Exercise therapy control apparatus and method using UI that includes segment selection progress bar

Abstract

Disclosed is an exercise therapy control apparatus using a UI that includes a segment selection progress bar, comprising: a signal input device for receiving a point selection signal from a patient in a step of generating therapy for the patient by a robot; a display for displaying a therapy setting UI for selecting at least one segment in a therapy progress step; and a processor for performing a segment setting process in the therapy progress step by means of the therapy setting UI.

Description

Exercise therapy control device and method using a UI including a segment selection progress bar

[0001] The present disclosure relates to an exercise therapy device and method using a robot. More specifically, it relates to an exercise therapy control device and method using a UI including a section selection progress bar.

[0002] While rehabilitation is sometimes performed using rehabilitation tools, robotic rehabilitation is used when the patient has difficulty standing independently or when appropriate treatment for the affected area is challenging.

[0003] Medical robots that include an Actuated Applied Part that physically contacts and controls a body part related to a patient's motor function perform clinical functions through a total of four functions: generate, select, execute, and monitor. These four functions are the basic functions of devices commonly referred to as rehabilitation robots, through which the patient's disability is rehabilitated, corrected, or alleviated.

[0004] Robotic rehabilitation can be applied to major joints of the body. Patients can wear articulated robots capable of being applied to the major joints of the shoulders, arms, and legs, which can assist, compel, or inhibit joint movement. Consequently, muscle resistance increases, leading to improved muscle strength and a greater range of motion in the joints.

[0005] Meanwhile, existing exercise rehabilitation systems have not been able to significantly deviate from passive and simple, repetitive methods. It is known that such approaches lead to poor rehabilitation effectiveness due to low patient engagement, making consistent treatment difficult and resulting in a lack of concentration on participating in training.

[0006] In particular, in the case of robot-assisted rehabilitation, since user recording is performed repeatedly, there was a problem in that unnecessary steps had to be repeated to perform treatment at the point of maximum action.

[0007] The purpose of the embodiments disclosed in this disclosure is to provide an exercise therapy control device and method that provide a UI enabling a patient, a treatment subject, or an operator to directly select a treatment section, and to control a robot so that an exercise motion corresponding to the treatment section selected through the UI is performed.

[0008] The problems that this disclosure aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below.

[0009] An apparatus according to the present disclosure for achieving the above-described technical problem comprises: a signal input device that receives a point selection signal from the patient during the treatment generation stage by the robot; a display that displays a treatment setting UI for receiving at least one section selection during the treatment progress stage; and a processor that performs a section setting process of the treatment selection stage through the treatment setting UI. The processor generates a 3D model that implements at least one movement motion according to the treatment progress in three dimensions, displays the treatment setting UI through the display that includes at least one bar shape indicating the progress status of the 3D model, a first pointer indicating the point where the point selection signal occurred on the progress bar, and a second pointer provided to be slidably movable on the progress bar to receive at least one playback section input, and controls the robot to perform a movement motion corresponding to at least one playback section set by the first pointer and the second pointer.

[0010] Meanwhile, the system further includes a shooting module that captures the progress stage of a patient's treatment by the robot to acquire a patient image; and the processor analyzes the patient image to generate a 3D model and can display the treatment setting UI through the display, which includes a progress bar with the first pointer and the second pointer displayed thereon, so that at least one section of the motion trajectory represented by the 3D model can be selected.

[0011] Additionally, the processor may obtain coordinate information of at least one rotation axis constituting the robot during the treatment progress step from the robot, generate the 3D modeling representing a motion trajectory using the coordinate information, and display the treatment setting UI through the display, which includes the first pointer and the second pointer displayed on the progress bar so that at least one section of the motion trajectory represented by the 3D modeling can be selected.

[0012] In addition, the processor can receive the number of repetitions and the repetition order of an exercise motion corresponding to at least one playback section through the UI, and control the robot so that the exercise motion corresponding to at least one playback section can be repeatedly performed according to the input number of repetitions and the repetition order.

[0013] In addition, the processor controls the robot so that it can sequentially perform motions corresponding to at least one playback section according to the playback progress order, and when a request for random execution is input through the UI, the processor can control the robot by randomly setting the order and number of repetitions of motions corresponding to at least one playback section.

[0014] Additionally, the system further includes a sensor module for acquiring the patient's biometric information; and a shooting module for capturing the patient to acquire a patient image; wherein the processor analyzes the biometric information and the patient image to calculate a feedback index during the performance of an exercise motion by the robot, and controls the robot so that the performance of the exercise motion by the robot is restricted according to the feedback index.

[0015] In addition, a method according to the present disclosure for achieving the aforementioned technical problem comprises: a control method performed by an exercise therapy control device using a treatment setting UI including a section selection progress bar, the method comprising: receiving a section selection signal from the patient during a treatment progress stage by a robot; generating a 3D model that reproduces at least one exercise motion according to the treatment progress stage in three dimensions; displaying the treatment setting UI including a progress bar that displays the playback progress status of the 3D model, a first pointer indicating a point where the section selection signal occurred on the progress bar, and a second pointer provided to be slidably movable on the progress bar to receive at least one playback section; and controlling the robot so that it can perform an exercise motion corresponding to at least one playback section set by the first pointer and the second pointer.

[0016] Additionally, the step of controlling the robot may include, when a random execution request is input through the UI, a step of controlling the robot by randomly setting the sequence and number of repetitions of the exercise motions corresponding to at least one playback section.

[0017] Additionally, the step of controlling the robot may include: a step of acquiring the patient's biometric information; a step of photographing the patient to acquire a patient image; a step of analyzing the biometric information and the patient image to calculate a feedback index during the performance of an exercise motion by the robot; and a step of controlling the robot so that the performance of an exercise motion by the robot is restricted according to the feedback index.

[0018] In addition to this, a computer program stored on a computer-readable recording medium for executing the present disclosure may be further provided.

[0019] In addition, a computer-readable recording medium for recording a computer program for executing a method for implementing the present disclosure may be further provided.

[0020] According to the aforementioned means for solving the problem of the present disclosure, by providing a UI that allows the patient to directly select a treatment section, it is possible to move away from repetitive and monotonous treatment methods and alleviate the boredom of treatment, and to reduce unnecessary treatment time, thereby providing the advantageous effect of enabling efficient medical services.

[0021] In addition, by providing a UI that allows users to directly set appropriate treatment intervals and repetition counts according to the patient's condition, safe and customized treatment can be provided.

[0022] In addition, compared to the existing UI, by visually presenting 3D models, patients can clearly recognize their treatment situation visually and selectively configure the treatment process they want.

[0023] In addition, as the human body adapts to high-repetition rehabilitation, treatment efficiency decreases and the patient's response to treatment also declines; therefore, treatment efficiency can be improved through an appropriate random treatment method.

[0024] In addition, after the patient selects a section, the right to choose the number of playbacks and the order of playback within that section can be granted, thereby increasing the patient's autonomy in treatment.

[0025] In addition, while the therapist is performing the registered treatment method, the patient's resistance is recognized through facial expressions, non-verbal expressions, and load measurements; to alleviate this, the therapist guides the treatment to be performed within a reduced range compared to the previously recorded range, thereby promoting treatment efficacy and patient safety.

[0026] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below.

[0027] FIG. 1 is a conceptual diagram of an exercise therapy control system according to one embodiment of the present disclosure.

[0028] FIG. 2 is a control block diagram of an exercise therapy control device according to one embodiment of the present disclosure.

[0029] FIG. 3 is a flowchart of an exercise therapy control method according to one embodiment of the present disclosure.

[0030] Figure 4 is a diagram illustrating the 3D modeling generation step of Figure 3.

[0031] FIG. 5 is a drawing for explaining a robot according to one embodiment of the present disclosure.

[0032] FIGS. 6 to 8 are drawings for explaining a treatment setting UI according to one embodiment of the present disclosure.

[0033] FIG. 9 is a control block diagram of an exercise therapy control device according to another embodiment of the present disclosure.

[0034] FIG. 10 is a flowchart of an exercise therapy control method according to another embodiment of the present disclosure.

[0035] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure pertains or content that overlaps between embodiments is omitted. The terms 'part, module, component, block' as used in the specification may be implemented in software or hardware, and depending on the embodiments, a plurality of 'parts, modules, components, blocks' may be implemented as a single component, or a single 'part, module, component, block' may include a plurality of components.

[0036] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are directly connected but also cases where they are indirectly connected, and indirect connections include connections made via a wireless communication network.

[0037] Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0038] Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components.

[0039] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0040] Singular expressions include plural expressions unless there is an obvious exception in the context.

[0041] In each step, identification codes are used for convenience of explanation and do not describe the order of the steps; the steps may be performed differently from the specified order unless a specific order is clearly indicated in the context.

[0042] The operating principles and embodiments of the present disclosure will be described below with reference to the attached drawings.

[0043] In this specification, the term "device according to the present disclosure" includes all various devices capable of performing computational processing and providing results to a user. For example, the device according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may be in the form of any one of these.

[0044] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0045] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0046] The above portable terminal may include, for example, all types of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0047] The functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0048] The predefined rules of operation or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined rules of operation or artificial intelligence models configured to perform desired characteristics (or objectives) are created by a basic artificial intelligence model being trained using multiple learning data by a learning algorithm. Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0049] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and performs neural network operations through operations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized. Artificial neural networks may include deep neural networks (DNNs), such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Bidirectional Recurrent Deep Neural Networks (BRDNNs), or Deep Q-Networks, but are not limited to the examples mentioned above.

[0050] According to an exemplary embodiment of the present disclosure, a processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that enables a machine to learn by mimicking human biological neurons. Methodologies of artificial intelligence can be classified according to the learning method into supervised learning, where input and output data are provided together as training data and the solution (output data) to the problem (input data) is predetermined; unsupervised learning, where only input data is provided without output data and the solution (output data) to the problem (input data) is not predetermined; and reinforcement learning, where a reward is given from an external environment whenever an action is taken from the current state, and learning proceeds in a direction that maximizes such reward. In addition, artificial intelligence methodologies can be classified according to the architecture, which is the structure of the learning model. The architectures of widely used deep learning technologies can be classified into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformers, and Generative Adversarial Networks (GAN).

[0051] The device and system may include an artificial intelligence model. The artificial intelligence model may be a single model or may be implemented as multiple models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include statistical learning algorithms in machine learning and cognitive science that mimic biological neurons. A neural network may refer to a model that possesses problem-solving capabilities by having artificial neurons (nodes) that form a network through synaptic connections and change the strength of synaptic connections through learning. The neurons of a neural network may include combinations of weights or biases. A neural network may include one or more layers composed of one or more neurons or nodes. For example, the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer a result (output) to be predicted from an arbitrary input by changing the weights of the neurons through learning.

[0052] The processor can create a neural network, train or learn a neural network, perform computations based on received input data, generate an information signal based on the results of the computation, or retrain the neural network. The neural network models may include, but are not limited to, various types of models such as Convolutional Neural Networks (CNN), Region with Convolutional Neural Networks (R-CNN), Region Proposal Networks (RPN), Recurrent Neural Networks (RNN), Stacking-based Deep Neural Networks (S-DNN), State-Space Dynamic Neural Networks (S-SDNN), Deconvolution Networks, Deep Belief Networks (DBN), Restructured Boltzmann Machines (RBM), Fully Convolutional Networks, Long Short-Term Memory Networks (LSTM), and Classification Networks, such as GoogleNet, AlexNet, and VGG Network. The processor may include one or more processors to perform computations according to the neural network models. For example, the neural network is a deep neural network It may include a (Deep Neural Network).

[0053] Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto) Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Depp Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), GAN (Generative Adversarial Network), LSM (Liquid State Machine), ELM (Extreme Learning Machine), ESN (Echo It will be understood by a person skilled in the art that any neural network may be included, but is not limited to, State Network, Deep Residual Network, Differential Neural Computer, Neural Turning Machine, Capsule Network, Kohonen Network, and Attention Network.

[0054] According to an exemplary embodiment of the present disclosure, the processor comprises a Convolutional Neural Network (CNN) such as GoogleNet, AlexNet, VGG Network, Region with Convolutional Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based Deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restructured Boltzmann Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA, Text Analysis, Dialog System, GPT-3, GPT-4 for Natural Language Processing, Visual Analytics, Visual Understanding, Video Synthesis for Vision Processing, Anomaly Detection, Prediction, Time-Series Forecasting, Optimization for ResNet Data Intelligence, Various artificial intelligence structures and algorithms, such as recommendation and data creation, may be used, but are not limited thereto. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0055] FIG. 1 is a conceptual diagram of an exercise therapy control system according to one embodiment of the present disclosure.

[0056] Referring to FIG. 1, an exercise therapy control system (10) according to one embodiment of the present disclosure may include an exercise therapy control device (100) and a robot (200).

[0057] The exercise therapy control device (100) can provide an exercise therapy control service according to the present invention.

[0058] The exercise therapy control service according to the present invention generates a 3D model that simulates a treatment scene, plays the 3D model, and displays a treatment setting UI (User Interface) that includes a playback progress bar of the 3D model, and can control the exercise motion of the treatment section to be repeated by receiving a specific treatment section through the treatment setting UI.

[0059] The robot (200) may be configured to include at least one multi-joint arm, and one end of the arm may be in contact with or connected to a body part of a patient.

[0060] The robot (200) can operate according to a predetermined control signal to guide the movement of the patient's body parts and assist with force.

[0061] The exercise therapy control device (100) and the robot (200) can be connected via a network to transmit and receive data.

[0062] For example, the exercise therapy control device (100) can transmit a control signal to the robot (200) to enable the robot (200) to perform a predetermined exercise motion included in exercise therapy. At this time, the control signal may be a control signal for performing an exercise motion corresponding to a specific treatment stage selected by the patient.

[0063] The robot (200) can transmit position coordinate information of at least one joint to the exercise therapy control device (100).

[0064] The exercise therapy control system (10) according to one embodiment of the present disclosure provides a UI that allows the patient to directly select a treatment section, thereby moving away from repetitive and monotonous treatment methods and reducing the boredom of treatment, and has the advantageous effect of enabling efficient medical services by reducing unnecessary treatment time.

[0065] In addition, an exercise therapy control system (10) according to one embodiment of the present disclosure can provide safe and customized treatment by providing a UI that allows the appropriate treatment interval and number of repetitions to be set directly according to the patient's condition.

[0066] FIG. 2 is a control block diagram of an exercise therapy control device according to one embodiment of the present disclosure.

[0067] Referring to FIG. 2, an exercise therapy control device (100) according to one embodiment of the present disclosure may include a processor (110), a communication module (120), a memory (130), a shooting module (140), and a display (150).

[0068] The components illustrated in FIG. 2 are not essential for implementing the exercise therapy control device (100) according to the present disclosure, so the patient rehabilitation therapy device (100) using vision artificial intelligence described in this specification may have more or fewer components than the components listed above.

[0069] The processor (110) can perform a rehabilitation treatment process according to the present disclosure.

[0070] The processor (110) may be implemented with a memory that stores data for an algorithm or a program that reproduces the algorithm for controlling the operation of components within the device, and at least one processor (not shown) that performs the aforementioned operation using the data stored in the memory. In this case, the memory and the processor may each be implemented as separate chips. Alternatively, the memory and the processor may be implemented as a single chip.

[0071] In addition, the processor (110) may control one or more of the components described above in combination to implement various embodiments according to the present disclosure described in the drawings below on the device.

[0072] A processor (110) receives a point selection signal from the patient during the treatment generation stage by the robot through a signal input device, displays a treatment setting UI for selecting at least one section during the treatment selection stage, generates a 3D model that plays at least one exercise motion according to the treatment selection stage in three dimensions, displays the treatment setting UI through the display including a progress bar that displays the progress status of the 3D model, a first pointer that indicates the point where the section selection signal is generated on the progress bar, and a second pointer that is slidably movable on the progress bar to receive at least one playback section, and controls the robot to perform an exercise motion corresponding to at least one playback section set by the first pointer and the second pointer.

[0073] Additionally, the processor (110) can acquire a patient image by capturing the patient's treatment progress steps by the robot, generate a 3D model by analyzing the patient image, and display the treatment setting UI through the display, which includes the progress bar with the first pointer and the second pointer displayed so that at least one section of the motion trajectory represented by the 3D model can be selected.

[0074] Additionally, the processor (110) may obtain coordinate information of at least one rotation axis constituting the robot during the treatment progress step from the robot, generate the 3D modeling representing the motion trajectory using the coordinate information, and display the treatment setting UI through the display, which includes the first pointer and the second pointer displayed on the progress bar so that at least one section of the motion trajectory represented by the 3D modeling can be selected.

[0075] Additionally, the processor (110) receives the number of repetitions and the repetition order of an exercise motion corresponding to at least one playback section through the UI, and can control the robot so that the exercise motion corresponding to at least one playback section can be performed repeatedly according to the input number of repetitions and the repetition order.

[0076] Additionally, the processor (110) controls the robot (200) so that it can sequentially perform exercise motions corresponding to at least one playback section according to the playback progress order, and when a request for random execution is input through the UI, it can control the robot (200) by randomly setting the order and number of repetitions of the exercise motions corresponding to at least one playback section.

[0077] Additionally, the processor (110) can analyze the bio-information and the patient image to calculate a feedback index during the performance of an exercise motion by the robot (200), and control the robot (200) so that the performance of an exercise motion by the robot (200) is restricted according to the feedback index.

[0078] The communication module (120) may include one or more components that enable communication with an external device, and may include, for example, at least one of a wired communication module, a wireless communication module, a short-range communication module, and a location information module.

[0079] The wired communication module may include various wired communication modules such as a Local Area Network (LAN) module, a Wide Area Network (WAN) module, or a Value Added Network (VAN) module, as well as various cable communication modules such as USB (Universal Serial Bus), HDMI (High Definition Multimedia Interface), DVI (Digital Visual Interface), RS-232 (recommended standard 232), power line communication, or POTS (plain old telephone service).

[0080] In addition to Wi-Fi modules and WiBro (Wireless broadband) modules, the wireless communication module may include wireless communication modules that support various wireless communication methods such as GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), UMTS (universal mobile telecommunications system), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G, 5G, and 6G.

[0081] The wireless communication module may include a wireless communication interface comprising an antenna and a transmitter that transmit a mobile communication signal. Additionally, the wireless communication module may further include a signal conversion module that modulates a digital control signal output from the processor (110) through the wireless communication interface into an analog wireless signal under the control of the processor (110).

[0082] A short-range communication module is for short-range communication and can support short-range communication by using at least one of Bluetooth™, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra Wideband), ZigBee, NFC (Near Field Communication), Wi-Fi (Wireless-Fidelity), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus) technologies.

[0083] The location information module is a module for obtaining the location (or current location) of the device according to the present disclosure, and representative examples thereof include a Global Positioning System (GPS) module or a Wireless Fidelity (WiFi) module. For example, if a GPS module is utilized, the location of the device can be obtained using signals sent from GPS satellites. As another example, if a Wi-Fi module is utilized, the location of the device can be obtained based on information from a Wireless Access Point (AP) that transmits or receives wireless signals from the Wi-Fi module. If necessary, the location information module may perform any of the functions of other modules of the communication unit to obtain data regarding the location of the device, either substituted or additionally. The location information module is a module used to obtain the location (or current location) of the device, and is not limited to a module that directly calculates or obtains the location of the device.

[0084] The memory (130) can store data supporting various functions of the device and programs for the operation of the processor (110), and can store input / output data, and can store a number of application programs (or applications) running on the device, data for the operation of the device, and instructions. At least some of these application programs can be downloaded from an external server via wireless communication.

[0085] Such memory (130) may include at least one type of storage medium among flash memory type, hard disk type, SSD type (Solid State Disk type), SSD type (Silicon Disk Drive type), multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (random access memory; RAM), SRAM (static random access memory), ROM (read-only memory; ROM), EEPROM (electrically erasable programmable read-only memory), PROM (programmable read-only memory), magnetic memory, magnetic disk, and optical disk. Additionally, the memory (130) may be a database that is separated from the device but connected via wired or wireless connection.

[0086] The shooting module (140) can take a picture of the patient to acquire a patient image and transmit the acquired patient image to the processor (110).

[0087] For example, the shooting module (140) may be implemented as an RGB camera, a TOF (Time of Flight) camera, etc., but is not limited thereto, and may include at least one light sensor, a lens that controls the path of light to shoot a specific point or a specific range, an image processor, an image element, and a wired / wireless data transmission module, etc.

[0088] The imaging module (140) can be positioned in the east, west, south, and north directions relative to the patient to take images, or attached to at least one means of transport to take images while moving.

[0089] The display (150) displays (outputs) information processed by the device (100). For example, the display (150) may display execution screen information of an application (e.g., an application) running on the device (100), or UI (User Interface) and GUI (Graphic User Interface) information based on such execution screen information.

[0090] Meanwhile, the device (100) according to one embodiment of the present disclosure may further include a signal input device.

[0091] For example, the signal input device may include a pedal-controllable switch or a foot mat including a pressure sensing sensor, and when pressure is applied by a patient or operator, it may generate a point selection signal and transmit it to the processor (110).

[0092] For example, when a patient or operator performs a movement motion to be applied to a future customized treatment design, such as a preferred movement motion, during the generation of treatment by the robot (200), the patient or operator may press a signal input device to generate a point selection signal. The processor (110) can identify the treatment section selected by the user in the treatment selection stage through the point selection signal and generate a treatment process that causes the corresponding treatment section to be performed repeatedly.

[0093] At least one component may be added or removed in response to the performance of the components shown in FIG. 2. Additionally, it will be readily understood by those skilled in the art that the relative positions of the components may be changed in response to the performance or structure of the system.

[0094] Meanwhile, each component illustrated in Figure 2 refers to a software and / or hardware component such as a Field Programmable Gate Array (FPGA) and an Application Specific Integrated Circuit (ASIC).

[0095] FIG. 3 is a flowchart of an exercise therapy control method according to one embodiment of the present disclosure.

[0096] Referring to FIG. 3, an exercise therapy control method according to one embodiment of the present disclosure may be performed by an exercise therapy control device (100) illustrated in FIG. 2 and may include the steps of: a processor (110) generating a 3D model (S10); a display (150) displaying a treatment setting UI (S20); a processor (110) acquiring an exercise motion segment (S30); acquiring the number of repetitions of the exercise motion segment (S40); and controlling a robot (200) (S50).

[0097] The step (S10) in which the processor (110) generates a 3D model may be a step of generating a 3D model that reproduces at least one movement motion in three dimensions according to the patient's treatment progress stage by the robot (200).

[0098] In this embodiment, the treatment includes a plurality of treatment progression stages, and each treatment stage may perform at least one exercise motion. For example, the treatment may be joint mobilization, and the treatment progression stages may consist of 0 to 10 stages.

[0099] The processor (110) can acquire a patient image capturing the patient's treatment progress stage by the robot (200) through the shooting module (140) and generate a 3D model by analyzing the patient image. This will be explained with reference to FIG. 4.

[0100] Figure 4 is a diagram illustrating the 3D modeling generation step of Figure 3.

[0101] Referring to FIG. 4, the shooting module (140) can obtain a patient image that records the entire process of the treatment progress stage by shooting a patient (P) who is performing a predetermined exercise motion by a robot (200) while lying on a bed (5).

[0102] The processor (110) can acquire a patient image from the imaging module (140) and can generate a 3D model from the patient image. For example, the processor (110) can generate a 3D model from the patient image using a computer-based 3D-CAD program.

[0103] Meanwhile, the signal input device (145) may be provided at a predetermined location adjacent to the bed (5) and may receive a section selection signal from the patient or operator during the treatment generation phase.

[0104] The processor (110) receives a point selection signal from a signal input device (145) and can identify a point corresponding to the point of origin of the point selection signal in a patient image that records the entire process of the treatment progress stage. Alternatively, the processor (110) can acquire coordinate information of at least one rotation axis constituting the robot (200) during the patient's treatment progress stage and generate a 3D model using the coordinate information. This will be explained with reference to FIG. 5.

[0105] FIG. 5 is a drawing for explaining a robot according to one embodiment of the present disclosure.

[0106] Referring to FIG. 5, a robot (200) according to one embodiment of the present disclosure may include a robot arm (220) having a multi-joint structure including at least one rotation axis (210a, 210b) and an end effector (230) installed at the end of the robot arm (220).

[0107] The rotation axis (210a, 210b) may include a drive capable of moving two or more axes.

[0108] The robot arm (220) can achieve positional movement by controlling the rotation axes (210a, 210b) according to a predetermined control signal.

[0109] The end effector (230) can be in contact with or coupled to a part of the patient's body.

[0110] The end effector (230) is not limited in its shape, but it is preferable that it be formed in a shape that can come into contact with or wrap around a body part required for rehabilitation.

[0111] A robot (200) can control rotation axes (210a, 210b) according to a control signal received from a processor (110) to move a part of a patient's body connected to an end effector (230) in an intended direction.

[0112] The processor (110) can receive coordinate information from the robot (200) indicating the position of the rotation axes (210a, 210b) during the patient's treatment progress phase. The processor (110) can generate a 3D shape of the robot (200) using the coordinate information and generate a 3D model by identifying the movement of the robot according to the position change of the rotation axes (210a, 210b) during the patient's treatment progress phase.

[0113] The step (S20) in which the display (150) displays a treatment setting UI may be a step of displaying a treatment setting UI including a progress bar that displays a 3D model and the playback progress status of the 3D model. This will be explained with reference to FIG. 6.

[0114] FIG. 6 is a drawing for explaining a treatment setting UI according to one embodiment of the present disclosure.

[0115] Referring to FIG. 6, the display (150) may display a treatment setting UI. The treatment setting UI may include a 3D modeling area (51), a progress bar (55), a section setting icon (52), a section repeat icon (53), and an execution icon (54).

[0116] The 3D modeling area (51) can output a 3D model that reproduces at least one exercise motion according to the treatment progress stage in three dimensions.

[0117] Compared to the existing UI, by visually presenting the 3D model, the patient can clearly recognize their treatment situation visually and selectively configure the desired treatment process. The progress bar (55) can display the playback progress status of the 3D model output from the 3D modeling area (51) and may include a first pointer (57) and a second pointer (56).

[0118] The first pointer (57) may indicate the point where a point selection signal is generated through the signal input device (145). That is, the first pointer (57) may indicate the point selected by the user during the treatment progress stage.

[0119] The second pointer (56) may be provided to be slidably movable on the progress bar (55) so as to select the playback position of the 3D model output from the 3D modeling area (51). The user can select the playback start position of the 3D model by moving the position of the second pointer (56) on the progress bar (55).

[0120] The section setting icon (52) is an icon that switches from the current screen to the section setting screen, and when input by the user, the screen can be switched to the section setting screen. The section setting screen can set at least one playback section of the 3D model.

[0121] The loop icon (53) is an icon that switches from the current screen to the loop setting screen, and when input by the user, the screen can be switched to the loop setting screen. The loop setting screen can set the number of repetitions for at least one playback section of the 3D model.

[0122] The execution icon (54) is an icon that commands the execution of a treatment progress step of the robot (200), and when input by a user, it can transmit a control signal to the robot (200) to enable the execution of a treatment progress step represented by a 3D model.

[0123] The step (S40) in which the processor (110) acquires an exercise motion segment (S30) and acquires the number of repetitions of the exercise motion segment may be a step of receiving at least one playback segment of a 3D model and the number of repetitions of each playback segment through a treatment setting UI. This will be explained with reference to FIGS. 7 and 8.

[0124] FIGS. 7 and FIGS. 8 are drawings for explaining a treatment setting UI according to one embodiment of the present disclosure.

[0125] Referring to FIG. 7, the display (150) can display the range setting screen of the treatment setting UI.

[0126] The section setting screen can set at least one playback section of the 3D model, and to this end, it may include a 3D modeling area (51), a progress bar (55), and at least one section classification icon (57a, 57b, 57c).

[0127] The 3D modeling area (51) can output a 3D model that reproduces at least one exercise motion according to the treatment progress stage in three dimensions.

[0128] The progress bar (55) can display the playback progress status of the 3D model output from the 3D modeling area (51), and may include a first pointer (57) and a second pointer (56) that is slidably movable on the progress bar (55) to set the playback section.

[0129] The user can select at least one playback section (s1) of the 3D model by moving the position of the second pointer (56) on the progress bar (55).

[0130] At least one segment classification icon (57a, 57b, 57c) is an icon that stores the playback segment (S1) of the currently selected 3D model, and when input by the user, the playback segment (S1) of the currently selected 3D model can be stored as each segment number.

[0131] For example, the user can set the playback section (s1) of the 3D model by moving the position of the second pointer (56) on the progress bar (55), and then save the currently set playback section (s1) as section 1 by inputting the section 1 icon (57a).

[0132] The processor (110) can obtain at least one playback section and a motion corresponding to the playback section in a 3D model using such a treatment setting UI.

[0133] For example, the processor (110) can obtain a playback section verifiable through the first pointer (57) and a playback section verifiable through the second pointer (56). The playback point verifiable through the first pointer (57) may be a playback point directly selected by the patient or operator through the signal input device (145) during treatment generation. The playback section verifiable through the second pointer (56) may be a playback section selected by the patient through the playback of the 3D model.

[0134] Referring to FIG. 8, the display (150) can display the interval repetition setting screen of the treatment setting UI.

[0135] The section repeat setting screen may include at least one section classification icon (57a, 57b, 57c), a repetition count setting icon (58), a random setting icon (59), and an execution icon (20).

[0136] At least one segment classification icon (57a, 57b, 57c) is an icon that loads a playback segment of a saved 3D model, and when input by a user, it can output a playback segment of a saved 3D model.

[0137] In addition, at least one segment classification icon (57a, 57b, 57c) may be provided to be selectable in a drop-down manner so as to set the execution order of the exercise motion corresponding to each playback segment.

[0138] For example, at least one segment classification icon (57a, 57b, 57c) may be initially placed in the 3D model according to the playback progress order of each playback segment, and the placement order may be reset by the user.

[0139] The repetition count setting icon (58) can select the number of repetitions for any playback section selected by the user.

[0140] For example, the user can select one of the segment classification icons (57a, 57b, 57c) to check the corresponding playback segment, and input the number of repetitions of the exercise motion appearing in the corresponding playback segment through the repetition count setting icon (58).

[0141] After the patient selects a section, the right to choose the number of playbacks and the order of playback within that section can be granted, thereby increasing the patient's autonomy in treatment.

[0142] The random setting icon (59) can randomly set the execution order of exercise motions corresponding to at least one playback section of the 3D model and the number of repetitions of each exercise motion. At this time, the number of repetitions of the exercise motions may be pre-set to a threshold value in consideration of patient safety.

[0143] When the performance of rehabilitation treatment is repeated frequently, the human body adapts, reducing treatment efficiency and the patient's response to treatment also decreases; therefore, treatment efficiency can be increased by using an appropriate random treatment method. The processor (110) can obtain the execution sequence and repetition count of exercise motions corresponding to at least one playback section of the 3D model using such a treatment setting UI.

[0144] The step (S50) in which the processor (110) controls the robot (200) may be a step of generating a control signal for the robot (200) and transmitting it to the robot (200) so that treatment can be performed according to the execution sequence and repetition count of the motion corresponding to at least one playback section of the 3D modeling acquired using the treatment setting UI.

[0145] For example, referring to FIG. 8, the execution icon (20) is an icon that commands the execution of a treatment progress step of the robot (200). When input by a user, it can transmit a control signal to the robot (200) to enable treatment to be executed according to the execution sequence of exercise motions set through the interval repetition setting screen and the number of repetitions of each exercise motion.

[0146] FIG. 9 is a control block diagram of an exercise therapy control device according to another embodiment of the present disclosure.

[0147] Referring to FIG. 9, an exercise therapy control device (100') according to another embodiment of the present disclosure may further include a sensor module (160) in addition to the processor (110), communication module (120), memory (130), shooting module (140), and display (150) included in the exercise therapy control device (100) shown in FIG. 2. Therefore, in the following description, only the sensor module (160) is described, and the description of the remaining other components is replaced by the description above.

[0148] The sensor module (160) senses at least one of internal information of the device (100'), surrounding environment information surrounding the device (100'), and user information, and generates a corresponding sensing signal. Based on this sensing signal, the processor (110) can control the operation or function of the device (100'), or perform data processing, functions, or operations related to an application installed on the device (100').

[0149] The sensor module (160) as described above may include at least one of a proximity sensor, an illumination sensor, a touch sensor, an acceleration sensor, a magnetic sensor, a gravity sensor (G-sensor), a gyroscope sensor, a motion sensor, an RGB sensor, an infrared sensor (IR sensor: infrared sensor), a fingerprint sensor, an ultrasonic sensor, an optical sensor (e.g., a camera), a microphone, an environmental sensor (e.g., including at least one of a barometer, a hygrometer, a thermometer, a radiation detection sensor, a heat detection sensor, a gas detection sensor), and a chemical sensor (e.g., a healthcare sensor, a biometric sensor, etc.). Meanwhile, the device (100') may combine and utilize information sensed from at least two of these sensors.

[0150] For example, in this embodiment, the sensor module (160) can sense the patient's heart rate, muscle tension, voice signal, etc., and generate sensor data.

[0151] FIG. 10 is a flowchart of an exercise therapy control method according to another embodiment of the present disclosure.

[0152] Referring to FIG. 10, an exercise therapy control method according to another embodiment of the present disclosure may be performed by an exercise therapy control device (100') illustrated in FIG. 9 and may include the steps of: a processor (110) generating a 3D model (S10); a display (150) displaying a treatment setting UI (S20); the processor (110) acquiring an exercise motion segment (S30); acquiring the number of repetitions of the exercise motion segment (S40); controlling a robot (200) (S50); acquiring feedback information (S60); and controlling the robot (200) according to the feedback information (S70).

[0153] The step (S10) in which the processor (110) generates a 3D model may be a step of generating a 3D model that reproduces at least one movement motion in three dimensions according to the patient's treatment progress stage by the robot (200).

[0154] The step (S20) of the display (150) showing the treatment setting UI may be a step of showing the treatment setting UI including a progress bar that displays the progress status of the 3D modeling.

[0155] The step (S40) in which the processor (110) acquires an exercise motion segment (S30) and acquires the number of repetitions of the exercise motion segment may be a step of receiving at least one playback segment of the 3D model and the number of repetitions of each playback segment through a treatment setting UI.

[0156] The step (S50) in which the processor (110) controls the robot (200) may be a step of generating a control signal for the robot (200) and transmitting it to the robot (200) so that treatment can be performed according to the execution sequence and repetition count of the motion corresponding to at least one playback section of the 3D modeling acquired using the treatment setting UI.

[0157] The step (S60) in which the processor (110) obtains feedback information may be a step of obtaining feedback information through a sensor module (160) or a shooting module (150) during the treatment performance according to step (S50).

[0158] Feedback information may be divided into first feedback information and second feedback information, the first feedback information may include the patient's heart rate and muscle tension, and the second feedback information may include the patient's voice, facial expressions, and feedback signals directly input through the treatment setting UI.

[0159] The processor (110) can obtain first feedback information including at least one of the patient's heart rate and muscle tension during treatment performed by the robot (200) through the sensor module (160).

[0160] The processor (110) can acquire a patient image through the shooting module (150) and analyze the patient image to extract the patient's voice.

[0161] For example, the processor (110) can extract an audio signal from a patient image and apply a deep learning-based voice separation model to the audio signal to extract the patient's voice.

[0162] A deep learning-based speech separation model may be constructed based on a deep learning algorithm comprising a hierarchical structure including an input layer, an output layer, and at least one intermediate layer (or hidden layer) between the input layer and the output layer. Based on such a multi-layered structure, the deep learning algorithm can derive highly reliable results through learning that optimizes the weights of activation functions between layers. Deep learning algorithms applicable to the present invention may include deep neural networks (DNN), convolutional neural networks (CNN), recurrent neural networks (RNN), Long Short Term Memory (LSTM), Bidirectional Short Term Memory (BLSTM), etc. Here, since speech is time-series data, a speech separation model may be implemented based on LSTM or BLSTM, which are specialized for learning iterative and sequential data, but is not limited thereto.

[0163] The processor (110) can extract the patient's voice from the patient image and convert the patient's voice into text, and can obtain second feedback information regarding whether a specific pre-specified text (e.g., "I'm sick") appears in the voice text.

[0164] Additionally, the processor (110) can acquire an image of the patient through the shooting module (150) and analyze the patient image to extract the patient's facial expression.

[0165] For example, the processor (110) can detect a face region from a patient image using a deep learning object detection technique, infer a facial expression by analyzing it by facial action unit, or classify the patient's facial expression into 6 to 8 types (anger, joy, sadness, surprise, disgust, dislike, etc.) using a conventional facial expression recognition module that recognizes facial expressions by extracting landmarks of major parts of the face, and obtain second feedback information regarding whether a specific pre-specified facial expression (e.g., surprise, etc.) appears in the patient's facial expression classification.

[0166] Additionally, the processor (110) can display a feedback request icon in the treatment setting UI and obtain a feedback signal generated according to the input of the feedback request icon as second feedback information.

[0167] In this way, the processor (110) can obtain first feedback information and second feedback information.

[0168] The step (S70) in which the processor (110) controls the robot (200) according to feedback information may be a step of calculating a feedback index according to the feedback information and controlling the robot (200) so that the performance of movement motion by the robot (200) is restricted according to the feedback index.

[0169] For example, the processor (110) can calculate a feedback index using first feedback information and second feedback information. The processor (110) can assign weights to the first feedback information and second feedback information, respectively, and calculate the sum of these values ​​as the feedback index.

[0170] [Mathematical Formula 1]

[0171] Feedback Index = k1 * 1st Feedback Information + k2 * 2nd Feedback Information

[0172] In mathematical formula 1, k1 is a weight assigned to the first feedback information and k2 is a weight assigned to the second feedback information, and each can be set in the system. The first feedback information is at least one measurement value among the patient's heart rate and muscle tension, and the second feedback information is a variable set according to the characteristics of the second feedback information, for example, can be set to 1 if a pre-specified text is included in the patient's voice, if the patient's facial expression is classified as a pre-specified facial expression, or if a feedback signal is generated, and 0 otherwise.

[0173] In mathematical formula 1, the first feedback information may be applied by taking a function such as a logarithm depending on its magnitude.

[0174] According to mathematical formula 1, the higher at least one of the patient's heart rate and muscle tension measurements, the higher the feedback index can be calculated when the patient's voice contains pre-specified text, the patient's facial expression is classified as a pre-specified expression, or a feedback signal is generated.

[0175] When the feedback index is calculated to be above a threshold, the processor (110) may stop the operation in the performance of the motion by the robot (200), control the execution order to perform the motion corresponding to the playback section designated in the first order, or control the number of repetitions.

[0176] During the performance of a registered treatment method, the therapist recognizes the patient's resistance through facial expressions, non-verbal expressions, and load measurements, and guides the treatment to be performed within a reduced range compared to the previously recorded range to alleviate this, thereby promoting treatment efficacy and patient safety.

[0177] Alternatively, the processor (110) can calculate the degree of change in the patient's facial expression, classify the facial expression change stage according to the degree of change in the patient's facial expression, and switch the operation mode of the robot (200) according to the patient's facial expression change stage.

[0178] For example, the processor (110) can calculate the degree of change in the patient's facial expression using frame changes in the patient's image, classify the stages of change in facial expression, and switch the operating mode of the robot (200) according to the stage of change in the patient's facial expression. In such a case, the operating mode of the robot (200) can be switched according to changes in facial expression, such as when the patient's facial expression becomes more distorted, thereby ensuring the safety of treatment by the robot (200).

[0179] The robot (200) can operate in either a random mode, which operates by randomly setting the exercise motion and the number of repetitions, or a default mode, which operates according to the exercise motion and the number of repetitions set by the user.

[0180] The processor (110) can switch the movement mode of the robot (200), which is currently operating in random mode, to default mode when the patient's facial expression change changes to level 1.

[0181] Alternatively, if the patient's facial expression change changes to level 2, which is higher than level 1, the processor (110) may switch the robot (200)'s movement mode to default mode or stop the operation, and then send a warning message to the manager's mobile terminal.

[0182] In this way, the processor (110) can ensure the safety of the patient by controlling the motion execution of the robot (200) by reflecting the patient's biosignals and feedback signals directly generated by the patient during the execution of the motion by the robot (200).

[0183] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. The instructions may be stored in the form of program code and, when executed by a processor, may generate a program module to perform the operation of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0184] Computer-readable recording media include all types of recording media that store instructions that can be decoded by a computer. Examples include ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, optical data storage devices, etc.

[0185] As described above, the disclosed embodiments have been explained with reference to the attached drawings. Those skilled in the art will understand that the present disclosure may be practiced in forms different from the disclosed embodiments without changing the technical spirit or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be interpreted restrictively.

Claims

1. A signal input device that receives a point selection signal from the patient during the patient treatment generation stage by the robot; A display for displaying a treatment setting UI for selecting at least one segment during the treatment selection stage; and A processor that performs a process of setting intervals of the treatment progress steps through the treatment setting UI; The above processor is, An exercise therapy control device using a UI including a segment selection progress bar, which generates a 3D modeling that implements at least one exercise motion according to the treatment progress in three dimensions, displays the treatment setting UI through the display, which includes at least one bar shape indicating the progress status of the 3D modeling, a first pointer indicating a point where the point selection signal is generated on the bar, and a second pointer provided to be slidably movable on the progress bar to receive at least one playback segment input, and controls the robot to perform an exercise motion corresponding to at least one playback segment set by the first pointer and the second pointer.

2. In Paragraph 1, It further includes a shooting module that captures the patient's treatment generation step by the robot to acquire a patient image, and The above processor is, An exercise therapy control device using a UI including a segment selection progress bar, characterized by analyzing the patient image to generate the 3D model, and displaying the treatment setting UI through the display, the UI including the first pointer and the second pointer, so that at least one segment in the exercise trajectory represented by the 3D model can be selected.

3. In Paragraph 1, The above processor is, An exercise therapy control device using a UI including a segment selection progress bar, characterized by acquiring coordinate information of at least one rotation axis constituting the robot from the robot during the treatment generation step, generating the 3D modeling representing a motion trajectory using the coordinate information, and displaying the treatment setting UI through the display, the UI including the first pointer and the second pointer, so that at least one segment in the motion trajectory represented by the 3D modeling can be selected.

4. In Paragraph 1, The above processor is, An exercise therapy control device using a UI including a section selection progress bar, characterized by receiving the number of repetitions and the repetition order of an exercise motion corresponding to at least one playback section through the UI, and controlling the robot so that the exercise motion corresponding to at least one playback section can be repeatedly performed according to the input number of repetitions and the repetition order.

5. In Paragraph 1, The above processor is, An exercise therapy control device using a UI including a section selection progress bar, characterized in that the robot is controlled to sequentially perform exercise motions corresponding to at least one playback section according to the playback progress order, wherein a random execution request is received through the UI, and when a random execution request is received through the UI, the number of random repetitions and the order within the exercise trajectory are set to control the robot.

6. In Paragraph 1, A sensor module for acquiring the biometric information of the above patient; and It further includes a shooting module that photographs the patient to acquire a patient image, and The above processor is, An exercise therapy control device using a UI including a section selection progress bar, characterized by analyzing the above bio-information and the above patient image to calculate a feedback index during the performance of an exercise motion by the robot, and controlling the robot so that the range of motion for the performance of the exercise motion by the robot is reduced according to the feedback index.

7. A control method performed by an exercise therapy control device using a treatment setting UI including a section selection progress bar, A step of receiving a segment selection signal from the patient during the treatment progress stage by the robot; A step of generating a 3D model that reproduces at least one exercise motion in three dimensions according to the above treatment progress stage; A step of displaying the treatment setting UI comprising a progress bar for displaying the playback progress status of the 3D model, a first pointer indicating a point where the segment selection signal is generated on the progress bar, and a second pointer slidably movable on the progress bar for receiving at least one playback segment input; and A method for controlling exercise therapy using a UI including a section selection progress bar, comprising the step of controlling the robot to perform an exercise motion corresponding to at least one playback section set by the first pointer and the second pointer.

8. In Paragraph 7, The step of generating the above 3D modeling is, A step of acquiring a patient image by capturing the patient's treatment progress stages by the above-mentioned robot; A step of obtaining coordinate information of at least one rotation axis constituting the robot from the robot during the treatment progress step; and A method for controlling exercise therapy using a UI including a segment selection progress bar, comprising the step of generating the 3D model using at least one of the patient image and the coordinate information.

9. In Paragraph 7, The step of controlling the above robot is, A method for controlling exercise therapy using a UI including a section selection progress bar, comprising the step of controlling the robot by randomly setting the sequence and number of repetitions of exercise motions corresponding to at least one playback section when a random execution request is input through the UI.

10. In Paragraph 7, The step of controlling the above robot is, A step of obtaining the biometric information of the above patient; A step of photographing the patient to obtain a patient image; A step of calculating a feedback index during the performance of an exercise motion by the robot by analyzing the above bio-information and the above patient image; and A method for controlling exercise therapy using a UI including a segment selection progress bar, comprising the step of controlling the robot so that the performance of exercise motion by the robot is restricted according to the feedback index.

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