System and method for providing user-customized service based on at least one of musculoskeletal system or nervous system

Abstract

A method for providing a user-customized service based on at least one of a musculoskeletal system or a nervous system comprises the steps of: receiving state information about a user; determining one or more musculoskeletal examination items on the basis of the received state information about the user; outputting the one or more determined musculoskeletal examination items; receiving, for each of the one or more output musculoskeletal examination items, one or more images including at least one of the user's posture or motion; receiving meta information about examination equipment for capturing the one or more images; and analyzing the user's state information, the one or more images, and the meta information by using a first machine learning model, and outputting a customized result for the user.

Description

System and method for providing customized services based on at least one of the musculoskeletal system and the nervous system

[0001] The present disclosure relates to a system and method for providing a user-tailored service based on at least one of the user's musculoskeletal system or nervous system, and more specifically, to a system and method for providing a user-tailored service based on at least one of the musculoskeletal system or nervous system, which utilizes the user's status information to design examination items and sequences, quantitatively and qualitatively evaluate the user's musculoskeletal function, and provide a service that provides a user-tailored result based on the same, and which can perform customized management for the user by utilizing the user's status information that is updated as the service is continuously used.

[0002] Musculoskeletal disorders are a key factor in chronic illness. Their recurrence rate is high, and their prevalence is steadily increasing, particularly among the elderly and across all age groups. Approximately 80% of these musculoskeletal disorders are classified as lifestyle-related conditions requiring prevention and management within the wellness sector. However, there is a lack of standardized screening, outcome interpretation, and management solutions.

[0003] Traditional musculoskeletal or wellness services typically rely on collecting standardized physical information from customers and then providing standardized interpretations and services based on this data. These traditional musculoskeletal function tests are limited to quantitative functional assessments, often excluding qualitative functional assessments closely related to the nervous system, such as poor posture, muscle imbalances, balance, movement coordination, and speed. This often results in discrepancies between comprehensive assessment results and actual injury risk.

[0004] Typically, users lack a thorough understanding of musculoskeletal function, leading them to focus solely on the quantitative aspects of exercise while lacking the qualitative aspects of musculoskeletal function, which are closely linked to the nervous system. This can lead to the perpetuation of faulty exercise patterns and lifestyle habits, which can actually harm health. For example, poor posture, joint movement patterns, and the persistent learning of imbalanced motor nerves can lead to accumulated imbalances in specific muscles or joints, resulting in long-term, chronic damage and injury. These issues highlight the limitations of existing services, which lack a personalized approach.

[0005] A user's individual status is defined by various factors, including gender, age, region, occupation, consumption level, physical and mental health, goals and objectives, interests, level of commitment, free time, lifestyle, nutritional status, sleep status, and preferred coaching methods. Therefore, standardized musculoskeletal function test items and fixed result interpretations often do not align with users' initial participation in health services and their motivations for continued use. Furthermore, many users struggle to interpret specialized musculoskeletal function information, indicating a strong desire for personalized information.

[0006] Most existing personalized health services have fixed test items, order, and interpretations, regardless of the user's characteristics or health status. They focus on personalized management services utilizing user-entered information based on limited and standardized health information. These service designs often misalign with the motivational factors needed to drive initial user engagement (service inflow) and continued engagement (service continuation). Users desire detailed information tailored to their health status and needs, and existing services that fail to meet this need for personalized management services based on customized tests and interpretations are contributing to lower user satisfaction.

[0007] The background technology described above is something that the inventor possessed or acquired in the process of deriving the disclosure of the present application, and cannot necessarily be said to be a publicly known technology disclosed to the general public prior to the present application.

[0008] The present disclosure provides a method and device (system) for providing a user-customized service based on at least one of the musculoskeletal system or the nervous system to solve the above-mentioned problems.

[0009] The present disclosure can be implemented in various ways, including methods, devices (systems), computer programs stored in computer-readable storage media, and / or computer-readable media having computer programs stored thereon.

[0010] According to one embodiment of the present disclosure, a method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system may be performed by at least one processor. In one embodiment, the method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system may include: receiving status information about a user; determining one or more musculoskeletal examination items based on the received status information about the user; outputting the determined one or more musculoskeletal examination items; receiving one or more images including at least one of a posture or a motion of the user for each of the one or more output musculoskeletal examination items; receiving meta information about an examination device that captures the one or more images; and analyzing the status information about the user, the one or more images, and the meta information using a first machine learning model to output a customized result for the tk user.

[0011] In one embodiment, the step of outputting a customized result for the user may include the step of analyzing one or more received images and the meta information to generate a result for the balance of at least one of the user's musculoskeletal system or nervous system, and the step of using a first machine learning model to generate a customized examination result for the user based on the result for the balance of at least one of the user's musculoskeletal system or nervous system and the received status information for the user.

[0012] In one embodiment, the step of determining one or more musculoskeletal examination items may include the step of determining a plurality of musculoskeletal examination items and an order of the plurality of musculoskeletal examination items based on status information about the user.

[0013] In one embodiment, the status information about the user includes information about the gender and age of the user, and the method further includes a step of determining a biological age for the user based on the result about the balance of at least one of the musculoskeletal system or the nervous system of the user and the gender and age information of the user, and the step of determining the biological age for the user based on the result about the balance of at least one of the musculoskeletal system or the nervous system of the user and the gender and age information of the user may include a step of generating a customized examination result for the user based on the biological age for the user, the result about the balance of at least one of the musculoskeletal system or the nervous system of the user, and the status information about the user using a first machine learning model.

[0014] In one embodiment, the customized test results for the user may include interest desire information about the user and function prediction information based on the interest desire information.

[0015] In one embodiment, the step of generating a customized examination result for the user based on the biological age of the user, the result of the balance of at least one of the musculoskeletal system or the nervous system of the user, and the status information of the user using the first machine learning model may include the step of determining elements and exposure priorities to be used in the customized examination result based on the interest desire information of the user, the step of determining customized function prediction information for the user based on the interest desire information of the user and the status of at least one of the musculoskeletal system or the nervous system of the user, and the step of generating customized function prediction information for the user as a customized result for the user using the determined elements and exposure priorities.

[0016] In one embodiment, the method may further include a step of generating step-by-step target information for the user and customized coaching information for the user based on function prediction information according to interest desire information and status information for the user using a second machine learning model.

[0017] In one embodiment, the method further includes a step of receiving step-by-step goal achievement information performed by the user and user-tailored coaching performance information, and the step of generating step-by-step goal information for the user and user-tailored coaching information for the user may include a step of regenerating step-by-step goal information for the user and user-tailored coaching information for the user based on the step-by-step goal achievement information performed by the user, the user-tailored coaching performance information, function prediction information according to interest desire information, and status information for the user using a second machine learning model.

[0018] In one embodiment, the step of receiving status information about the user may include the step of receiving updated status information about the user based on at least one of step-by-step goal achievement information performed by the user, customized coaching performance information about the user, interest desire information about the user, or function prediction information based on interest desire information.

[0019] In one embodiment, state information about a user may include fixed information about the user and change information about the user.

[0020] In one embodiment, a computer-readable non-transitory recording medium having recorded thereon instructions for executing a method on a computer may be provided.

[0021] A system and method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system according to one embodiment utilizes accumulated user status information to evaluate the user's musculoskeletal function and provide a user-tailored result based thereon, thereby having the advantage of being able to clearly and easily identify the user's physical vulnerabilities and to automate the inflow and ongoing management of health services by selecting information of interest.

[0022] In addition, while conventional methods have focused only on quantitative evaluation of musculoskeletal function, a system and method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system according to one embodiment utilizes the user's status information to simultaneously perform quantitative functional evaluations such as the number of repetitions of the user's actions and / or movements, distance traveled, etc., as well as qualitative functional evaluations highly related to the nervous system such as postural alignment, muscle imbalance, and compensatory movements, thereby having the advantage of enabling a more comprehensive evaluation of the user's musculoskeletal function.

[0023] In addition, conventional musculoskeletal function tests tested each item separately (e.g., flexibility by region, muscle strength by region, endurance by region, balance, posture, etc.), and since each piece of information was not integrated, only a partial interpretation of a single region and element was possible for musculoskeletal functions with complex correlations. In contrast, a system and method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system according to one embodiment simultaneously perform quantitative and qualitative evaluations of the user's musculoskeletal function, thereby identifying and interpreting the source of vulnerabilities and fundamental problems in the body's musculoskeletal function, as well as the correlation between the vulnerable musculoskeletal function regions and motor nervous system test elements, thereby enabling the design of a clear solution for solving body functional vulnerabilities and problems.

[0024] In addition, the system and method for providing a user-customized service based on at least one of the musculoskeletal system and the nervous system according to one embodiment has the advantage of being able to comprehensively analyze and interpret a user-customized test that combines the test parts, items, and order required by the user, unlike a conventional musculoskeletal function test that can test each single item of each part and function, thereby identifying repetitive muscle and nerve balance and imbalance patterns that the user is not aware of.

[0025] In addition, the system and method for providing a user-customized service based on at least one of the musculoskeletal system and the nervous system according to one embodiment has the advantage of being able to improve expertise and usability in designing unmanned services by comprehensively configuring the types and order of musculoskeletal function evaluation items, thereby drastically reducing the time required.

[0026] In addition, the system and method for providing a user-tailored service based on at least one of the musculoskeletal system or the nervous system according to an embodiment has the advantage of being able to induce the user to continuously participate in the health service by setting detailed goals step by step to achieve the final goal corresponding to the user's interest and desire, and guiding and coaching the user to achieve the step-by-step goal information.

[0027] The effects of the system and method for providing a user-tailored service based on at least one of the musculoskeletal system or the nervous system according to the embodiment are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

[0028] The following drawings attached to this specification illustrate preferred embodiments of the present disclosure and, together with the detailed description of the invention, serve to further understand the technical idea of ​​the present disclosure, and therefore, the present disclosure should not be interpreted as being limited to matters described in such drawings.

[0029] FIG. 1 is a diagram illustrating a process of generating musculoskeletal-based user-tailored results through an information processing system according to one embodiment of the present disclosure.

[0030] FIG. 2 is a schematic diagram showing a configuration in which an information processing system is connected to a plurality of inspection devices so as to be able to communicate with each other to provide search results according to one embodiment of the present disclosure.

[0031] FIG. 3 is a block diagram showing the internal configuration of an inspection device and an information processing system according to one embodiment of the present disclosure.

[0032] FIG. 4 is a diagram illustrating user status information according to one embodiment of the present disclosure.

[0033] FIG. 5 is a diagram illustrating a process of deriving user-customized inspection items through an information processing system according to one embodiment of the present disclosure.

[0034] FIG. 6 is a diagram illustrating a process of deriving balance of at least one of the musculoskeletal system and the nervous system through an information processing system according to one embodiment of the present disclosure.

[0035] FIG. 7 is a diagram illustrating a process of generating a musculoskeletal-based user-tailored result through a first machine learning model according to one embodiment of the present disclosure.

[0036] FIG. 8 is a diagram illustrating a process of generating a musculoskeletal-based user-tailored result considering biological age through a first machine learning model according to one embodiment of the present disclosure.

[0037] FIG. 9 is a diagram illustrating a musculoskeletal-based user-tailored result according to one embodiment of the present disclosure.

[0038] FIG. 10 is a flowchart illustrating a method for generating customized results for a user based on interest desire information about the user according to one embodiment of the present disclosure.

[0039] FIG. 11 is a diagram illustrating a machine learning model according to one embodiment of the present disclosure.

[0040] FIG. 12 is a diagram illustrating a process of generating user-level goal information and customized coaching information through a second machine learning model according to one embodiment of the present disclosure.

[0041] FIG. 13 is a diagram illustrating a process of regenerating user-level goal information and customized coaching information through a second machine learning model according to one embodiment of the present disclosure.

[0042] FIG. 14 is a diagram illustrating a user interface (UI) that provides a skeleton-based user-customized service according to one embodiment of the present disclosure.

[0043] FIG. 15 is a flowchart illustrating a method for providing a user-customized service based on at least one of the musculoskeletal system or the nervous system according to one embodiment of the present disclosure.

[0044] The various embodiments described in this specification are exemplified for the purpose of clearly explaining the technical concept of the present disclosure and are not intended to be limited to specific embodiments. The technical concept of the present disclosure includes various modifications, equivalents, alternatives, and embodiments selectively combined from all or part of the embodiments described herein. Furthermore, the scope of the technical concept of the present disclosure is not limited to the various embodiments presented below or the specific descriptions thereof.

[0045] Terms used herein, including technical or scientific terms, unless otherwise defined, may have the meaning commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0046] As used herein, expressions such as "includes," "may include," "comprises," "may have," "have," and "may have" indicate the presence of a target feature (e.g., a function, operation, or component), but do not exclude the presence of other additional features. In other words, such expressions should be understood as open-ended terms that imply the possibility of including a second embodiment.

[0047] In this specification, singular expressions include plural expressions unless the context clearly indicates otherwise. Furthermore, plural expressions include singular expressions unless the context clearly indicates otherwise. When a part of the specification is said to include a component, this does not exclude other components, but rather implies that other components may be included, unless otherwise specifically stated.

[0048] Also, the term 'module' or 'part' used in the specification means a software or hardware component, and the 'module' or 'part' performs certain roles. However, the 'module' or 'part' is not limited to software or hardware. The 'module' or 'part' may be configured to reside on an addressable storage medium and may be configured to execute one or more processors. Thus, as an example, the 'module' or 'part' may include at least one of components such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, or variables. The functionality provided within the components and 'modules' or 'parts' may be combined into a smaller number of components and 'modules' or 'parts', or further separated into additional components and 'modules' or 'parts'.

[0049] According to one embodiment of the present disclosure, a 'module' or 'unit' may be implemented as a processor and a memory. 'Processor' should be broadly construed to include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, a graphics processing unit (GPU), a neural network processing unit (NPU, TPU, VPU, etc.), and the like. In some circumstances, a 'processor' may also refer to an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field-programmable gate array (FPGA), and the like. A 'processor' may also refer to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors in conjunction with a DSP core, or any other such combination of configurations. In addition, 'memory' should be broadly construed to include any electronic component capable of storing electronic information. 'Memory' may refer to various types of processor-readable media, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, etc. Memory is said to be in electronic communication with the processor if the processor can read information from, and / or write information to, the memory. Memory integrated in a processor is in electronic communication with the processor.

[0050] As used herein, the expressions “first,” “second,” or “first,” “second,” etc., unless the context indicates otherwise, are used to refer to multiple similar objects and to distinguish one object from another, and do not limit the order or importance among the objects.

[0051] As used herein, the expressions "A, B, and C," "A, B, or C," "A, B, and / or C," or "at least one of A, B, and C," "at least one of A, B, or C," "at least one of A, B, and / or C," "at least one selected from A, B, and C," "at least one selected from A, B, or C," "at least one selected from A, B, and / or C," and the like can mean each listed item or all possible combinations of the listed items. For example, "at least one selected from A and B" can refer to (1) A, (2) at least one of A, (3) B, (4) at least one of B, (5) at least one of A and at least one of B, (6) at least one of A and B, (7) at least one of B and A, and (8) both A and B.

[0052] The expression "based on" as used herein is used to describe one or more factors that influence the decision, act of judgment, or action described in the phrase or sentence containing the expression, and this expression does not exclude additional factors that influence the decision, act of judgment, or action.

[0053] As used herein, the expression that a component (e.g., a first component) is “connected” or “connected” to another component (e.g., a second component) may mean that the component is directly connected or connected to the other component, as well as connected or connected via a new other component (e.g., a third component).

[0054] The expression "configured to" used herein may have the meanings of "set to", "having the ability to", "modified to", "made to", "capable of", etc., depending on the context. The expression is not limited to the meaning of "specifically designed in hardware", and for example, a processor configured to perform a specific operation may mean a generic-purpose processor that can perform the specific operation by executing software.

[0055] Hereinafter, various embodiments of the present disclosure will be described with reference to the attached drawings. In the attached drawings and the description of the drawings, identical or substantially equivalent components may be assigned the same reference numerals. Furthermore, in the description of various embodiments below, duplicate descriptions of identical or corresponding components may be omitted, but this does not mean that the corresponding components are not included in the embodiments.

[0056] FIG. 1 is a diagram illustrating a process of generating musculoskeletal-based user-tailored results through an information processing system according to one embodiment of the present disclosure.

[0057] Referring to FIG. 1, in one embodiment, the information processing system (230) may provide a user-customized service based on at least one of the musculoskeletal system and the nervous system. Here, the user-customized service may refer to a user-customized examination, interpretation, and / or management service.

[0058] In one embodiment, the information processing system (230) can generate musculoskeletal-based user-tailored results (140) using user status information (110), one or more user images (120), and meta-information (130) of the inspection equipment that captures the images.

[0059] Here, user status information (110) may refer to information related to the user's status. For example, user status information (110) may include, but is not limited to, the user's gender and age information, medical information, disease information, lifestyle information, health information, and preference information.

[0060] Additionally, here, one or more user images (120) may mean one or more images including at least one of the user's posture or movement for each of one or more musculoskeletal examination items.

[0061] At this time, one or more musculoskeletal examination items are determined based on user status information (110), and may be determined based on rules or determined through a machine learning model. For example, the information processing system (230) may determine one or more musculoskeletal examination items based on user status information (110), and may guide the user to take a specific posture or perform a specific movement based on the determined one or more musculoskeletal examination items, and may acquire a user image (120) generated by photographing or filming a scene in which the user takes a specific posture or performs a specific movement. For example, the information processing system (230) may receive 2D and / or 3D images of the user as the user image (120), and may extract information on body joints and / or muscles in the user image (120) using a segmentation model or the like. Based on this extracted information, the posture and / or movement of the user in the user image (120) may be analyzed.

[0062] Additionally, the meta information (130) of the inspection equipment capturing the image may refer to detailed information, characteristics, or related data regarding the inspection equipment. For example, the meta information (130) of the inspection equipment capturing the image may include, but is not limited to, the performance of the inspection equipment capturing the image, environmental information at the time of image capture, etc.

[0063] In addition, the user-tailored result (140) herein may refer to a result derived by analyzing user status information (110), one or more images (120), and meta information (130). For example, the user-tailored result (140) may include, but is not limited to, the user's biological age, interest and desire information, function prediction information, and customized result interpretation. Here, the user's biological age may refer to an indicator generated based on at least one of the functional status of the user's musculoskeletal system or nervous system, body composition, or health functional physical strength.

[0064]

[0065] FIG. 2 is a schematic diagram showing a configuration in which an information processing system is connected to a plurality of inspection devices so as to be able to communicate with each other to provide search results according to one embodiment of the present disclosure.

[0066] As illustrated in FIG. 2, a plurality of inspection devices (210_1, 210_2, 210_3, 210_4) may be connected to an information processing system (230) that can provide user-customized services (e.g., user-customized inspection, interpretation, and / or management services) based on at least one of the musculoskeletal system or the nervous system via a network (220). Here, the plurality of inspection devices (210_1, 210_2, 210_3, 210_4) may include a terminal of a user who receives user-customized services based on at least one of the musculoskeletal system or the nervous system. In addition, here, the network (220) may be connected to the information processing system (230) not only through a communication method utilizing a communication network (e.g., mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.) but also through internal communication of multiple inspection equipment (210_1, 210_2, 210_3, 210_4).

[0067] In one embodiment, the information processing system (230) may include one or more server devices and / or databases capable of storing, providing, and executing computer executable programs (e.g., downloadable applications) and data associated with user-tailored services, etc., based on at least one of the musculoskeletal system or the nervous system, or one or more distributed computing devices and / or distributed databases based on cloud computing services.

[0068] A user-customized service based on at least one of the musculoskeletal system or the nervous system provided by the information processing system (230) can be provided to the user through a user-customized service application, a web browser, a web browser extension program, etc., based on at least one of the musculoskeletal system or the nervous system installed in each of the plurality of testing devices (210_1, 210_2, 210_3, 210_4). For example, the information processing system (230) can provide information corresponding to a request for provision of customized results received from the testing devices (210_1, 210_2, 210_3, 210_4) through a user-customized service application, etc., based on at least one of the musculoskeletal system or the nervous system, or perform corresponding processing.

[0069] Multiple inspection equipment (210_1, 210_2, 210_3, 210_4) can communicate with the information processing system (230) via a network (220).

[0070] The network (220) may be configured to enable communication between a plurality of inspection devices (210_1, 210_2, 210_3, 210_4) and an information processing system (230). Depending on the installation environment, the network (220) may be configured as a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device, and RS-serial communication, a wireless network such as a mobile communication network, WLAN (Wireless LAN), Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and may include not only a communication method utilizing a communication network (e.g., a mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.) that the network (220) may include, but also short-range wireless communication between inspection equipment (210_1, 210_2, 210_3, 210_4), internal communication within the inspection equipment (210_1, 210_2, 210_3, 210_4), etc.

[0071] In FIG. 2, a mobile phone terminal (210_1), a tablet terminal (210_2), a PC terminal (210_3), and a kiosk (210_4) are illustrated as examples of the inspection equipment, but are not limited thereto, and the inspection equipment (210_1, 210_2, 210_3, 210_4) may be any computing device capable of wired and / or wireless communication and capable of installing and executing a user-customized service application or web browser based on at least one of the musculoskeletal system or the nervous system. For example, the inspection equipment may include an AI speaker, a smartphone, a mobile phone, a navigation system, a computer, a laptop, a digital broadcasting terminal, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a tablet PC, a game console, a wearable device, an IoT (Internet of Things) device, a VR (virtual reality) device, an AR (augmented reality) device, a set-top box, etc.

[0072] In addition, although FIG. 2 illustrates four inspection devices (210_1, 210_2, 210_3, 210_4) communicating with an information processing system (230) via a network (220), this is not limited thereto, and a different number of inspection devices may be configured to communicate with an information processing system (230) via a network (220).

[0073] In FIG. 2, a configuration in which a user's request is transmitted to an information processing system (230) through inspection equipment (210_1, 210_2, 210_3, 210_4) is exemplarily illustrated, but the present invention is not limited thereto, and the user's request may be provided to the information processing system (230) through an input device associated with the information processing system (230) without passing through the inspection equipment (210_1, 210_2, 210_3, 210_4), and the result of processing the user's request may be provided to the user through an output device (e.g., a display, etc.) associated with the information processing system (230).

[0074] Although FIG. 2 illustrates that the inspection equipment (210_1, 210_2, 210_3, 210_4) receives a user-customized service based on at least one of the musculoskeletal system or the nervous system from the information processing system (230), the present invention is not limited thereto. For example, without communication with the information processing system (230), a user-customized service may be provided based on at least one of the musculoskeletal system or the nervous system through a user-customized service providing program / application installed in the inspection equipment (210_1, 210_2, 210_3, 210_4). In addition, although the information processing system (230) is illustrated as a single device, the present invention is not limited thereto, and the information processing system (230) may be configured with a plurality of devices.

[0075] In Fig. 2, a configuration is illustrated in which inspection equipment (210_1, 210_2, 210_3, 210_4) is connected to an information processing system (230) via a network. The network can be configured not only as a network under the cloud, but also through internal communication within the equipment.

[0076] FIG. 3 is a block diagram showing the internal configuration of an inspection device and an information processing system according to one embodiment of the present disclosure.

[0077] Referring to FIG. 3, the inspection equipment (210) may refer to any computing device capable of executing an application, web browser, etc. and capable of wired / wireless communication. For example, the inspection equipment (210) of FIG. 2 may include a mobile phone terminal (210_1), a tablet terminal (210_2), a PC terminal (210_3), and a kiosk (210_4).

[0078] As illustrated in FIG. 3, the inspection equipment (210) may include a memory (312), a processor (314), a communication module (316), and an input / output interface (318). Similarly, the information processing system (230) may include a memory (332), a processor (334), a communication module (336), and an input / output interface (338).

[0079] As illustrated in FIG. 3, the inspection equipment (210) and the information processing system (230) may be configured to communicate information and / or data via a network (220) using respective communication modules (316, 336). For example, the network (220) may include an internal communication network processed on-premise within the equipment, as well as an external communication network such as a cloud. In addition, the input / output device (320) may be configured to input information and / or data to the inspection equipment (210) or output information and / or data generated from the inspection equipment (210) via an input / output interface (318).

[0080] The memory (312, 332) may include any non-transitory computer-readable recording medium. In one embodiment, the memory (312, 332) may include a permanent mass storage device such as a read-only memory (ROM), a disk drive, a solid-state drive (SSD), or flash memory. As another example, a permanent mass storage device such as a ROM, an SSD, a flash memory, or a disk drive may be included in the inspection equipment (210) or the information processing system (230) as a separate permanent storage device distinct from the memory. In addition, an operating system and at least one program code may be stored in the memory (312, 332).

[0081] These software components may be loaded from a computer-readable recording medium separate from the memory (312, 332). This separate computer-readable recording medium may include a recording medium directly connectable to the inspection equipment (210) and the information processing system (230), for example, a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, a memory card, etc. As another example, the software components may be loaded into the memory (312, 332) via a communication module (316, 336) other than a computer-readable recording medium. For example, at least one program may be loaded into the memory (312, 332) based on a computer program that is installed by files provided by developers or a file distribution system that distributes installation files of applications via a network (220).

[0082] The processor (314, 334) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processor (314, 334) by a memory (312, 332) or a communication module (316, 336). For example, the processor (314, 334) may be configured to execute instructions received according to program code stored in a storage device such as the memory (312, 332).

[0083] The communication module (316, 336) may provide a configuration or function for the inspection equipment (210) and the information processing system (230) to communicate with each other via the network (220), and may provide a configuration or function for the inspection equipment (210) and / or the information processing system (230) to communicate with other inspection equipment or other systems (e.g., a separate cloud system, etc.). For example, a request or data (e.g., a request for providing customized results, etc.) generated by the processor (314) of the inspection equipment (210) according to a program code stored in a recording device such as a memory (312) may be transmitted to the information processing system (230) via the network (220) under the control of the communication module (316). Conversely, a control signal or command provided under the control of the processor (334) of the information processing system (230) can be received by the inspection equipment (210) through the communication module (316) of the inspection equipment (210) via the communication module (336) and the network (220).

[0084] The input / output interface (318) may be a means for interfacing with an input / output device (320). As an example, the input device may include various sensors such as cameras, lidar sensors, and 3D sensors including audio sensors and / or image sensors, keyboards, microphones, and mice, and the output device may include devices such as displays, speakers, and haptic feedback devices. As another example, the input / output interface (318) may be a means for interfacing with a device that integrates a configuration or function for performing input and output, such as a touch screen. For example, when the processor (314) of the inspection equipment (210) processes a command of a computer program loaded into the memory (312), a service screen configured using information and / or data provided by the information processing system (230) or other inspection equipment may be displayed on the display through the input / output interface (318). In FIG. 3, the input / output device (320) is illustrated as not being included in the inspection equipment (210), but is not limited thereto, and may be configured as a single device with the inspection equipment (210). In addition, the input / output interface (338) of the information processing system (230) may be a means for interfacing with a device (not shown) for input or output that is connected to the information processing system (230) or that the information processing system (230) may include. In FIG. 3, the input / output interfaces (318, 338) are illustrated as elements configured separately from the processors (314, 334), but is not limited thereto, and the input / output interfaces (318, 338) may be configured to be included in the processors (314, 334).

[0085] The inspection equipment (210) and the information processing system (230) may include more components than those in FIG. 3. However, it is not necessary to clearly illustrate most of the conventional components. In one embodiment, the inspection equipment (210) may be implemented to include at least some of the input / output devices (320) described above. In addition, the inspection equipment (210) may further include other components, such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, a database, etc. For example, when the inspection equipment (210) is a smartphone, it may include components that a smartphone generally includes, and various components, such as an acceleration sensor, a gyro sensor, a microphone module, a camera module, various physical buttons, buttons using a touch panel, input / output ports, and a vibrator for vibration, may be implemented to be further included in the inspection equipment (210).

[0086] While a program or application for a user-customized service, etc. based on at least one of the musculoskeletal system or the nervous system is being operated, the processor (314) can receive text, images, videos, voices and / or actions, etc. input or selected through an input device such as a camera, microphone, including a touch screen, keyboard, audio sensor and / or image sensor connected to an input / output interface (318), and can store the received text, images, videos, voices and / or actions, etc. in a memory (312) or provide them to an information processing system (230) through a communication module (316) and a network (220).

[0087] The processor (314) of the inspection equipment (210) may be configured to manage, process, and / or store information and / or data received from an input / output device (320), other inspection equipment, an information processing system (230), and / or multiple external systems. The information and / or data processed by the processor (314) may be provided to the information processing system (230) via a communication module (316) and a network (220). The processor (314) of the inspection equipment (210) may transmit the information and / or data to the input / output device (320) via an input / output interface (318) and output the information and / or data. For example, the processor (314) may output or display the received information and / or data on a screen associated with the inspection equipment (210).

[0088] The processor (334) of the information processing system (230) may be configured to manage, process, and / or store information and / or data received from a plurality of inspection equipment (210) and / or a plurality of external systems. The information and / or data processed by the processor (334) may be provided to the inspection equipment (210) via a communication module (336) and a network (220).

[0089] FIG. 4 is a diagram illustrating user status information according to one embodiment of the present disclosure.

[0090] Referring to FIG. 4, user state information (110) applicable to one embodiment of the present disclosure may include user fixed information (401) and user change information (402).

[0091] First, user-fixed information (401) may refer to fixed information that does not change with respect to the user. For example, user-fixed information (401) may include, but is not limited to, gender and age information (410), medical information (420) (e.g., health checkup records, family history, etc.), and disease information (430) (e.g., past disease diagnoses, treatment records, etc.).

[0092] Next, user change information (402) may refer to changeable information related to the user. For example, user change information (402) may include, but is not limited to, lifestyle information (440) (e.g., region, occupation, consumption level, free time, lifestyle pattern, sleep status, etc.), health information (450) (e.g., posture and body type / muscle imbalance, mobility and stability, muscle tension, health functional physical strength (e.g., muscle strength, muscle endurance, flexibility, balance, agility, etc.), body composition, stress intensity, nutritional status, biometric information, etc.), and preference information (460) (e.g., health service purpose, goal, interest, priority, willpower for improvement, desired coaching type, etc.).

[0093] FIG. 5 is a diagram illustrating a process of deriving user-customized inspection items through an information processing system according to one embodiment of the present disclosure.

[0094] Referring to FIG. 5, in one embodiment, the information processing system (230) can determine a user-customized inspection item (510) using user fixed information (401) and user variable information (402).

[0095] In one embodiment, the information processing system (230) can determine a plurality of musculoskeletal examination items using user fixed information (401) and user variable information (402), and can determine the order of the determined plurality of musculoskeletal examination items.

[0096] In one embodiment, the information processing system (230) can determine a plurality of musculoskeletal examination items and the order of the plurality of musculoskeletal examination items based on rules using user fixed information (401) and user change information (402). For example, the information processing system (230) can set in advance rules for musculoskeletal examination items required for each user fixed information (401) and user change information (402) and the order of each examination item, and when the user fixed information (401) and the user change information (402) are acquired, the musculoskeletal examination items and the order corresponding to the user fixed information (401) and the user change information (402) can be determined based on the rules set in advance. Here, the rules set in advance can be arbitrarily modified or changed by a user with authority (e.g., an administrator, an actual user, etc.), but are not limited thereto.

[0097] In one embodiment, the information processing system (230) can determine a plurality of musculoskeletal examination items and the order of the plurality of musculoskeletal examination items based on a machine learning model using user fixed information (401) and user variable information (402).

[0098] Here, the machine learning model (hereinafter referred to as the “third machine learning model”) may be a model that learns according to a machine learning method (e.g., semi-supervised learning, supervised learning, etc.) using user fixed information (401) and user variable information (402) as input data and learning data that includes musculoskeletal examination items and order as correct answer data, thereby deriving musculoskeletal examination items required for the user and the order of each examination item by inputting user fixed information (401) and user variable information (402).

[0099] For example, the third machine learning model may be, but is not limited to, a CNN (Convolutional Neural Network), a Transformer, a RL (Reinforcement Learning)-based model, an MLP (Multi-Layer Perceptron) model, and an ensemble model.

[0100] Here, the test items determined from the user fixed information (401) and the user change information (402) may include quantitative functional assessment metrics and qualitative functional assessment metrics.

[0101] First, quantitative functional assessment items are items that measure quantitative aspects of musculoskeletal function, and may include, but are not limited to, muscle contraction count indicating the total number of times a muscle contracts during exercise, exercise distance indicating the total distance moved during exercise, repetition count indicating the number of repetitions of a specific exercise movement, exercise intensity indicating the degree of force or resistance used during exercise, load amount indicating the weight or resistance applied to the muscles during exercise, and exercise volume indicating the total amount of exercise.

[0102] In addition, qualitative functional evaluation items are items that evaluate the qualitative aspects of musculoskeletal functions that are highly related to the motor nervous system, such as posture alignment that indicates the posture and alignment of the body during exercise, muscle imbalance that indicates the muscle development status between the left and right or upper and lower body parts, range of motion (ROM) that indicates the range of motion of the joints, movement accuracy that indicates the accuracy and consistency of exercise movements, balance and stability that indicate the balance and stability of the body during exercise, exercise tempo that indicates the speed of exercise execution, muscle activation pattern that indicates the activation status of specific muscles during exercise, breathing pattern that indicates the consistency and efficiency of breathing during exercise, and fatigue levels that indicate the state of fatigue during and after exercise.

[0103] For example, if the information processing system (230) determines that the user has a sedentary lifestyle, is an office worker, is a woman in her 40s, and has a history of neck pain based on user fixed information (401) and user change information (402), the information processing system (230) can determine each area, test items, and order through a comprehensive judgment of body functions that are mechanically and kinematically related to the areas around the shoulders, neck, and pelvis.

[0104] At this time, the information processing system (230) can determine the order of the inspection items by considering the operation flow (location and position, overlapping of inspections, factors that may affect inspections, priority, precautions during inspection, classification of inspection items to be careful of, inspection time, etc.) of each inspection item, but is not limited thereto.

[0105] FIG. 6 is a diagram illustrating a process of deriving balance of at least one of the musculoskeletal system and the nervous system through an information processing system according to one embodiment of the present disclosure.

[0106] Referring to FIG. 6, in one embodiment, the information processing system (230) may derive a balance (630) of at least one of the musculoskeletal system or the nervous system by using one or more user images (120) and meta information (130) of the examination equipment that captures the images. Here, the balance (630) of at least one of the musculoskeletal system or the nervous system may include status information for each of posture, healthy physical fitness, body shape, stability, mobility, and body composition, and / or balance information for a plurality of organic relationships among posture, healthy physical fitness, body shape, stability, mobility, or body composition. For example, the balance (630) of at least one of the musculoskeletal system or the nervous system may be expressed as an index for the status of each of posture, healthy physical fitness, body shape, stability, mobility, and body composition. Additionally or alternatively, the balance (630) of at least one of the musculoskeletal system or the nervous system may represent an indicator of the balance of multiple organic relationships among posture, health function physical strength, body shape, stability, mobility, or body composition.

[0107] In one embodiment, the information processing system (230) can analyze one or more user images (120) to recognize a user's motion (action). For example, the information processing system (230) can receive 2D and / or 3D images of the user as the user image (120) and extract information about body joints and / or muscles within the user image (120) using a segmentation model or the like. Based on this extracted information, the motion (action) of the user within the user image (120) can be analyzed. Based on the analyzed actions (e.g., static actions, dynamic actions, etc.), a quantitative / qualitative functional assessment of the user's musculoskeletal function can be performed. For example, the user's musculoskeletal function can include information about posture, body shape, mobility, stability, health functional fitness, and / or body composition. Based on this quantitative / qualitative functional assessment, a balance (630) of at least one of the musculoskeletal system or the nervous system can be derived. For example, the information processing system (230) may pre-define at least one balance (630) of the musculoskeletal system or the nervous system according to a quantitative / qualitative functional evaluation in advance, and may derive at least one balance (630) of the musculoskeletal system or the nervous system corresponding to the result of the quantitative / qualitative functional evaluation performed on the user based on the pre-defined information.

[0108] Here, the balance (630) of at least one of the musculoskeletal system or the nervous system may mean, but is not limited to, balance within the musculoskeletal system, balance within the nervous system, or balance between the musculoskeletal system and the nervous system.

[0109] In one embodiment, the information processing system (230) can use the integrated result information of the test items to derive the balance (630) of at least one of the musculoskeletal system or the nervous system.

[0110] Here, the integrated result information of the test items may include, but is not limited to, information by type of test item, such as the user's posture and body type, mobility and stability derived from performing quantitative and dynamic function evaluations simultaneously, and health function physical strength and body composition derived from performing quantitative and dynamic function evaluations simultaneously.

[0111] FIG. 7 is a diagram illustrating a process of generating a musculoskeletal-based user-tailored result through a first machine learning model according to one embodiment of the present disclosure.

[0112] Referring to FIG. 7, the information processing system (230) can derive a musculoskeletal-based user-tailored result (140) through a machine learning model (hereinafter referred to as “first machine learning model (700)”) using user status information (110) and at least one balance (630) of the musculoskeletal system or nervous system.

[0113] Here, the first machine learning model (700) may refer to a model learned according to a machine learning method (e.g., semi-supervised learning and / or supervised learning) that uses user status information (110) and the balance (630) of at least one of the musculoskeletal system or the nervous system as input data, and trains to output a user-customized result (140) using learning data including correct answer data (ground truth) corresponding to the user-customized result (140). For example, the user-customized result (140) may include, but is not limited to, interest desire information, function prediction information according to interest desire information, etc.

[0114] In one embodiment, the first machine learning model (700) includes two different input networks and one output network, and can extract two different results through each of the two input networks, and derive one result data by fusing the two results through the output network.

[0115] For example, the first machine learning model (700) may include, but is not limited to, a first input network that extracts a first feature vector related to the balance (630) of at least one of the musculoskeletal system or the nervous system by inputting information on the examination environment (e.g., distance between the equipment and the user, angle of the equipment, examination environment (indoor, outdoor, lighting, etc.), meta information of 2D / 3D unstructured data (e.g., resolution, Aspect Ratio, etc.)) and information on the integrated results of the examination items (e.g., posture and body shape, mobility and stability derived from simultaneous performance of quantitative and qualitative functional evaluations, and health functional physical strength and body composition derived from simultaneous performance of quantitative and qualitative functional evaluations), a second input network that receives user status information (110) and extracts a second feature vector, and a first output network that extracts a latent vector by fusing the first feature vector and the second feature vector, and derives a user-customized result (140) based on the extracted latent vector.

[0116] Here, the first machine learning model (700) may be, but is not limited to, a CNN (Convolutional Neural Network), a Transformer, a RL (Reinforcement Learning)-based model, an MLP (Multi-Layer Perceptron), and an ensemble model.

[0117] In addition, here, the process of extracting a latent vector by fusing the first feature vector and the second feature vector is known to include various techniques such as Concat, Multiply, Average, Dot, Plus, and Minus, and various techniques such as these techniques can be selectively utilized. Therefore, this specification does not describe a specific method of extracting a latent vector by fusing the first feature vector and the second feature vector.

[0118] The musculoskeletal-based user-tailored results (140) generated through the above-described process may include, but are not limited to, biological age, interest desire information, function prediction information based on interest desire, and customized result interpretation.

[0119] FIG. 8 is a diagram illustrating a process of generating a musculoskeletal-based user-tailored result considering biological age through a first machine learning model according to one embodiment of the present disclosure.

[0120] Referring to FIG. 8, in one embodiment, the information processing system (230) can derive a musculoskeletal-based user-tailored result (140) through the first machine learning model (700) by taking into account the user's biological age (810).

[0121] More specifically, first, the information processing system (230) can determine the biological age (810) of the user based on the balance (630) of at least one of the musculoskeletal system or the nervous system and the user's gender and age information (410) among the user status information (110).

[0122] For example, the information processing system (230) can determine the biological age (810) of the user using a rule-based biological age model. For example, the information processing system (230) can determine rules for determining the biological age (e.g., biometric indicators, values, etc. that appear according to the biological age), and when the user's gender and age information (410) and the balance (630) of at least one of the musculoskeletal system or the nervous system are acquired, the biological age (810) can be estimated by applying the predetermined rules to the user's gender and age information (410) and the balance (630) of at least one of the musculoskeletal system or the nervous system.

[0123] As another example, the information processing system (230) can extract the biological age (810) by inputting the user's gender and age information (410) and the balance (630) of at least one of the musculoskeletal system or the nervous system into an artificial intelligence-based biological age model. Here, the artificial intelligence-based biological age model may be a model trained to input the user's gender and age information (410) and the balance (630) of at least one of the musculoskeletal system or the nervous system as input data, and output the biological age (810) using correct answer data corresponding to the biological age (810), but is not limited thereto.

[0124] Thereafter, the information processing system (230) can generate a customized examination result (140) for the user based on the user's biological age (810), balance (630) of at least one of the skeletal system or nervous system, and user status information (110) through the first machine learning model (700).

[0125] FIG. 9 is a diagram illustrating a musculoskeletal-based user-tailored result according to one embodiment of the present disclosure.

[0126] Referring to FIG. 9, the musculoskeletal-based user-tailored results (140) may include biological age (810), interest desire information (910), function prediction information (920), and customized result interpretation (930).

[0127] First, biological age (810) refers to the biological age, not the user's actual age. For example, it may be a numerical value derived through a biological age model, but is not limited thereto.

[0128] Next, interest desire information (910) refers to information indicating a health or body-related desire that the user wishes to improve. For example, interest desire information (910) may include, but is not limited to, function indicating interest in maintaining or improving a specific bodily function, beauty indicating desire related to appearance or body shape, and pain indicating desire for the purpose of alleviating physical pain.

[0129] This interest need information (920) may be information derived by analyzing the balance (630) of at least one of the musculoskeletal system or nervous system and user status information (110) through an artificial intelligence-based interest need model (Wellness Needs Model), but is not limited thereto.

[0130] Next, the function prediction information (920) refers to a physical function or condition predicted according to the user's interest and desire. For example, the function prediction information (920) includes sex (physiological), exercise (sports, etc.), learning (brain function), digestion (endocrine system), breathing (circulatory system), sleep (hormones), disability (loss of function), immunity (infection), and mental health (depression, anxiety) in the function area, includes body shape change (body shape and diet) in the beauty area, and may include physical disease (medical) in the pain area, but is not limited thereto.

[0131] Such function prediction information (920) may be information derived by analyzing the balance (630) of at least one of the musculoskeletal system or nervous system and user status information (110) through an artificial intelligence-based function prediction model, but is not limited thereto.

[0132] Lastly, the customized result interpretation (930) means the result of interpreting the biological age (810), interest desire information (910), and function prediction information (920) in a way that suits the user, and may include, for example, text and visual effects corresponding to the biological age (810), interest desire information (910), and function prediction information (920), information on the user's condition derived based on the biological age (810), interest desire information (910), and function prediction information (920), or information on recommended lifestyle habits, diet, exercise methods, disease prevention and / or treatment methods related to the user's condition, but is not limited thereto.

[0133] Here, the customized result interpretation (930) may include all contents corresponding to biological age (810), interest desire information (910) and function prediction information (920), but in some cases, may include only information corresponding to the user's area of ​​interest.

[0134] FIG. 10 is a flowchart illustrating a method (S1000) for generating customized results for a user based on interest desire information about the user according to one embodiment of the present disclosure.

[0135] Referring to FIG. 10, in one embodiment, the information processing system (230) can generate customized results for the user based on interest desire information about the user.

[0136] At step S1010, the information processing system (230) can determine the elements and exposure priorities to be used in the customized test results based on the interest desire information about the user.

[0137] Here, the elements to be used in the customized test results may be text corresponding to the user's biological age, interest desire information, and function prediction information, but in some cases, may be images generated by visualizing information corresponding to the user's biological age, interest desire information, and function prediction information.

[0138] In step S1020, the information processing system (230) can determine user-tailored function prediction information based on information about the user's interest desires and the status of at least one of the user's musculoskeletal or nervous system.

[0139] In step S1030, the information processing system (230) can generate a customized result for the user by processing the customized function prediction information determined through step S1020 using the elements and exposure priorities determined through step S1010.

[0140] FIG. 11 is a diagram illustrating a machine learning model according to one embodiment of the present disclosure.

[0141] Referring to FIG. 11, a machine learning model (1100) may include an input layer (1120) into which input data (1110) is input, a hidden layer (1130_1, 1130_n), and an output layer (1140) that outputs result data (1150).

[0142] Here, the machine learning model (1100) may include, but is not limited to, a model that derives musculoskeletal-based user-tailored results from user status information and information on the balance of at least one of the musculoskeletal system or the nervous system (e.g., the first machine learning model (700 of FIGS. 7 and 8)), a model that derives user-stage goal information and customized coaching information from user status information and function prediction information (e.g., the second machine learning model (1200 of FIGS. 12 and 13)), and a model that derives musculoskeletal examination items and order from user status information (e.g., the third machine learning model).

[0143] More specifically, a machine learning model (e.g., a neural network) consists of one or more network functions, which may be comprised of a set of interconnected computational units, generally referred to as "nodes." These "nodes" may also be referred to as "neurons." One or more network functions are comprised of at least one node. The nodes (or neurons) comprising one or more network functions may be interconnected by one or more "links."

[0144] Within a machine learning model, one or more nodes connected via links can form a relationship between input nodes and output nodes. The concepts of input nodes and output nodes are relative, meaning that any node in an output node relationship with one node can also be in an input node relationship with another node, and vice versa. As described above, input node-to-output node relationships can be created based on links. One input node can be connected to one or more output nodes via links, and vice versa.

[0145] In a relationship between input nodes and output nodes connected through a single link, the value of the output node can be determined based on the data input to the input node. Here, the node interconnecting the input nodes and output nodes can have a weight. The weight can be variable and can be varied by a user or an algorithm so that the machine learning model can perform a desired function. For example, when one or more input nodes are interconnected to one output node through each link, the output node can determine the output node value based on the values ​​input to the input nodes connected to the output node and the weight set for the link corresponding to each input node.

[0146] As described above, a machine learning model consists of one or more nodes interconnected through one or more links, forming input and output node relationships within the model. The characteristics of a machine learning model can be determined based on the number of nodes and links, the relationships between nodes and links, and the weights assigned to each link. For example, if two machine learning models exist with the same number of nodes and links but different weight values ​​between the links, the two models may be perceived as different from each other.

[0147] Some of the nodes that make up a machine learning model can form a layer based on their distances from the initial input node. For example, a set of nodes that are n distances from the initial input node can form n layers. The distance from the initial input node can be defined by the minimum number of links required to reach the node from the initial input node. However, this definition of a layer is arbitrary for explanatory purposes, and the order of layers within a machine learning model can be defined in a different way than described above.

[0148] The initial input node may refer to one or more nodes in the machine learning model into which data is directly input without going through links in their relationships with other nodes. Similarly, the final output node may refer to one or more nodes in the machine learning model that do not have an output node in their relationships with other nodes. In addition, the hidden node may refer to nodes that constitute the machine learning model other than the initial input node and the final output node. The machine learning model according to one embodiment of the present invention may be a machine learning model in which the number of nodes in the input layer may be greater than the number of nodes in the hidden layer closer to the output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer.

[0149] A machine learning model may include one or more hidden layers. Hidden nodes in a hidden layer can receive the output of the previous layer and the output of surrounding hidden nodes as input. The number of hidden nodes in each hidden layer may be the same or different. The number of nodes in the input layer may be determined based on the number of data fields in the input data and may be the same as or different from the number of hidden nodes. Input data input to the input layer may be computed by the hidden nodes in the hidden layer and output by the output layer, a fully connected layer (FCL).

[0150] In various embodiments, the machine learning model may be a deep learning model.

[0151] A deep learning model (e.g., a deep neural network (DNN)) can refer to a machine learning model that includes multiple hidden layers in addition to an input layer and an output layer. Using a deep neural network, it is possible to identify latent structures in data. That is, the latent structures of photos, text, videos, and / or voices (e.g., what objects are in the photo, the user's posture, the user's movements, the user's body shape, etc.) can be identified.

[0152] Deep neural networks may include, but are not limited to, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, generative adversarial networks (GANs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), Q networks, U networks, Siamese networks, etc.

[0153] In various embodiments, the network function may include an autoencoder, which may be a type of artificial neural network that outputs output data similar to the input data.

[0154] An autoencoder may include at least one hidden layer, and one or more hidden layers may be positioned between input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called a bottleneck layer (encoding), and then expanded symmetrically from the bottleneck layer to the output layer (symmetrical to the input layer). The nodes of the dimensionality reduction layer and the dimensionality restoration layer may or may not be symmetrical. In addition, the autoencoder may perform nonlinear dimensionality reduction. The number of input and output layers may correspond to the number of sensors remaining after preprocessing the input data. In the autoencoder structure, the number of nodes in the hidden layer included in the encoder may have a structure that decreases as it moves away from the input layer. The number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and the decoder) may be maintained above a certain number (e.g., more than half of the number of nodes in the input layer) because too small a number may not convey sufficient information.

[0155] FIG. 12 is a diagram illustrating a process of generating user-level goal information and customized coaching information through a second machine learning model according to one embodiment of the present disclosure.

[0156] Referring to FIG. 12, in one embodiment, the information processing system (230) can generate step-by-step target information for the user and customized coaching information (1210) for the user based on function prediction information (920) and user status information (110) according to interest desire information.

[0157] Here, the step-by-step goal information for the user refers to information about detailed goals set for each step to achieve the final goal corresponding to the user's interest and desires, and may include information about goals for the user to achieve for each step (e.g., lifestyle habits, exercise adjustments, nutritional diets, mental management, etc. that are detailed to be easily performed and achieved for each step). For example, if the interest and desire of a female office worker in her 40s with a sedentary lifestyle is beauty (diet) or simultaneously includes pain (back) management, the step-by-step goal information for the user may be lifestyle habits and exercise adjustments that can be performed in a daily environment to address potential factors that may be related to back pain, as well as nutritional diets for body composition adjustment and weight loss, or may be, but is not limited to, the pain level and body fat distribution and body measurements by part, including weight, through these.

[0158] In addition, customized coaching information for users is information designed to guide and coach users to achieve step-by-step goal information, and may be information designed by analyzing the user's service participation patterns such as access frequency, time, and prescription execution intensity, as well as taking into account updated user status information such as real-time input conversations, changes in health status, changes in preferences, and changes in lifestyle patterns. For example, customized coaching information may include, but is not limited to, notification cycles, prescription execution intensity, etc.

[0159] In one embodiment, the information processing system (230) can generate step-by-step target information for the user and customized coaching information (1210) for the user through a machine learning model (hereinafter referred to as “second machine learning model (1200)”) based on function prediction information (920) and user status information (110) according to interest desire information.

[0160] Here, the second machine learning model (1200) may mean a model learned according to a machine learning method (e.g., semi-supervised learning and / or supervised learning) using learning data that uses user status information (110) and function prediction information (920) based on interest desire information as input data and step-by-step target information and customized coaching information (1210) as correct answer data.

[0161] In one embodiment, the second machine learning model (1200) includes two different input networks and one output network, and can extract two different results through each of the two input networks, and derive one result data by fusing the two results through the output network.

[0162] For example, the second machine learning model (1200) may include, but is not limited to, a third input network that receives function prediction information (920) based on the user's interest desire information and extracts a third feature vector, a fourth input network that receives user status information (110) and extracts a fourth feature vector, and a second output network that extracts a latent vector by fusing the third feature vector and the fourth feature vector and derives a user-customized result (140) based on the extracted latent vector.

[0163] Here, the second machine learning model (1200) may be, but is not limited to, a CNN (Convolutional Neural Network), a Transformer, a RL (Reinforcement Learning)-based model, an MLP (Multi-Layer Perceptron), and an ensemble model.

[0164] In one embodiment, if user status information is updated based on at least one of user interest and desire information, function prediction information based on the interest and desire information, step-by-step goal achievement information performed by the user, and customized coaching performance information for the user, the information processing system (230) may receive the updated user status information and regenerate step-by-step goal information and customized coaching information for the user based on the updated user status information. Under this configuration, based on the user status information, the inspection-interpretation-management process is repeatedly performed, so that the user's musculoskeletal system and / or musculoskeletal vulnerabilities may be gradually resolved.

[0165] FIG. 13 is a diagram illustrating a process of regenerating user-level goal information and customized coaching information through a second machine learning model according to one embodiment of the present disclosure.

[0166] Referring to FIG. 13, the information processing system (230) can generate regenerated user step-by-step goal information and customized coaching information (1330) by regenerating step-by-step goal information and customized coaching information based on step-by-step achievement goals and coaching performance information performed by the user.

[0167] In one embodiment, the information processing system (230) can receive step-by-step goal achievement information (1310) performed by the user and user-customized coaching performance information (1320), and can generate regenerated user step-by-step goal information and user-customized coaching information (1330) by regenerating step-by-step goal information and user-customized coaching information (1320) based on the step-by-step goal achievement information (1310) performed by the user, the user-customized coaching performance information (1320), the function prediction information (920) based on the interest and desire information, and the user status information (110) using a second machine learning model.

[0168] For example, the information processing system (230) can extract a user's behavioral pattern based on step-by-step goal achievement information (1310) performed by the user and user-tailored coaching performance information (1320), and can adjust step goal information for the user based on the extracted behavioral pattern.

[0169] That is, the information processing system (230) can monitor the extent to which the user has achieved the step-by-step goal, and adjust upward, maintain, or downward the step-by-step goal set in advance for the user based on whether the user has achieved the step-by-step goal, thereby enabling the user to set an appropriate goal.

[0170] FIG. 14 is a diagram illustrating a user interface (UI) that provides a skeleton-based user-customized service according to one embodiment of the present disclosure.

[0171] Referring to FIG. 14, in one embodiment, the information processing system (230) may provide a user interface (UI) that provides a user-customized service based on at least one of the musculoskeletal system or the nervous system.

[0172] Here, the UI may include, but is not limited to, a first area (1401) and a second area (1402).

[0173] First, the first region (1401) may refer to a region that displays the results of a user's mobility and stability test along with an image of the user's examination behavior. In one embodiment, the first region (1401) may include a region (1410) that measures the angle of the user's shoulder flexion (left) by analyzing the user's examination behavior image (1410) and a region (1420) that evaluates a compensation pattern, such as the degree of center movement, by analyzing the user's examination behavior image (1430). Under this configuration, when the user's stability and mobility are analyzed, the range of joint motion, which is a quantitative functional evaluation, may be provided through the region (1410) of the first region (1401), and at the same time, the results of evaluating a compensation pattern, such as the degree of center movement, which is a qualitative functional evaluation, may be provided together. Accordingly, a customized result report that enables an integrated interpretation of results including both quantitative and qualitative functional evaluations may be provided to the user. In the present disclosure, the image (1430) of the first area (1401) is depicted as an image of a user's inspection action, but is not limited thereto, and may be output as a video or 3D image including the user's inspection action.

[0174] Next, the second region (1402) may refer to a region that outputs the results of a health function physical fitness test. The second region (1402) may determine and output the layout and text wording of the user interface by considering the user status information among the simultaneously analyzed quantitative and qualitative functional evaluations. Specifically, based on the user status information including the user's age, gender, interests, etc., the balance and / or status of at least one of the user's musculoskeletal system or nervous system may be output from the expert's perspective. For example, the second region (1402) may include, but is not limited to, a biological age region (1440) that displays the user's biological age, an average motion speed region (1450) that displays the user's average motion speed, a muscle strength level region (1460) that displays the user's muscle strength level, and a record comparison region (1470) that displays a comparison result with the user's previous record. That is, the configuration and / or layout output in the second region (1402) may vary depending on the user's status information.

[0175] Additionally or alternatively, the text wording may be provided as a synthesized voice generated through a previously known text-to-speech (TTS) technology. For example, the UI of FIG. 14 may include an AI speaker icon (not shown). When the AI ​​speaker icon is selected (e.g., by clicking or touching), the order of explanation of the test results, selective wording, and / or explanation time may be determined by considering at least one functional state and / or attention requirement information of the user's musculoskeletal or nervous system. Based on the order of explanation of the test results, selective wording, and / or explanation time, a function may be provided that allows the user's test results to be explained to the user from the perspective of an expert (e.g., an AI coach) through a synthesized voice, as if an AI expert were explaining the test results to the user right next to them. In other words, through the UI of FIG. 14, the user may be provided with an audio version of the test results, and further, a 3D result sheet.

[0176] FIG. 15 is a flowchart illustrating a method (S1500) for providing a user-customized service based on at least one of the musculoskeletal system or the nervous system according to one embodiment of the present disclosure.

[0177] Here, it is described that the entity performing each step included in the method (S1500) for providing a user-customized service based on at least one of the musculoskeletal system or nervous system according to FIG. 15 is an information processing system (230), but it is not limited thereto, and in some cases, it may be performed by an inspection device (210), or the information processing system (230) and the inspection device (210) may be performed together.

[0178] Additionally, some steps may be performed by the information processing system (230), and the remaining steps may be performed by the inspection equipment (210).

[0179] Referring to FIG. 15, at step S1510, the information processing system (230) can receive status information about the user.

[0180] In one embodiment, the information processing system (230) can collect user status information through a user data channel.

[0181] Here, the status information about the user may include, but is not limited to, fixed information such as the user's gender and age information, medical information, and disease information, and variable information such as lifestyle information, health information, and preference information, as information related to the user's status.

[0182] Additionally, here, the user data channel may be, but is not limited to, the user's wearable device or a mobile application installed on the user's mobile phone.

[0183] In one embodiment, the information processing system (230) may repeatedly collect user status information at predetermined intervals, but is not limited thereto.

[0184] At step S1520, the information processing system (230) can determine one or more musculoskeletal examination items for the user based on the status information about the user received through step S1510.

[0185] In one embodiment, the information processing system (230) may determine one or more musculoskeletal examination items and their order required for the user by analyzing status information about the user through a third machine learning model, or may determine one or more musculoskeletal examination items and their order required for the user by analyzing status information about the user based on a preset rule.

[0186] In one embodiment, the information processing system (230) can dynamically adjust the type and order of one or more musculoskeletal examination items based on user status information collected at predetermined intervals.

[0187] In the conventional case, unnecessary inspection items were included regardless of user information, and all inspection items were inspected in a predetermined order once they were determined. However, according to the present disclosure, the user's status information is updated in real time (tagged and categorized), and the inspection items and order are flexibly and dynamically adjusted based on this information, thereby enabling appropriate inspections to be performed for the user. For example, when the user's status information is updated, categorized tags (e.g., normal / caution / danger, etc.) are tagged, and the inspection items and / or order can be adjusted based on the updated status information and tags. Here, the inspection items corresponding to risk factors can be designed to be performed first.

[0188] In addition, by selecting only the test items that are absolutely necessary for the user among the numerous musculoskeletal function test items, the cost and time required for the test can be drastically reduced.

[0189] At step S1530, the information processing system (230) can output one or more musculoskeletal examination items and order determined through step S1520.

[0190] At step S1540, the information processing system (230) may receive one or more user images for one or more postures or movements of the user in response to one or more musculoskeletal examination items output through step S1530.

[0191] For example, when one or more musculoskeletal examination items are determined based on user status information (110), the information processing system (230) can guide the user to assume a specific posture or perform a specific action based on the determined one or more musculoskeletal examination items, and in response, can acquire a user image generated by capturing a scene of the user assuming a specific posture or performing a specific action.

[0192] At step S1550, the information processing system (230) can output a customized result for the user by analyzing the image received at step S1540 and the status information about the user received at step S1510 through the first machine learning model.

[0193] At this time, the information processing system (230) analyzes the image and, when multiple postures and movements of the user are recognized, selects only the postures and movements requested from the user among the multiple postures and movements, or filters out postures and movements that were not requested from the user as noise.

[0194] In one embodiment, the information processing system (230) can generate a user-customized result sheet based on the user's musculoskeletal function test results.

[0195] For example, the information processing system (230) can determine the configuration, structure, and arrangement order of the UI that outputs the user's musculoskeletal function test results according to the user.

[0196] In addition, the information processing system (230) can select language and words to be used in creating a customized result sheet using interest and desire information, process the analyzed results using a biological age model and a function prediction model to suit the user's taste, or adjust the difficulty of language and words or select expressions according to the user's level.

[0197] A customized report generated using the above method may include all information related to the user's musculoskeletal function test results. However, in some cases, it may only include information relevant to the user's area of ​​interest or information selected by the user or administrator. For example, the user or administrator may arbitrarily modify and change the information included in the customized report, but this is not limited to this.

[0198] As described above, those skilled in the art will appreciate that the present disclosure can be implemented in other specific forms without altering the technical spirit or essential characteristics thereof. Therefore, the above-described embodiments should be understood as illustrative in all respects and not restrictive. The scope of the present disclosure is defined by the following claims rather than the detailed description, and all changes or modifications derived from the meaning and scope of the claims and equivalent concepts should be construed as being included within the scope of the present disclosure.

[0199] The features and advantages described in this specification are not exhaustive, and many additional features and advantages will become apparent to those skilled in the art upon review of the drawings, specification, and claims. Furthermore, it should be noted that the language used in this specification has been primarily selected for readability and instructional purposes, and may not be intended to delineate or circumscribe the subject matter of the present disclosure.

[0200] The above description of the embodiments of the present disclosure has been presented for illustrative purposes. It is not intended to limit the present disclosure to the precise form disclosed, nor is it intended to be exhaustive. Those skilled in the art will appreciate that numerous modifications and variations are possible in light of the above disclosure.

[0201] Therefore, the scope of this disclosure is not limited by the detailed description, but is defined by any claims of the application based on this description. Accordingly, the disclosure of embodiments of this disclosure is illustrative and does not limit the scope of this disclosure, which is set forth in the following claims.

Claims

1. A method for providing a user-customized service based on at least one of the musculoskeletal system and the nervous system, performed by at least one processor, A step of receiving status information about a user; A step of determining one or more musculoskeletal examination items based on the status information about the user received above; A step of outputting one or more musculoskeletal examination items determined above; For each of the one or more musculoskeletal examination items outputted above, a step of receiving one or more images including at least one of the user's posture or movement; A step of receiving meta information about an inspection device that captures one or more of the images; and A step of analyzing the status information of the user, the one or more images, and the meta information using a first machine learning model to output a customized result for the user, A method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system.

2. In paragraph 1, The step of outputting customized results for the above user is: A step of analyzing one or more of the received images and the meta information to generate a result regarding the balance of at least one of the user's musculoskeletal system or nervous system; and A step of generating a customized examination result for the user based on the result of the balance of at least one of the musculoskeletal system or nervous system of the user and the received status information for the user, using the first machine learning model. A method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system.

3. In paragraph 1, The step of determining one or more of the above musculoskeletal examination items is: A step of determining a plurality of musculoskeletal examination items and an order of the plurality of musculoskeletal examination items based on status information about the user, A method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system.

4. In paragraph 2, The status information about the user includes information about the user's gender and age, The method further comprises a step of determining a biological age for the user based on the result of the balance of at least one of the musculoskeletal system or nervous system of the user and the gender and age information of the user, The step of determining the biological age of the user based on the result of the balance of at least one of the musculoskeletal system or nervous system of the user and the gender and age information of the user, A step of generating a customized examination result for the user based on the biological age of the user, the result of the balance of at least one of the musculoskeletal system or nervous system of the user, and the status information of the user, using the first machine learning model. A method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system.

5. In paragraph 4, The customized test results for the above user include interest desire information for the user and function prediction information based on the interest desire information. A method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system.

6. In paragraph 5, The step of generating a customized examination result for the user based on the biological age of the user, the result of the balance of at least one of the musculoskeletal system or nervous system of the user, and the status information of the user using the first machine learning model is as follows: A step of determining elements and exposure priorities to be used in the customized test results based on interest desire information about the user; A step of determining user-tailored function prediction information based on the interest desire information of the user and the status of at least one of the user's musculoskeletal or nervous system; and A step of generating customized function prediction information for the user as a customized result for the user by using the determined elements and the exposure priority, A method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system.

7. In paragraph 5, Further comprising a step of generating step-by-step target information for the user and customized coaching information for the user based on function prediction information according to the interest desire information and status information for the user using a second machine learning model. A method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system.

8. In paragraph 7, The method further includes a step of receiving step-by-step goal achievement information performed by the user and user-tailored coaching performance information, The step of generating step-by-step goal information for the above user and customized coaching information for the above user is as follows: A step of regenerating step goal information for the user and customized coaching information for the user based on step-by-step goal achievement information performed by the user, user-customized coaching performance information, function prediction information according to the interest and desire information, and status information for the user, using the second machine learning model. A method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system.

9. In paragraph 5, The step of receiving status information about the above user is: A step of receiving updated status information about a user based on at least one of step-by-step goal achievement information performed by the user, customized coaching performance information about the user, interest desire information about the user, or function prediction information based on the interest desire information, A method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system.

10. In paragraph 1, The status information for the user includes fixed information for the user and change information for the user. A method for providing a user-tailored service based on at least one of the musculoskeletal system and the nervous system.

11. A method for evaluating musculoskeletal or nervous system function, performed by at least one processor, A step of determining at least one of an assessment item or an assessment sequence for examining a user's musculoskeletal function or nervous system function, wherein the assessment item includes a quantitative function assessment item and a qualitative function assessment item; A step of performing a functional evaluation of a musculoskeletal function or a nervous system function of a user by receiving at least one user image including at least one of a static motion or a dynamic motion of the user associated with the determined evaluation item, or sensor data associated with at least one of a static motion or a dynamic motion of the user, wherein the functional evaluation includes a quantitative functional evaluation and a qualitative functional evaluation; A step of generating a customized result for the user based on the result of the above functional evaluation; and A step for outputting customized results for the above-mentioned generated user. A method for evaluating musculoskeletal or nervous system function, including:

12. In paragraph 11, The above decision step is, A step of receiving status information about the user and determining evaluation items and evaluation order corresponding to the received status information about the user based on a rule set in advance; A step of receiving status information about the user and using a machine learning model to determine evaluation items and evaluation order corresponding to the received status information about the user; or A step of determining evaluation items and evaluation order based on a predefined set of evaluation items. A method for assessing musculoskeletal or nervous system function, comprising at least one of:

13. In paragraph 11, The above decision step is, A step of changing at least one of the determined evaluation items or the determined evaluation order in response to receiving a user input that changes at least one of the determined evaluation items or the determined evaluation order. Including, The steps performed above are: A step of performing a quantitative functional evaluation and a qualitative functional evaluation of the user's musculoskeletal function or nervous system function based on at least one of the above-mentioned changed evaluation items or the above-mentioned changed evaluation order. A method for evaluating musculoskeletal or nervous system function, including:

14. In paragraph 11, The above quantitative function evaluation items are: Includes quantitative evaluation items of musculoskeletal function or nervous system function associated with the user's exercise performance, The above quantitative evaluation items are: Associated with at least one of information about the number of muscle contractions during exercise, information about exercise distance, information about the number of repetitions of a specific exercise movement, information about exercise intensity, information about muscle load during exercise, or information about exercise volume. Methods for assessing musculoskeletal or nervous system function.

15. In paragraph 11, The above qualitative function evaluation items are: Includes qualitative evaluation items of musculoskeletal function or nervous system function associated with the user's exercise performance, The above qualitative evaluation items are: Associated with at least one of information about postural alignment during exercise, information about muscle imbalances, information about range of motion, information about accuracy of exercise movements, information about body balance and stability, information about movement speed, information about muscle activation patterns during exercise, information about breathing patterns during exercise, or information about fatigue. Methods for assessing musculoskeletal or nervous system function.

16. In paragraph 11, The steps performed above are: A step of outputting first guidance information for static motion associated with the determined evaluation item and second guidance information for dynamic motion associated with the determined evaluation item; A step of receiving at least one of the user images including the static motion of the user and the dynamic motion of the user corresponding to each of the outputted first guidance information and the outputted second guidance information, or the sensor data associated with the static motion of the user and the dynamic motion of the user corresponding to each of the outputted first guidance information and the outputted second guidance information; and A step of performing a static function evaluation associated with a static motion of the user and a dynamic function evaluation associated with a dynamic motion of the user based on at least one of the received at least one user image or the received sensor data. A method for evaluating musculoskeletal or nervous system function, including:

17. In paragraph 16, A method for evaluating musculoskeletal or nervous system function, wherein the above static function evaluation and the above dynamic function evaluation are performed sequentially or in parallel.

18. In paragraph 11, The above outputting step is: A step of outputting at least one of an image or text associated with the generated customized result for the user as at least a part of the user interface; or A step of outputting a synthetic voice associated with the customized result for the user generated above. A method for assessing musculoskeletal or nervous system function, comprising at least one of:

19. In paragraph 18, The image associated with the customized results for the user generated above is: At least one of the received at least one user image or a 3D image generated based on the received at least one user image, Methods for assessing musculoskeletal or nervous system function.

20. In paragraph 18, A method for evaluating musculoskeletal or nervous system function, wherein an image associated with the generated customized result for the user and a synthetic voice associated with the generated customized result for the user are output sequentially or in parallel.

21. In paragraph 11, The above decision step is, A step of determining the user's action flow associated with the evaluation item based on the determined evaluation item and the determined evaluation order. A method for evaluating musculoskeletal or nervous system function, including:

22. In paragraph 11, Step of receiving status information about the above user Including more, The above decision step is, A step of updating the determined evaluation items and the determined evaluation order based on the status information about the user received above. A method for evaluating musculoskeletal or nervous system function, further comprising:

23. In paragraph 11, The steps performed above are: A step of performing segmentation on at least one user image received above, thereby extracting at least one of information on a body joint or information on a muscle within the at least one user image received above; and A step of analyzing the static or dynamic motion of the user based on at least one of the extracted information about the body joint or information about the muscle. A method for evaluating musculoskeletal or nervous system function, including:

24. In paragraph 11, The steps for generating customized results for the above users are: A step of generating a customized result for the user including at least one user image received above, information associated with the result of performing the quantitative function evaluation, and information associated with the result of performing the qualitative function evaluation. A method for evaluating musculoskeletal or nervous system function, including:

25. In paragraph 11, The steps performed above are: A step of receiving meta information about an inspection device that captures at least one user image received above. Including, The steps for generating customized results for the above users are: A step of generating a result for the balance of at least one of the user's musculoskeletal function or nervous system function by using at least one user image received and the received meta information. A method for evaluating musculoskeletal or nervous system function, including:

26. In paragraph 25, The steps for generating customized results for the above users are: A step of generating at least one of the user's biological age information, interest desire information, or function prediction information based on the status information about the user received and the result of the generated balance; and A step of generating a customized result for the user based on at least one of the generated biological age information, interest desire information, or function prediction information. A method for evaluating musculoskeletal or nervous system function, further comprising:

27. A computer-readable non-transitory recording medium recording commands for executing the method according to paragraph 1 on a computer.

28. As an information processing system, Communication module; memory; and At least one processor connected to said memory and configured to execute at least one computer-readable program contained in said memory Including, The above program is, Receive status information about the user, Based on the status information about the user received above, determining at least one of the evaluation items or evaluation sequences for examining the musculoskeletal function or nervous system function of the user, wherein the evaluation items include quantitative function evaluation items and qualitative function evaluation items, By receiving at least one user image including at least one of the static or dynamic motion of the user associated with the determined evaluation item, or sensor data associated with at least one of the static or dynamic motion of the user, a functional evaluation of the musculoskeletal function or nervous system function of the user is performed, wherein the functional evaluation includes a quantitative functional evaluation and a qualitative functional evaluation. Based on the results of the above functional evaluation, a customized result for the user is generated, An information processing system comprising commands for outputting customized results for the generated user.

29. In paragraph 28, An input / output interface for receiving data for examining the musculoskeletal function or nervous system function of the user. Including more, The above input / output interface is, An information processing system configured to receive at least one user image or sensor data.

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