in real time so that the patient can intuitively recognize the joint points at which analysis is performed for the exercise movement.

[0397] Specifically, as shown in (b) and (c) of FIG 9b, the control unit 130 may output the functional evaluation analysis target image on the user terminal 10 in real time in conjunction with the functional evaluation analysis target image being photographed by the user terminal 10. In addition, the control unit 130 may provide by overlapping or rendering graphic objects corresponding to the extracted key points P1 and P2 in a region of the subject U corresponding to a preset joint point.

[0398] In addition, when the position of a preset joint point is changed as the patient performs an exercise, the control unit 130 may provide a key point graphic object by superimposing it on an area of ​​the subject U corresponding to the changed joint point. That is, the control unit 130 may superimpose a key point graphic object on an area corresponding to the joint point in the image to be analyzed for functional evaluation so that the position of the joint point, which is changed in real time, is reflected.

[0399] Meanwhile, the control unit 130 may analyze the exercise motion corresponding to the exercise item performed by the patient using the functional evaluation analysis target image and at least one of the key points P1 and P2.

[0400] In this case, the control unit 130 can analyze the patient's exercise movements based on at least one of an artificial intelligence model (artificial intelligence movement analysis model) that has undergone machine learning for the analysis of exercise movements and predefined rules (or rule information).

[0401] On the other hand, analysis of the patient's movement can be performed not only on the range of motion of the joints, but also on at least one of the following: the moving distance of the joints, the movement speed (or acceleration) of the joints, and the body balance, body equilibrium, and body alignment (e.g., leg alignment, spinal alignment, etc.) of the subject (corresponding to the patient) included in the movement video being analyzed.

[0402] As described above, the present invention not only provides rehabilitation exercises to patients, but also provides cognitive behavioral therapy in conjunction with rehabilitation exercises, so that information on the patient's pain level, pain duration, and mental health status can be monitored. Furthermore, the present invention uses the above-mentioned AI function evaluation to analyze the patient's exercise movements, thereby extracting information on the patient's exercise performance ability, body parts where the patient's exercise ability is lacking (e.g., body parts with weak muscles), etc.

[0403] Thus, in the present invention, the patient's exercise plan can be updated using at least one of the results of the patient's cognitive behavioral therapy and the results of the exercise analysis, rather than relying solely on the evaluation information of the patient. In this case, the update of the patient's exercise plan may include various adjustments or changes related to the update of the exercise plan, such as adjustment of the difficulty of the patient's exercise, change of the type of exercise, etc.

[0404] For example, the control unit 130 may update the exercise plan based on the patient's feedback on pain (example 1: negative feedback on pain (example: my legs can't function anymore), example 2: positive feedback on pain (example: I feel like I can run for some reason) based on the cognitive behavioral evaluation results (or feedback, e.g., which may be received through a worksheet module or other modules) received from the patient during the cognitive behavioral treatment process. As an example, if negative feedback is received as a result of the cognitive behavioral evaluation, the control unit 130 may lower the difficulty of the exercise or change the type of exercise in the patient's exercise plan. The change in the type of exercise may be a change from the type of exercise performed before the feedback on the patient's pain was received to another exercise. As another example, if positive feedback is received as a result of the cognitive behavioral evaluation, the control unit 130 may increase the difficulty of the exercise or change the type of exercise in the patient's exercise plan. The change in the type of exercise may be a change to an exercise that is more difficult than the type of exercise performed before the feedback on the patient's pain was received.

[0405] As another example, the control unit 130 may analyze the exercise movements of the patient from the AI ​​function evaluation, and thereby extract information on the patient's exercise performance ability, the patient's body parts with insufficient exercise ability (e.g., body parts with weak muscles), and the like. In addition, the exercise plan may be updated from the extracted information so that the patient's body parts with insufficient exercise ability are strengthened, or the exercise plan may be updated so that the patient's body parts with insufficient exercise ability are not overworked. As an example, if the result of the function evaluation shows that a specific body part of the patient (e.g., muscles of the left leg) is weak, the control unit 130 may analyze whether or not there is an excessive exercise for the specific body part of the patient in the current exercise plan. The control unit 130 may analyze whether or not there is an excessive exercise for the specific body part of the patient based on information (e.g., explanatory information) on each type of exercise. In addition, if there is an excessive exercise for the specific body part of the patient as a result of the analysis, the corresponding exercise may be excluded from the exercise plan. In addition, the control unit 130 may update the patient's exercise plan by extracting an exercise item that can strengthen the specific body part of the patient from the storage unit and including it in the patient's exercise plan.

[0406] In this way, the control unit 130 can update the patient's exercise plan based on at least one of the patient's condition information from the cognitive behavioral treatment and the evaluation information from the functional evaluation. Furthermore, the control unit 130 can update the patient's exercise plan based on at least one of the patient's condition information from the cognitive behavioral treatment and the evaluation from the functional evaluation, and the patient's evaluation information from the evaluation page described above.

[0407] Meanwhile, as shown in FIG. 10, the control unit 130 can provide a summary page 1000 including results for the service provided by the present invention on the user terminal 10 .

[0408] The control unit 130 can display at least one of the following on the summary page 1000: i) rehabilitation exercise information according to the exercise plan, ii) evaluation information for the exercise plan, iii) AI function evaluation information based on an AI function evaluation, and iv) result information of cognitive behavioral treatment according to the cognitive behavioral treatment plan.

[0409] As shown in (a) of FIG. 10, the control unit 130 can display, on a summary page 1000, at least one of exercise execution rate information for each of multiple days that make up the rehabilitation period and average exercise execution rate information during the rehabilitation period as rehabilitation exercise information in accordance with the exercise plan.

[0410] Furthermore, as shown in (b) of FIG. 10, the control unit 130 can display, on the summary page 1000, at least one of evaluation information (e.g., pain score, etc.) for each of the multiple days that make up the rehabilitation period and information on changes in the evaluation information (e.g., comparison information between the initial pain score and the most recent pain score) as evaluation information for the exercise plan.

[0411] 10(c), the control unit 130 can display AI function evaluation result information performed at preset intervals during the rehabilitation period on the summary page 1000. In this case, the control unit 130 can display at least one of the number of times (or the time) each of the preset exercise items was performed and the average number of times (or the average time) each of the preset exercise items was performed on the summary page 1000.

[0412] Furthermore, as shown in (d) of Fig. 10, the control unit 130 may provide a calendar on the summary page 1000 for checking the progress of the cognitive behavioral treatment according to the cognitive behavioral treatment plan. The calendar may display whether a worksheet module provided in the cognitive behavioral treatment plan has been performed. The control unit 130 may display visually different graphic objects for days when the worksheet module has been performed and days when the worksheet module has not been performed.

[0413] As described above, the method and system for providing digital-based musculoskeletal rehabilitation therapy according to the present invention can provide a patient with an exercise plan for musculoskeletal rehabilitation therapy through an application based on prescription information including an exercise plan for a patient being assigned from a doctor terminal.

[0414] In particular, the method and system for providing digital-based musculoskeletal rehabilitation treatment according to the present invention can play back exercise videos corresponding to a plurality of exercise items constituting an exercise list on a user terminal on which an application is executed. As a result, even if a doctor and a patient do not meet in person for rehabilitation treatment for a musculoskeletal disease, the doctor can prescribe to the patient, and the patient can receive rehabilitation through an exercise plan according to the doctor's prescription, thereby eliminating spatial, time, and economic constraints on musculoskeletal rehabilitation treatment and improving access to exercise treatment.

[0415] Furthermore, the method and system for providing digital-based musculoskeletal rehabilitation treatment according to the present invention may provide an evaluation page for evaluating exercise items based on whether the degree of playback of an exercise video satisfies a preset standard, and may update an exercise plan based on the evaluation information received through the evaluation page. This allows a patient to perform an exercise plan and provide appropriate feedback, thereby receiving customized rehabilitation treatment to which the feedback is applied. In particular, a patient may receive customized rehabilitation exercise treatment, such as adjusting the difficulty of exercise items according to the patient's own condition and excluding exercise items that are difficult for the patient.

[0416] Furthermore, the digital-based musculoskeletal rehabilitation therapy delivery method and system of the present invention can provide patients with treatment for not only their rehabilitation areas but also their mental health by providing a cognitive behavioral treatment plan in conjunction with a rehabilitation exercise plan.

[0417] In particular, the digital-based method and system for providing musculoskeletal rehabilitation therapy according to the present invention can receive questionnaire response data for a plurality of questionnaire data via a user terminal, and detect patient condition information related to the patient's pain duration and the degree of cognitive distortion based on the questionnaire response data. In addition, a treatment protocol for a cognitive behavioral treatment customized for the patient can be provided based on the patient's condition information. As a result, the method and system for providing rehabilitation therapy according to the present invention can provide a patient with a behavioral treatment program customized in consideration of the patient's pain duration and the degree of cognitive distortion even if the patient has the same musculoskeletal disease, rather than providing the patient with a uniform cognitive behavioral treatment due to a musculoskeletal disease. Furthermore, the patient can receive a customized cognitive behavioral treatment suited to his or her own condition.

[0418] Furthermore, the digital-based musculoskeletal rehabilitation therapy providing method and system according to the present invention can sequentially provide multiple treatment programs that are performed in conjunction with an exercise plan during the rehabilitation period, allowing patients to systematically receive cognitive behavioral therapy along with rehabilitation exercises and complete the rehabilitation therapy without interruption.

[0419] Furthermore, the method and system for providing digital-based musculoskeletal rehabilitation treatment according to the present invention updates a treatment program to be useful for the patient's cognitive behavioral treatment based on treatment response data collected during the progress of the cognitive behavioral treatment, thereby making it possible to provide an updated cognitive behavioral treatment taking into account the patient's improvement status, rather than continuously providing the patient with an initially determined cognitive behavioral treatment method.

[0420] Meanwhile, as described above, the present invention can provide exercise therapy for musculoskeletal disorders online, and the method and system for providing exercise therapy will be discussed in more detail below. In particular, the method and system for providing exercise therapy will be discussed below, which can analyze the exercise movements of a patient performing prescribed exercise based on exercise video captured of the patient's exercise. In this way, the present invention can provide a method and system for providing exercise therapy that can analyze the exercise movements of a patient from exercise video based on an artificial intelligence model specialized for musculoskeletal disorders. Furthermore, the present invention can provide a method and system for providing exercise therapy that can provide a user environment in which patients can easily access treatment for musculoskeletal disorders.

[0421] The present invention relates to a method for analyzing a patient's exercise motion contained in an exercise video based on the exercise video received from a patient's terminal, and providing an analysis result. In particular, the present invention relates to a method for analyzing an exercise motion based on a patient's joint points using an artificial intelligence model specialized for musculoskeletal disorders.

[0422] In the present invention, the analysis of the motion of rehabilitation exercise for musculoskeletal disorders will be mainly described, but is not necessarily limited thereto. That is, the motion analysis in the present invention may include not only the analysis of the motion of exercise, but also the analysis of various motions such as motions in daily life and motions during stretching.

[0423] On the other hand, the "exercise action" described in this invention is a gesture (movement) performed in the process of exercising, and may be used interchangeably with terms such as bodily "movement," "action," "movement," and "gesture."

[0424] Moreover, the "exercise video" is a video (image or video) that captures (includes) the process of the patient performing an exercise movement, as shown in FIG. 16, and includes at least a part of the patient U's body.

[0425] In the present invention, a patient object included in an exercise video is named and described as "subject U." In the present invention, "subject U" may refer to a patient exercising in an exercise video or a part of the patient's body. In the present invention, "subject" and "patient" may be used interchangeably, and are described with the same reference numeral "U."

[0426] Hereinafter, the method and system for providing exercise therapy using an artificial intelligence posture estimation model and a motion analysis model according to the present invention will be described in detail with reference to the accompanying drawings. FIG. 11 is a conceptual diagram for explaining the system for providing exercise therapy according to the present invention. FIG. 12 and FIG. 13 are flow charts for explaining the method for providing exercise therapy according to the present invention, FIG. 14a and FIG. 14b are conceptual diagrams for explaining a doctor's prescription, FIG. 15 and FIG. 16 are conceptual diagrams for explaining a method for analyzing a patient's exercise motion from an exercise video, FIG. 17, FIG. 18a, FIG. 18b, FIG. 18c, FIG. 18d, FIG. 18e, and FIG. 18f are conceptual diagrams for explaining an artificial intelligence posture estimation model, and FIG. 19 and FIG. 20 are conceptual diagrams for explaining an example of use in which a user's motion analysis result is provided. Furthermore, FIG. 21a, FIG. 21b, and FIG. 21c are conceptual diagrams for explaining a user environment in which a patient's motion analysis result is provided.

[0427] As shown in FIG. 11, the exercise therapy providing system 1000 according to the present invention uses an artificial intelligence posture estimation and motion analysis model to analyze the exercise motion of a patient in an exercise video received from a patient terminal 10, and may be configured to include at least one of an application 100 installed on the patient terminal 10 and an artificial intelligence server (or cloud server) 200. In addition, the exercise therapy providing system 1000 according to the present invention may include a posture estimation model and a motion analysis model trained using training data. Of course, at least one of the configurations and functions of the exercise therapy providing system 1000 discussed below may be included as at least one of the configurations of the digital-based musculoskeletal rehabilitation treatment providing system described above. The digital-based musculoskeletal rehabilitation treatment providing system may be configured to include at least one of the functions or configurations described below.

[0428] The application 100 according to the present invention is installed in the patient terminal 10, and can perform a function of analyzing the exercise motion of a patient U having a musculoskeletal disease and providing feedback information based on the analysis result. Therefore, the application 100 according to the present invention may be named a "digital exercise treatment solution", a "digital rehabilitation treatment solution", a "digital exercise evaluation solution", a "non-face-to-face exercise treatment solution", a "non-face-to-face rehabilitation treatment solution", a "non-face-to-face exercise evaluation solution", a "mobile exercise treatment program", a "mobile rehabilitation treatment program", a "mobile exercise evaluation program", and a "Mobile Orthopedic Rehabilitation Assistant (MORA)".

[0429] The application 100 according to the present invention is installed in a patient terminal 10 and can play a role in connecting a patient U with a musculoskeletal disease with an orthopedic surgeon D and supporting the rehabilitation of the patient U. For the sake of convenience, the application 100 installed in the patient terminal 10 will be described below as an "exercise therapy application."

[0430] Meanwhile, the exercise therapy application 100 according to the present invention can be installed in a patient terminal 10. The patient terminal 10 described in the present invention means an electronic device logged in with a user account of a patient U, and for example, the electronic device may include at least one of a smart phone, a mobile phone, a tablet PC, a kiosk, a computer, a laptop, a digital broadcast terminal, a PDA (Personal Digital Assistants), and a PMP (Portable Multimedia Player).

[0431] Here, the user account of the patient U may refer to an account of the patient U that is registered in advance in the exercise therapy providing system 1000 according to the present invention. Such a user account of the patient U may be understood as a "patient account" or a "patient ID (identification, identification number)." In the present invention, the terms "patient," "patient account (or patient user account)," and "patient terminal" may be used interchangeably.

[0432] Meanwhile, a doctor can prescribe exercise to patient U via doctor terminal 20. Doctor terminal 20 in the present invention may refer to an electronic device logged in with doctor D's user account. Doctor D's user account is an account of doctor D registered in advance in exercise treatment providing system 1000 according to the present invention, and can be understood as a "doctor account" or a "doctor ID (identification, identification number)." In the present invention, the terms "doctor," "doctor account (or doctor's user account)," and "doctor terminal" may be used interchangeably.

[0433] Doctor D can prescribe a medicine to patient U by referring to user DB 30 that contains user information of patient U.

[0434] The user DB30 may contain user information (or patient information) of patient U matched to each patient account. The user information of patient U may include various information necessary for providing exercise therapy. For example, the user information of patient U may include at least one of the following: disease information, age information, gender information, surgical history information, exercise plan information, exercise execution information, height information, and weight information of patient U. However, the above-mentioned user information of the patient is merely an example, and it goes without saying that the user information of the patient may include various information necessary for providing exercise therapy to the patient.

[0435] Meanwhile, the exercise therapy application 100 described in the present invention is installed on the patient terminal 10, and can analyze the exercise movements of a patient who has performed exercise according to a doctor D's prescription through an artificial intelligence posture estimation model and an artificial intelligence motion analysis model, and provide the results on the patient terminal 10.

[0436] In addition, the exercise therapy application 100 is configured to communicate with the artificial intelligence server 200, and can provide the patient's motion analysis results analyzed by the artificial intelligence server 200 on the patient terminal 10. The patient's motion analysis results analyzed by the artificial intelligence server 200 can be generated by at least one of the artificial intelligence motion analysis unit 212 and the rule-based motion analysis unit 213 included in the motion analysis unit 210.

[0437] The exercise therapy application 100 is configured to transmit and receive data to and from the artificial intelligence server 200 via wireless communication, and there is no limitation on the wireless communication method. The exercise therapy application 100 according to the present invention can communicate with the artificial intelligence server 200 using a communication module provided in the patient terminal 10. The communication module provided in the patient terminal 10 can be various.

[0438] For example, the communication module provided in the patient terminal 10 may be a WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance: registered trademark), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed ​​Downlink Packet Access), HSUPA (High Speed ​​Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), 5G (5th Generation Mobile Telecommunication), Bluetooth (Bluetooth TMThe device may be configured to communicate with the artificial intelligence server 200 using at least one of the following technologies: Wireless LAN Card (registered trademark), Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra-Wideband (UWB), ZigBee, Near Field Communication (NFC), Wi-Fi Direct, and Wireless Universal Serial Bus (Wireless USB).

[0439] Meanwhile, the artificial intelligence server 200 described in the present invention may be a cloud server that analyzes the exercise movements of the patient U from the exercise video. Such an artificial intelligence server 200 can analyze the exercise movements of the patient U using the exercise video received from the exercise treatment application 100. The "artificial intelligence server" described in the present invention may be named an "artificial intelligence exercise treatment server", an "artificial intelligence rehabilitation treatment server", a "digital treatment server", etc. Hereinafter, for convenience of explanation, it will be named as an "artificial intelligence server" and described.

[0440] Meanwhile, at least one of the exercise therapy application 100 and the artificial intelligence server 200 according to the present invention can analyze the relative positional relationship between key points P1 and P2 corresponding to a plurality of joint points of the patient U extracted from the exercise video 300 through a posture estimation model 52 (corresponding to the artificial intelligence posture estimation unit 121a of FIG. 11) trained using training data related to the joint points, as shown in FIG. 17. The relative positional analysis between the key points can be performed by the motion analysis unit 120, 210. In particular, the motion analysis can be performed by one of the artificial intelligence motion analysis unit 122, 212 and the rule-based motion analysis unit 123, 213 of the motion analysis unit. One of the artificial intelligence motion analysis unit 122, 212 or the rule-based motion analysis unit 123, 213 can be named an artificial intelligence motion analysis model.

[0441] Here, "joint points" may refer to multiple joints of patient U (or a part of patient U's body that includes joints).

[0442] Moreover, the term "key point" may refer to an area in the motion video 300 that corresponds to each of a plurality of joint points of the subject U.

[0443] Therefore, in the present invention, the terms "joint point" and "key point" may be used interchangeably, and the joint points and key points are described by using the same reference numerals "P1" and "P2", respectively.

[0444] The exercise therapy providing system 1000 can extract key points P1 and P2 corresponding to joint points from the patient's exercise video using the posture estimation model 52, and analyze the exercise motion of the patient U based on an analysis of the positional relationship between the extracted key points P1 and P2. In the present invention, a series of processes for analyzing the patient's exercise motion from the exercise video using the key points extracted through the artificial intelligence posture estimation model 52 can be named a "exercise motion analysis process."

[0445] Such an exercise motion analysis process can be performed by at least one of the exercise therapy application 100 and the artificial intelligence server 200. Specifically, the exercise motion analysis process may include at least one of i) a first data processing method performed by the exercise therapy application 100, ii) a second data processing method performed by the artificial intelligence server 200, and iii) a third data processing method performed by both the exercise therapy application 100 and the artificial intelligence server 200.

[0446] Here, in the third data processing method, the exercise therapy application 100 and the artificial intelligence server 200 may process data sequentially or simultaneously.

[0447] Therefore, in the present invention, the exercise motion analysis process will be described as being performed in the exercise treatment providing system 1000 without distinguishing between a physical space and a subject in which the exercise motion analysis process is performed.

[0448] 17, the motion analysis process may be performed using key points extracted from the AI ​​posture estimation model 52. The AI ​​posture estimation model 52 can identify or estimate the patient's joint points from the motion video through learning data specialized for the joint points, and extract the corresponding key points.

[0449] In the present invention, the learning data used by AI posture estimation model 52 to learn may be stored in database 40, and such database 40 may be named "learning data DB." The learning data will be described in detail later.

[0450] As shown in Fig. 17, posture estimation server 50 may include at least one of learning unit 51 and posture estimation model 52. Posture estimation server 50 may be provided inside exercise treatment providing system 1000 according to the present invention, or may be formed as an external server. That is, posture estimation server 50 according to the present invention performs the function of learning posture estimation, and can be understood as having no physical space constraints. The posture estimation server 50 will be described in detail later together with learning data.

[0451] Meanwhile, as shown in FIG. 11, a therapeutic exercise application 100 according to the present invention may include at least one of a video receiving unit 110, a motion analyzing unit 120, a video processing unit 130, and a control unit 140.

[0452] The video receiving unit 110 of the exercise therapy application 100 may be configured to receive exercise video including the state of the patient's exercise from the patient terminal 10 in which the application 100 is installed. Such exercise video can be captured by a camera provided in the patient terminal 10. In the present invention, "receiving exercise video from the patient terminal 10" may mean that the video receiving unit 110 of the exercise therapy application 100 accesses the exercise video recorded in the memory of the patient terminal 10.

[0453] The motion analysis unit 120 of the exercise therapy application 100 can analyze the patient's exercise motion (or exercise posture) based on the exercise video received from the patient terminal 10. For this purpose, the motion analysis unit 120 of the exercise therapy application 100 may be configured to include at least one of a key point extraction unit 121, an artificial intelligence motion analysis unit 122, and a rule-based motion analysis unit 123. The artificial intelligence motion analysis unit 122 or the rule-based motion analysis unit 123 may be named an "artificial intelligence motion analysis model."

[0454] The key point extraction unit 121 can extract key points P1 and P2, which are paired pieces of coordinate information on the x-axis and y-axis, from the motion video. In this case, the key point extraction unit 121 can extract key points from the video using an artificial intelligence model.

[0455] In the present invention, extraction of keypoints using an artificial intelligence model will be described as being performed by an artificial intelligence posture estimation unit 121 a included in the keypoint extraction unit 121 .

[0456] The AI ​​posture estimation unit 121a may be called an "AI posture estimation model" and may extract key points corresponding to the patient's joint points from the motion video using an AI model trained for object detection from the video. The AI ​​posture estimation model may be a model based on object detection. For example, the AI ​​posture estimation unit 121a may extract key points from the motion video using an object detection AI model that ensembles multiple bounding boxes. Meanwhile, the AI ​​posture estimation unit 121a may use various object detection AI models, and the above-mentioned object detection AI model is one example.

[0457] Furthermore, in the present invention, the artificial intelligence motion analysis unit 122 and the rule-based motion analysis unit 123 can analyze the patient's exercise motion (or exercise posture) using at least one of the exercise video received from the patient terminal and the key points extracted by the key point extraction unit 120.

[0458] More specifically, the artificial intelligence motion analysis unit 122 and the rule-based motion analysis unit 123 can i) analyze the patient's motion based on motion video, ii) analyze the patient's motion based on key points, or iii) analyze the patient's motion using both the motion video and key points.

[0459] For the sake of convenience, the following description will be focused on a method for analyzing a patient's motion based on key points. However, it goes without saying that the AI ​​motion analysis unit 122 and the rule-based motion analysis unit 123 can receive motion video as input data instead of key points, and can immediately analyze a patient's motion from the motion video. Meanwhile, the AI ​​motion analysis unit 122 or the rule-based motion analysis unit 123 can also be expressed as the above-mentioned "AI motion analysis model."

[0460] Meanwhile, the artificial intelligence action analysis unit 122 can classify (or identify) the type of exercise performed by the patient and judge the accuracy of the exercise based on an artificial intelligence model (or posture estimation model, see drawing reference number "52" in FIG. 17) learned to analyze the patient's exercise (or exercise posture) from key points.

[0461] In addition, the rule-based motion analysis unit 123 can classify the type of motion (or identify the type of motion) of the motion performed by the patient and determine the accuracy of the motion, based on rule information defined for analyzing the motion of the patient.

[0462] Here, the "rule information" refers to information that includes various rules used in analyzing exercise movements, and may include, for example, reference joint movable range information for each exercise movement (or type of exercise). Such rule information may be used interchangeably with terms such as "reference information" and "standard information."

[0463] Furthermore, the rule information may include various rule information for analyzing at least one of the movable distance of the joint, the movement speed (or acceleration) of the joint, the body balance, body equilibrium, and body alignment state (e.g., leg axis alignment state, spine alignment state, etc.) of the subject (corresponding to the patient) included in the analysis target motion video other than the movable range of the joint. The rule-based motion analysis unit 123 can derive various analysis results from the analysis target motion video of the patient based on such rule information.

[0464] In the present invention, the patient's exercise motion can be analyzed from the exercise video by at least one of the artificial intelligence motion analysis unit 122 and the rule-based motion analysis unit 123.

[0465] Specifically, in the present invention, i) the patient's movement movements are analyzed by the artificial intelligence motion analysis unit 122 ("first analysis execution method"), ii) the patient's movement movements are analyzed by the rule-based motion analysis unit 123 ("second analysis execution method"), or iii) the patient's movement movements may be analyzed by both the artificial intelligence motion analysis unit 122 and the rule-based motion analysis unit 123 ("third analysis execution method").

[0466] Here, in the third analysis execution method, the AI ​​action analysis unit 122 and the rule-based action analysis unit 123 may process data sequentially or simultaneously.

[0467] Meanwhile, the image processor 130 of the therapeutic exercise application 100 may be configured to overlap and render graphic objects corresponding to the extracted key points P1 and P2 on the subject U of the patient included in the exercise image 300. This allows the patient to intuitively recognize the joint points at which analysis is performed on his / her exercise motion.

[0468] The control unit 140 of the exercise therapy application 100 may be configured to perform overall control over the configuration included in the exercise therapy application 100. The control unit 140 of the exercise therapy application 100 can control the configuration of the exercise therapy application 100 using a CPU (Central Processing Unit) of the patient terminal 10, and can further control the configurations provided in the patient terminal 10 (e.g., a communication module, a camera module, a sensing module, an output module (e.g., a display, a speaker), and an input module (e.g., a touch screen, a microphone).

[0469] On the other hand, as shown in FIG. 11, the artificial intelligence server 200 is a cloud server configured to analyze a patient's exercise posture using an artificial intelligence posture estimation model, and may be configured to include at least one of a motion analysis unit 210 and a control unit 220.

[0470] The motion analysis unit 210 of the artificial intelligence server 200 can analyze the motion (or motion posture) of the patient based on the motion video received from the patient terminal 10.

[0471] The motion analysis unit 210 of the artificial intelligence server 200 can receive the patient's exercise video from the exercise therapy application 100, and the reception of the exercise video may be performed by a communication unit (or communication module) of the artificial intelligence server 200.

[0472] The motion analysis unit 210 of the artificial intelligence server 200 may be configured to include at least one of a key point extraction unit 211, an artificial intelligence motion analysis unit 212, and a rule-based motion analysis unit 213. The artificial intelligence motion analysis unit 212 or the rule-based motion analysis unit 213 may be named an "artificial intelligence motion analysis model."

[0473] The key point extraction unit 211, the AI ​​motion analysis unit 212, and the rule-based motion analysis unit 213 included in the AI ​​server 200 may perform the same functions as the key point extraction unit 121, the AI ​​motion analysis unit 122, and the rule-based motion analysis unit 123 of the above-mentioned exercise therapy application 100. Therefore, detailed description will be omitted.

[0474] The control unit 220 of the artificial intelligence server 200 may be configured to exercise overall control over the components included in the artificial intelligence server 200.

[0475] Hereinafter, an exercise and motion analysis process will be described that uses the above-described configuration of the exercise treatment providing system 1000 according to the present invention to analyze the exercise and motion of the patient U from an exercise video and provide an exercise and motion analysis result.

[0476] 12, the doctor terminal 20 may prescribe exercise to the patient U (S210). The exercise treatment providing system 1000 can receive prescription information for the exercise prescription from the doctor terminal 20 based on the exercise prescription for the patient being prescribed by the doctor terminal 20.

[0477] The exercise treatment provision system 1000 can assign an exercise plan including at least one prescribed exercise according to the prescription information to the patient account based on the prescription information being received from the doctor terminal 20. The assigned exercise plan can be transmitted to the patient terminal 10 (S220).

[0478] The exercise therapy providing system 1000 may include a communication unit that communicates with at least one of the patient terminal 10, the doctor terminal 20, the user DB 30, and the database 40. For example, the communication unit may be a wireless LAN (WLAN), wireless-fidelity (Wi-Fi), wireless fidelity (Wi-Fi) Direct, digital living network alliance (DLNA: registered trademark), wireless broadband (WiBro), world interoperability for microwave access (WiMAX), high speed downlink packet access (HSDPA), high speed uplink packet access (HSUPA), long term evolution (LTE), long term evolution-advanced (LTE-A), 5th generation mobile telecommunication (5G), Bluetooth (Bluetooth TM Communication can be performed using at least one of the following technologies: Wireless LAN (registered trademark), Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra-Wideband (UWB), ZigBee, Near Field Communication (NFC), Wi-Fi Direct, and Wireless Universal Serial Bus (Wireless USB).

[0479] Meanwhile, the patient terminal 10 can capture an exercise video of the patient performing the prescribed exercise included in the exercise plan (S230). The exercise treatment application 100 can activate the camera provided in the patient terminal 10 and control it to capture the exercise video.

[0480] The exercise video captured by the patient terminal 10 can be used by the exercise treatment providing system 1000 as analysis target data (or analysis target exercise video) for the patient's exercise motion analysis.

[0481] As described above, the exercise motion analysis process may be performed by at least a part of the exercise therapy application 100 and the artificial intelligence server 200 installed on the patient terminal 10, and in the present invention, the exercise motion analysis process is described as being performed in the exercise therapy provision system 1000, without separately distinguishing the physical space and subject in which the exercise motion analysis process is performed.

[0482] Meanwhile, the therapeutic exercise providing system 1000 can extract key points P1 and P2 corresponding to a plurality of joint points from the exercise video. The key points P1 and P2 can be extracted by at least a part of the key point extracting unit 121 included in the therapeutic exercise application 100 and the key point extracting unit 221 included in the artificial intelligence server 200.

[0483] The exercise treatment provision system 1000 can analyze the relative positional relationship between the extracted key points P1 and P2 (S250). The exercise treatment provision system 1000 can also analyze the exercise motion of the patient U based on the analysis of the positional relationship between the key points P1 and P2 (S260). Such exercise motion analysis may be performed by at least a part of the motion analysis unit 120 of the application 100 and the motion analysis unit 210 of the artificial intelligence server 200.

[0484] Furthermore, the exercise treatment providing system 1000 can provide the results of the exercise motion analysis of the patient U to the patient terminal 10 as feedback information and to the doctor terminal 20 as monitoring information (S270).

[0485] In this way, the exercise treatment provision system 1000 can perform overall control over the exercise motion analysis process, which in the present invention can be understood to be performed by the control unit of the exercise treatment provision system 1000. That is, the control unit of the exercise treatment provision system 1000 is a concept including the control unit 140 of the exercise treatment application 100 and the control unit 220 of the artificial intelligence server 200, and can perform overall control over the exercise treatment provision system 1000.

[0486] The exercise motion analysis process performed by the exercise treatment delivery system 1000 will now be described in more detail.

[0487] In the present invention, a process of receiving prescription information regarding exercise for a patient from a doctor terminal can be performed (S310, see FIG. 3).

[0488] 14a and 14b, the exercise treatment providing system 1000 can provide an exercise prescription page (or an exercise allocation page) including a prescription function for patient exercise on a doctor terminal 20 logged in with a doctor account. In the present invention, the term "exercise prescription" may be used interchangeably with "exercise allocation."

[0489] The exercise treatment providing system 1000 can provide an exercise prescription page for each patient account on the doctor terminal 20 so that a prescription can be made for a specific patient U account among the patient accounts matched with the doctor account.

[0490] For example, in the present invention, it is assumed that a first patient account (e.g., patient account of "Kim Woo-young") and a second patient account (e.g., patient account of "Kim So-hee") are matched with a specific doctor D account. The exercise treatment providing system 1000 can provide an exercise prescription page corresponding to the first patient account on the doctor terminal 20 based on receiving an exercise prescription request for the first patient account (e.g., patient account of "Kim Woo-young") from the doctor terminal 20.

[0491] The exercise therapy providing system 1000 can receive prescription information for a specific patient from the doctor terminal 20 based on a user's selection (or user's input) made on an exercise prescription page corresponding to the specific patient. The prescription information may include various information for prescribing exercise to the patient. For example, the prescription information may include at least one of the following: i) information on at least one exercise action to be included in the exercise plan (e.g., "Put your hands against the wall to stretch your calves," "Sit down and roll a ball to massage the soles of your feet"), ii) difficulty information of the exercise action, iii) exercise action duration information, iv) exercise action execution count information, v) exercise action execution schedule information, vi) physical information matched to the exercise action (e.g., "ankle," "knee"), and vii) caution information (e.g., "Please apply ice after exercise") (see (a) of FIG. 14a and FIG. 14b).

[0492] The exercise therapy providing system 1000 can receive prescription information for a specific patient U from the doctor terminal 20 based on the input (or selection) of prescription information for the specific patient on an exercise prescription page corresponding to the specific patient. In this case, guidance information informing that a prescription has been made for the specific patient may be output on the doctor terminal 20 (see (b) of FIG. 14).

[0493] Meanwhile, in the present invention, a process of assigning an exercise plan including at least one prescribed exercise to a patient account based on the prescription information can be performed (S320, see FIG. 13).

[0494] As shown in (a) of FIG. 15, the exercise therapy delivery system 1000 can assign an exercise plan E including at least one prescribed exercise to a specific patient account based on prescription information for a specific patient U, and provide the assigned exercise plan (e.g., “digital therapeutic agent for patellofemoral arthritis”) E on a patient terminal 10 logged in with the specific patient account.

[0495] Here, the "prescribed exercise" can be understood as an exercise action that is identified based on prescription information and assigned to a patient account from among multiple exercise actions (or exercise types) included in the exercise treatment provision system 1000. Therefore, in the present invention, the "prescribed exercise" may be used interchangeably with the "exercise action." Also, in the present invention, the "exercise plan" may be used interchangeably with the "digital therapeutic agent."

[0496] The exercise treatment provision system 1000 can provide an exercise page linked to an exercise guide video provision function on the patient terminal 10 based on a request for an exercise plan assigned to a specific patient account from the patient terminal 10 logged in with a specific patient account, so that the patient can perform the prescribed exercises included in the exercise plan.

[0497] As shown in (b) of FIG. 15, the exercise page may include an exercise list L, which may include items V1 to V6 corresponding to exercise guide images for each of a number of prescribed exercises (e.g., “straight leg raise,” “standing and bending the knees”) included in an exercise plan assigned to a particular account.

[0498] When the exercise plan includes a specific prescribed exercise (e.g., "straight leg raise") with multiple exercise sets, the exercise therapy provision system 1000 can control the exercise list L to include items V1 to V3 corresponding to the exercise guide image of the specific prescribed exercise for the number of sets (e.g., "3").

[0499] On the other hand, the exercise treatment provision system 1000 can control, upon receiving a request to start exercise from the patient terminal 10, to play multiple exercise guide videos in sequence on the patient terminal 10 based on the order of items V1 to V6 included in the exercise list L.

[0500] Meanwhile, in the present invention, a process of receiving an exercise video of an exercise corresponding to a prescribed exercise from a patient terminal (S330, see FIG. 13) can be performed.

[0501] As shown in FIG. 16, the exercise treatment providing system 1000 can control the camera provided in the patient terminal 10 to capture an exercise video of the patient U based on the exercise guide video being played on the patient terminal 10.

[0502] The exercise therapy application 100 installed on the patient terminal 10 can control the activation state of the camera on the patient terminal 10 from an inactive state to an active state, thereby controlling the camera to capture exercise video of patient U performing exercise movements in accordance with the exercise guide video.

[0503] As shown in (a) of Figure 16, in order to detect a subject U corresponding to the patient from the exercise video captured by the camera, the exercise therapy application 100 may output a guidance message (e.g., "Please stand within the screen") on the patient terminal 10 so that the patient's entire body is entirely contained within a specific area of ​​the exercise video (or the display of the patient terminal).

[0504] The exercise therapy application 100 can detect the object U from the video 300 using an object detection algorithm, based on the fact that the object U corresponding to the patient's entire body is included within a specific region.

[0505] The exercise therapy application 100 can use various object detection algorithms. For example, the exercise therapy application 100 can use an algorithm (Weighted Box Fusion, WBF) that ensembles multiple bounding boxes. However, it goes without saying that the exercise therapy application 100 is not limited to the above-mentioned object detection algorithm, and can use various object detection algorithms that can detect an object corresponding to the subject U from the training target exercise video 300.

[0506] Furthermore, the exercise therapy application 100 can use a camera to capture an exercise video of the patient performing an exercise movement according to a prescribed exercise, based on detection of a subject U corresponding to the patient's entire body within a specific area.

[0507] In this case, the exercise therapy application 100 can capture an image of the patient performing the prescribed exercise while playing back an exercise guide video corresponding to the prescribed exercise assigned to the patient.

[0508] In addition, the exercise therapy application 100 can match the exercise video captured by the camera of the patient terminal 10 to the exercise plan (or each of multiple prescribed exercises included in the exercise plan) and control it to be recorded in the memory of the patient terminal 10.

[0509] Meanwhile, in the present invention, a process of extracting key points corresponding to a plurality of pre-set joint points from a motion image can be performed (S340, see FIG. 13).

[0510] In the present invention, key points P1 and P2 corresponding to preset joint points P1 and P2 can be extracted from an exercise video by at least a part of the exercise therapy application 100 and the artificial intelligence server 200. As described above, the extraction of key points P1 and P2 may be performed i) by the exercise therapy application 100, ii) by the artificial intelligence server 200, or iii) by both the exercise therapy application 100 and the artificial intelligence server 200. In the following description, the extraction of key points P1 and P2 will be described as being performed by the exercise therapy providing system 1000 without distinguishing between different entities.

[0511] The exercise therapy providing system 1000 can extract, from the exercise video 300, areas corresponding to predefined (or pre-set) joint points among a plurality of joint points of the patient, as key points P1 and P2.

[0512] Here, "joint points" may refer to multiple joints of patient U (or a part of patient U's body that includes joints).

[0513] Moreover, the term "key point" may refer to an area in the motion video 300 that corresponds to each of a plurality of joint points of the subject U.

[0514] In the present invention, the terms "joint point" and "key point" may be used interchangeably, and the joint points and key points are described by using the same reference numerals "P1" and "P2", respectively.

[0515] On the other hand, the human body is made up of about 200 bones, and a joint is a part where bones are connected, and the human body is made up of multiple joints.

[0516] In the present invention, among a plurality of joint points constituting a human body, joint points to be key points may be specified in advance and may exist as joint point definition information 500. For example, in the joint point definition information 500, a first joint point P1 corresponding to a head center 510 and a second joint point P2 corresponding to a neck center 520 may be defined and exist in advance (see FIG. 18d).

[0517] The exercise therapy provision system 1000 can extract key points P1 and P2 corresponding to joint points from the exercise video 300 based on a posture estimation model 52 trained using a training data set (Set) including position information of predetermined joint points.

[0518] In this case, the exercise therapy provision system 1000 can identify the positions of key points P1 and P2 in the exercise video 300 based on the fact that the position information of each of the joint points previously set by the posture estimation model is extracted in the form of pairs of coordinate information on the x-axis and y-axis.

[0519] Meanwhile, the exercise therapy provision system 1000 can extract (or identify) key points P1, P2 corresponding to the joint points by either a first key point extraction process or a second key point extraction process based on whether the joint points are visible in the exercise video 300.

[0520] In the present invention, whether or not a joint point is visible can be understood to mean whether or not the joint point is visible in the motion image 300 .

[0521] The exercise therapy providing system 1000 can determine that a joint point is visible in the exercise video 300 if the exercise video 300 includes a part of the subject U's body that corresponds to the joint point.

[0522] When a specific joint point is visible in the exercise video 300, the exercise therapy providing system 1000 can extract a key point corresponding to the specific joint point through a first key point extraction process.

[0523] Specifically, the exercise treatment providing system 1000 can identify visible joint points of the subject U that are visible in the exercise image 300 from among a plurality of preset joint points. For example, when a first joint point and a second joint point are visible in the exercise image from among a plurality of preset joint points, the exercise treatment providing system 1000 can identify the first joint point and the second joint point as the visible joint points.

[0524] Additionally, the exercise therapy delivery system 1000 can extract the identified visible joint points as key points.

[0525] In this case, the exercise therapy providing system 1000 may extract position information of an area (or pixel) corresponding to a visible joint point from within an exercise image, and extract key points corresponding to the visible joint point. For example, the exercise therapy providing system 1000 may extract position information of a visible joint point using an object detection algorithm, and extract key points corresponding to the visible joint point.

[0526] In the present invention, the position information of the visible joint points extracted by the first keypoint extraction process may be named and described as "first type information (first type position information)" or "actual position information".

[0527] On the other hand, if the exercise therapy providing system 1000 does not include a part of the subject U's body that corresponds to the joint point, the exercise video 300 can determine that the joint point is not visible in the exercise video 300.

[0528] When a specific joint point is not visible in the exercise video 300, the exercise therapy providing system 1000 can predict and extract a key point corresponding to the specific joint point using the posture estimation model 52 through a second key point extraction process.

[0529] The exercise therapy providing system 1000 can predict position information of invisible joint points of the subject U that are not visible in the exercise image 300, among a plurality of preset joint points, based on the posture estimation model 52. In this case, the posture estimation model 52 can predict position information of the invisible joint points based on position information of the visible joint points.

[0530] In the present invention, the position information of the articulation points extracted by the second keypoint identification process may be named and described as "second type information (second type position information)" or "predicted position information".

[0531] The exercise therapy delivery system 1000 can extract (or identify) key points corresponding to invisible joint points by matching predicted position information of invisible joint points with key points corresponding to invisible joint points.

[0532] In this way, in the present invention, key points P1 and P2 corresponding to the joint points can be extracted (or identified) through different processes based on a posture estimation model that has been trained on position information of the joint points depending on whether the predefined joint points are visible in the motion video 300. Therefore, in the present invention, analysis of invisible joint points that cannot be seen in the motion video is also possible.

[0533] Meanwhile, the exercise treatment providing system 1000 can extract key points P1 and P2 from the exercise video in real time in conjunction with the exercise video being captured by the patient terminal 10. Also, the exercise treatment providing system 1000 can provide the extracted key points P1 and P2 on the patient terminal 10 in real time so that the patient can intuitively recognize the joint points at which the exercise motion is analyzed.

[0534] 16(b) and (c), the exercise therapy providing system 1000 can output the exercise video 300 on the patient terminal 10 in real time in conjunction with the exercise video 300 being captured by the patient terminal 10. In addition, the exercise therapy providing system 1000 can provide graphic objects corresponding to the extracted key points P1 and P2 by overlapping them in a region of the subject U corresponding to a preset joint point.

[0535] The data processing for providing the key point graphic object by overlaying it on the exercise image 300 may be performed by the image processor 130 of the exercise therapy application 100. The image processor 130 may render each of the graphic objects corresponding to the extracted key points P1 and P2 on the area of ​​the subject U corresponding to the joint points P1 and P2 matched to the key points P1 and P2.

[0536] Furthermore, when the position of a preset joint point is changed as the patient performs an exercise, the image processor 130 may provide a key point graphic object by overlaying it on an area of ​​the subject U corresponding to the changed joint point. That is, the image processor 130 may overlay a key point graphic object on an area corresponding to the joint point in the exercise image so that the position of the joint point, which is changed in real time, is reflected.

[0537] Meanwhile, in the present invention, a process of analyzing a relative positional relationship between key points from key points extracted through a posture estimation model trained using training data related to joint points, and analyzing a patient's exercise motion for a prescribed exercise based on the analysis of the positional relationship can be performed (S350, see FIG. 13). The analysis of the relative positions between key points can be performed by the motion analysis units 120 and 210. In particular, the analysis of the exercise motion can be performed by one of the artificial intelligence motion analysis units 122 and 212 and the rule-based motion analysis units 123 and 213 of the motion analysis unit.

[0538] 16(d), the exercise therapy application 100 can provide guidance information (e.g., "Calculating result value" or "Providing Kim Cheol-soo's exercise motion analysis result") on the patient terminal 10 to guide the patient through the exercise motion analysis. The patient exercise motion analysis method will be described in detail below.

[0539] The exercise therapy provision system 1000 can analyze the relative positional relationship between key points P1 and P2 corresponding to each of a plurality of pre-set joint points using key points extracted from a posture estimation model trained using training data.

[0540] The exercise therapy delivery system 1000 can analyze the relative positions between key points P1 and P2 corresponding to each of a plurality of pre-set joint points using both key points corresponding to visible joint points and key points corresponding to invisible joint points.

[0541] Here, the "relative position between keypoints" can be understood as the position of a particular keypoint (e.g., a first keypoint, "P1") relative to another keypoint (e.g., a second keypoint, "P2") between at least two keypoints P1 and P2.

[0542] In the following, for convenience of explanation, key points corresponding to visible joint points are named "first type key points" and key points corresponding to invisible joint points are named "second type key points".

[0543] The exercise therapy delivery system 1000 can perform analysis on at least one of i) the relative positional relationship between a plurality of first type key points, ii) the relative positional relationship between the first type key points and the second type key points, and iii) the relative positional relationship between a plurality of second type key points.

[0544] In this case, the exercise therapy providing system 1000 can analyze the relative positional relationship between some of the associated key points among the plurality of joint points based on the type of prescribed exercise performed by the patient.

[0545] For example, when a patient performs a prescribed exercise according to a first type of exercise, the exercise therapy providing system 1000 can analyze the relative positional relationship between key points corresponding to a first joint point and a second joint point among a plurality of joint points.

[0546] As another example, the exercise therapy providing system 1000 may analyze the relative positional relationship between key points corresponding to the first joint point and the third joint point among the multiple joint points when the patient performs a prescribed exercise according to a second type of exercise different from the first type of exercise. Such relative positional relationship may be used for motion analysis as a result.

[0547] The results of the motion analysis performed by the exercise treatment providing system 1000 according to the present invention may be very diverse. For example, the exercise treatment providing system 1000 may analyze at least one of the range of motion of a joint, the distance of motion, the speed (or acceleration) of joint movement, the body balance, body equilibrium, and body alignment (e.g., leg axis alignment, spine alignment, etc.) of a subject (corresponding to a patient) included in the exercise image to be analyzed from the extracted key points or image. Meanwhile, the exercise treatment providing system 1000 according to the present invention may analyze the relative positional relationship between key points based on rule information regarding prescribed exercise.

[0548] Here, the rule information can be understood as information in which rules are predefined for analyzing the relative positional relationship between key points.

[0549] The exercise therapy providing system 1000 can analyze the patient's exercise motion by determining whether the relative positional relationship between key points satisfies the rule information. In the following, as an example, a method for analyzing a joint range of motion based on the relative positional relationship of key points and the rule information will be described. However, the content described below is merely one embodiment for analyzing the patient's motion based on the relative positional relationship of key points and the rule information, and in the present invention, various patient motions can be analyzed based on the relative positional relationship of key points and the rule information.

[0550] The joint range of motion analyzed by the exercise treatment provision system 1000 according to the present invention will be considered in more detail. The exercise treatment provision system 1000 can analyze the patient's joint range of motion according to the relative positional relationship between key points based on rule information for the reference joint range of motion related to the prescribed exercise.

[0551] Furthermore, the exercise treatment provision system 1000 can analyze the relative positional relationship between the key points based on rule information regarding the prescribed exercise. Also, the exercise treatment provision system 1000 can analyze the exercise motion of the patient by determining whether the relative positional relationship between the key points satisfies the rule information.

[0552] The exercise therapy delivery system 1000 can extract relative positional relationships between associated key points matched to a specific prescribed motion from multiple consecutive frames related to the specific prescribed motion, and obtain (or calculate) the patient's joint range of motion for the specific prescribed motion using the extracted relative positional relationships.

[0553] Specifically, it is assumed that a motion video consists of a plurality of frames having a first type corresponding to a first prescribed motion, and a plurality of frames having a second type corresponding to a second prescribed motion.

[0554] When a patient's exercise motion analysis is performed for the first prescribed exercise among a first prescribed exercise and a second prescribed exercise, the exercise treatment provision system 1000 can analyze the patient's exercise motion using key points extracted from a plurality of frames having a first type.

[0555] Meanwhile, when a patient's exercise motion analysis is performed for a second prescribed exercise, the exercise therapy delivery system 1000 can analyze the patient's exercise motion using key points extracted from a plurality of frames having a second type.

[0556] That is, the exercise therapy delivery system 1000 can analyze key point positional relationships for a series of movements (or postures) and obtain (or calculate) the patient's range of motion for a specific prescribed exercise.

[0557] In the following, for ease of explanation, consecutive frames (i.e., frames having a first type) corresponding to a particular prescribed motion (e.g., a first prescribed motion) will be named the "first analyzed frame" and the "second analyzed frame" depending on the time before and after the frames are formed.

[0558] Here, the first analyzed frame can be understood as a frame formed before in time, and the second analyzed frame can be understood as a frame formed after in time.

[0559] The exercise treatment delivery system 1000 can extract key points in each of the first and second frames under analysis.

[0560] A first analysis target keypoint group corresponding to each of the plurality of joint points can be extracted from the first analysis target frame, and a second analysis target keypoint group corresponding to each of the plurality of joint points can be extracted from the second analysis target frame.

[0561] The exercise treatment provision system 1000 can analyze a "first positional relationship" between key points included in a first analysis target key point group to perform a first motion analysis of the subject U included in a first analysis target frame. Also, the exercise treatment provision system 1000 can analyze a "second positional relationship" between key points included in a second analysis target key point group to perform a second motion analysis of the subject U included in a second analysis target frame.

[0562] The exercise therapy delivery system 1000 can obtain (extract or calculate) the patient's joint range of motion for a particular prescribed exercise based on the first key point positional relationship and the second key point positional relationship.

[0563] In this case, the exercise therapy provision system 1000 can obtain the patient's joint range of motion by referring to the user DB 30 and taking into consideration at least one of the patient's age information, gender information, height information, weight information, surgical history information, and musculoskeletal disease information.

[0564] Meanwhile, the exercise therapy providing system 1000 can determine whether the acquired range of motion of the patient satisfies the reference joint range of motion corresponding to the rule information regarding a specific prescribed exercise. In the present invention, the rule-based analysis of the range of motion of the patient may be performed by the rule-based motion analysis unit 213 of the artificial intelligence server 200 (see FIG. 11 ), but is not limited to the analysis by the rule-based motion analysis unit 213.

[0565] In the present invention, rule information for the reference joint movable range may exist for each of a plurality of exercise types. Such rule information may include information on the reference joint movable range that differs from one another for each age, sex, height, weight, and musculoskeletal disease.

[0566] The exercise therapy provision system 1000 can compare the patient's joint range of motion for a specific prescribed exercise with a standard joint range of motion for the specific prescribed exercise included in the rule information to determine whether the patient's joint range of motion satisfies the standard joint range of motion.

[0567] Based on the judgment result, the exercise treatment provision system 1000 can provide the analysis result of the patient's exercise motion on the patient terminal 10 as feedback for the prescribed exercise.

[0568] Meanwhile, in the present invention, a process of transmitting the analysis result of the patient's exercise motion to the patient terminal can be performed (S360, see FIG. 13).

[0569] The exercise therapy delivery system 1000 can provide the motion analysis results in various ways so that the patient can intuitively recognize the analysis results of the exercise motion and increase the patient's compliance with the exercise.

[0570] The exercise treatment providing system 1000 can provide graphic objects corresponding to key points P1 and P2 by superimposing them on the exercise video 300 in real time while the exercise video 300 is being captured by the patient terminal 10 (see FIG. 16).

[0571] In this case, the exercise therapy delivery system 1000 can arrange the patient's joint range of motion information around key points P1 and P2 related to the joint range of motion.

[0572] Furthermore, the exercise therapy provision system 1000 can provide key point graphic objects (or graphic objects corresponding to the positional relationship between key points) having different visual appearances superimposed on the exercise image so that the patient can recognize whether the patient's joint range of motion meets the reference joint range of motion.

[0573] Furthermore, visual appearances of graphic objects superimposed on a motion image may be configured to differ from one another depending on whether the relative positional relationship between the extracted key points satisfies the rule information.

[0574] For example, if the patient's joint range of motion satisfies the reference joint range of motion, a graphic object A having a first visual appearance may be superimposed on the exercise video 300. On the other hand, if the patient's joint range of motion does not satisfy the reference joint range of motion, a graphic object B having a second visual appearance different from the first visual appearance may be superimposed on the exercise video 300.

[0575] Furthermore, the exercise treatment provision system 1000 can provide the patient's evaluation score for the prescribed exercise as a motion analysis result (e.g., "Kim Woo-young's squat posture is 70 points") based on key points extracted from each of the multiple frames that make up the exercise video 300.

[0576] Meanwhile, in the present invention, the exercise therapy application 100 and the artificial intelligence server 200 installed in the patient terminal 10 can each analyze the exercise motion and generate the exercise motion analysis result.

[0577] For example, the exercise therapy application 100 may overlay graphic objects corresponding to the key points P1 and P2 on the exercise video in real time to generate the first analysis result.

[0578] As another example, the artificial intelligence server 200 consisting of a cloud server can generate a patient's evaluation score for the prescribed exercise as a second analysis result based on key points extracted from each of a plurality of frames constituting the exercise video.

[0579] The exercise treatment provision system 1000 can provide analysis results of the patient's exercise motion, including a first analysis result generated by the exercise treatment application 100 and a second analysis result generated by the artificial intelligence server 200, on the patient terminal 10.

[0580] Meanwhile, the exercise treatment providing system 1000 can transmit the patient's exercise motion analysis result to the doctor terminal 20. The doctor terminal 20 may be provided with both the first analysis result and the second analysis result.

[0581] In this way, the present invention provides various user environments related to the provision of analysis results so that the patient can intuitively recognize the analysis results for the exercise movement. Other embodiments related to the provision of analysis results will be described later.

[0582] 17, the present invention is directed to analyzing the exercise motion of a patient U included in an exercise video 300 based on the exercise video 300 received from a patient terminal 10, and providing the analysis result. In particular, the present invention relates to a method of processing a learning data set centered on important joint points and learning the data set in order to analyze the exercise motion of a patient based on artificial intelligence.

[0583] The learning data used to learn the posture estimation model of the present invention will be specifically described below.

[0584] 17, the database 40 is a storage in which a learning data set is stored, and may be provided in the exercise therapy providing system 1000 according to the present invention itself, or may be formed as an external storage (or an external DB). The database 40 according to the present invention can be understood to be sufficient as long as it is a space in which a learning data set is stored, and there is no restriction on the physical space.

[0585] The system may be configured to include at least one of a database 40, a posture estimation server 50, and an exercise therapy providing system 1000.

[0586] In database 40, training data for training pose estimation model 52 may be stored and present as a training data set.

[0587] 18b, the learning data set 400 in the present invention may be composed of a plurality of data groups 410-450 corresponding to different information attributes 410a-450a, respectively. The information included in each of the plurality of data groups 410-450 may be extracted and composed from an exercise video 300 including a subject U performing an exercise motion.

[0588] Here, the "exercise video 300" refers to a video (image or video) that captures (includes) the process of a user performing an exercise motion, as shown in FIG. 18a, and may include at least a part of the user U's body.

[0589] In the present invention, a user object included in the exercise video 300 is named and described as "subject U." In the present invention, "subject U" may refer to a user exercising in the exercise video or a part of the user's body. Therefore, in the present invention, "subject" and "user" may be used interchangeably, and are described with the same reference numeral "U."

[0590] Meanwhile, the 'exercise video 300' described in the present invention may include an 'analysis target exercise video' and a 'learning target exercise video'.

[0591] The "analysis target motion video" can be understood as a motion video that is a target of posture estimation analysis of the subject U, and the "learning target motion video" can be understood as a motion video that is a target of machine learning for a posture estimation model. Here, the posture estimation analysis can mean extracting key points from the video.

[0592] The learning unit 51 may be configured to perform learning for a posture estimation model based on the training object exercise video 300. The learning unit 51 can train the posture estimation model using the training data.

[0593] As shown in (a) of FIG. 18b, the learning unit 51 can detect a subject U from the training object motion video 300 and extract various learning data used for motion posture estimation from the detected subject U. Such learning data may be used interchangeably with "information", "data", "data value", or "data value". Meanwhile, extraction of learning data may be performed by other means instead of the learning unit 51.

[0594] The learning unit 51 can use various algorithms for object detection to detect the subject U from the training object motion video 300. For example, the learning unit 51 can use an algorithm (Weighted Box Fusion, WBF) that ensembles a plurality of bounding boxes. However, it goes without saying that the learning unit 51 is not limited to the above-mentioned object detection algorithm, and can use various object detection algorithms that can detect an object corresponding to the subject U from the training object motion video 300.

[0595] The learning unit 51 can classify the extracted learning data into any one of a plurality of data groups 410 to 450 corresponding to each of a plurality of different information attributes 410a to 450a.

[0596] The multiple information attributes 410a-450a different from each other described in the present invention may be predefined and exist as shown in (b) of Fig. 18b. Also, the multiple data groups 410-450 corresponding to the multiple information attributes 410a-450a, respectively, may include learning data corresponding to the predefined information attributes.

[0597] For example, i) the data group 410 corresponding to the first information attribute 410a may include joint point position information of the subject U, ii) the data group 420 corresponding to the second information attribute 420a may include information indicating whether the joint points of the subject U are visible or not, iii) the data group 430 corresponding to the third information attribute 430a may include information regarding the shooting direction of the subject U, iv) the data group 440 corresponding to the fourth information attribute 440a may include exercise code information for classifying the exercise action (or exercise type) performed by the subject U, and v) the data group 450 corresponding to the fifth information attribute 450a may include the size and center position information of a bounding box for the subject U.

[0598] Here, the "joint points P1, P2" may refer to the user's joints or a region corresponding to the subject U's joints in the motion image 300.

[0599] The learning unit 51 can mutually link a plurality of data groups 410-450 extracted from the training object exercise video 300, and generate (construct) a data set for the training object exercise video 300. Furthermore, the learning unit 51 can store the generated training data set 400 in the database 40. The database 40 can be constructed as the database 40 for the posture estimation model 52 based on the storage of the training data set 400 generated by the learning unit 51.

[0600] Furthermore, the learning unit 51 can perform learning for the posture estimation model 52 based on a learning dataset 400 present in the database 40. As described above, the learning dataset 400 may include position information of joint points.

[0601] The posture estimation model 52 is a posture estimation model trained using a training data set including position information of joint points, and is capable of estimating the motion posture of the subject U from the analysis target motion video.

[0602] Meanwhile, the posture estimation model 52 uses the learning dataset 400 generated by the learning unit 51 to extract key points corresponding to the joint points of the subject from the motion video 300, and at least one of the artificial intelligence motion analysis units 122, 212 and the rule-based motion analysis units 123, 213 can analyze the motion of the subject in the motion video 300 using the extracted key points.

[0603] There may be various motion postures of the subject U that can be estimated from the analysis target motion video 300 using the key points estimated from the posture estimation model 52. For example, at least one of the artificial intelligence motion analysis units 122, 212 and the rule-based motion analysis units 123, 213 may estimate and analyze information regarding at least one of i) positions of joint points, ii) joint movable ranges of the joint points, iii) movement paths of the joint points, iv) connection relationships between the joint points, and v) symmetrical relationships of the joint points for the subject U.

[0604] In addition, the artificial intelligence motion analysis unit 122, 212 can analyze at least one of the following from the key points extracted from the movement video 300 to be analyzed or from the video 300: the moving distance of the joint, the movement speed (or acceleration) of the joint, the body balance, body equilibrium, and body alignment (e.g., leg alignment, spinal alignment, etc.) of the subject (corresponding to the patient) contained in the movement video to be analyzed.

[0605] In the present invention, posture estimation model 52 may be configured to include learning unit 51. Conversely, learning unit 51 may include posture estimation model 52. In this case, the posture estimation function can be achieved by having posture estimation model 52 learn by learning unit 51. Therefore, in the present invention, the functions performed by posture estimation model 52 can be explained by mixing them with those performed by learning unit 51.

[0606] On the other hand, the user terminals 10, 20 may be configured to provide a posture analysis result service that provides the user terminals 10, 20 with a user's movement motion analysis result (or a movement motion analysis report) that is analyzed based on key points extracted and estimated from the posture estimation model 52 (see FIG. 11).

[0607] Here, the user terminals 10, 20 may be at least one of a patient terminal 10, a doctor terminal 20, and a third party terminal.

[0608] Such an exercise treatment provision system 1000 may be configured to communicate with the user terminals 10 and 20. In the present invention, the communication of the exercise treatment provision system 1000 can also be understood as being performed by a communication unit of the exercise treatment provision system 1000.

[0609] For example, the communication unit of the exercise therapy providing system 1000 may be a WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance: registered trademark), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed ​​Downlink Packet Access), HSUPA (High Speed ​​Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), 5G (5th Generation Mobile Telecommunication), Bluetooth (Bluetooth TM The device may be configured to communicate with the user terminals 10, 20 using at least one of the following technologies: Wireless LAN Card (registered trademark), Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra-Wideband (UWB), ZigBee, Near Field Communication (NFC), Wi-Fi Direct, and Wireless Universal Serial Bus (Wireless USB).

[0610] Meanwhile, the user terminals 10 and 20 described in the present invention refer to electronic devices and may include at least one of a smart phone, a mobile phone, a tablet PC, a kiosk, a computer, a laptop, a digital broadcasting terminal, a PDA (Personal Digital Assistants), and a PMP (Portable Multimedia Player). Furthermore, the user terminals 10 and 20 may be electronic devices to which a user account is logged in, connected, or registered.

[0611] Here, the user account may refer to an account registered in advance in the exercise treatment providing system 1000 according to the present invention. Such a user account may be understood as a user ID (identification, identification number).

[0612] Meanwhile, in the present invention, a process of receiving an exercise video from the user terminal 10, 20 can be performed. The exercise treatment providing system 1000 can receive an exercise video 300 in which a user performs an exercise motion by communicating with the user terminal 10, 20.

[0613] In this case, the exercise video 300 that the exercise treatment providing system 1000 receives from the user terminals 10 and 20 can be understood as an analysis target exercise video that is to be the subject of analysis of the exercise motion of the user.

[0614] The exercise treatment providing system 1000 can receive exercise images to be analyzed from the user terminals 10 and 20 according to various times and routes.

[0615] 11, the exercise treatment providing system 1000 can control the camera state to an activated state so that the camera 201 provided in the user terminal 10, 20 captures an exercise video to be analyzed based on the selection of a graphic object corresponding to "start exercise" from the user terminal 10, 20. In addition, the exercise treatment providing system 1000 can receive the exercise video to be analyzed captured by the camera 201 from the user terminal 10, 20 in real time or based on the completion of the user's exercise.

[0616] Next, in the present invention, a process of analyzing a movement motion related to a specific movement motion of a user included in a movement video can be performed based on key points extracted from a posture estimation model trained using a training data set including position information of joint points.

[0617] When the learning unit 51 receives an analysis target exercise video from the user terminal 10, 20, it can extract key points corresponding to joint points of the user U included in the analysis target exercise video based on the posture estimation model 52 learned using the learning target exercise video 300. In addition, at least one of the artificial intelligence action analysis units 122, 212 and the rule-based action analysis units 123, 213 can analyze the exercise action of the subject in the exercise video 300 using the extracted key points.

[0618] The posture estimation information of the user U estimated by the learning unit 51 may include various information. For example, the learning unit 51 can estimate ii) position information of the joint points P1 and P2 of the subject U, and ii) joint movable range information (angle information) of the subject U.

[0619] Next, in the present invention, based on the completion of the analysis, a process of providing the user U's exercise motion analysis result related to a specific exercise motion to the user terminal 10, 20 can be performed.

[0620] The exercise treatment providing system 1000 can process the analysis result of the user's exercise motion to generate an exercise motion analysis report. Also, the exercise treatment providing system 1000 can provide the exercise motion analysis report on the user terminal 10, 20.

[0621] 11, for example, the exercise treatment providing system 1000 can provide a user's exercise video by rendering joint point graphic objects corresponding to the joint points P1 and P2 of the user U at positions corresponding to the joint points P1 and P2 of the user U. In addition, the exercise treatment providing system 1000 can display joint movable range information 221 of a specific joint point P1 around the specific joint point P1.

[0622] In this way, the exercise therapy providing system 1000 according to the present invention can perform learning for the posture estimation model 52 using the database 40 constructed based on the training object exercise video 300. In addition, the posture estimation model 52 can be used to estimate the user's exercise posture and provide an exercise motion analysis result providing service based on the estimated posture.

[0623] The information included in the analysis result may be various. For example, the analysis result may include analysis information on at least one of the range of motion of a joint, the distance of motion, the speed (or acceleration) of joint movement, the body balance, the body equilibrium, and the body alignment state (e.g., leg axis alignment state, spine alignment state, etc.) of a subject (corresponding to a patient) included in the motion image to be analyzed, analyzed from the extracted key points or image.

[0624] Furthermore, such analytical information may further include a score, which may be an analysis score of the user's athletic movement (or posture). Such an analysis score may be calculated based on various methods (e.g., rule-based analysis based on pre-set criteria or analysis by an artificial intelligence algorithm).

[0625] In the database 40, a learning data set 400 extracted and generated from the training object exercise video 300 by the learning unit 51 may be stored.

[0626] The learning data set 400 used for estimating a user's exercise posture will be described in more detail below.

[0627] As shown in FIG. 18 a, the training data set 400 may be configured to include data related to a subject U extracted from the training object exercise video 300.

[0628] The learning unit 51 can extract data for the subject U from the training object exercise video 300 to configure a training data set 400.

[0629] Such a learning data set 400 may be made up of a plurality of sub-data sets 401 to 403. In the present invention, the learning data set 400 can be understood as a higher-level concept, and the sub-data sets 401 to 403 can be understood as data sets corresponding to lower-level concepts.

[0630] The learning unit 51 may extract data for the subject U from each of the reference frames 301-306 selected based on a preset criterion from among the multiple frames constituting the training object exercise video 300, and construct sub-data sets 401-403.

[0631] The learning unit 51 can select the reference frames 301 to 306 based on various criteria. The training exercise video 300 may be a moving image or a plurality of still images.

[0632] When the training object exercise video 300 is a video, the learning unit 51 may select reference frames 301-306 from among a plurality of frames constituting the training object exercise video 300 based on a certain time interval T. As another example, when the amount of change in the motion of the subject included in the previous and next frames corresponds to a certain amount of change or more, the learning unit 51 may select the previous and next frames as the reference frames 301-306.

[0633] The learning data included in the learning dataset according to the present invention may be composed of learning data extracted from each of reference frames selected based on a preset criterion from among a plurality of frames constituting a training target motion video, with a focus on the subject included in the training target motion video.

[0634] For convenience of explanation, the following explanation will be based on the "learning dataset 400" without distinguishing between the "learning dataset 400" and the "sub-datasets 401-403". The information included in the learning dataset 400 described below may be the information included in the sub-datasets 401-403. In this case, the learning dataset 400 according to the present invention can be understood as including a plurality of sub-datasets 401-403 including the information described below.

[0635] On the other hand, as shown in FIG. 18b(a), the learning data set 400 may be made up of a plurality of data groups 410 to 460 each corresponding to a plurality of different information attributes.

[0636] The learning unit 51 can extract data corresponding to each of multiple information attributes from the training object exercise video, and classify (or match) the extracted data having the same information attributes into the same data group to generate a learning dataset 400.

[0637] Here, the information attributes 410a to 450a can be understood as a criterion for classifying the types of information required for estimating the motion posture of the subject U from the learning motion video 300. As shown in (b) of Fig. 18b, in the present invention, a plurality of different information attributes (first to fifth information attributes 410a to 450a) may be predefined and exist.

[0638] The learning unit 51 can extract learning data corresponding to each of the multiple information attributes 410a to 450a from the training object exercise video, and classify the learning data corresponding to the same information attribute into the same data group to generate a learning data set 400.

[0639] Furthermore, the learning unit 51 can identify the association between the plurality of data groups 410 to 460 on a group-by-group basis based on the association between the plurality of information attributes 410a to 450a, and perform learning on the learning data set 400 and the association between each group.

[0640] The multiple data groups and the relationships between the groups will be specifically described below.

[0641] As shown in FIG. 18c, a first data group 410 among the plurality of data groups 410 to 450 may include position information 411 and 412 for joint points P1 and P2 of a subject U included in a motion image 300.

[0642] As shown in (a) of Fig. 18c, the joint points P1 and P2 in the present invention may refer to an area of ​​the subject U corresponding to the user's joints in the training motion video 300. Also, as shown in (b) of Fig. 18c, the position information 411 and 412 of the joint points may be understood as the positions of the areas in the training motion video 300 where the joint points P1 and P2 are located.

[0643] On the other hand, the human body is made up of about 200 bones, and a joint is a part where bones are connected, and the human body is made up of multiple joints.

[0644] The learning unit 51 may have predefined joint points to be learned among a plurality of joint points of the subject U. That is, the "learning target joint points" described in the present invention may be understood as joint points predefined for learning in the present invention among a plurality of joint points of the user.

[0645] 18d, in the database 40, among the multiple joint points, training target joint points to be used as training targets for a posture estimation model may be specified in advance and may exist as reference information 500. Also, in the reference information 500, the order of the multiple training target joint points may be defined in advance.

[0646] The first training joint point can be defined as the center of head. More specifically, the first training joint point can be understood as a point that is predicted or corresponds to the first cervical vertebra level.

[0647] The second learning joint point can be defined as the center of the neck. More specifically, the second learning joint point can be understood as the C3-C4 level, which is the center of the neck lordotic curve, and as the midpoint between the first and third midpoints when viewed from the front.

[0648] The third training joint point can be defined as the lower end of the neck. More specifically, the third training joint point can be understood as the C7-T1 level, which is the center point of the line connecting both clavicle levels.

[0649] The fourth learning joint point may be defined as the shoulder center. More specifically, the fourth learning joint point may be the humerus head center, which may be understood as a position corresponding to the rotation center of a continuous motion in a rotation motion in which the arm is abducted to a position that is the central axis of the shoulder joint rotation motion. In an image that is not a continuous motion of a rotation motion, a point corresponding to predicted position information of the shoulder center may be the fourth learning joint point. In addition, the fourth learning joint point may exist at each of the left shoulder center and the right shoulder center.

[0650] The fifth learning joint point may be defined as the elbow center. More specifically, the fifth learning joint point may be a part corresponding to the humerus medial-lateral epicondyle center, and may be understood as a central point at the elbow level. The fifth learning joint point may exist at each of the left elbow center and the right elbow center.

[0651] The sixth training joint point can be defined as the wrist center. More specifically, the sixth training joint point is the radius-ulnar styloid process center, which can be understood as a central point at the wrist level. The sixth training joint point can be present at each of the left wrist center and the right wrist center.

[0652] The seventh training joint point may be defined as the hand center. More specifically, the seventh training joint point may be understood to correspond to the third metacarpal head, and may exist at the left hand center and the right hand center.

[0653] The eighth learning joint point may be defined as the hip joint center (femoral head center). More specifically, the eighth learning joint point may be understood as a position that is the central axis of the hip joint rotational motion, and as a position that corresponds to the rotation center of a continuous motion in a rotational motion of abducting the leg. In an image that is not a continuous motion of a rotational motion, a point that corresponds to predicted position information of the hip joint center may be understood as the eighth learning joint point. The eighth learning joint point may exist at each of the left hip joint center and the right hip joint center.

[0654] The ninth training joint point can be understood as the knee center. More specifically, the ninth training joint point is the femoral medial-lateral epicondyle center, which can be understood as a central point at the knee level. The ninth training joint point can exist at each of the left knee center and the right knee center.

[0655] The tenth training joint point can be defined as the ankle center. More specifically, the tenth training joint point can be understood as a midpoint at the ankle level between the medial-lateral malleolus centers. The tenth training joint point can be located at the left ankle center and the right ankle center, respectively.

[0656] The eleventh training joint point may be defined as the foot center. More specifically, the eleventh training joint point may correspond to the second metatarsal head and may be present at the left foot center and the right foot center.

[0657] The 12th training joint point can be defined as the heel. More specifically, the 12th training joint point can be present on each of the left and right heels at the level where the heels contact the floor. The 12th training joint point may not be visible when the subject U is standing completely forward in the image, but may be visible if the foot is twisted even slightly.

[0658] The 13th learning target joint point can be defined as the lumbar curve start point (SUP. END OF LORDOSIS). More specifically, the 13th learning target joint point can be understood as the midpoint between the average level of the 4th on both sides and the average level of the 8th on both sides, which is the level of about 8-10T Spine at the level of the xiphoid process of the sternum.

[0659] The 14th learning joint point can be defined as the center of lordosis. More specifically, the 14th learning joint point is at a level of about L2-4 Spine, and can be understood as the midpoint between the 13th level and the average level of the 8th level on both sides.

[0660] The 15th learning joint point can be defined as the lumbar curve end point (INF. END OF LORDOSIS). More specifically, the 15th learning joint point is at a level of about S1-2 spine and can be understood as the midpoint between the 14th level and the midlevel of the average of the 8th levels on both sides.

[0661] Meanwhile, the first learning target joint point P1 may be defined as the head center 510, and the second learning target joint point P2 may be defined as the neck center 520. A first order having the highest priority may be defined for the first learning target joint point P1, and a second order having a lower priority than the second order may be defined for the second learning target joint point P2.

[0662] In this case, the order of the training joint points present corresponding to the left and right sides of the subject U may be such that the training joint point corresponding to the first side (e.g., left side) of the body has a higher priority than the training joint point corresponding to the second side (e.g., right side) of the body. For example, a training joint point P3 corresponding to the left wrist center 530 may be matched with a higher priority than a training joint point P4 corresponding to the right shoulder center 540.

[0663] The learning unit 51 can extract coordinate information as position information 411, 412 of each of a plurality of training target joint points P1, P2 designated in advance from the training target exercise video 300.

[0664] The coordinate information may include at least one of two-dimensional or three-dimensional coordinates. When two-dimensional coordinate information is extracted, the learning unit 120 can extract coordinate information of the x-axis and y-axis of each of the multiple learning target joint points P1 and P2 from the learning target motion video 300. In contrast, when two-dimensional coordinate information is extracted, the learning unit 120 can extract coordinate information of the x-axis, y-axis, and z-axis of each of the multiple learning target joint points P1 and P2 from the learning target motion video 300.

[0665] The coordinate information can be extracted by various methods. In particular, the z-axis coordinate information can be extracted by a camera (e.g., an RGB camera, etc.) or various types of sensors (e.g., a distance measurement sensor, etc.). Furthermore, the z-axis coordinate information can be extracted from the learning target image 300 through various types of artificial intelligence algorithms. When the z-axis coordinate information is extracted through an artificial intelligence algorithm, it can be expressed as being "estimated" or "predicted".

[0666] On the other hand, the learning unit 51 can classify the position information 411, 412 of each of the multiple learning target joint points P1, P2 into a first data group 410 based on the correspondence between the position information 411, 412 and the first information attribute 410a, and generate the first data group 410, and generate a learning dataset 400 including the first data group 410.

[0667] Considering an example in which two-dimensional coordinate information (x, y coordinate information) is extracted, the learning unit 121 can extract position information 411, 412 of a plurality of learning target joint points P1, P2 in a form in which the coordinate information of the x axis and the y axis are paired. The learning unit 51 can extract position information "[599, 463]" of the first learning target joint point P1, and extract position information "[586, 545]" of the second learning target joint point P2. In addition, the learning unit 51 can generate a learning data set 400 constituted of a first data group 410 including the above-mentioned "[599, 463]" and "[586, 545]".

[0668] The learning unit 51 can perform learning to estimate the positions of the joint points P1, P2 of the subject U included in the training object exercise video 300 based on the position information 411, 412 of the training object joint points P1, P2 constituting the first data group 410.

[0669] On the other hand, as shown in (b) of FIG. 18c, the learning unit 51 can configure (generate) a learning dataset 400 by sequentially arranging the position information 411, 412 of each of the multiple learning target joint points P1, P2 in a first data group 410 based on a predefined order between the multiple learning target joint points P1, P2.

[0670] As described above, the database 40 may contain a predefined order of a plurality of training target joint points P1 and P2.

[0671] The learning unit 51 can refer to the database 40 and sequentially arrange the position information 411, 412 of the multiple training target joint points P1, P2 in the first data group 410 in accordance with the order corresponding to the training target joint points P1, P2, to generate a training data set 400. In addition, such a training data set 400 can be stored in the database 40 to construct the database 40 for posture estimation.

[0672] Specifically, as shown in (b) of FIG. 18c, the learning unit 51 can preferentially arrange, within the first data group 410, the first position information 411 of the first learning target joint point P1 corresponding to the first order, and arrange the second position information 412 of the second learning target joint point P2 corresponding to the second order following the first position information 411.

[0673] On the other hand, the learning unit 51 can extract (or identify) the position information 411, 412 of...

Claims

1. 1. A method for providing digital-based musculoskeletal rehabilitation therapy provided by an application, comprising: Selecting an exercise plan to be provided to the patient based on prescription information including an exercise plan corresponding to the indication of the patient being assigned from a doctor terminal; Executing the application on a user terminal to which the patient account is logged in; providing an exercise list according to the exercise plan to the user terminal on which the application is executed; In the user terminal, exercise videos corresponding to the plurality of exercise items constituting the exercise list are played back; providing an evaluation page for evaluating the exercise item based on whether the degree of playback of the exercise video satisfies a preset criterion; and updating the exercise plan based on evaluation information received via the evaluation page.

2. The exercise plan includes: The method for providing digital-based musculoskeletal rehabilitation therapy according to claim 1, characterized in that the exercise items related to the patient's indications included in the patient's prescription information are assigned at least in part to each of a plurality of different days constituting a preset rehabilitation period.

3. In the step of providing the exercise list, providing the exercise list including the plurality of exercise items assigned to specific days on which the exercise video is played based on a reference date on which counting of the preset rehabilitation period is started in the user terminal; The evaluation page includes: The method for providing digital-based musculoskeletal rehabilitation therapy according to claim 2, characterized in that, in order to perform an evaluation related to the plurality of exercise items provided to the patient on each specific day, if the playback level of the exercise video satisfies the preset criterion, the video is provided to the user terminal on the specific day.

4. The evaluation page includes: A first evaluation area for evaluating the exercise difficulty of the plurality of exercise items assigned to the specific day; A second evaluation area for selecting a high difficulty exercise from the plurality of exercise items; and a third evaluation area for evaluating exercise pain associated with the plurality of exercise items; In the step of updating the exercise plan, The method for providing digital-based musculoskeletal rehabilitation therapy according to claim 3, characterized in that the difficulty level of the exercise plan or at least a portion of the exercise items constituting the exercise plan are changed based on evaluation information received through at least one of the first evaluation area, the second evaluation area, and the third evaluation area.

5. The method for providing digital-based musculoskeletal rehabilitation therapy according to claim 4, characterized in that exercise items according to the updated exercise plan are provided to the user terminal from the day after the specific day has passed.

6. The second evaluation area displays the plurality of exercise items assigned to the specific day, In the step of updating the exercise plan, 5. The method for providing digital-based musculoskeletal rehabilitation therapy according to claim 4, characterized in that when at least one of the plurality of exercise items is selected from the user terminal, the selected exercise item is excluded from the exercise plan, and another exercise item that has an efficacy corresponding to the selected exercise item and induces less pain than the selected exercise item is included in the exercise plan.

7. further comprising assigning to the patient account a cognitive behavioral treatment plan to be performed in conjunction with the exercise plan; The step of assigning a cognitive behavioral treatment plan includes: receiving, via the user terminal, questionnaire response data for a plurality of questionnaire data; Detecting condition information of the pain patient related to a pain duration and a degree of cognitive distortion of the pain patient based on the questionnaire response data; Identifying a user group corresponding to the condition information of the pain patient from among a plurality of user groups classified according to pain duration and degree of cognitive distortion; determining an initial treatment protocol corresponding to the user group from among a plurality of treatment protocols; and providing a plurality of specific treatment programs included in the initial treatment protocol in sequence during a pre-set rehabilitation period.

8. providing an initial screen page in response to the application being executed on the user terminal; The initial screen page is a first menu item for accessing a list of exercises according to said exercise plan; a second menu item for accessing the rating page; a third menu item for accessing the cognitive behavioral treatment plan assigned along with the exercise plan; and a fourth menu item for accessing a page for performing a functional evaluation for a specific movement of the patient; The method for providing digital-based musculoskeletal rehabilitation therapy as described in claim 7, characterized in that if the playback level of the exercise video does not meet the preset standard, provision of the evaluation page to the user terminal is restricted even if the second menu item is selected on the user terminal.

9. The functional evaluation for the specific movement is configured to be performed at predetermined intervals during a predetermined rehabilitation period to which the exercise plan is assigned, The fourth menu item is The initial screen page is configured to be included on a specific day according to the preset day interval, The method of claim 8, wherein the initial screen page does not include a particular day during the rehabilitation period.

10. A communication unit that receives prescription information including an exercise plan corresponding to the indication of the patient from a doctor terminal; and a control unit that provides the user terminal with an exercise list according to the exercise plan in response to the application being executed on the user terminal to which the patient account is logged in; The control unit is In the user terminal, in accordance with the plurality of exercise items constituting the exercise list, exercise videos corresponding to the plurality of exercise items are reproduced; providing an evaluation page for performing an evaluation related to the exercise item based on whether the degree of playback of the exercise video satisfies a preset criterion; A digital-based musculoskeletal rehabilitation therapy delivery system, comprising: a computer; a computer-readable recording medium; and a display displaying a display of the display of the display.

11. A program executed by one or more processes in an electronic device and stored on a computer-readable recording medium, Selecting an exercise plan to be provided to the patient based on prescription information including an exercise plan corresponding to the indication of the patient being assigned from a doctor terminal; Executing the application on a user terminal to which the patient account is logged in; providing an exercise list according to the exercise plan to the user terminal on which the application is executed; In the user terminal, exercise videos corresponding to the plurality of exercise items constituting the exercise list are played back; providing an evaluation page for evaluating the exercise item based on whether the degree of playback of the exercise video satisfies a preset criterion; and updating the exercise plan based on the evaluation information received via the evaluation page.

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