Method and system for creating customized therapeutic exercise program

Abstract

The present invention relates to a method and system for generating a customized therapeutic exercise program, which can customize an exercise program for therapeutic purposes and provide it to a patient. The method for generating a customized therapeutic exercise program according to the present invention may include the steps of: executing an exercise program generation mode on a terminal logged in with a specific medical staff account; selecting at least one exercise item to be included in the exercise program in the exercise program generation mode; setting program information corresponding to the exercise program; generating the exercise program including the exercise item and the program information; storing the exercise program in association with the specific medical staff account; and allocating the exercise program to the patient account based on receiving prescription information for the patient account from the specific medical staff account.

Description

[Technical field]

[0001] The present invention relates to a method and system for generating a customized therapeutic exercise program, which can customize an exercise program for therapeutic purposes and provide it to a patient. [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. 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]

[0011] The present invention relates to a method and system for generating a customized therapeutic exercise program, which can customize an exercise program and provide it to a patient. Furthermore, the present invention relates to a method and system for generating a customized exercise treatment program, which allows the customized exercise program to be shared among medical staff. In particular, the present invention relates to a method and system for generating a customized therapeutic exercise program that can provide an exercise program sharing environment on a hospital basis.

[0012] Furthermore, the present invention relates to a method and system for generating a customized exercise treatment program, which can provide a therapeutic exercise program to a patient based on the authority of medical staff. [Means for solving the problem]

[0013] In order to solve the above problem, the method for generating a customized exercise treatment program of the present invention may include the steps of executing an exercise program generation mode on a terminal logged in with a specific medical staff account, selecting at least one exercise item to be included in the exercise program in the exercise program generation mode, setting program information corresponding to the exercise program, generating the exercise program including the exercise item and the program information, storing the exercise program in association with the specific medical staff account, and assigning the exercise program to a patient account based on receiving prescription information for the patient account from the specific medical staff account.

[0014] In one example, the step of selecting the exercise item includes the steps of displaying on the terminal a plurality of exercise items registered in the exercise therapy provision service, selecting at least one of the plurality of exercise items from the specific medical staff account, and displaying on the terminal a list including the selected exercise items, and the exercise program may be composed of the exercise items included in the list.

[0015] Furthermore, the exercise program may be set to either a non-shareable state in which it is accessible only to the specific medical staff account, or a shareable state in which it is accessible to medical staff accounts other than the specific medical staff account.

[0016] In this case, the program information may include information identifying the target with which the exercise program is to be shared, and the other medical staff accounts that can access the exercise program may be determined based on the program information.

[0017] In one example, the information for identifying the sharing target may be at least one of hospital information and medical staff information registered in the exercise treatment providing service.

[0018] In one example, if the exercise program is in a shareable state, the exercise program may be assigned to the patient account based on prescription information received from the other medical staff account.

[0019] Furthermore, the specific medical staff account may be one of a first type medical staff account that has its own prescription authority for the exercise program and a second type medical staff account that does not have its own prescription authority for the exercise program, and the step of assigning the exercise program to the patient account may include a step of confirming the type of the specific medical staff account, and if the confirmation results in the type of the specific medical staff account being the first type, a step of assigning the exercise program to the patient account.

[0020] Further, the step of allocating the exercise program to the patient account may allocate the exercise program to the patient account based on an approval event occurring for the exercise program prescription from a medical staff account of the first type when confirmation indicates that the particular medical staff account is of the second type.

[0021] Meanwhile, the customized exercise treatment program generation system according to the present invention may include a control unit that executes an exercise program generation mode on a terminal logged in with a specific medical staff account, a communication unit that receives a selection of at least one exercise item to be included in the exercise program in the exercise program generation mode, and a storage unit in which the exercise program is stored, wherein the control unit sets program information corresponding to the exercise program, generates the exercise program including the exercise item and the program information, stores the exercise program in the storage unit in cooperation with the specific medical staff account, and assigns the exercise program to the patient account based on receiving prescription information for the patient account from the specific medical staff account.

[0022] 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: executing an exercise program generation mode on a terminal logged in with a specific medical staff account; selecting at least one exercise item to be included in an exercise program in the exercise program generation mode; setting program information corresponding to the exercise program; generating the exercise program including the exercise item and the program information; storing the exercise program in association with the specific medical staff account; and assigning the exercise program to the patient account based on receiving prescription information for the patient account from the specific medical staff account. Effect of the Invention

[0023] As described above, the method and system for generating a customized exercise treatment program according to the present invention can provide a user environment in which medical staff can directly customize an exercise program by executing an exercise program generation mode on a terminal logged in with a specific medical staff account.

[0024] Specifically, the method and system for generating a customized exercise treatment program according to the present invention generates an exercise program by selecting at least one exercise item to be included in the exercise program in an exercise program generation mode, thereby enabling medical staff to generate an effective and optimal exercise program by taking into consideration various factors such as the patient's characteristics such as disease, symptoms, age, age group, and exercise ability.

[0025] In particular, the method and system for generating a customized exercise treatment program according to the present invention stores the generated exercise program in association with a specific medical staff account, thereby enabling the exercise program generated by the specific medical staff account to be managed and the exercise program to be shared with other medical staff accounts.

[0026] Furthermore, the method and system for generating a customized exercise treatment program according to the present invention can assign the exercise program to a patient account based on receiving prescription information for the patient account from a specific medical staff account, thereby allowing a doctor to prescribe a customized exercise program for treatment to a patient, and allowing the patient to receive an optimal exercise program and exercise effectively. [Brief description of the drawings]

[0027] [Figure 1] 1 is a conceptual diagram for explaining a customized exercise therapy providing system according to the present invention. [Figure 2a] FIG. 2 is a conceptual diagram for explaining medical staff accounts and patient accounts according to the present invention. [Figure 2b] FIG. 2 is a conceptual diagram for explaining medical staff accounts and patient accounts according to the present invention. [Diagram 3] FIG. 1 is a conceptual diagram for explaining an exercise program provided by the present invention. [Figure 4a] FIG. 1 is a conceptual diagram for explaining an exercise program provided by the present invention. [Figure 4b] FIG. 1 is a conceptual diagram for explaining an exercise program provided by the present invention. [Diagram 5] 1 is a flowchart illustrating a method for providing customized exercise therapy according to the present invention. [Figure 6a] FIG. 2 is a conceptual diagram for explaining a method for generating an exercise program in the present invention. [Figure 6b] FIG. 2 is a conceptual diagram for explaining a method for generating an exercise program in the present invention. [Figure 7] FIG. 1 is a conceptual diagram for explaining an exercise program prescribing method according to the present invention. [Figure 8a] FIG. 2 is a conceptual diagram for explaining a method for sharing an exercise program in the present invention. [Figure 8b] FIG. 2 is a conceptual diagram for explaining a method for sharing an exercise program in the present invention. [Figure 9a] FIG. 2 is a conceptual diagram for explaining the exercise program prescription process according to the authority of a medical staff account in the present invention. [Figure 9b] FIG. 2 is a conceptual diagram for explaining the exercise program prescription process according to the authority of a medical staff account in the present invention. [Figure 9c] FIG. 2 is a conceptual diagram for explaining the exercise program prescription process according to the authority of a medical staff account in the present invention. [Figure 10] FIG. 13 is a conceptual diagram for explaining a change in place of employment of a medical staff account in 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

[0028] 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.

[0029] 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.

[0030] 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. The singular expression includes the plural expression unless the context clearly dictates otherwise.

[0031] 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.

[0032] The exercise therapy provision system 100 of the present invention can provide an exercise program (which may be called an "exercise plan" or "exercise therapy program") prescribed by medical staff U1 to a patient between medical staff and the patient.

[0033] In particular, the exercise therapy provision system 100 according to the present invention can provide a patient with an exercise program that is directly customized and generated by medical staff. Therefore, the exercise therapy provision system 100 according to the present invention may be named an "exercise program generation system."

[0034] Meanwhile, the "medical staff" described in the present invention refers to those who work in the medical institution (eg, hospital) 1, and may include, for example, at least one of a doctor, a nurse, and a physical therapist.

[0035] For the sake of convenience, the present invention will be described with reference to a doctor and a physical therapist who can provide (or prescribe) an exercise program to the patient U2 as examples of medical staff. However, the medical staff is not limited to these, and any user who works at a medical institution to provide an exercise treatment service can be considered as a medical staff according to the present invention. The medical staff U1 can generate a treatment program including at least one treatment item according to the patient's symptoms requiring treatment and an appropriate treatment method.

[0036] In the present invention, a method for generating an exercise program including at least one exercise item for exercise therapy (e.g., rehabilitation therapy, physical therapy, cognitive therapy, etc.) will be mainly described. However, the present invention is not limited to the generation of an exercise program for the purpose of exercise therapy, and may be used for the generation of a treatment program requested by a medical institution such as a hospital.

[0037] Meanwhile, the medical staff U1 can customize at least one exercise item and an exercise method for the exercise item (e.g., the number of exercises, the exercise time, the exercise sequence, etc.) to generate various exercise programs.

[0038] 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.

[0039] In the present invention, an exercise program created by a specific medical staff member can be named a "unique exercise program (or unique program)", a "customized exercise program (or customized program)", a "customized type exercise program (or customized type program)", an "individual exercise program (or individual program)", or a "personal exercise program (or personal program)". Furthermore, the present invention can provide a user environment in which original exercise programs created by specific medical staff can be shared among medical staff. In the present invention, an exercise program shared among medical staff can be named a "shared exercise program (or shared program)."

[0040] Furthermore, the "recommended exercise program (or recommended program)" described in the present invention can be understood as a program that is registered in the exercise treatment provision service by the system administrator and provided to medical staff.

[0041] Meanwhile, medical staff U1 can prescribe an exercise program (or exercise plan) to patient U2 based on at least one of their own exercise program, a shared exercise program, and a recommended exercise program.

[0042] In the present invention, a service can be provided to allow medical staff U1 to create an original exercise program using a page (or screen) provided by the exercise treatment provision system 100. In addition, in the present invention, a user environment is provided in which medical staff U1 can prescribe an exercise program to patient U2, and information on the exercise program prescribed by medical staff U1 can be received. In addition, in the present invention, the prescribed exercise program received from medical staff U1 is provided to patient U2, thereby providing exercise treatment services to patient U2.

[0043] Hereinafter, a method and system for generating a unique exercise program and providing a customized exercise therapy to a patient will be described in detail with reference to the accompanying drawings. FIG. 1 is a conceptual diagram for explaining a customized exercise therapy providing system according to the present invention. FIG. 2a and FIG. 2b are conceptual diagrams for explaining a medical staff account and a patient account according to the present invention, FIG. 3, FIG. 4a and FIG. 4b are conceptual diagrams for explaining an exercise program provided by the present invention, FIG. 5 is a flow chart for explaining a customized exercise therapy providing method according to the present invention, FIG. 6a and FIG. 6b are conceptual diagrams for explaining a method for generating an exercise program in the present invention, FIG. 7 is a conceptual diagram for explaining a method for prescribing an exercise program in the present invention, FIG. 8a and FIG. 8b are conceptual diagrams for explaining a method for sharing an exercise program in the present invention, FIG. 9a, FIG. 9b and FIG. 9c are conceptual diagrams for explaining an exercise program prescribing process according to the authority of a medical staff account in the present invention, and FIG. 10 is a conceptual diagram for explaining a change of place of work of a medical staff account in the present invention. As shown in FIG. 1, an exercise therapy providing system 100 according to the present invention may be configured to communicate with user terminals 10 and 20.

[0044] The user terminals 10 and 20 in the present invention can be understood as electronic devices that can use the exercise therapy provision service. For example, the user terminals 10 and 20 may include mobile phones, smart phones, notebook computers, laptop computers, slate PCs, tablet PCs, ultrabooks, desktop computers, digital broadcast terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), navigation, wearable devices such as smart watches, smart glasses, and head mounted displays (HMDs).

[0045] A medical staff member U1 can use his / her user terminal 10 to generate and prescribe a therapeutic exercise program through a screen (or page) 400 provided by the exercise treatment provision system 100. The patient U2 can use his / her own user terminal 20 to receive exercise therapy services according to the exercise program prescribed by the medical staff U1 and undergo treatment.

[0046] Meanwhile, the "screen (or page) 400" described in the present invention can be understood to mean at least one piece of information (text, image, video, graphic object, etc.) displayed (or output) on the display of the user terminal 10, 20. Therefore, the "screen (or page)" in the present invention may be used interchangeably with "information."

[0047] On the other hand, in the present invention, information related to the exercise treatment service can be provided on the user terminals 10 and 20 logged in with the user accounts of the medical staff U1 or the patient U2.

[0048] In this specification, for convenience of explanation, the medical staff U1 account is named "medical staff account", and a user terminal logged in with the medical staff account is named "medical staff terminal".

[0049] As shown in Fig. 2b, an "account" can be created through a page (e.g., "membership registration page") 200a, 200b provided by the exercise treatment providing system 100. Alternatively, an "account" can be created in at least one other system linked to the exercise treatment providing system according to the present invention. Details related to user accounts will be described later.

[0050] Meanwhile, as shown in FIG. 1, an exercise therapy providing system 100 according to the present invention may include at least one of a communication unit 110, a storage unit 120, and a control unit .

[0051] The communication unit 110 may be configured to perform at least one of communication with the medical staff terminal 10 and the patient terminal 20. For example, the communication unit 110 may support 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 TMCommunication 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).

[0052] The communication unit 110 can provide the medical staff terminal 10 with a screen (or page) 400 including information about a unique exercise program. The medical staff U1 can create a unique exercise program through the screen output to the medical staff terminal 10 and prescribe the unique exercise program that he / she created to the patient U2.

[0053] The communication unit 110 can provide an original exercise program on the patient terminal 20 based on the original exercise program prescribed from the medical staff terminal 10. The patient U2 can receive an exercise therapy service according to the exercise program prescribed by the medical staff U1 through the patient terminal 20.

[0054] Furthermore, the storage unit 120 may be configured to store various information related to the exercise treatment provision service. For example, the storage unit 120 may store an original exercise program created by a specific medical staff member U1, which is matched with the specific medical staff member account U1.

[0055] In the present invention, the storage unit 120 can be understood as being sufficient as long as it is a space in which information related to an exercise treatment provision service is stored, and there is no restriction on the physical space. Specifically, the storage unit 120 can exist in at least one of the inside and outside spaces (e.g., external server) of the exercise treatment provision system 100. That is, at least a part of the storage unit 120 can exist inside the exercise treatment provision system 100. Also, at least a part of the storage unit 120 can exist in database 2 in the external space (e.g., external server). In this case, the exercise treatment provision system 100 can use the information stored in database 2 in the external space. Furthermore, the control unit 130 can perform a series of controls related to the configurations included in the exercise treatment provision system 100 and the exercise treatment provision service.

[0056] In particular, the control unit 130 can provide a user environment in which the medical staff terminal 10 can generate a unique exercise program and prescribe the generated unique exercise program to a patient.

[0057] Furthermore, the control unit 130 can share a unique exercise program created in a specific medical staff account U1 with another medical staff account U3 based on the settings of the specific medical staff account U1.

[0058] Below, the concepts of "user account" and "exercise program" of the present invention will be explained, and then a service for creating, prescribing and sharing a unique exercise program will be specifically described.

[0059] 2, the control unit 130 can separately manage medical staff accounts of medical staff U1, U3, U4, and U5 and a patient account of patient U2. In the following, for convenience of explanation, the medical staff accounts will be described with the same reference numbers "U1", "U3", "U4", and "U5" as the medical staff, and the patient account will be described with the same reference number "U2" as the patient.

[0060] 2a(a), the control unit 130 can manage medical staff accounts U1, U3, U4, and U5 for each hospital 1a or 1b. The control unit 130 can match the medical staff accounts U1, U3, U4, and U5 with at least one hospital (or hospital information) 1a or 1b where the medical staff work (or belong), and store them in the storage unit 120.

[0061] For example, the control unit 130 can match and manage the medical staff accounts U1 and U3 of medical staff working at the first hospital 1a ("Doctor A" and "Therapist B") with the first hospital 1a, and can match and manage the medical staff accounts U4 and U5 of medical staff working at the second hospital 1b ("Doctor B" and "Therapist B") with the second hospital 1b.

[0062] In order to manage medical staff accounts on a hospital-by-hospital basis, the control unit 130 may input hospital information of the medical staff's hospital (or affiliated hospital, working hospital, associated hospital, or affiliated hospital) when creating a medical staff account.

[0063] For example, as shown in FIG. 2b(a), the control unit 130 may include, on a medical staff account registration page 210, an input area 211 for inputting hospital information where the medical staff works.

[0064] Meanwhile, the medical staff accounts in the present invention can be classified into a first type and a second type depending on whether or not they have the authority to independently prescribe an exercise program to a patient.

[0065] A "first type of medical staff account" may be understood as a medical staff account that is authorized to independently prescribe exercise programs to patients.

[0066] In addition, a "second type medical staff account" may be understood as a medical staff account that can prescribe an exercise program to a patient based on the approval of the first type medical staff account. The control unit 130 can set the medical staff account to either the first type or the second type based on the occupational information of the medical staff.

[0067] For example, when the occupation of the medical staff is a first type of occupation (e.g., "doctor"), the control unit 130 can set the type of the medical staff account to a first type. On the other hand, when the occupation of the medical staff is a second type of occupation (e.g., "therapist"), the control unit 130 can set the type of the medical staff account to a second type.

[0068] In the present invention, an exercise program can be assigned to a patient through different processes depending on whether the type of medical staff account prescribing the exercise program to the patient is a first type or a second type, as will be described in more detail below.

[0069] Meanwhile, as shown in (b) of Fig. 2a, a patient account U2 may exist in the storage unit 120. The patient account U2 may exist in a state where it is matched with at least one of symptom information regarding the patient's symptoms, an exercise program assigned to the patient, medical staff information of the medical staff who assigned the exercise program, and hospital information of the hospital to which the medical staff belongs. The control unit 130 may provide a patient account registration page 220 configured differently from the page 210 for medical staff account registration.

[0070] For example, as shown in (b) of FIG. 2b, the control unit 130 can configure a patient account registration page 220 to allow input of at least one of the patient's name information 221, gender information 222, and mobile phone number information 223.

[0071] That is, the control unit 130 can generate a patient account even if the occupational information of the patient and the hospital information of the associated hospital are not input, which is different from the medical staff account. Such a patient account registration page 210 may be provided on at least one of the medical staff terminal 10 and the patient terminal 20.

[0072] For example, when a patient visits a hospital, the control unit 130 can provide a patient account registration page 220 to the medical staff terminal 10. The medical staff can input the patient information of the patient through the medical staff terminal 10 to create a patient account. As another example, a patient can directly create a patient account through a patient account registration page 220 provided on the patient terminal 20.

[0073] Meanwhile, as shown in FIG. 3, the exercise program 300 provided by the present invention may correspond to at least one of a unique exercise program (or a first attribute exercise program) 310, a shared exercise program (or a second attribute exercise program) 320, and a recommended exercise program (or a third attribute exercise program) 330 depending on the attributes. The unique exercise program 310 can be understood as an exercise program generated by a particular medical staff account U1.

[0074] A specific medical staff account U1 can select at least one exercise item from among multiple exercise items registered in the exercise treatment provision service. In addition, the medical staff account can set the exercise method of the selected exercise item (e.g., number of exercise repetitions, exercise time, exercise sequence, exercise schedule, etc.) to create a unique exercise program 310.

[0075] That is, a specific medical staff account U1 can create a unique exercise program 310 by customizing at least one exercise item and an exercise method for the exercise item.

[0076] Such a unique exercise program 310 is accessible to the particular medical staff account U1 that created the unique exercise program 310, and can be prescribed to patients by the particular medical staff account U1.

[0077] 4a, the control unit 130 may provide a unique exercise program entry page 400a to the terminal 10 logged in with a specific medical staff account U1. In this case, the unique exercise program entry page 400a may provide information 410, 420 about the exercise program created by the specific medical staff account U1.

[0078] The information 410 regarding the unique exercise program may include at least one of: i) information regarding the number of exercise items included in the exercise program (e.g., “12 movements”) 411; ii) a shared graphic object 412 indicating whether the exercise program can be shared; iii) a graphic object 413 indicating whether the exercise program has been set as a favorite; iv) a thumbnail 414 of a video related to the exercise program; v) a title 415 of the exercise program; and vi) a date and time the exercise program was created 416. Here, the sharing graphic object 412 may be displayed to intuitively guide whether the unique exercise program 310 can be shared or not. In the present invention, the unique exercise program 310 may be set to either a non-sharable state or a sharable state. "Exercise program sharing" in the present invention may mean allowing access to and prescription of an exercise program.

[0079] In addition, the "non-sharing state" can be understood as a state in which the unique exercise program 310 can be accessed only through a specific medical staff account U1, and the unique exercise program information 410 can be viewed and the unique exercise program can be assigned to a patient only through a specific medical staff account U1.

[0080] The "shareable state" can be understood as a state in which the unique exercise program can be accessed by other medical staff accounts U3 other than the specific medical staff account U1, and the other medical staff accounts U3 can also view the unique exercise program information 410 and assign the unique exercise program 310 to a patient.

[0081] The unique exercise program 310 may be determined to be either shareable or non-shareable based on the settings (or preferences) of the particular medical staff account U1.

[0082] When the unique exercise program 310 is in a shareable state, the control unit 130 can display the shared graphic object 412 on the page 400a so that specific medical staff can intuitively recognize that the program is in a shareable state.

[0083] Meanwhile, when the unique exercise program 310 is in a non-shareable state, the control unit 130 can restrict the shared graphic object 412 from being displayed on the page 400a.

[0084] On the other hand, if a particular medical staff account U1 shares its own exercise program 310 with another medical staff account U3, the shared exercise program can be named "shared exercise program 320."

[0085] For example, assume that an exercise program created in a specific medical staff account U1 is shared with another medical staff account U3. In this case, the exercise program may correspond to a unique exercise program 310 in the specific medical staff account U1 and a shared exercise program 320 in the other medical staff account U3.

[0086] 4b, the control unit 130 can provide a shared exercise program entry page 400b to the terminal 10 logged in with another medical staff account U3. In addition, the control unit 130 can display information 420 about the shared exercise program shared from a specific medical staff account U1 to another medical staff account U3 on the shared exercise program entry page 400b.

[0087] On the other hand, the recommended exercise program 330 is an exercise program provided by the exercise treatment provision system 100, and can be understood as an exercise program that can be accessed by all medical staff accounts registered in the exercise treatment provision system 100 (or some of the medical staff accounts set by the system administrator).

[0088] The control unit 130 can generate at least one recommended exercise program 330 based on settings by a system administrator, or generate the recommended exercise program 330 based on prescription history information stored in the storage unit 120. For example, the control unit 130 can generate a "recommended exercise program for treating stiff neck" including at least one exercise item that is effective for treating stiff neck.

[0089] Medical staff can select one of the custom exercise programs 310, the shared exercise programs 320, and the recommended exercise programs 330 to prescribe a customized exercise plan for the patient.

[0090] The following describes in detail how to generate a unique exercise program so that medical staff can customize and prescribe a unique exercise program.

[0091] In the present invention, a process of executing an exercise program generation mode can be performed on a terminal logged in with a specific medical staff account (S510, see FIG. 5).

[0092] The control unit 130 may control the exercise program generation mode to be executed on the terminal logged in with the specific medical staff account U1 based on receiving a request to execute the exercise program generation mode from the terminal logged in with the specific medical staff account U1.

[0093] Here, the "exercise program generation mode" can be understood as a mode in which an original exercise program can be generated, and may be used interchangeably with the "original exercise program generation mode" or the "new program generation mode."

[0094] For example, as shown in Fig. 4a, the control unit 130 may provide a graphic object (e.g., "Create new program") 430 associated with an exercise program generation mode execution function on a medical staff terminal logged in with a specific medical staff account U1. The control unit 130 may receive an exercise program generation mode execution request (or an exercise program generation mode execution event) from the specific medical staff account U1 based on the selection of the specific medical staff account U1 for the graphic object 430.

[0095] When the control unit 130 receives a request to execute the exercise program generation mode, the control unit 130 may control the exercise program generation mode to be executed on the terminal logged in with a specific medical staff account U1. That is, the control unit 130 may execute the exercise program mode based on the occurrence of an exercise program generation mode execution event.

[0096] Meanwhile, in the present invention, in the exercise program creation mode, a process of selecting at least one exercise item to be included in the exercise program can be performed (S520, see FIG. 5).

[0097] As shown in FIG. 6a(a), the control unit 130 can provide an exercise program creation page (or a unique exercise program creation page or exercise list configuration page) 600a on the terminal of a specific medical staff account U1.

[0098] The control unit 130 can be controlled to include a first area (also called a "selection area") 610 for selecting at least one exercise item included in a unique exercise program, and a second area (also called a "list area") 620 in which an exercise list composed of the selected exercise items is output.

[0099] The control unit 130 can display on the first area 610 information relating to at least a portion of the multiple exercise items registered in the exercise therapy provision service (e.g., information on the name (or title) of the exercise item, a representative image (e.g., a thumbnail of the video corresponding to the exercise item), information on the exercise part corresponding to the exercise item, etc.).

[0100] The control unit 130 may receive selection information of a specific medical staff account U1 from the medical staff terminal 10 based on the selection of any one of the exercise items displayed in the first area 610. For example, the control unit 130 may receive selection information for a selected exercise item based on the selection in the first area 610 of information about a specific exercise item or an icon corresponding to a specific exercise item (e.g., "Add" or "Select" or "Save").

[0101] Furthermore, the control unit 130 can display information 611, 612 about at least one exercise program (unique exercise program, shared exercise program, recommended exercise program) registered in the exercise treatment provision service on the first area 610. The information 611, 612 about the recommended exercise program may include at least one of information about the number of exercise items included in the recommended exercise program, information about the exercise program name (or title), information about the exercised body part, and information about a representative image of the exercise item.

[0102] The control unit 130 may receive selection information (or a selection event) of a specific medical staff account U1 from the medical staff terminal 10 based on the selection of any one of the exercise programs displayed in the first area 610. For example, as shown in (a) of FIG. 6a, the control unit 130 may receive selection information for an exercise program based on the selection of information about a specific exercise program or an icon (e.g., "Add" or "Select" or "Save") 611a corresponding to a specific exercise program in the first area 610.

[0103] In this case, the control unit 130 may determine that at least one exercise item included in the selected exercise program has been selected based on the selection of the exercise program (or the occurrence of a selection event) in the first area 610. For example, when an exercise program consisting of "three" exercise items (e.g., a first exercise item, a second exercise item, and a third exercise item) is selected, the control unit 130 may determine that all three exercise items have been selected.

[0104] In this way, when the unique exercise program generation mode is executed, the control unit 130 can provide a user environment in which at least one of the multiple exercise items registered in the exercise treatment provision service can be selected, to a terminal logged in with a medical staff account.

[0105] Meanwhile, as shown in (b) of FIG. 6a, when at least one exercise item is selected in the first area 610, the control unit 130 can display the selected exercise items 621, 622 in the second area (also called the “list area”) 620.

[0106] In this case, the control unit 130 may arrange the selected exercise items in order to correspond to the order in which they were selected, and may arrange and display them in the second area 620. The control unit 130 may also generate an exercise program in order to correspond to the order in which the exercise items are arranged. Furthermore, the control unit 130 can provide an editing function through the second area 620 so that the selected exercise items 621 and 622 can be edited.

[0107] Here, the "edit function" can be understood as a function of performing at least one of the exercise method (exercise duration or number of exercise repetitions) for the selected exercise items 621, 622, the exercise order, and copying and deleting the selected exercise items 621, 622. Hereinafter, for convenience of explanation, the edit function will be described using a specific exercise item 621 as an example.

[0108] The control unit 130 may include an exercise method (exercise duration or number of exercise repetitions) setting area 621a for the exercise item 621 on the second area 620. The control unit 130 may display a plurality of exercise method option information (e.g., "maintain for 15 seconds," "maintain for 20 seconds," etc.) on the exercise method setting area 621a. The control unit 130 may also set the selected exercise method option information as the exercise method for the exercise item based on the selection of any one of the plurality of exercise method option information (e.g., "maintain for 20 seconds"). On the other hand, the control unit 130 may set the exercise method set as the basic value (or default value, default value) as the exercise method for the exercise item when there is no selection of the medical staff account for the exercise method option information.

[0109] Furthermore, the control unit 130 can display a graphic object 621b associated with a function for changing the arrangement of the exercise items 621 on the second area 620. The control unit 130 can change the arrangement of the exercise items 621 based on a selection of the medical staff account for the graphic object 621b associated with the arrangement change function. For example, the control unit 130 can change the arrangement of the exercise items 621 based on a user input of dragging the graphic object 621b in the medical staff account to move the exercise items 621 from a first arrangement order (or a first position) to a second arrangement order (or a second position) different from the first arrangement order. In this case, the changed arrangement order of the exercise items 621 can correspond to the second arrangement order (or the second position).

[0110] Furthermore, the control unit 130 can display a check box 621c corresponding to the exercise item 621 on the second area 620. When the copy graphic object 623 displayed in the second area 620 is selected with the check box 621c corresponding to the exercise item selected, the control unit 130 can additionally display the copied exercise item 621 on the second area 620. Furthermore, when the delete graphic object 624 displayed in the second area 620 is selected with the check box 621c corresponding to the exercise item selected, the control unit 130 can delete the copied exercise item 621 from the second area 620.

[0111] In this way, the present invention provides a user environment in which a medical staff account can select at least a part of a plurality of exercise items registered in the exercise treatment provision service and edit the selected exercise items. This allows the medical staff to customize and diversify exercise programs according to the patient's symptoms, characteristics of the patient (e.g., age group, age, exercise performance ability, etc.), and the patient's exercise environment. Meanwhile, in the present invention, a process of setting program information corresponding to an exercise program can be performed (S530, see FIG. 5).

[0112] As shown in FIG. 6b, the control unit 130 can provide program information setting pages (hereinafter also referred to as "information setting pages" or "exercise program information setting pages") 600b, 600c, 600d, and 600e for setting program information corresponding to an exercise program on a terminal logged in with a medical staff account.

[0113] The control unit 130 may provide the program information setting pages 600b, 600c, 600d, and 600e before the exercise list configuration page 600a (see FIG. 6a) or after the exercise list configuration page 600a. That is, in the present invention, the order in which the exercise list configuration page 600a and the program information setting pages 600b, 600c, 600d, and 600e are provided may be variously configured.

[0114] The control unit 130 may input required information (exercise part information, exercise tool information) for generating an exercise program, and selection information (program name information, disease and surgery name information, step information, precautions information, shared information) through program information setting pages 600b, 600c, 600d, and 600e.

[0115] As shown in (a) of FIG. 6b, the control unit 130 may provide a program information setting page 600b for inputting motion part information on a terminal logged in with a medical staff account. For example, the control unit 130 may display motion part graphic objects 631, 632 corresponding to at least one motion part (e.g., whole body, knee, etc.) on the program information setting page 600b. Based on the selection of at least one of the motion part graphic objects 631, 632, the control unit 130 may match the motion part (e.g., knee) information corresponding to the selected motion part graphic object 632 to an exercise program to generate an exercise program.

[0116] Further, as shown in (b) of FIG. 6b, the control unit 130 may provide a program information setting page 600c for inputting exercise tool information on a terminal logged in with a medical staff account. For example, the control unit 130 may display exercise tool graphic objects 641, 642 corresponding to at least one exercise tool (e.g., elastic band, mat, etc.) on the program information setting page 600c. In addition, the control unit 130 may check exercise tool information matched to an exercise item included in the exercise program, and automatically select (e.g., highlight) an exercise tool graphic object (e.g., mat) 642 corresponding to the checked exercise tool.

[0117] Exercise tool information may be matched for each exercise item and stored in the storage unit 120. The control unit 130 may automatically set (match) exercise tool information for an exercise program based on the exercise tool information matched to the exercise item.

[0118] In addition, based on the selection of at least one of the exercise tool graphic objects 641, 642, the control unit 130 can match the exercise part (e.g., knee) information corresponding to the selected exercise tool graphic object 632 to an exercise program to generate an exercise program.

[0119] Furthermore, a setting page for inputting at least one of the program name information, disease and surgery name information, step information, and caution information of the exercise program can be provided on a terminal logged in with a medical staff account. For example, as shown in (c) of FIG. 6b, the control unit 130 can provide a program information setting page 600c including a caution input area 651 to a terminal logged in with a medical staff account. The medical staff can input caution information to be noted when prescribing an exercise program in the caution input area 651. The control unit 130 can match the caution information input in the caution input area 651 to the exercise program to generate the exercise program.

[0120] Furthermore, as shown in FIG. 6b(d), the control unit 130 can provide a program information setting page (hereinafter also referred to as the "shared setting page") 600d for inputting shared information (or shared setting information) on a terminal logged in with a medical staff account.

[0121] As described above, in the present invention, a unique exercise program created by a specific medical staff account U1 can be restricted so that it is accessible only to the specific medical staff account U1, or the unique exercise program can be shared so that it is accessible to other medical staff accounts U3. The medical staff can set sharing information for the exercise program on the sharing setting page 600d.

[0122] Here, the sharing information may include one of sharing status information (or sharing setting information, information regarding whether the exercise program can be shared or not) and sharing target information (medical staff account information to which the exercise program is to be shared).

[0123] As shown in Fig. 6b(d), the control unit 130 may provide an icon 661 associated with a sharing status setting function on the sharing setting page 600d. Such an icon 661 may be displayed as a first visual appearance corresponding to a shareable state and a second visual appearance corresponding to a non-shareable state. The control unit 130 may set sharing status information for either shareable or non-shareable to match the exercise program based on a selection of the medical staff account for the icon 661.

[0124] On the other hand, if there is no setting (or input or selection) of a medical staff account for the sharing information, the control unit 130 may match and set the sharing status (e.g., shareable) set as a basic value (or default value, default value) to the exercise program.

[0125] Furthermore, although not shown, the control unit 130 may further include a sharing target setting area on the sharing setting page 600d. The control unit 130 may set another medical staff account U3 with which the exercise program is to be shared, based on the selection (or input) of the medical staff account in the sharing target setting area. Details related to the sharing target setting will be described later.

[0126] Meanwhile, the present invention can perform a process of generating an exercise program including selected exercise items and set program information (S540, see FIG. 5). Furthermore, the present invention can perform a process of storing the generated exercise program in association with a specific medical staff account U1 (S550, see FIG. 5).

[0127] The control unit 130 can generate an exercise program composed of the selected exercise items. Specifically, the control unit 130 can generate a unique exercise program by matching at least a part of the exercise order according to the arrangement of the selected exercise items, the exercise method (exercise duration and number of exercise repetitions) set for each exercise item, exercise part information corresponding to the exercise program, exercise tool information, program name information, disease and surgery name information, step information, caution information, and shared information.

[0128] That is, the unique exercise program created in the present invention may be configured such that exercise items selected by a specific medical staff account U1 are performed (or progressed) in a certain order according to an exercise method (exercise duration and number of exercise repetitions) based on the settings of the specific medical staff account U1. Also, the created unique exercise program may be configured to be shared with another medical staff account U3 based on the settings of the specific medical staff account U1.

[0129] In addition, the control unit 130 can end the execution of the exercise program generation mode by storing the exercise program generated in cooperation with the specific medical staff account U1 in the storage unit 120.

[0130] Meanwhile, in the present invention, a process of allocating an exercise program to a patient account can be performed based on receiving prescription information for the patient account from a specific medical staff account U1 (S560, see FIG. 5). The control unit 130 can receive prescription information regarding exercise for the patient from the medical staff terminal 10.

[0131] As shown in FIG. 7(a), the control unit 130 can provide an exercise prescription page 700 including a prescription function for a patient's exercise on the medical staff terminal 10 logged in with a specific medical staff account U1.

[0132] The control unit 130 can provide an exercise prescription page 700 for each patient account on the medical staff terminal 10 so that prescriptions can be made for a specific patient account among the patient accounts matched to a specific medical staff account U1.

[0133] 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 medical staff account U1. The control unit 130 can provide an exercise prescription page 700 corresponding to the first patient account on the medical staff terminal 10 based on receiving an exercise prescription request for the first patient account (e.g., patient account of "Kim Woo-young") from the medical staff terminal 10.

[0134] The control unit 130 can display, on the exercise prescription page 700, information regarding at least a portion of the multiple exercise items registered in the exercise treatment provision service, or information regarding at least one exercise program registered in the exercise treatment provision service.

[0135] Specifically, the control unit 130 can display on the exercise prescription page 700 information regarding at least one of a unique exercise program 710 linked to a specific medical staff account U1, a shared exercise program 720 shared with a specific medical staff account U1, and a recommended exercise program 730 provided by the exercise treatment provision service.

[0136] In this case, based on the fact that the unique exercise programs and shared exercise programs associated with each medical staff account are different from each other, the control unit 130 can display information regarding the different unique exercise programs or different shared exercise programs on the exercise prescription page 700 provided for each medical staff account.

[0137] The control unit 130 can receive selection information (or a selection event) of a specific medical staff account U1 from the medical staff terminal 10 based on the selection of any one of the exercise programs displayed on the exercise prescription page 700.

[0138] For example, as shown in (a) of Fig. 7, it is assumed that information 711 corresponding to a unique exercise program linked to a specific medical staff account U1 and an icon (e.g., "Add" or "Select" or "Save") 711a are displayed on an exercise prescription page 700. The control unit 130 can receive selection information for the exercise program based on the information 711 about the exercise program or the icon 711a corresponding to the exercise program being selected on the exercise prescription page 700.

[0139] Further, the control unit 130 can assign the selected exercise program to the patient account based on receiving prescription information including the selected exercise program from the medical staff terminal 10.

[0140] The patient U2 can receive exercise treatment according to the exercise program assigned to the patient account by using the patient terminal 20 logged in with the patient account.

[0141] Specifically, the control unit 130 can provide an exercise video corresponding to an exercise program assigned to a patient account on the patient terminal 20 logged in with the patient account so that the patient U2 can imitate the exercise treatment while watching the exercise video. That is, the control unit 130 can check the exercise program assigned to the patient account and the exercise items included in the exercise program, and provide an exercise video corresponding to the exercise items on the patient terminal 20.

[0142] Meanwhile, in the present invention, a unique exercise program created in a specific medical staff account U1 can be shared with another medical staff account U3. When the control unit 130 receives sharing information of the unique exercise program from the specific medical staff account U1, the control unit 130 can share the unique exercise program with the other medical staff account U3 based on the received sharing information.

[0143] Here, the sharing information may include any one of sharing status information (information regarding whether the exercise program can be shared or not) and sharing target information (information on medical staff accounts to which the exercise program is to be shared).

[0144] As described above, when the exercise program generation mode is executed, the control unit 130 can receive sharing information of the unique exercise program from a specific medical staff account U1. The control unit 130 can set the sharing state and sharing target of the unique exercise program based on the sharing information received from the specific medical staff account U1.

[0145] Furthermore, when the exercise program generation mode is terminated, the control unit 130 can receive sharing information of the unique exercise program from a specific medical staff account U1. The control unit 130 can change the sharing state and sharing target of the unique exercise program based on the received sharing information.

[0146] In this way, when the exercise program generation mode is executed or terminated, the control unit 130 can receive sharing information of a unique exercise program from a specific medical staff account U1, and set the sharing status and sharing target of the unique exercise program based on the received sharing information.

[0147] For the sake of convenience, the following description will be given with an example in which the sharing state of the unique exercise program and the settings for the sharing target are performed after the exercise program generation mode is terminated. However, the data processing described below is also applicable in the same manner when the exercise program generation mode is executed.

[0148] As shown in FIG. 8a, the control unit 130 can provide a page 800 including a sharing setting function for a unique exercise program on the medical staff terminal 10 logged in with a specific medical staff account U1.

[0149] The control unit 130 may provide the unique exercise program page 800 on the terminal logged in with the specific medical staff account U1 based on receiving a request to provide the unique exercise program page 800 from the specific medical staff account U1. In this case, the unique exercise program for which the page is requested may correspond to any one of the unique exercise programs associated with the specific medical staff account U1.

[0150] For example, as shown in Fig. 4a, the control unit 130 can display information 410, 420 about at least one unique exercise program associated with a specific medical staff account U1 on a unique exercise program entry page 400a. Based on the selection of any one of the information 410, 420 displayed on the unique exercise program entry page 400a, the control unit 130 can receive a request from the specific medical staff account U1 to provide a unique exercise program page (hereinafter, described as a "program page") 800 corresponding to the selected information.

[0151] Meanwhile, the control unit 130 may display on the program page 800 a first graphic object 810 associated with a shared activation setting function for a unique exercise program, and a second graphic object 820 associated with a shared target setting function.

[0152] The control unit 130 can set the sharing status of the unique exercise program to either shareable or non-shareable based on the input (or selection) of the medical staff account for the first graphic object 810.

[0153] For example, in a state where the unique exercise program is set to "shareable", the control unit 130 can change the sharing status of the unique exercise program from "shareable" to "not shareable" based on an input by a user of a specific medical staff account U1 to the first graphic object 810. On the other hand, in a state where the unique exercise program is set to "not shareable", the control unit 130 can change the sharing status of the unique exercise program from "not shareable" to "shareable" based on an input by a user of a specific medical staff account U1 to the first graphic object 810.

[0154] In this case, the control unit 130 may display the visual appearance of the first graphic object 810 differently and display different guidance information around the first graphic object 810 based on whether the sharing status of the unique exercise program corresponds to either shareable or non-shareable.

[0155] For example, when a unique exercise program is set to be sharable, the control unit 130 can display a first graphic object 810 having a first visual appearance on the program page 800. In addition, the control unit 130 can display a first guidance information (e.g., "The program will be shared with the hospital (workplace) as a shared program") 811 around the first graphic object 810 to inform the user that the unique exercise program is in a sharable state.

[0156] Meanwhile, when the unique exercise program is set to be unshareable, the control unit 130 may display a first graphic object 810' having a second visual appearance different from the first visual appearance on the program page 800. In addition, the control unit 130 may display a second guidance message (e.g., "The program can only be viewed by the user") 811' around the first graphic object 810 to inform the user that the unique exercise program is in a unshareable state.

[0157] Meanwhile, as shown in FIG. 8a, the control unit 130 can identify at least one other medical staff account with which to share the unique exercise program based on input (or selection) of a particular medical staff account U1 to a second graphic object (e.g., “Select Medical Staff”) 820.

[0158] The control unit 130 can provide information about other medical staff accounts (e.g., “Physical Therapist / Kim Hyun-gi (1234)”, “Doctor / Lee Ji-eun (2222)”) 821, 822 other than the specific medical staff account U1 to the program page 800. For example, the control unit 130 can provide information about at least one other medical staff account 821, 822 to the program page 800 based on input (or selection) of the specific medical staff account U1 to a second graphic object (e.g., “Select Medical Staff”) 820.

[0159] The control unit 130 may identify at least one other medical staff account selected through the program page 800 as a unique exercise program sharing target. For example, based on the information 821 on the medical staff account of the medical staff "Lee Ji-eun" being selected, the control unit 130 may identify the medical staff "Lee Ji-eun" as a unique exercise program sharing target.

[0160] Furthermore, the control unit 130 can provide information corresponding to all other medical staff accounts (e.g., "select all" or "share with all medical staff in the hospital") so that a particular medical staff account U1 can select all other medical staff accounts as targets for sharing its own exercise program.

[0161] The control unit 130 can identify all other medical staff accounts as targets for sharing the unique exercise program based on the selection of information corresponding to all other medical staff accounts (e.g., “Select all” or “Share with all medical staff in the hospital”).

[0162] Meanwhile, as shown in Fig. 8b, the control unit 130 may identify a sharing target of the unique exercise program based on at least one of the hospitals 1a, 1b and the individual medical staff members U3, U4, U5, and U6. That is, the control unit 130 may identify a sharing target of the exercise program on a hospital-by-hospital basis, or on a medical staff-by-individual basis, U3, U4, U5, and U6.

[0163] Specifically, the control unit 130 can identify at least some of a plurality of other medical staff members who work in the same hospital (eg, "Hospital A") as the specific medical staff member, as sharing targets of the unique exercise program.

[0164] The control unit 130 can refer to the storage unit 120 and provide information on other medical staff accounts U3 and U6 matched to the same hospital 1a as the specific medical staff account U1 to the program page 800. In addition, the control unit 130 can provide information corresponding to all other medical staff accounts matched to the same hospital 1a (e.g., "select all" or "share with all medical staff in the hospital") to the program page 800.

[0165] The control unit 130 can identify a sharing target for the unique exercise program based on the selection of a particular medical staff account U1 relative to at least one of the other medical staff account information provided on the program page 800.

[0166] For example, based on receiving a selection corresponding to “overall sharing within the same hospital 910” from a specific medical staff account U1, the control unit 130 can identify all other medical staff accounts U3, U6 that are matched with the same hospital 1a as the specific medical staff account U1 as sharing targets.

[0167] As another example, based on receiving a selection corresponding to “individual sharing within the same hospital 920” from a specific medical staff account U1, the control unit 130 can identify only the selected other medical staff account U3 from among other medical staff accounts U3 and U6 that are matched with the same hospital 1a as the specific medical staff account U1 as the sharing target.

[0168] As another example, the control unit 130 may set "total sharing within the same hospital 910" as a basic value (or default value) for the sharing target for the unique exercise program. In this case, if the control unit 130 does not receive setting information for the sharing target from the specific medical staff account U1, it may specify all of the other medical staff accounts U3 and U6 that are matched with the same hospital 1a as the specific medical staff account U1 as the sharing target.

[0169] That is, the control unit 130 can share a unique exercise program created by a particular medical staff member with at least some of a plurality of other medical staff members who work in the same hospital as the particular medical staff member account.

[0170] Furthermore, as shown in FIG. 8b, the control unit 130 may extend and share a unique program of a particular medical staff member working at a first hospital (e.g., “Hospital A”) to a second hospital (e.g., “Hospital B”) different from the first hospital.

[0171] In this case, other hospitals with which the unique program can be shared may be set in advance. For example, assume that a first hospital, a second hospital, and a third hospital, which are different from one another, are registered in the exercise therapy providing service. In this case, the control unit 130 may set so that the unique exercise program can be shared between the first hospital and the second hospital. On the other hand, the control unit 130 may set so that the sharing of the unique exercise program is restricted between the third hospital and the first hospital and the second hospital. Such unique exercise program sharing setting between hospitals may be performed based on a request from the hospital. The hospital may request the sharing setting between the hospitals based on a business agreement contract between the hospitals, etc.

[0172] As shown in FIG. 8b, when the first hospital 1a and the second hospital 1b are configured to allow sharing of unique exercise programs, the control unit 130 can share a unique exercise program linked to a specific medical staff account U1 of the first hospital 1a with at least some of the medical staff accounts U4 and U5 of the second hospital 1b.

[0173] For example, based on receiving a selection corresponding to “share overall within other hospitals 930” from a particular medical staff account U1, the control unit 130 can identify all of the medical staff accounts U4 and U5 of the second hospital 1b as sharing targets.

[0174] As another example, the control unit 130 can identify a part of a medical staff account U4 of the second hospital 1b as a sharing target based on receiving a selection corresponding to “individual sharing within other hospitals 940” from a particular medical staff account U1.

[0175] Furthermore, in the present invention, unique exercise programs can be shared between different hospitals registered in the exercise therapy provision service of the present invention, or between medical staff belonging to different hospitals. In other words, the unique exercise programs of the present invention can be freely shared by hospitals or medical staff registered in the system of the present invention. As a result, the system of the present invention can play the role of a medical platform that allows hospitals and medical staff to share and activate each other's treatment know-how.

[0176] Meanwhile, as described above, the medical staff accounts in the present invention can be divided into a first type and a second type based on whether or not they have the authority to independently prescribe an exercise program to a patient. In the present invention, the "authority to independently prescribe an exercise program" can be understood as the authority to prescribe an exercise program without the approval of another person.

[0177] As shown in FIG. 9a, a "first type medical staff account U1" can be understood as a medical staff account that has the authority to independently prescribe exercise programs to patients.

[0178] Such a first type medical staff account U1 may further have the authority to approve an exercise program prescribed by a second type medical staff account U3.

[0179] A "second type medical staff account U3" can be understood as a medical staff account that can prescribe an exercise program to a patient based on the approval of the first type medical staff account U1.

[0180] In other words, the "second type medical staff account U3" may correspond to a medical staff account that does not have the authority to independently prescribe an exercise program to a patient. Such a second type of medical staff account U3 may not even have the authority to approve exercise programs prescribed by other medical staff accounts. The control unit 130 can set the type of the medical staff account to either the first type or the second type based on the occupational information of the medical staff.

[0181] For example, when the occupation of the medical staff is a first type of occupation (e.g., "doctor"), the control unit 130 can set the type of the medical staff account to a first type. Also, when the occupation of the medical staff is a second type of occupation (e.g., "therapist"), the control unit 130 can set the type of the medical staff account to a second type.

[0182] Meanwhile, in the present invention, different exercise program prescription processes can be performed based on whether the medical staff account that prescribed the exercise program to the patient is one of the first type and the second type.

[0183] In the present invention, a first exercise prescription process can be performed when the type of the medical staff account that prescribed the exercise program is a first type, and a second exercise prescription process can be performed when the type of the medical staff account that prescribed the exercise program is a second type.

[0184] To this end, when the control unit 130 receives prescription information from a medical staff account, it can check the type of the corresponding medical staff account and, depending on the check result, can perform either a first exercise prescription process or a second exercise prescription process.

[0185] Specifically, the control unit 130 can receive exercise program prescription information for a patient from a first type medical staff account U1 (S1010, see FIG. 9b).

[0186] The control unit 130 can assign the prescribed exercise program to the patient account U2 based on receiving exercise program prescription information for the patient from the first type medical staff account U1 (S1020, see FIG. 9b).

[0187] That is, when the control unit 130 receives prescription information from a first type of medical staff account, it can perform a first exercise prescription process to immediately assign an exercise program to a patient account based on the existence of authority in the first type of medical staff account to independently prescribe an exercise program.

[0188] Meanwhile, the control unit 130 may receive exercise program information for the patient from the second type medical staff account U3 (S1030, see FIG. 9b).

[0189] Based on receiving exercise program prescription information for the patient from the second type medical staff account U3, the control unit 130 can send approval request information for the prescribed exercise program to the first type medical staff account U1 (S1040, see FIG. 9b).

[0190] In this case, the control unit 130 can identify a first type medical staff account U1 that is the subject of a request for prescription approval of an exercise program based on the selection of a second type medical staff account U3 and any one of the pre-set matching information.

[0191] The control unit 130 may request approval of prescription of the exercise program from a specific first type medical staff account selected by the second type medical staff account U3 that prescribed the exercise program among the multiple first type medical staff accounts. In addition, the control unit 130 may request approval of prescription of the exercise program from a specific first type medical staff account matched with the second type medical staff account U3 that prescribed the exercise program among the multiple first type medical staff accounts.

[0192] When the control unit 130 approves the prescription for the exercise program from the first type medical staff account U1 that received the prescription approval request (i.e., an exercise program prescription approval event occurs), it can receive approval information from the first type medical staff account U1 (S1040, see FIG. 9b).

[0193] The control unit 130 can assign the exercise program prescribed by the second type medical staff account U3 to the patient account U2 based on the approval of the prescription of the exercise program by the first type medical staff account U1 (or the occurrence of an exercise program prescription event) (S1050, see FIG. 9b).

[0194] That is, when the control unit 130 receives prescription information from the second type medical staff account, it can send an exercise program prescription request to the first type medical staff account U1 having approval authority, based on the existence of authority to prescribe an exercise program independently in the second type medical staff account. Also, the control unit 130 can perform a second exercise prescription process to assign an exercise program to a patient account, based on the occurrence of an approval event for the exercise program prescription from the first type medical staff account.

[0195] Meanwhile, as shown in (a) of FIG. 9c, the control unit 130 can provide a page 1000a on a terminal logged in with a second type medical staff account, the page 1000a including an area 1011 for identifying a first type medical staff account that has approved the exercise program prescription.

[0196] The control unit 130 may display information about at least one first type medical staff account (e.g., “Yoon Chang (5678)” or “Lee Yoo Hyun (1234)”) 1011, 1011 in the area 1010.

[0197] In this case, the control unit 130 can display information about the first type medical staff account that is matched with the same hospital as the second type medical staff account in the area 1010. That is, the control unit 130 can provide information about medical staff having a first type of occupation (e.g., doctor) among medical staff who work in the same hospital as the medical staff having a second type of occupation (e.g., therapist) in the first area 1010.

[0198] Based on the selection of a specific first type medical staff account (e.g., “Yun-Chan”) through the area 1010, the control unit 130 may request approval of the exercise program prescription of the second type medical staff account from the selected first type medical staff account.

[0199] As shown in FIG. 9c(b), the control unit 130 can provide an exercise program approval page 1000b on the terminal logged in with the first type medical staff account U1.

[0200] The control unit 130 can display at least one of second type medical staff account information 1021, date information 1022 on which the exercise program was prescribed, exercise program information 1023, and patient information 1024 for which the exercise program is prescribed, on the exercise program approval page 1000b. The control unit 130 may also include, on the exercise program approval page 1000b, an approval graphic object 1025 associated with an exercise program approval function, and a rejection graphic object (or a disapproval graphic object) 1026 associated with an exercise program rejection (or disapproval) function.

[0201] The control unit 130 can assign the exercise program prescribed by the second type medical staff account U3 to the patient account U2 based on the approval graphic object 1025 being selected on the exercise program approval page 1000b.

[0202] On the other hand, the control unit 130 can restrict the exercise program prescribed by the second type of medical staff account U3 from being assigned to the patient account U2 based on the selection of the rejection graphic object (or disapproval graphic object) 1026 on the exercise program approval page 1000b.

[0203] On the other hand, when the control unit 130 receives exercise program prescription information for a patient from the second type medical staff account U3, it can assign the prescribed exercise program to the patient account based on the fact that automatic approval for the exercise program prescription is set.

[0204] In other words, when automatic approval for an exercise program is set (or activated), the control unit 130 can restrict the request for prescription approval of an exercise program prescribed in a second type account from the first type medical staff account U1, and immediately assign the prescribed exercise program to the patient account.

[0205] On the other hand, if automatic approval for the exercise program is not set (or is deactivated), the control unit 130 may request the first type medical staff account U1 to approve the prescription of the exercise program prescribed by the second type account.

[0206] For this purpose, as shown in (c) of Fig. 9c, the control unit 130 may provide a page 1000c including an icon 1030 associated with an automatic approval setting function for an exercise program prescription on a terminal logged in with a first type medical staff account U1. For example, the control unit 130 may include the automatic approval icon 1030 in the My Page 1000c.

[0207] The control unit 130 can set the automatic approval function for the exercise program to either activated or deactivated based on the input (or selection) of the first type medical staff account U1 to the automatic approval icon 1030.

[0208] For example, when the automatic approval function is set to “activated”, the control unit 130 can change the automatic approval function from “activated” to “deactivated” based on an input by the user of the first type medical staff account U1 to the automatic approval icon 1030. On the other hand, when the automatic approval function is set to “deactivated”, the control unit 130 can change the automatic approval function from “deactivated” to “activated” based on an input by the user of the first type medical staff account U1 to the automatic approval icon 1030.

[0209] In the present invention, a user environment can be provided in which medical staff can share their own exercise programs with each other on a hospital basis. Therefore, when a medical staff member is transferred or the hospital where the medical staff member works is changed, there is a possibility that the shared programs shared between the medical staff member will not be shared, so it is very important to update the hospital information of the changed medical staff member.

[0210] Therefore, the present invention can provide a function for changing the work hospital information of the medical staff. In the present invention, the work hospital described can mean the hospital where the medical staff works (or belongs) or an associated hospital.

[0211] As shown in FIG. 10, the control unit 130 can provide a page 1100 for changing the hospital (or hospital information) where the medical staff member works on the terminal logged in with the medical staff member account. When a medical staff member is transferred from a first hospital to a second hospital, the medical staff member can change the hospital where the medical staff member works from the first hospital to the second hospital through the page 1100 .

[0212] The control unit 130 can update the hospital information matched to the medical staff account from the first hospital to the second hospital based on the medical staff account inputting the second hospital as the medical staff's changed place of employment hospital.

[0213] Meanwhile, when the hospital matched with the medical staff account is changed from the first hospital to the second hospital, the control unit 130 may discontinue sharing the exercise program that was shared with other medical staff accounts working at the first hospital so as not to share it any further.

[0214] For example, assume that a unique program generated by a particular medical staff account U1 based on the particular medical staff working at the first hospital has been shared with a medical staff account U3 of another medical staff member working at the first hospital.

[0215] When the work hospital information matched to the specific medical staff account U1 is changed from a first hospital to a second hospital, the control unit 130 may discontinue sharing the exercise program of the specific medical staff account U1 with another medical staff account U3 matched to the first hospital.

[0216] Furthermore, when the hospital matched with a medical staff account is changed from a first hospital to a second hospital, the control unit 130 can discontinue sharing the exercise program shared with other medical staff accounts working at the first hospital so that the exercise program is no longer shared with the medical staff account transferred to the second hospital.

[0217] As described above, the method and system for generating a customized exercise treatment program according to the present invention can provide a user environment in which medical staff can directly customize an exercise program by executing an exercise program generation mode on a terminal logged in with a specific medical staff account.

[0218] Specifically, the method and system for generating a customized exercise treatment program according to the present invention generates an exercise program by selecting at least one exercise item to be included in the exercise program in an exercise program generation mode, thereby enabling medical staff to generate an effective and optimal exercise program by taking into consideration various factors such as the patient's characteristics such as disease, symptoms, age, age group, and exercise ability.

[0219] In particular, the method and system for generating a customized exercise treatment program according to the present invention stores the generated exercise program in association with a specific medical staff account U1, thereby enabling the exercise program generated by the specific medical staff account U1 to be managed and the exercise program to be shared with other medical staff accounts.

[0220] Furthermore, the method and system for generating a customized exercise treatment program according to the present invention can assign the exercise program to a patient account based on receiving prescription information for the patient account from a specific medical staff account U1. This allows a doctor to prescribe a customized exercise program for treatment to a patient, and the patient can receive an optimal exercise program and exercise effectively.

[0221] 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.

[0222] Furthermore, the present invention described above may be embodied as computer readable codes or instructions on a medium having a program recorded thereon, i.e., the present invention may be provided in the form of a program.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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."

[0227] 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.

[0228] 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."

[0229] 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.

[0230] 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.

[0231] 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)".

[0232] 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."

[0233] 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).

[0234] 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.

[0235] 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. Doctor D can prescribe a medicine to patient U by referring to user DB 30 that contains user information of patient U.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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. Here, "joint points" may refer to multiple joints of patient U (or a part of patient U's body that includes joints). 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.

[0243] 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 with the same reference numerals "P1" and "P2", respectively.

[0244] 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."

[0245] 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.

[0246] Here, in the third data processing method, the exercise therapy application 100 and the artificial intelligence server 200 may process data sequentially or simultaneously.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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."

[0254] 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.

[0255] 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 .

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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."

[0260] 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.

[0261] 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.

[0262] Here, the "rule information" refers to information including 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."

[0263] 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. In the present invention, the patient's motion can be analyzed from the motion video by at least one of the artificial intelligence motion analysis unit 122 and the rule-based motion analysis unit 123.

[0264] 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").

[0265] 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.

[0266] 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.

[0267] 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).

[0268] 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. 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.

[0269] 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.

[0270] 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."

[0271] 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. 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.

[0272] 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.

[0273] 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.

[0274] 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).

[0275] 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).

[0276] 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.

[0277] 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.

[0278] 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.

[0279] 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.

[0280] 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.

[0281] 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).

[0282] 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. The exercise motion analysis process performed by the exercise treatment delivery system 1000 will now be described in more detail.

[0283] 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).

[0284] 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."

[0285] 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.

[0286] 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.

[0287] 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).

[0288] 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).

[0289] 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).

[0290] 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.

[0291] 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."

[0292] 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.

[0293] As shown in FIG. 1, 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 raises," "standing and bending the knees") included in an exercise plan assigned to a particular account.

[0294] 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").

[0295] 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. 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.

[0296] 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.

[0297] 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.

[0298] 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).

[0299] 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.

[0300] 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.

[0301] 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.

[0302] 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.

[0303] 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.

[0304] 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).

[0305] 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.

[0306] 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. Here, "joint points" may refer to multiple joints of patient U (or a part of patient U's body that includes joints). 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.

[0307] 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. 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.

[0308] 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 in advance and exist (see FIG. 18d).

[0309] 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.

[0310] 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.

[0311] 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. 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 .

[0312] 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.

[0313] When a specific joint point is visible in the exercise video 300, the exercise therapy providing system 1000 can extract key points corresponding to the specific joint point through a first key point extraction process.

[0314] 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. Additionally, the exercise therapy delivery system 1000 can extract the identified visible joint points as key points.

[0315] 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.

[0316] 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".

[0317] 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.

[0318] 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.

[0319] 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.

[0320] 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".

[0321] 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.

[0322] 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.

[0323] 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.

[0324] 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.

[0325] 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.

[0326] 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.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] 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.

[0332] 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".

[0333] 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.

[0334] 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.

[0335] 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.

[0336] 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.

[0337] 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. Here, the rule information can be understood as information in which rules are predefined for analyzing the relative positional relationship between key points. The exercise therapy delivery 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.

[0338] A method for analyzing a joint range of motion based on the relative positional relationship of key points and rule information will be described below as an example. However, the content described below is merely one embodiment for analyzing a patient's motion based on the relative positional relationship of key points and rule information, and the present invention can analyze various patient motions based on the relative positional relationship of key points and rule information.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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.

[0344] 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.

[0345] 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.

[0346] 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.

[0347] Here, the first analysis target frame can be understood as a frame formed before in time, and the second analysis target frame can be understood as a frame formed after in time. The exercise treatment delivery system 1000 can extract key points in each of the first and second frames under analysis.

[0348] 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.

[0349] 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.

[0350] 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.

[0351] 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.

[0352] 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.

[0353] 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.

[0354] 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.

[0355] 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. 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).

[0356] 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.

[0357] 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). 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.

[0358] 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.

[0359] 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.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] 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 of the exercise movement. Other embodiments related to the provision of analysis results will be described later.

[0368] 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. The learning data used to learn the posture estimation model of the present invention will be specifically described below.

[0369] 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. 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. In database 40, training data for training pose estimation model 52 may be stored and present as a training data set.

[0370] 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.

[0371] 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.

[0372] 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." Meanwhile, the 'exercise video 300' described in the present invention may include an 'analysis target exercise video' and a 'learning target exercise video'.

[0373] 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.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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. 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.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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). 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.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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. 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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). In the database 40, a learning data set 400 extracted and generated from the training object exercise video 300 by the training unit 51 may be stored. The learning data set 400 used for estimating a user's exercise posture will be described in more detail below. 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. 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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 generate a learning data set 400 by classifying the learning data corresponding to the same information attribute into the same data group.

[0414] 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. The multiple data groups and the relationships between the groups will be specifically described below.

[0415] 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.

[0416] 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. 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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".

[0439] 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.

[0440] 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]".

[0441] 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.

[0442] 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. As described above, the database 40 may contain a predefined order of a plurality of training target joint points P1 and P2.

[0443] 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.

[0444] 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.

[0445] On the other hand, the learning unit 51 can extract (or identify) the position information 411, 412 of the learning target joint points P1, P2 by either the first process or the second process based on whether the learning target joint points P1, P2 are visible in the exercise video 300.

[0446] In the present invention, whether the training target joint points are visible or not can be understood to mean whether the training target joint points P1 and P2 are visible in the training target motion image 300 or not.

[0447] In the present invention, a visible joint point in a training motion image can be named a "training target visible joint point", and an invisible joint point in a training motion image can be named a "training target invisible joint point".

[0448] If the learning object motion video 300 includes parts of the subject U's body corresponding to the learning object joint points P1 and P2, the learning unit 51 can determine that the learning object joint points are visible in the learning object motion video 300.

[0449] Based on the fact that the learning target joint points P1 and P2 are visible in the learning target movement video 300, the learning unit 51 can extract position information of the actual positions where the learning target joint points P1 and P2 are located from the learning target movement video 300 through the first process.

[0450] In the present invention, the position information of the learning target joint points P1, P2 extracted by the first process can be named and explained as "first type information (first type position information)" or "actual position information".

[0451] On the other hand, if the exercise video 300 does not include a part of the subject U's body corresponding to the learning joint points P1 and P2, the learning unit 51 can determine that the learning joint points P1 and P2 are not visible in the learning exercise video 300.

[0452] Based on the fact that the learning target joint points P1 and P2 are not visible in the movement video 300, the learning unit 51 can predict the expected positions of the learning target joint points P1 and P2 through the second process and extract (or identify) the predicted position information.

[0453] In the present invention, the position information of the learning target joint points P1, P2 extracted by the second process can be named and explained as "second type information (second type position information)" or "predicted position information".

[0454] In this manner, in the present invention, the plurality of position information 411, 412 included in the first data group 410 can be defined to have different extraction processes and type information depending on whether the plurality of training target joint points P1, P2 are visible in the training target exercise image 300. Meanwhile, the second process may include various data processing operations for extracting (identifying) predicted position information of the learning target joint points P1 and P2 that are not visible in the motion image 300.

[0455] For example, the learning unit 51, which extracts predicted position information through the second process, can predict predicted position information of the learning target joint points P1 and P2 that are not visible in the exercise video 300 based on the actual position information of the learning target joint points that are visible in the exercise video 300.

[0456] In this case, the learning unit 51 can identify predicted position information by assigning weights to multiple learning target joint points P1, P2 that are visible in the exercise video 300 based on their relevance to learning target joint points P1, P2 that are not visible in the exercise video 300.

[0457] For example, the closer the orders of the learning joint points P1 and P2 are, the higher the relevance between the learning joint points may be set. The relevance between the learning joint points corresponding to the third order may be set such that the learning joint point corresponding to the second order is higher than the learning joint point corresponding to the first order.

[0458] As another example, the relevance between the learning target joint points P1 and P2 may be set highest between the learning target joint points P1 and P2 that exist corresponding to the left and right sides of the subject U. For example, the relevance between the learning target joint point P3 corresponding to the center of the left wrist and the learning target joint point P3 corresponding to the center of the right wrist may be set highest (see (a) of FIG. 18c).

[0459] Furthermore, the learning unit 51 can extract predicted position information of the learning target joint points P1 and P2 that are not visible in the movement video 300 based on motion information of the movement action performed by the subject U in the movement video 300. The database 40 may store motion information including a moving path (e.g., a moving position, a moving direction) of a body (or a joint point) during a motion.

[0460] The learning unit 51 can identify predicted position information of the training target joints that are not visible in the exercise video 300 by referring to the position information of the training target joint points P1 and P2 that are visible in the exercise video 300 and the motion information in the database 40.

[0461] Meanwhile, as shown in FIG. 18c, a second data group 420 among the multiple data groups 410 to 450 may include data values ​​421 and 422 indicating whether or not the learning target joint points P1 and P2 of a subject U included in the motion image 300 are visible.

[0462] As shown in (b) of FIG. 18c, the data values ​​of the data 421, 422 included in the second data group 420 may be either a first data value (e.g., “1”) or a second data value (e.g., “2”), depending on whether the learning target joint points P1, P2 are visible or not.

[0463] Data having a first data value (e.g., “1”) is data indicating that the learning target joint points P1 and P2 are visible in the exercise image 300, and can be understood as information indicating that the position information included in the first data group 410 is of the first type (actual position information).

[0464] The learning unit 51 may extract a first type of position information (actual position information) of the learning target joint points when the learning target joint points P1 and P2 are visible in the exercise video 300. Based on the extraction of the first type of position information (actual position information) from the exercise video 300, the learning unit 51 may generate (configure) the learning data set 400 by including data having a first data value (e.g., “1”) in the second data group 420.

[0465] On the other hand, the second data value (e.g., “2”) is data indicating that the learning target joint points P1 and P2 are not visible in the exercise video 300, and can be understood as information indicating that the position information included in the first data group 410 is of the second type (predicted position information).

[0466] The learning unit 51 can extract (or identify) the second type of position information (actual position information) of the learning target joint point when the learning target joint point is not visible in the exercise video 300. Based on the extraction (or identification) of the second type of position information (predicted position information) from the exercise video 300, the learning unit 51 can generate (configure) the learning dataset 400 by including data having a second data value (e.g., “2”) in the second data group 420.

[0467] Meanwhile, as shown in (b) of FIG. 18c, the learning unit 51 can generate (configure) the learning dataset 400 by arranging the data (or data values) 421, 422 included in the second data group 420 in the same order as the predefined order in which the position information 411, 412 of each of the multiple training target joint points is arranged, so as to indicate whether each of the multiple training target joint points P1, P2 is visible or not.

[0468] The learning unit 51 can sequentially arrange the data 421, 422 indicating whether each of the multiple learning target joint points is visible or not in the second data group 420 based on a predefined order between the multiple learning target joint points P1, P2.

[0469] For example, as shown in (b) of FIG. 18c, the learning unit 51 can arrange data 421 having a first data value (e.g., “1”) in a first order corresponding to the first learning target joint point P1 within the second data group 420 based on the first learning target joint point P1 being visible in the movement video 300.

[0470] 18c(a), the second learning target joint point P2 is shown as being visible in the motion video 300, but it is assumed that the second learning target joint point P2 is not visible in the motion video 300. The learning unit 51 can arrange data 422 having a second data value (e.g., “2”) in a second order corresponding to the second learning target joint point P2 based on the fact that the second learning target joint point P2 is visible in the motion video 300.

[0471] Meanwhile, in the present invention, it can be understood that the definition of the type of the location information included in the first data group 410 is performed according to the data value of the data included in the second data group 420.

[0472] As shown in (b) of FIG. 18c, assume that in the second data group 420, data 421 arranged in a first order has a first data value (e.g., “1”), and data 422 arranged in a second order has a second data value (e.g., “2”).

[0473] In the present invention, based on the fact that the data 421 arranged in a first order in the second data group 420 has a first data value (e.g., “1”), the type of the location information 411 arranged in a first order in the first data group 410 can be defined as a first type of location information (actual location information).

[0474] On the other hand, based on the fact that the data 422 arranged in the second order in the second data group 420 has a second data value (e.g., “2”), the type of the location information 411 arranged in the second order in the first data group 410 can be defined as a second type of location information (predicted location information).

[0475] Meanwhile, the posture estimation model 52 according to the present invention can perform learning by setting different learning weights for each of the position information 411, 412 of a plurality of training target joint points included in the first data group 410 based on the data values ​​included in the second data group 420.

[0476] Specifically, when data 421 arranged in a first order in second data group 420 has a first data value (e.g., “1”), posture estimation model 52 can set a first learning weight to position information 411 arranged in a first order in first data group 410 and perform learning.

[0477] On the other hand, if the data 422 arranged in the second order in the second data group 420 has a second data value (e.g., “2”), the posture estimation model 52 can set a second learning weight for the position information 412 arranged in the second order in the first data group 410 and perform learning.

[0478] That is, the posture estimation model 52 can perform learning by setting different learning weights for the first type of position information (actual position information) and the second type of position information (predicted position information) based on the data values ​​of the second data group 420. In this case, posture estimation model 52 may set the first learning weight higher than the second learning weight.

[0479] 18b, the data groups 410 to 450 may further include a third data group 430 including information related to a photographing direction of a subject included in the motion image 300. The third data group 430 may include a data value indicating a photographing direction in which a subject U included in the motion image 300 was photographed. As shown in FIG. 18e(a), the subject U may be photographed from different photographing directions (eg, "front" or "side").

[0480] Here, the term "shooting direction" may be understood as the direction of the axis of a camera (see reference numeral "201" in FIG. 19) relative to the subject U. Here, the camera 201 may be understood as the camera 201 that shoots the motion image 300 including the subject U. Such a camera 201 may include the camera 201 provided in the user terminal 10 or 20.

[0481] The data values ​​included in the third data group 430 shown in Fig. 18e(b) correspond to the shooting direction of the object U and may have different data values ​​(e.g., "0" or "1"). The data values ​​included in the third data group may be configured to have different data values ​​according to the shooting direction of the object U based on the camera that shot the object. In the following, to avoid confusion with the data values ​​included in the second data group 420, the data values ​​corresponding to the shooting direction are named and described as "data object values".

[0482] Data having a first data object value (e.g., “0”) can be understood as data indicating that the shooting direction of the training object video 300 is a first direction (e.g., forward) (see FIG. 18e(b)).

[0483] The learning unit 51 can generate a learning dataset 400 by including data having a first data object value (e.g., “0”) in the third data group 430 based on the fact that the shooting direction of the subject U included in the training object exercise video 300 corresponds to a first direction (e.g., forward direction).

[0484] On the other hand, the second data object value (e.g., “1”) can be understood as data indicating that the shooting direction for the motion image 300 is a second direction (e.g., a side direction) different from the first direction (see FIG. 18e(b)).

[0485] The learning unit 51 can generate a learning dataset 400 by including data having a second data object value (e.g., “1”) in the third data group 430 based on the fact that the shooting direction of the subject U included in the exercise video 300 corresponds to the second direction (e.g., side direction).

[0486] Furthermore, although not shown, a third data object value (e.g., “2”) can be understood as data indicating that the shooting direction for the motion image 300 is a third direction (e.g., diagonal direction) different from the first and second directions.

[0487] The learning unit 51 can store in the database 40 the third data group 430 including data having a third data object value (e.g., "2") based on the fact that the shooting direction of the subject U included in the training object video 300 corresponds to the third direction (e.g., diagonal direction).

[0488] On the other hand, the first to third directions described in the present invention can be understood as the cases where the angles between the subject U and the axis of the camera 201 based on a pre-set direction (e.g., clockwise) correspond to the first to third ranges, respectively.

[0489] For example, the first direction can be understood as a range in which the angle between the subject U and the axis of the camera 201 corresponds to a range from a first angle to a second angle that is larger than the first angle. The second direction can be understood as a range in which the angle between the subject U and the axis of the camera 201 corresponds to a range from the second angle to a third angle that is larger than the second angle. Moreover, the third direction can be understood as a range in which the angle between the subject U and the axis of the camera 201 corresponds to a range from the third angle to a fourth angle that is larger than the third angle.

[0490] Meanwhile, the learning unit 51 may configure an angle (or angle value) between the subject U and the axis of the camera 201 based on a preset direction (e.g., clockwise) as the data value of the data included in the third data group 430. For example, when the subject U and the axis of the camera 201 form a right angle based on the clockwise direction, the learning unit 51 may configure a data value of "90" as the data of the third data group 430. Meanwhile, the learning unit 51 may perform learning for estimating the posture of the subject U by linking the learning data sets 400 having different shooting direction information included in the third data group 430.

[0491] For example, it is assumed that the data included in the third data group 430 includes a first learning data set having a first data object value (e.g., “0”) and a second learning data set having a second data object value (e.g., “1”). The learning unit 51 can perform learning for estimating the posture of the subject U by linking the position information of the first data group 410 included in the first data set with the position information of the first data group included in the second data set.

[0492] On the other hand, when the learning unit 51 estimates the exercise posture of the subject U from the exercise video to be analyzed captured in the first direction, the learning unit 51 can estimate the exercise posture of the subject U based on a posture estimation model learned from the exercise video to be studied 300 captured in the first direction.

[0493] The exercise therapy provision system 1000 of the present invention can analyze the exercise movements of subject U based on key points extracted from a posture estimation model trained using a learning dataset 400 including shooting direction information (data object values ​​or data values) corresponding to the shooting direction of the exercise video to be analyzed.

[0494] On the other hand, when the motion analysis units 120 and 210 estimate the movement posture of the subject U from the analysis target movement video 300 captured in a first direction, they can analyze the movement motion of the subject U using posture estimation information estimated from a posture estimation model learned from the training target movement video 300 captured in each of the first and second directions.

[0495] That is, when the motion of the subject U is captured from the analysis target motion video 300 captured in a first imaging direction, the motion analysis units 120 and 210 can analyze the motion of the subject U using posture estimation information estimated from a posture estimation model trained on a first learning data set including data object values ​​corresponding to the first imaging direction and a second learning data set including data object values ​​corresponding to a second imaging direction different from the first imaging direction. In this case, the posture estimation model 52 may correspond to a posture estimation model trained by setting a weight on the first learning data set.

[0496] In this way, the posture estimation model 52 may be configured to be trained by taking into consideration the shooting direction of the subject through the training data set having different data values ​​according to the shooting direction of the subject included in the training video. Furthermore, the user's motion analysis result may be a result of analyzing a specific motion of the user based on posture estimation information extracted in the posture estimation model by taking into consideration the shooting direction of the user included in the motion video.

[0497] Meanwhile, as shown in FIG. 18 b , a fourth data group 440 of the plurality of data groups may include an exercise code that matches an exercise motion performed by a subject U included in the training exercise image 300 .

[0498] As shown in FIG. 18f, in the database 40, a plurality of different exercise actions 710, 720, 730 may be matched with different exercise codes (eg, "502", "503", "504"), respectively.

[0499] The "exercise code" described in the present invention is a data value that distinguishes different exercise actions from each other, and may be used interchangeably with "exercise key", "action code" or "action key".

[0500] The learning unit 51 can generate a learning dataset 400 by including in the fourth data group 440 a specific movement code ("502") that matches a specific exercise movement (e.g., "standing and extending one leg forward") 710 performed by the subject U included in the training exercise video 300. The learning unit 51 can perform learning for posture estimation by linking a plurality of learning data sets 400 including the same movement code.

[0501] For example, assume that there is a first learning data set based on a first training object exercise video and a second learning data set based on a second training object exercise video 300. The learning unit 51 can link the first learning data set and the second learning data set with each other and perform learning for posture estimation based on the fact that the exercise code (e.g., “502”) included in the first learning data set and the second learning data set is the same.

[0502] Such an exercise code may be included in the fourth data group 440 based on the exercise movement performed by the subject U being identified by at least one of information received from the user terminal 10, 20, a system administrator, or the learning unit 51.

[0503] The learning unit 51 can identify the exercise action performed by the subject U based on the information received from the user terminals 10 and 20. For example, based on the selection of a graphic object corresponding to "start exercising" from the user terminals 10 and 20, the learning unit 51 can control the state of the camera 201 provided in the user terminals 10 and 20 to an activated state so that the camera 201 captures an exercise video.

[0504] In this case, the graphic object may correspond to a particular exercise movement, and the learning unit 51 may determine that the subject U included in the exercise video 300 received from the user terminals 10 and 20 has performed the particular exercise movement. Furthermore, the learning unit 51 can identify the exercise and movement performed by the subject U based on the information input by the system administrator.

[0505] Furthermore, the learning unit 51 can identify the exercise / motion performed by the subject U based on the position information of the learning target joint of the subject U included in the exercise video 300. In this case, the exercise / motion performed by the subject U can be identified by referring to the motion information of the exercise / motion stored in the database 40.

[0506] Meanwhile, the learning unit 51 may match a plurality of learning data sets 400 including the same motion code with each other based on the motion code included in the fourth data group 440 and store the matched data in the database 40 .

[0507] In this case, the learning unit 51 may divide and allocate the memory (or memory space) of the database 40 based on the learning code. In the present invention, dividing the memory (or memory space) of the database 40 may be understood as generating folders on the database 40 based on the motion codes. Furthermore, the learning unit 51 may store the learning data set 400 including a specific motion code in a folder corresponding to the specific motion code.

[0508] On the other hand, the motion analysis units 120 and 210 can analyze the exercise movements in the exercise video to be analyzed using posture estimation information estimated from a posture estimation model learned using a learning dataset including movement codes corresponding to specific exercise movements performed by the subject U.

[0509] Furthermore, the motion analysis unit 120, 210 can estimate the movement movement of subject U for a specific movement movement using posture estimation information estimated from a posture estimation model learned based on the learning target movement video 300 related to the same specific movement movement as that of subject U included in the analysis movement target.

[0510] Meanwhile, as shown in FIG. 18c, a fifth data group 450 among the multiple data groups 410 to 450 may include size information 451 of a bounding box 301 for a subject U included in the learning exercise video 300, and position information 452 of the center of the bounding box 301. In the present invention, the "size information" of the bounding box 301 is sometimes mixed with the "scale".

[0511] The learning unit 51 extracts size information 451 of the bounding box 301 corresponding to the subject U detected in the training object motion video 300, extracts center position information 452 of the bounding box 301, and includes this in a fifth data group 450 to generate a learning dataset 400.

[0512] As described above, the learning unit 51 can use various algorithms for object detection to detect the subject U from the training target 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 target motion video 300.

[0513] Based on an object detection algorithm, the learning unit 51 can extract size information 451 and center position information 452 of a bounding box corresponding to the subject U from the learning target video 300, and include the size information 451 and center position information 452 in a fifth data group 450 to generate a learning dataset 400. In this case, the learning unit 51 can extract the center position information 452 of the bounding box 301 in the form of a pair of coordinate information on the x-axis and y-axis. Meanwhile, the learning unit 51 can configure the learning data set 400 including the video identification information of the training object exercise video 300 .

[0514] Here, the "video identification information" refers to information for identifying the video 300 from which the information contained in the learning dataset was extracted, and may include, for example, file name information and file format type information (or extension information, e.g., "JPG", "TIF") of the training object exercise video 300. In the present invention, the video identification information can be named a sixth data group corresponding to a sixth information attribute. The learning unit 51 can generate the learning data set 400, including the sixth data group constituted by the video identification information.

[0515] Meanwhile, in the present invention, the analysis of the user's exercise movements contained in the exercise video to be analyzed is completed through posture estimation information extracted based on the posture estimation model 52, and the exercise movement analysis results can be provided to the user terminal 200 based on the completion of the analysis of the user's exercise movements contained in the exercise video to be analyzed.

[0516] 19(a), the control unit 130 can control the camera state to an active state so that the camera 201 provided in the user terminal 200 captures an exercise video to be analyzed, based on the selection of a graphic object 210 corresponding to "start exercise" from the user terminal 200. In addition, the control unit 130 can receive the exercise video to be analyzed captured by the camera 201 from the user terminal 200 in real time or based on the completion of the user's exercise.

[0517] As shown in (b) of FIG. 19, the control unit 130 may provide the user terminal 200 with a motion analysis result using the analysis result based on the completion of the analysis of the motion of the user included in the analysis target image based on the posture estimation model 52. The motion analysis result provided on the user terminal 200 by the control unit 130 may include various information. For example, the control unit 130 may render a graphic object corresponding to joint points P1 and P2 for the subject U corresponding to the user and provide it on the user terminal 200. In this case, the control unit 130 may also provide joint movable range information (angle information) 221 of the subject U.

[0518] 19(c), the control unit 130 can provide, on the user terminal 200, motion posture information 820 for each of a plurality of joint points of the subject U performing a specific motion posture (e.g., "stretching arms out to the side") 810. For example, the control unit 130 can provide, on the user terminal 200, the joint movable ranges of the joint points located on the first side (e.g., left side) of the user and the joint movable ranges of the joint points located on the second side (e.g., right side).

[0519] Meanwhile, the control unit 130 may prescribe (provide) an exercise plan (or exercise program) including at least one exercise movement to the user based on the motion analysis result for the specific exercise movement of the user.

[0520] For example, as shown in (a) of FIG. 20, the control unit 130 can add an exercise action to an exercise plan (or exercise program) based on the exercise action analysis results, and provide a service page 910 on the user terminal 200 including information on the exercise plan (or exercise program) including the additional exercise action.

[0521] As another example, as shown in (b) of FIG. 20, the control unit 130 can adjust the difficulty of the exercise plan (or exercise program) based on the movement analysis posture results, and provide a service page 920 including the adjusted difficulty information on the user terminal 200.

[0522] As another example, as shown in (c) of FIG. 20, the control unit 130 can exclude some of the exercise movements from the exercise plan (or exercise program) based on the results of the exercise movement analysis, and provide a service page 930 on the user terminal 200 informing the user that the exercise movements have been excluded.

[0523] Meanwhile, in the present invention, when the analysis of the patient's motion is completed based on the above-mentioned motion analysis units 120 and 210, the motion analysis result can be provided to the patient terminal 10. As shown in Figures 21a, 21b and 21c, the motion analysis result can be provided through the exercise treatment application 100 installed in the patient terminal 10.

[0524] As shown in (a) of Fig. 21a, the exercise therapy application 100 can provide a service page configured to enable access to each of the multiple services provided by the present invention on the patient terminal 10. For example, the service page may be configured to enable access to at least one of i) an exercise guide page linked to an exercise guide information providing function for an exercise plan assigned to a patient account, ii) an exercise page related to the execution of an exercise plan assigned to a patient account (see (b) of Fig. 21a), iii) a function evaluation page linked to a function evaluation (see (c) of Fig. 21a), and iv) a plan evaluation page linked to an exercise plan evaluation function.

[0525] Furthermore, as shown in Fig. 21b, the exercise therapy application 100 can provide an exercise report page that provides an exercise report based on the exercise motion analysis result and the exercise execution result on the patient terminal 10. For example, the exercise report page may include at least one of exercise execution rate information (see (a) and (b) of Fig. 21b) and exercise plan difficulty information (see (c) of Fig. 21b). Furthermore, as shown in FIG. 21c, the exercise therapy application 100 can provide the patient's exercise motion analysis results on the patient terminal 10.

[0526] As shown in (a) of Fig. 21c, the exercise therapy application 100 can provide motion analysis information for each of a plurality of joint points of a subject U performing a specific prescribed motion (e.g., "stretching arms out to the side") on the patient terminal 10. For example, the exercise therapy application 100 can provide a joint range of motion of a joint point located on a first side (e.g., left side) of the patient and a joint range of motion of a joint point located on a second side (e.g., right side) of the patient on the patient terminal 10 as a graph.

[0527] As shown in (b) of FIG. 21c, the exercise therapy application 100 can provide daily exercise motion analysis results of a patient who has performed prescribed exercises according to an exercise plan for a certain period of time. For example, the exercise therapy application 100 may provide joint range of motion information corresponding to a first exercise day and joint range of motion information corresponding to a second exercise day. The exercise therapy application 100 may also provide average joint range of motion for the first exercise day and the second exercise day.

[0528] As shown in (c) of FIG. 21c, the exercise therapy application 100 may render graphic objects corresponding to key points P1 and P2 in the exercise video 300 and provide them on the patient terminal 10. In this case, the exercise therapy application 100 may also provide the patient's joint range of motion information (angle information) together.

[0529] Meanwhile, the exercise treatment providing system 1000 according to the present invention can provide the exercise motion analysis results provided to the patient terminal 10 to the doctor terminal 20 so that the doctor can monitor the execution of the patient's exercise plan.

[0530] As described above, the system and method for providing exercise therapy using an AI posture estimation model and a motion analysis model according to the present invention can receive prescription information on exercises for a patient from a doctor terminal, and assign an exercise plan including at least one prescribed exercise to the patient account based on the prescription information. This allows the doctor to prescribe to the patient even if the doctor and the patient do not meet in person for exercise therapy for musculoskeletal disorders, and the patient receives an exercise plan according to the doctor's prescription, thereby eliminating spatial, time, and economic constraints on exercise therapy and improving access to exercise therapy.

[0531] Furthermore, the system and method for providing exercise therapy using the artificial intelligence posture estimation model and motion analysis model of the present invention can analyze the user's exercise movements by extracting key points corresponding to a plurality of pre-set joint points from the exercise video, focusing on the joints required for exercise therapy of musculoskeletal diseases.

[0532] Furthermore, the system and method for providing exercise therapy using an AI posture estimation model and a motion analysis model according to the present invention can analyze a motion related to a specific motion of a user included in an exercise video based on a posture estimation model trained using a training data set including position information of joint points. As a result, the present invention can accurately analyze the posture of a patient from an exercise video, and in particular, obtain information on the range of motion of the patient's joints, alignment state, and disengagement state, thereby improving the quality of medical services.

[0533] Furthermore, the system and method for providing exercise therapy using the artificial intelligence posture estimation model and motion analysis model of the present invention transmits the analysis results of the patient's exercise motion to the patient terminal, so that the patient can receive feedback on the exercise video and improve the effectiveness of the exercise therapy without having to go directly to a hospital located far away.

[0534] On the other hand, the computer readable medium includes any kind of recording device in which data readable by a computer system is stored. Examples of the computer readable medium include HDD (Hard Disk Drive), SSD (Solid State Disk), SDD (Silicon Disk Drive), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

[0535] Furthermore, the computer-readable medium may include a storage and may be a server or cloud storage that the electronic device can access through communication. In this case, the computer can download the program according to the present invention from the server or cloud storage through wired or wireless communication.

[0536] Furthermore, in the present invention, the above-mentioned computer is an electronic device equipped with a processor, that is, a CPU (Central Processing Unit), and the type of the computer is not particularly limited.

[0537] However, the above detailed description should not be construed as limiting in all respects, but should be considered as illustrative. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the scope of the present invention are included in the scope of the present invention.

Claims

1. Executing an exercise program generation mode on a terminal logged in with a specific medical staff account; A step of selecting at least one exercise item to be included in the exercise program in the exercise program creation mode; setting program information corresponding to the exercise program; generating the exercise program including the exercise items and the program information; storing the exercise program in association with the particular medical staff account; and assigning the exercise program to the patient account based on receiving prescription information for the patient account from the particular medical staff account.

2. The step of selecting an exercise item includes: Displaying a plurality of exercise items registered in the exercise treatment provision service on the terminal; Selecting at least one exercise item from the plurality of exercise items from the specific medical staff account; Displaying a list including the selected exercise item on the terminal; The method of claim 1 , wherein the exercise program is composed of the exercise items included in the list.

3. The exercise program comprises: The method for generating a customized therapeutic exercise program according to claim 2, characterized in that the customized therapeutic exercise program is set to either a non-shareable state accessible only to the specific medical staff account, or a shareable state accessible to medical staff accounts other than the specific medical staff account.

4. The program information includes information for identifying a target to which the exercise program is to be shared, The method for generating a customized therapeutic exercise program according to claim 3, wherein the other medical staff accounts that can access the exercise program are determined based on the program information.

5. The information for identifying the shared object is:

5. The method for generating a customized therapeutic exercise program according to claim 4, wherein the customized therapeutic exercise program is generated based on at least one of hospital information and medical staff information registered in the therapeutic exercise service.

6. If the exercise program is in a shareable state, The method for generating a customized exercise treatment program according to claim 5, further comprising assigning the exercise program to the patient account based on prescription information received from the other medical staff account.

7. The specific medical staff account is: The medical staff account is one of a first type of medical staff account having an independent prescription authority for the exercise program and a second type of medical staff account having no independent prescription authority for the exercise program; The step of assigning the exercise program to the patient account comprises: identifying a type of said particular medical staff account; The method for generating a customized exercise treatment program according to claim 1, further comprising the step of: if the type of the particular medical staff account is the first type as a result of the confirmation, allocating the exercise program to the patient account.

8. The step of assigning the exercise program to the patient account comprises:

8. The method for generating a customized exercise treatment program according to claim 7, characterized in that, if the confirmation result indicates that the specific medical staff account is the second type, the exercise program is assigned to the patient account based on the occurrence of an approval event for the exercise program prescription from the medical staff account of the first type.

9. A control unit that executes an exercise program generation mode on a terminal logged in with a specific medical staff account; a communication unit that receives a selection for at least one exercise item to be included in the exercise program in the exercise program generation mode; A storage unit in which the exercise program is stored, The control unit is Setting program information corresponding to the exercise program; generating the exercise program including the exercise items and the program information; storing the exercise program in the storage unit in association with the specific medical staff account; A system for generating a customized exercise treatment program, comprising: assigning the exercise program to the patient account based on receiving prescription information for the patient account from the specific medical staff account.

10. A program executed by one or more processes in an electronic device and stored on a computer-readable recording medium, The program is Executing an exercise program generation mode on a terminal logged in with a specific medical staff account; A step of selecting at least one exercise item to be included in the exercise program in the exercise program creation mode; setting program information corresponding to the exercise program; generating the exercise program including the exercise items and the program information; storing the exercise program in association with the particular medical staff account; and assigning the exercise program to the patient account based on receiving prescription information for the patient account from the specific medical staff account.

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