KR20230161117A - Electronic apparatus providing nursing simulation education program for virtual reality based, and virtual reality apparatus - Google Patents

Abstract

The present invention includes a communication unit that communicates with a VR device that provides VR images, and a processor connected to the memory and the communication unit to provide at least one scenario to the user based on memory and VR (Virtual Reality) interaction. An electronic device that provides a virtual reality-based nursing simulation education program for patients with hypovolemic shock, including: Accordingly, when the processor selects the emergency room mode according to a user command obtained from the VR device through a communication unit, the VR device A clinical practice scenario is provided to the user, and the interactions obtained for the clinical practice scenario are stored in memory as practice information.

Description

An electronic device that provides a virtual reality-based nursing simulation education program for patients with hypovolemic shock, and a VR device {ELECTRONIC APPARATUS PROVIDING NURSING SIMULATION EDUCATION PROGRAM FOR VIRTUAL REALITY BASED, AND VIRTUAL REALITY APPARATUS}

The present invention relates to an electronic device and a VR (Virtual Reality) device that provides a virtual reality-based nursing simulation education program for patients suffering from hypovolemic shock. In detail, the present invention relates to an electronic device and a VR (Virtual Reality) device that provides an education program in virtual reality using a VR program. This is a technology that allows nursing trainees to experience clinical practice according to the situation based on the scenario of a patient with hypovolemic shock, which is difficult to experience.

Hypovolemic shock is shock caused by a lack of body fluid in the circulatory system. It can usually be caused by bleeding, burns, and dehydration. Symptoms, tests, and treatments may appear and be applied differently depending on the patient's age and cause. there is.

Meanwhile, conventional simulation training requires expensive equipment and facilities, and there has been a problem of low cost-effectiveness compared to practice in that changes in the subject's condition cannot be realistically confirmed.

Specifically, in nursing school education, a facility with an environment similar to a medical field is built and scenarios developed through a human practice model (simulator) are applied to experience actual nursing interventions such as assessing the patient's condition, discovering health problems, and providing appropriate treatment. The training that allows you to do this is simulation training.

However, simulation practice training has various limitations. Specifically, the liveliness of the human practice model is low, so it does not have the same sense of reality as the actual subject. Although it is attempted to implement various medical situations through simulators, there are limitations to the situations that can be implemented. In addition, the types of scenarios in which nursing actions can be performed on human practice models are small, and the reactions of subjects after nursing actions are limited.

In addition, expensive equipment and environments must be set up for simulation training, only a small number of people can receive training within a limited time, and medical treatment is required for situations such as hypovolemic shock that occurs due to hemorrhagic shock in a medical environment. There is limited practice of on-site reproduction or response.

Meanwhile, the matters described as background technology above are only for the purpose of improving understanding of the background of the present invention, and should not be taken as recognition that they correspond to prior art already known to those skilled in the art. will be.

Registered Patent Publication No. 10-1591775, 2016.01.29.

The problem that the present invention seeks to solve is in the field of nursing, by incorporating IT into simulation practice education that replaces clinical practice, and using virtual reality for nursing trainees to provide a simulation practice education program for patients with hypovolemic shock. To provide an electronic device and a VR device that provide a virtual reality-based nursing simulation education program for patients with hypovolemic shock that allows trainees to receive realistic and effective hands-on training without restrictions on time and space.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood by those skilled in the art from the description below.

An electronic device that provides a virtual reality-based nursing simulation education program for hypovolemic shock patients according to one aspect of the present invention to solve the above-mentioned problems is based on memory and VR (Virtual Reality) interaction, and includes at least one In order to provide a scenario to a user, it includes a communication unit that communicates with the VR device providing a VR image, and a memory, and a processor connected to the communication unit, whereby the processor, through the communication unit, receives user commands from the VR device. If the emergency room mode is selected according to this, a clinical practice scenario is provided to the user through the VR device, and the interactions obtained for the clinical practice scenario are stored in memory as practice information.

Meanwhile, the communication unit communicates with at least one terminal, and accordingly, the processor can provide practice information to the evaluator terminal through the communication unit and obtain evaluation information about the practice information from the evaluator terminal.

Additionally, when the classroom mode is selected according to a user command obtained from the VR device through the communication unit, the processor provides the user with an image of the virtual classroom through the VR device and creates a personal character registered for the user as a virtual classroom. It is output to the classroom, controlled to operate based on the interaction acquired through the VR device, and based on the lecture schedule registered for the user, the pre-registered lecture video is output to the virtual display included in the virtual classroom. And, for the output lecture video, a virtual anatomy diagram and virtual tools are provided to the user through a VR device, and interaction about using the virtual tools for the virtual anatomy diagram is obtained from the user through the VR device. You can.

In addition, in providing a clinical practice scenario to the user, the processor provides a first scenario in which a virtual patient is treated, examined, and examined, and a second scenario in which a prescription is performed based on the test results of the first scenario. , and a third scenario in which actions for prescriptions according to the second scenario are performed on a virtual patient can be provided to the user through a VR device.

Additionally, if the virtual patient matches at least one of a bleeding patient, a burn patient, and a dehydration patient, the processor may control the virtual patient to perform symptoms according to pre-registered hypovolemic shock.

Additionally, when the selected mode is the emergency room mode for practice, if interaction with the first to third scenarios is not obtained within a certain time, the processor provides a guide image to the user through the VR device, and provides the first to third scenarios with a guide image. Among the third scenarios, the scenario in which the guide image is provided can be provided again to the user through the VR device.

Meanwhile, when the selected mode is a practical emergency room mode, when a golden time is identified for a virtual patient, the processor provides a counter for the identified golden time to the user through the VR device, and provides a counter for the identified golden time to the user through the VR device. If the interaction for the action is not obtained within the identified golden time, the virtual patient can be controlled to perform symptoms according to shock.

A VR device that provides a virtual reality-based hypovolemic shock patient nursing simulation education program according to this aspect of the present invention provides at least one scenario to the user based on memory and VR (Virtual Reality) interaction. In order to do so, it includes a display module that provides a VR image, a controller for obtaining interaction from the user, and a control unit connected to the memory, the display module, and the controller. Accordingly, the control unit operates the emergency room according to the user command obtained through the controller. When the mode is selected, the clinical practice scenario is provided to the user through the display module, the acquired interactions about the clinical practice scenario are stored in memory as practice information through the controller, and the classroom is operated according to user commands obtained through the controller. When the mode is selected, an image of the virtual classroom is provided to the user through the display module, the personal character registered for the user is output to the virtual classroom, and the operation is controlled based on the interaction obtained through the controller. Based on the lecture schedule registered for the user, pre-registered lecture videos are output for the virtual display included in the virtual classroom, and for the output lecture videos, virtual anatomy diagrams and virtual tools are displayed through the display module. It is provided to the user and, through the controller, obtains interaction from the user about using a virtual tool on the virtual anatomy.

Other specific details of the invention are included in the detailed description and drawings.

According to the electronic device and VR (Virtual Reality) device that provide a virtual reality-based nursing simulation education program for patients with hypovolemic shock of the present invention, nursing trainees can conduct simulation clinical practice on nursing patients with hypovolemic shock at a time and place. You can improve your practical clinical knowledge and performance ability by experiencing and performing nursing directly in virtual reality without any limitations. It is cost-effective because it does not require facilities, equipment, or consumables for education, and nursing trainees can use virtual reality repeatedly. You can maximize the effectiveness of education by experiencing clinical practice, and you can actively experience clinical practice with interest through a game-like feel.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 is a configuration diagram of an electronic device according to an embodiment of the present disclosure.
Figure 2 is an opening diagram of an electronic device according to an embodiment of the present disclosure.
Figure 3 is a system configuration diagram according to an embodiment of the present disclosure.
Figure 4 is a basic flowchart according to an embodiment of the present disclosure.
5 to 7 are scenario prediction diagrams according to an embodiment of the present disclosure.
Figure 8 is a configuration diagram of a VR device according to an embodiment of the present disclosure.

The advantages and features of the present invention and methods for achieving them will become clear by referring to the embodiments described in detail below along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms. The present embodiments are merely provided to ensure that the disclosure of the present invention is complete and to provide a general understanding of the technical field to which the present invention pertains. It is provided to fully inform the skilled person of the scope of the present invention, and the present invention is only defined by the scope of the claims.

The terminology used herein is for describing embodiments and is not intended to limit the invention. As used herein, singular forms also include plural forms, unless specifically stated otherwise in the context. As used in the specification, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other elements in addition to the mentioned elements. Like reference numerals refer to like elements throughout the specification, and “and/or” includes each and every combination of one or more of the referenced elements. Although “first”, “second”, etc. are used to describe various components, these components are of course not limited by these terms. These terms are merely used to distinguish one component from another. Therefore, it goes without saying that the first component mentioned below may also be a second component within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention pertains. Additionally, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless clearly specifically defined.

As used in the specification, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and the “unit” or “module” performs certain roles. However, “part” or “module” is not limited to software or hardware. A “unit” or “module” may be configured to reside on an addressable storage medium and may be configured to run on one or more processors. Thus, as an example, a “part” or “module” refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within components and “parts” or “modules” can be combined into smaller components and “parts” or “modules” or into additional components and “parts” or “modules”. Could be further separated.

Spatially relative terms such as “below”, “beneath”, “lower”, “above”, “upper”, etc. are used as a single term as shown in the drawing. It can be used to easily describe the correlation between a component and other components. Spatially relative terms should be understood as terms that include different directions of components during use or operation in addition to the directions shown in the drawings. For example, if you flip a component shown in a drawing, a component described as "below" or "beneath" another component will be placed "above" the other component. You can. Accordingly, the illustrative term “down” may include both downward and upward directions. Components can also be oriented in other directions, so spatially relative terms can be interpreted according to orientation.

In this specification, a computer refers to all types of hardware devices including at least one processor, and depending on the embodiment, it may be understood as encompassing software configurations that operate on the hardware device. For example, a computer can be understood to include, but is not limited to, a smartphone, tablet PC, desktop, laptop, and user clients and applications running on each device.

Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

FIG. 1 is a configuration diagram of an electronic device according to an embodiment of the present disclosure, FIG. 2 is an opening diagram of an electronic device according to an embodiment of the present disclosure, and FIG. 3 is a system configuration diagram according to an embodiment of the present disclosure. am.

As shown, the electronic device 100, which provides a virtual reality-based hypovolemic shock patient nursing simulation education program, provides at least one scenario to the user based on the memory 110 and VR (Virtual Reality) interaction. In order to provide VR images, it includes a communication unit 120 that communicates with the VR device 200 that provides VR images, a memory 110, and a processor 130 connected to the communication unit 120.

At this time, the processor 130 can communicate with the VR device 200 through the communication unit 120, and for this purpose, the VR device 200 may also include a separate communication unit.

In one embodiment, the electronic device 100 includes a server, a smartphone, a tablet personal computer, a mobile phone, a video phone, a laptop PC, and a netbook computer. ), a laptop computer, and medical equipment.

In one embodiment, the VR device 200 may be a PC device, a mobile device (connected to a smartphone), a console-based device, an all-in-one device, etc.

Meanwhile, the electronic device 100 is connected to at least one terminal (smartphone, tablet personal computer, mobile phone, video phone, laptop PC, etc.) through the communication unit 120. It is possible to communicate with a netbook computer (netbook computer, laptop computer, etc.), and for this purpose, the evaluator terminal, which is a terminal owned by an evaluator who conducts an evaluation according to clinical practice, may include a separate communication unit.

Meanwhile, the memory 110 can store various programs and data necessary for the operation of the electronic device 100. The memory 110 may be implemented as a non-volatile memory 110, a volatile memory 110, a flash-memory 110, a hard disk drive (HDD), or a solid state drive (SSD).

The communication unit 120 can communicate with an external device. In particular, the communication unit 120 may include various communication chips such as a Wi-Fi chip, a Bluetooth chip, a wireless communication chip, an NFC chip, and a low-energy Bluetooth chip (BLE chip). At this time, the Wi-Fi chip, Bluetooth chip, and NFC chip communicate in the LAN method, WiFi method, Bluetooth method, and NFC method, respectively. When using a Wi-Fi chip or Bluetooth chip, various connection information such as SSID and session key are first transmitted and received, and various information can be transmitted and received after establishing a communication connection using this. A wireless communication chip refers to a chip that performs communication according to various communication standards such as IEEE, ZigBee, 3G (3rd Generation), 3GPP (3rd Generation Partnership Project), LTE (Long Term Evolution), and 5G (5th Generation).

The processor 130 can control the overall operation of the electronic device 100 using various programs stored in the memory 110. The processor 130 may be comprised of RAM, ROM, a graphics processing unit, a main CPU, first to n interfaces, and a bus. At this time, RAM, ROM, graphics processing unit, main CPU, first to n interfaces, etc. may be connected to each other through a bus.

RAM stores O/S and application programs. Specifically, when the electronic device 100 is booted, the O/S is stored in RAM, and various application data selected by the user may be stored in RAM.

ROM stores a set of instructions for booting the system. When a turn-on command is input and power is supplied, the main CPU copies the O/S stored in the memory 110 to RAM according to the command stored in the ROM, executes the O/S, and boots the system. When booting is complete, the main CPU copies various application programs stored in the memory 110 to RAM and executes the application programs copied to RAM to perform various operations.

The graphics processing unit uses a calculation unit (not shown) and a rendering unit (not shown) to create a screen containing various objects such as items, images, and text. Here, the calculation unit may be configured to calculate attribute values such as coordinate values, shape, size, color, etc. for each object to be displayed according to the layout of the screen using a control command received from the input unit. Additionally, the rendering unit may be configured to generate screens of various layouts including objects based on attribute values calculated by the calculation unit. The screen generated by this rendering unit may be displayed within the display area of the display (display included in the display module 220 of the VR device 200).

The main CPU accesses the memory 110 and performs booting using the OS stored in the memory 110. And, the main CPU performs various operations using various programs, contents, data, etc. stored in the memory 110.

The first to n interfaces are connected to the various components described above. One of the first to n interfaces may be a network interface connected to an external device through a network.

Meanwhile, furthermore, the processor 130 can control an artificial intelligence model. In this case, of course, the processor 130 may include a graphics processor (eg, GPU) for controlling the artificial intelligence model.

Meanwhile, the artificial intelligence model according to the present invention may be a model based on supervised learning or unsupervised learning. Furthermore, the artificial intelligence model according to the present invention may include SVM (support vector machine), decision tree, neural network, etc., and methodologies applied thereto.

In one embodiment, the artificial intelligence model according to the present invention may be an artificial intelligence model based on convolutional deep neural networks (CNN) that is learned by inputting learning data. However, it is not limited to this, and of course, various artificial intelligence models can be applied to the present invention. For example, models such as Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Bidirectional Recurrent Deep Neural Network (BRDNN) can be used as artificial intelligence models, but are not limited to these.

At this time, Convolutional deep Neural Networks (CNN) are a type of multilayer perceptrons designed to use minimal preprocessing. A convolutional neural network consists of one or several convolutional layers and general artificial neural network layers on top of them, and additionally utilizes weights and pooling layers. Thanks to this structure, the convolutional neural network can fully utilize input data with a two-dimensional structure. Additionally, convolutional neural networks can be trained via standard back propagation. Convolutional neural networks have the advantage of being easier to train and using fewer parameters than other feedforward artificial neural network techniques.

Additionally, Deep Neural Networks (DNN) are Artificial Neural Networks (ANN) consisting of multiple hidden layers between an input layer and an output layer.

At this time, the structure of the deep neural network may be composed of a perceptron. A perceptron consists of several inputs, one processor, and one output. The processor multiplies multiple input values by their respective weights and then adds up all of the input values multiplied by the weights. Next, the processor substitutes the combined value into the activation function and outputs one output value. If you want a specific value to come out as the output value of the activation function, you can modify the weight multiplied by each input value and recalculate the output value using the modified weight. At this time, each perceptron can use a different activation function. Additionally, each perceptron accepts the outputs from the previous layer as input and then obtains the output using an activation function. The obtained output is passed to the input of the next layer. By going through the above-described process, you can finally obtain several output values.

Recurrent Neural Network (RNN) refers to a neural network in which the connections between units that make up the artificial neural network form a directed cycle. Unlike a forward neural network, a recurrent neural network can utilize the memory inside the neural network to process arbitrary input.

Deep Belief Networks (DBN) is a generative graphical model used in machine learning. In deep learning, it refers to a deep neural network composed of multiple layers of latent variables. It has the characteristic that there is a connection between layers, but no connection between units within the layer.

Due to the nature of the deep trust neural network as a generative model, it can be used for prior learning, and after learning the initial weights through prior learning, the weights can be fine-tuned through backpropagation or other discriminant algorithms. This characteristic is very useful when there is little training data, because the smaller the training data, the stronger the influence of the initial value of the weights on the resulting model. The pre-learned weight initial value becomes closer to the optimal weight compared to the arbitrarily set initial weight value, which enables improved performance and speed of the fine-tuning stage.

The content regarding the above-described artificial intelligence and its learning method is described for illustrative purposes, and the artificial intelligence and its learning method used in the above-described embodiments are not limited. For example, all types of artificial intelligence technologies and learning methods that can be applied by a person skilled in the art to solve the same problem can be used to implement the system according to the disclosed embodiments.

Meanwhile, the processor 130 may include one or more cores (not shown) and a graphics processing unit (not shown) and/or a connection path (e.g., bus, etc.) for transmitting and receiving signals with other components. You can.

Processor 130 according to one embodiment performs the method described in connection with the present invention by executing one or more instructions stored in memory 110.

For example, the processor 130 acquires new training data by executing one or more instructions stored in the memory 110, performs a test on the acquired new training data using a learned model, and performs the test. As a result, first learning data in which labeled information is obtained with an accuracy greater than a predetermined first reference value is extracted, the extracted first learning data is deleted from the new learning data, and the new learning data from which the extracted learning data is deleted is extracted. The learned model can be retrained using training data.

Meanwhile, the processor 130 includes random access memory (RAM) (not shown) and read-only memory (ROM) that temporarily and/or permanently store signals (or data) processed within the processor 130. , not shown) may be further included. Additionally, the processor 130 may be implemented in the form of a system on chip (SoC) that includes at least one of a graphics processing unit, RAM, and ROM.

The memory 110 may store programs (one or more instructions) for processing and control of the processor 130. Programs stored in the memory 110 may be divided into a plurality of modules according to their functions.

FIG. 4 is a basic flowchart according to an embodiment of the present disclosure, and FIGS. 5 to 7 are scenario prediction diagrams according to an embodiment of the present disclosure.

As shown, in the electronic device 100 that provides a virtual reality-based nursing simulation education program for patients with hypovolemic shock, the processor 130 receives the classroom mode information from the VR device 200 through the communication unit 120. and acquire a user command to select one of the emergency room modes (S410), and if the selected mode is the emergency room mode, provide a clinical practice scenario to the user through the VR device 200 (S420), and provide information about the clinical practice scenario The acquired interactions are stored in the memory 110 as practice information (S430).

Meanwhile, the processor 130 may provide practice information about the user to the evaluator terminal through the communication unit 120 and obtain evaluation information about the practice information from the evaluator terminal.

As shown in FIG. 5, when the virtual reality-based hypovolemic shock patient nursing simulation education program starts, the processor 130 creates a personal character (avatar) already registered for the user's account through the VR device 200. Enter virtual reality with .

At this time, the user can control the movements of the avatar through the VR device 200 and select the training content (classified into classroom mode, emergency room mode, emergency room mode, practice emergency room mode, and actual emergency room mode).

The classroom mode is a mode that provides a virtual education scenario that allows students to learn the theoretical basis of clinical practice scenarios in advance through video lectures in a virtual classroom, and the emergency room mode is a mode that provides a virtual education scenario for actual patients in virtual reality. This is a mode that provides a scenario for clinical practice experience. It involves responding to and examining patients who visit the hospital, prescribing based on examination and test results, and taking appropriate actions according to the prescription (MRI test, blood test, X-ray test, etc.). Interaction can be obtained from the user through the VR device 200.

Accordingly, the scenario may be an interaction-based image composed based on symptoms, movements, conversations, examination methods, prescription details, and action methods according to the type of disease that occurs in an actual patient.

Meanwhile, in step S420, when the selected mode is the classroom mode, the processor 130 provides an image of a virtual classroom to the user through the VR device 200, and converts the personal character registered for the user into a virtual classroom. It is output to the classroom and controlled to operate based on the interaction obtained through the VR device 200.

As shown in FIG. 7, the processor 130 outputs a pre-registered lecture video to a virtual display included in a virtual classroom based on the lecture schedule registered for the user, and provides information about the output lecture video. , a virtual anatomy diagram and a virtual tool can be provided to the user through the VR device 200, and interaction about using the virtual tool on the virtual anatomy diagram can be obtained from the user through the VR device 200. .

For example, the processor 130 provides a view of a personal character to the user through the VR device 200, and when the lecture schedule registered for the user includes an electrocardiogram examination lecture, a virtual heart, a virtual electrocardiogram measurement tool, And the virtual ECG monitoring screen is included in the personal character's field of view, and based on the interaction with the virtual heart, the ECG test results for each region of the virtual heart where the virtual ECG measurement tool is located are displayed on the virtual ECG monitoring screen. can be provided to.

As another example, the processor 130 allows the user to include a virtual keyboard in the field of view of the personal character through the VR device 200, allows text input to be performed based on interaction with the virtual keyboard, and inputs the input text. By including it in the chat screen for the virtual classroom, the text entered can be shared with other users connected to the virtual classroom and the character for each lecturer.

Meanwhile, in steps S420 and S430, the processor 130 configures a first scenario to perform a response, examination, and examination on a virtual patient, and a second scenario to perform a prescription based on the test results according to the first scenario. A clinical practice scenario including a scenario and a third scenario in which actions for prescriptions according to the second scenario are performed on a virtual patient can be provided to the user through the VR device 200.

At this time, if the virtual patient matches at least one of a bleeding patient, a burn patient, and a dehydration patient, the processor 130 controls the virtual patient to perform symptoms according to pre-registered hypovolemic shock. You can.

Specifically, the processor 130 collects interaction about the first scenario from the user through the VR device 200, including the user's voice responding to the virtual patient, the palpation area for the virtual patient, and the user's interaction with the first scenario. By selecting at least one virtual inspection tool, interactions for each can be collected.

Accordingly, the processor 130 provides 1-1 practice information about whether a pre-registered correct answer keyword exists for the first scenario based on the voice of the user responding to the virtual patient, and the palpation site for the virtual patient. 1-2 practice information about whether the selected virtual test tool matches a pre-registered part for the symptom shown by the virtual patient, and whether the selected virtual test tool matches a pre-registered test tool for the symptom shown by the virtual patient. Create practice information for 1-3.

In collecting interaction for the second scenario from the user through the VR device 200, the processor 130 collects an interaction for selecting at least one prescription, and provides basis information for the selected prescription on a virtual keyboard. It can be obtained through interaction with at least one of input and voice input.

Accordingly, the processor 130 compares at least one prescription previously registered for the symptom displayed by the virtual patient with the selected prescription, and generates 2-1 practice information about whether a missing prescription exists, 2-2 practice information is generated regarding whether the keyword included in the evidence information matches at least one keyword already registered for the symptom displayed by the virtual patient.

The processor 130, in collecting interactions for the third scenario from the user through the VR device 200, generates third practice information according to the interaction for performing the selected prescription.

The processor 130 calculates a false mark score based on the 1-1 practice information to the third practice information, and provides the calculated false mark score to the evaluator terminal along with the 1-1 practice information to the third practice information. And, the score can be provided to the user through the VR device 200.

In an embodiment, the processor 130 operates at a first time when the collection of interactions for the first scenario is completed, at a second time when the collection of interactions for the second scenario is completed, and at a time when the collection of interactions for the third scenario is completed. 3 Identify the point in time.

Accordingly, the processor 130 calculates the first average time required for the second scenario and the third scenario based on the time required indicators previously stored in the memory 110 for the first to third scenarios. Obtain the second average time, calculate the first difference for the first to second time points, the second difference for the second to third time points, and compare the first difference with the first average time, If the first difference value is less than or equal to the first average time, a score for judgment proficiency is calculated proportional to the difference value between the first difference value and the first average time, and if the first difference value exceeds the first average time, the first average time is calculated. A score for judgment proficiency is calculated that is inversely proportional to the difference between the difference value and the first average time.

In addition, the processor 130 compares the second difference value with the second average time, and when the second difference value is less than or equal to the second average time, the score for prescription proficiency is proportional to the difference value between the second difference value and the second average time. is calculated, and when the second difference value exceeds the second average time, a score for prescription proficiency is calculated that is inversely proportional to the difference value between the second difference value and the second average time.

The processor 130 may provide scores for judgment proficiency and scores for prescription proficiency to the evaluator terminal and VR device.

At this time, since the goal is to ensure accurate response, diagnosis, and inspection for the first scenario, the processor 130 does not calculate a score according to time for the first scenario.

Meanwhile, the time required indicator is data on the average time required to determine a prescription according to the disease and symptoms and implement the prescription.

In step S420, when the selected mode is the practice emergency room mode, the processor 130 sends a guide image to the user through the VR device 200 if interaction for the first to third scenarios is not obtained within a certain time. is provided, and among the first to third scenarios, the scenario in which the guide image is provided is provided again to the user through the VR device 200, so that the user can practice repeatedly.

The guide image is an image that explains the operation of the interaction that must be collected for the time period in which the user did not input interaction through the VR device 200 for the first to third scenarios.

On the other hand, when the selected mode is the actual emergency room mode, when a golden time is identified for a virtual patient, the processor 130 provides a counter for the identified golden time to the user through the VR device 200, and provides a counter for the identified golden time to the user through the VR device 200. If the interaction for the preset action for the patient is not obtained within the identified golden time, the virtual patient can be controlled to perform symptoms due to shock.

Additionally, the processor 130 may additionally provide a scenario according to the interaction training mode to the user through the VR device 200. For example, the processor 130 may provide a VR device ( 200), it can be provided to the user.

Figure 8 is a configuration diagram of a VR device according to an embodiment of the present disclosure.

As shown, the VR device 200, which provides a virtual reality-based hypovolemic shock patient nursing simulation education program, includes a memory 210 and VR to provide at least one scenario to the user based on VR interaction. It includes a display module 220 that provides an image, a controller 230 for obtaining interaction from the user, and a control unit 240 connected to the memory 210, the display module 220, and the controller 230.

The display module 220 is configured to visually output various information.

In one embodiment, the display module 220 includes at least one of a head mounted display (HMD) and a face mounted display (FMD), and includes a speaker and a microphone for output and input of sound. It can be included.

Meanwhile, the VR device 200 may further include a motion tracker, and the motion tracker may replace the controller 230.

The controller 230 is a VR controller for collecting interaction from the user.

The control unit 240 can control the overall operation of the VR device 200 using various programs stored in the memory 210.

Accordingly, the control unit 240 obtains a user command to select one of the classroom mode and the emergency room mode through the controller 230, and when the selected mode is the emergency room mode, the control unit 240 displays the clinical practice mode through the display module 220. Provide scenarios to users.

At this time, for the clinical practice scenario, the control unit 240 stores the acquired interactions as practice information in the memory 210 through the controller 230, and when the selected mode is the classroom mode, through the display module 220. An image of the virtual classroom is provided to the user, the personal character registered for the user is output to the virtual classroom, and the operation is controlled based on the interaction obtained through the controller 230, and the lecture registered for the user is performed. Based on the schedule, pre-registered lecture videos are output on virtual displays included in the virtual classroom, and virtual anatomy diagrams and virtual tools are provided to users for the output lecture videos through the display module 220. And, through the controller 230, interaction about using a virtual tool for a virtual anatomy can be obtained from the user.

Meanwhile, when the selected mode is the emergency room mode, the control unit 240 creates a first scenario in which response, examination, and examination are performed on a virtual patient, and a second scenario in which a prescription is performed based on the examination results by the first scenario. , and a clinical practice scenario including a third scenario in which the prescription action according to the second scenario is performed on a virtual patient, is provided to the user through the display module 220, wherein the first to third scenarios are provided to the user through the display module 220. If the virtual patient that is the target of the scenario matches at least one of a bleeding patient, a burn patient, and a dehydration patient, the virtual patient may be controlled to perform symptoms according to pre-registered hypovolemic shock.

The steps of the method or algorithm described in connection with embodiments of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof. The software module may be RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which the present invention pertains.

Additionally, different embodiments of the present invention may complement or be combined with each other.

The components of the present invention may be implemented as a program (or application) and stored in a medium in order to be executed in conjunction with a hardware computer. Components of the invention may be implemented as software programming or software elements, and similarly, embodiments may include various algorithms implemented as combinations of data structures, processes, routines or other programming constructs, such as C, C++, , it can be implemented in a programming or scripting language such as Java, assembler, Python, etc. Functional aspects may be implemented as algorithms running on one or more processors.

Above, embodiments of the present invention have been described with reference to the attached drawings, but those skilled in the art will understand that the present invention can be implemented in other specific forms without changing its technical idea or essential features. You will be able to understand it. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive.

100: electronic device
110: memory
120: Department of Communications
130: processor
200: VR device
210: memory
220: display module
230: controller
240: control unit

Claims (8)

In the electronic device that provides a virtual reality-based nursing simulation education program for patients with hypovolemic shock,
Memory;
A communication unit that communicates with a VR device that provides VR images to provide at least one scenario to the user based on VR (Virtual Reality) interaction; and
Includes a processor connected to the memory and the communication unit,
The processor,
When the emergency room mode is selected according to a user command obtained from the VR device through the communication unit, a clinical practice scenario is provided to the user through the VR device,
An electronic device that stores interactions obtained for the clinical practice scenario as practice information in the memory. According to claim 1,
The Department of Communications,
communicate with at least one terminal,
The processor,
Through the communication unit, the practice information is provided to the evaluator terminal,
An electronic device that obtains evaluation information about the practice information from the evaluator terminal. According to claim 1,
The processor,
When a classroom mode is selected according to a user command obtained from the VR device through the communication unit, an image of a virtual classroom is provided to the user through the VR device,
Outputting the personal character registered for the user to the virtual classroom and controlling it to operate based on the interaction obtained through the VR device,
Based on the lecture schedule registered for the user, output a pre-registered lecture video on a virtual display included in the virtual classroom,
For the output lecture video, provide a virtual anatomy diagram and virtual tools to the user through the VR device,
An electronic device that obtains interaction about using the virtual tool on the virtual anatomy from the user via the VR device. According to claim 1,
The processor,
In providing the clinical practice scenario to the user, a first scenario in which a virtual patient is treated, examined, and examined, a second scenario in which a prescription is performed based on test results from the first scenario, and An electronic device that provides a third scenario in which an action for a prescription according to the second scenario is performed on the virtual patient to the user through the VR device. According to clause 4,
The processor,
When the virtual patient matches at least one of a bleeding patient, a burn patient, and a dehydration patient, an electronic device that controls to perform symptoms according to pre-registered hypovolemic shock on the virtual patient. According to clause 4,
The processor,
When the selected mode is a practice emergency room mode, if interaction for the first to third scenarios is not obtained within a certain time, a guide image is provided to the user through the VR device,
An electronic device that provides a scenario in which the guide image is provided among the first to third scenarios again to the user through the VR device. According to clause 4,
The processor,
When the selected mode is a practical emergency room mode, when a golden time is identified for the virtual patient, a counter for the identified golden time is provided to the user through the VR device,
An electronic device that controls the virtual patient to perform symptoms of shock when an interaction for a preset action is not obtained within the identified golden time. In the VR device that provides a virtual reality-based nursing simulation education program for hypovolemic shock patients,
Memory;
A display module that provides VR images to provide at least one scenario to a user based on VR (Virtual Reality) interaction;
a controller for obtaining interaction from the user; and
Includes a control unit connected to the memory, the display module, and the controller,
The control unit,
When the emergency room mode is selected according to a user command obtained through the controller, a clinical practice scenario is provided to the user through the display module,
For the clinical practice scenario, the acquired interactions are stored in the memory as practice information through the controller,
When the classroom mode is selected according to a user command obtained through the controller, an image of the virtual classroom is provided to the user through the display module,
Outputting the personal character registered for the user to the virtual classroom and controlling it to operate based on the interaction obtained through the controller,
Based on the lecture schedule registered for the user, output a pre-registered lecture video on a virtual display included in the virtual classroom,
For the output lecture video, provide a virtual anatomy diagram and virtual tools to the user through the display module,
A VR device that obtains, through the controller, interaction from the user about using the virtual tool on the virtual anatomy.

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