Apparatus and method for skin vibration stimulation

Abstract

The present disclosure relates to an apparatus and a method for providing skin vibration stimulation, the apparatus for providing skin vibration stimulation comprising: a communication module that performs communication with an external device; a storage module for storing at least one process for providing vibration-based stimulation to the skin of a user; a vibration module for generating vibration by using at least one vibrator on the basis of a vibration pattern; and a control module that performs an operation for providing vibration-based stimulation to the skin of the user on the basis of the at least one process, wherein the control module, when request information is input from the user, provides mode information configured to allow selection of at least one of a plurality of modes on the basis of the request information, and when at least one mode among the plurality of modes is selected, configures the vibration pattern on the basis of selection information for the selected at least one mode, and provides vibration stimulation according to the configured vibration pattern.

Description

Skin vibration stimulation device and method

[0001] The present disclosure relates to a skin vibration stimulation device and method, and more particularly, to a skin vibration stimulation device and method configured to be wearable.

[0002] Recently, brain stimulation technology has been receiving attention as an emerging technology in the fields of psychiatry and rehabilitation medicine, and technologies such as ECT, rTMS, tDCS, and MNS are being developed.

[0003] Among them, ECT, the most effective, is a treatment technique that has been around for decades and is very safe and effective, but it has the disadvantage of being used only in patients with severe schizophrenia or suicidal thoughts because it provides strong, invasive electrical brain stimulation in an operating room.

[0004] Although less powerful forms of brain stimulation technologies such as rTMS and tDCS have been developed, they are still large and not portable, which poses significant limitations for patients to use on their own in daily life.

[0005] To solve these problems, MNS, which pursues brain stimulation through wearable technology, is being developed, but other problems such as unpleasant stimulation from electrical stimulation and skin trouble caused by attaching gel pads are occurring.

[0006] Therefore, there is a need to develop a technology that can be configured to be wearable by the user and perform biofeedback functions by stimulating the brain through micro-vibrations instead of electrical stimulation.

[0007] The present disclosure provides a skin vibration stimulation device and method that are configured to be wearable by a user and provide brain stimulation through micro-vibrations instead of electrical stimulation, thereby enabling the user to control symptoms such as depression that are acutely worsening when they occur.

[0008] Meanwhile, the present disclosure provides a skin vibration stimulation device and method that enable a user to visually check his or her own symptoms and control the symptoms based on a cognitive behavioral therapy technique, thereby enhancing the patient's ability to control the symptoms.

[0009] The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

[0010] According to an aspect of the present disclosure for achieving the above-described technical problem, a skin vibration stimulation device comprises: a communication module for performing communication with an external device; a storage module for storing at least one process for providing vibration-based stimulation to a user's skin; a vibration module for generating vibration using at least one vibrator based on a vibration pattern; and a control module for performing an operation for providing vibration-based stimulation to the user's skin based on the at least one process, wherein the control module, when request information is input from the user, provides mode information configured to select at least one of a plurality of modes based on the request information, and when at least one mode is selected from the plurality of modes, configures the vibration pattern based on selection information for the selected at least one mode, and provides a vibration stimulation according to the configured vibration pattern.

[0011] In addition, a skin vibration stimulation method according to one aspect of the present disclosure may include a step of providing mode information configured to select at least one of a plurality of modes based on request information input from a user; a step of configuring a vibration pattern based on selection information for at least one of the selected modes when at least one of the plurality of modes is selected; and a step of providing a vibration stimulus according to the configured vibration pattern.

[0012] According to the aforementioned problem solving means of the present disclosure, the device is configured to be wearable by a user and stimulates the brain through micro-vibrations instead of electrical stimulation, thereby enabling the user to control symptoms such as depression that are acutely worsening when they occur.

[0013] Meanwhile, according to the present disclosure, based on cognitive behavioral therapy techniques, the user can visually check his / her own symptoms and control the symptoms.

[0014] The effects of the present disclosure 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.

[0015] FIG. 1 is a drawing showing a skin vibration stimulation system based on a skin vibration stimulation device configured to be wearable according to one embodiment of the present disclosure.

[0016] FIG. 2 is a diagram showing the driving flow of a skin vibration stimulation system according to one embodiment of the present disclosure.

[0017] FIG. 3 is a diagram showing the configuration of a skin vibration stimulation device according to one embodiment of the present disclosure.

[0018] FIG. 4 is a drawing showing a skin vibration stimulation method according to one embodiment of the present disclosure.

[0019] FIG. 5 is a diagram showing a series of operations for generating analysis information based on accumulated stored vibration driving history according to one embodiment of the present disclosure.

[0020] FIG. 6 is a diagram showing a series of operations for improving the performance of an artificial intelligence-based pre-learning model according to one embodiment of the disclosure.

[0021] FIG. 7 is a drawing showing an example of displaying and providing mode information on a display module of a skin vibration stimulation device according to an embodiment of the disclosure.

[0022] FIG. 8 is a drawing showing an example of providing a control bar by displaying it on a display module of a skin vibration stimulation device according to one embodiment of the present disclosure.

[0023] The advantages and features of the present disclosure, and methods for achieving them, will become clearer with reference to the embodiments described below in detail with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. These embodiments are provided solely to ensure that the present disclosure is complete and to fully inform those skilled in the art of the scope of the present disclosure, and the present disclosure is defined solely by the scope of the claims.

[0024] The terminology used herein is for the purpose of describing embodiments only and is not intended to limit the present disclosure. In this specification, the singular also includes the plural unless specifically stated otherwise. As used herein, the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other components in addition to the mentioned components. Like reference numerals refer to like components throughout the specification, and "and / or" includes each and any combination of one or more of the mentioned components. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms. These terms are only used to distinguish one component from another. Therefore, it should be understood that a first component mentioned below may also be a second component within the technical spirit of the present disclosure.

[0025] Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used in their common sense to those of ordinary skill in the art to which this disclosure pertains. Furthermore, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise.

[0026] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general contents in the technical field to which this disclosure belongs or contents overlapping between embodiments are omitted. The term "part" or "module" as used in the specification means a hardware component such as a software or FPGA or ASIC, and the "part" or "module" performs certain roles. However, the "part" or "module" is not limited to software or hardware. The "part" or "module" may be configured to be in an addressable storage medium and may be configured to play one or more processors. Thus, as an example, the "part" or "module" includes components such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within the components and “sub-components” or “modules” may be combined into a smaller number of components and “sub-components” or “modules” or further separated into additional components and “sub-components” or “modules”.

[0027] Throughout the specification, when a part is said to be "connected" to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection via a wireless communication network.

[0028] Additionally, when a part is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise specifically stated.

[0029] Throughout the specification, when we say that an element is "on" another element, this includes not only cases where the element is in contact with the other element, but also cases where another element exists between the two elements.

[0030] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0031] Singular expressions include plural expressions unless the context clearly indicates otherwise.

[0032] The identification codes for each step are used for convenience of explanation and do not describe the order of each step. Each step may be performed in a different order than specified unless the context clearly indicates a specific order.

[0033] The terms used in the following explanation are defined as follows.

[0034] Although the description herein is limited to the "service providing device (200)", this is a terminal for generating and providing analysis information based on performance information regarding the user's training, and may include various devices capable of performing computational processing. That is, the service providing device (200) may further include a server, a computer, a server, and / or a portable terminal, or may be in the form of any one of them, but is not limited thereto.

[0035] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0036] The above server is a server that processes information by communicating with external devices, 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.

[0037] The above portable terminal may include, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as a 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) terminal, a smart phone, and a wearable device such as a watch, a ring, a bracelet, an anklet, a necklace, glasses, contact lenses, or a head-mounted device (HMD).

[0038] In this specification, the 'user terminal (100)' is a skin vibration stimulation device, which is a terminal carried by a user (patient), and the 'guardian terminal (300)' includes a terminal carried by a guardian, medical staff, and / or manager of the user (patient) or provided at a medical institution and / or management institution.

[0039] In this specification, the "AI-based pre-training model" refers to a deep learning-based analysis model, and each of these can generate analysis information by analyzing the results of a user's biofeedback training. The deep learning method is not limited, and at least one method may be applied depending on the situation (need). Examples of AI algorithms that can be applied include, but are not limited to, a Recurrent Neural Network (RNN) or a Transformer, and other AI algorithms may also be applied.

[0040] In this specification, "biofeedback" refers to a training method that allows individuals to learn self-regulation by directly observing and feeling the responses of the autonomic nervous system, such as muscle tension, brain waves, heart rate, skin resistance, and body temperature, on a computer screen. This biofeedback training can be particularly helpful in cases where medication is difficult to take or has not been effective despite prolonged use. It can also be helpful in treating chronic headaches, stress relief, chronic fatigue, constipation, Raynaud's disease, insomnia, and irritable bowel syndrome.

[0041] The operating principle and embodiments of the present disclosure are described below with reference to the attached drawings.

[0042] FIG. 1 is a diagram illustrating a skin vibration stimulation system based on a skin vibration stimulation device configured to be wearable according to one embodiment of the present disclosure.

[0043] Referring to FIG. 1, a skin vibration stimulation system (hereinafter referred to as a “providing system”) (10) according to one embodiment of the present disclosure may be configured to include a user terminal (100), a service providing device (200), and at least one guardian terminal (300).

[0044] First, the user terminal (100) is a terminal carried by the user (patient) for performing training through vibration stimulation, and is referred to as a "skin vibration stimulation device." This user terminal (100) can generate and provide the accumulated and stored vibration driving history for training performed over a preset period as performance information for the training to the service providing device (200). At this time, the user terminal (100) can be configured to be wearable on at least one body part of the user.

[0045] Additionally, the user terminal (100) receives analysis information generated based on the performance information from the service providing device (200).

[0046] Meanwhile, the service providing device (200) collects training performance information for the user based on the user terminal (100) and a separate database, and inputs the performance information into an artificial intelligence-based pre-learning model to analyze the correlation between vibration patterns and the user's biometric data. Accordingly, the service providing device (200) can generate analysis information based on the analysis results and provide it to the user terminal (100) and at least one guardian terminal (300).

[0047] In addition, at least one guardian terminal (300) is a terminal possessed by at least one guardian that is preset in response to the patient, and receives analysis information generated based on performance information on training performed by the user terminal (100), i.e., the user (patient), from the service providing device (200). At this time, at least one guardian terminal (300) is a concept that includes not only the guardian but also a medical staff terminal, a medical institution terminal, a management institution terminal, etc., as described above, and may be pre-registered so that analysis information can be provided by the service providing device (200) or the user terminal (100).

[0048] Meanwhile, the user terminal (100) and at least one guardian terminal (300) may be a computer, an Ultra Mobile PC (UMPC), a workstation, a netbook, a Personal Digital Assistants (PDA), a portable computer, a web tablet, a wireless phone, a mobile phone, a smart phone, a pad, a smart watch, a wearable terminal, an e-book, a portable multimedia player (PMP), a portable game console, a navigation device, a black box, a digital camera, or other mobile communication terminals, etc., on which a plurality of application programs (i.e., applications) desired by medical staff (including medical institutions, administrators, and management institutions) and / or users (including patients and guardians) can be installed and executed. That is, the user terminal (100) and at least one guardian terminal (300) can be provided in various forms, and their type and number are not limited.

[0049] FIG. 2 is a diagram illustrating the operating flow of a skin vibration stimulation system according to one embodiment of the present disclosure. While FIG. 2 illustrates only one guardian terminal (300), this is merely for convenience of explanation, and the system may be configured with at least one or more guardian terminals.

[0050] Referring to FIG. 2, a user performs training via a user terminal (100) (S101) and generates the results of the training as performance information (S103). At this time, the performance information may include vibration drive history accumulated and stored according to training performed over a preset period.

[0051] Next, when the user terminal (100) transmits the performance information generated by step S103 to the service providing device (200), the service providing device (200) that receives the performance information analyzes the performance information (S107) and generates analysis information based on the analysis result (S109).

[0052] Next, the service providing device (200) transmits the analysis information generated by step S109 to the user terminal (100) and the guardian terminal (300), respectively (S111, S113). At this time, steps S111 and S113 may be performed simultaneously or sequentially, and the order in which they are performed is not limited.

[0053] Next, the user terminal (100) and guardian terminal (300) that received the analysis information display it through their respective display modules, thereby allowing the user and guardian to visually check the user's training status (S115, S117).

[0054] Meanwhile, FIGS. 1 and 2 illustrate one embodiment, and the service providing device (200) may be omitted. In this case, the function (operation) of the service providing device (200) described above may be performed by the user terminal (100). Hereinafter, the specific configuration and operation of the skin vibration stimulation device, i.e., the user terminal (100), will be described based on the case where the service providing device (200) is omitted.

[0055] FIG. 3 is a drawing showing the configuration of a skin vibration stimulation device according to one embodiment of the present disclosure.

[0056] Referring to FIG. 3, a skin vibration stimulation device (i.e., referred to as a 'user terminal') (100) according to one embodiment of the present disclosure may be configured to include a communication module (110), a vibration module (130), a storage module (150), and a control module (170).

[0057] The communication module (110) transmits and receives at least one piece of information or data to and from at least one device / terminal. Here, at least one device / terminal may be a device / terminal that seeks to receive analysis information generated based on the performance results of the user's training, or a device / terminal that provides various data / information that enables the generation of such information, and the type and form thereof are not limited.

[0058] In addition, the communication module (110) can perform communication with other devices and transmit and receive wireless signals in a communication network according to wireless Internet technologies.

[0059] Wireless Internet technologies include, for example, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed ​​Downlink Packet Access), HSUPA (High Speed ​​Uplink Packet Access), LTE (Long Term Evolution), and LTE-A (Long Term Evolution-Advanced), and the user terminal (100) transmits and receives data according to at least one wireless Internet technology, including Internet technologies not listed above.

[0060] As for short range communication, short range communication can be supported 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. Such short range wireless communication networks (Wireless Area Networks) can support wireless communication between a user terminal (100), a service providing device (200), and / or at least one guardian terminal (300). At this time, the short range wireless communication network can be a short range wireless personal area network (Wireless Personal Area Network).

[0061] The vibration module (130) includes at least one vibrator that generates vibration, and generates vibration using the at least one vibrator based on a vibration pattern to perform vibration stimulation.

[0062] The storage module (150) can store data for at least one process (algorithm) for providing skin vibration stimulation or a program that reproduces the process. Furthermore, the storage module (150) can further store processes for performing other operations, but this is not limited thereto.

[0063] The storage module (150) can store various data that support various functions of the user terminal (100), as well as basic information about the user (including personal information such as age, gender, and name), medical information (disease, weight, average blood pressure, average heart rate, and average body temperature), and vibration driving history accumulated and stored through training. The storage module (150) can store a plurality of application programs (or applications) run on the user terminal (100), data for the operation of the user terminal (100), and commands. At least some of these application programs can be downloaded from an external server via wireless communication. Meanwhile, the application programs can be stored in at least one memory provided in the storage module (150), installed on the user terminal (100), and driven to perform operations (or functions) by at least one processor stored in the storage module (150) via the control module (170).

[0064] Meanwhile, at least one memory may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. In addition, the memory may store information temporarily, permanently, or semi-permanently, and may be provided as a built-in or removable type.

[0065] The storage module (150) may build a database to generate and provide analysis information based on artificial intelligence, or may be linked to a separate external server.

[0066] In addition to operations related to the application program, the control module (170) controls all components within the user terminal (100) based on at least one processor to process input or output signals, data, information, etc., or executes commands, algorithms, and application programs stored in at least one memory to perform various processes, and can provide or process appropriate information or functions to generate and provide analysis information based on artificial intelligence. In addition, the control module (170) can collect medical information of the user based on the storage module (150) or a separate database.

[0067] Specifically, when request information is input from a user, the control module (170) provides mode information configured to enable selection of at least one of a plurality of modes based on the request information. In addition, when at least one of a plurality of modes is selected by the user, the control module (170) configures a vibration pattern based on selection information for the selected at least one mode, and provides a vibration stimulus based on the vibration pattern configured accordingly. Here, at least one mode may be configured with a plurality of vibrations having different intensities and times.

[0068] Meanwhile, the control module (170) provides a visual display of biometric data measured for the user so that the user can recognize his or her condition while providing the vibration stimulus. In addition, the control module (170) may provide a control bar that allows the user to adjust at least one of the intensity and duration of the vibration stimulus currently being provided while providing the vibration stimulus.

[0069] In addition, the control module (170) records the vibration driving history until the vibration stimulus provision ends and stores it as a customized training mode corresponding to the user, thereby enabling additional registration of a customized training mode for the user.

[0070] Meanwhile, the control module (170) analyzes the vibration driving history accumulated and stored according to a preset cycle to generate analysis information and transmits the analysis information to at least one preset guardian terminal (300). At this time, the vibration driving history may include at least one of selection information, adjustment information including information on vibration and / or intensity adjusted by the user while providing vibration stimulation according to the selection information, and biometric information generated by collecting (measuring) the user's biometric data that changes according to the provided vibration stimulation.

[0071] To this end, the control module (170) generates analysis information by analyzing the correlation between the control information and the biometric information based on an artificial intelligence-based pre-learning model. This pre-learning model is learned using the vibration driving history of multiple different users as learning data, and when the vibration driving history of a user is accumulated and stored a preset number of times or more, the control module (170) performs learning on the pre-learning model using the vibration driving history of that user as learning data, thereby improving the performance to suit the user.

[0072] In addition, the control module (170) can provide different mode information depending on the body position where the user terminal (100) is worn. That is, the control module (170) detects the position where the user terminal (100) is worn (wearing position) and provides the user with mode information mapped to the detected wearing position by displaying it on the display module.

[0073] In addition, the specific operation of the control module (170) will be described below based on each drawing.

[0074] FIG. 4 is a diagram illustrating a skin vibration stimulation method according to one embodiment of the present disclosure.

[0075] Referring to FIG. 4, when request information is input from a user, the user terminal (100) provides mode information configured to enable selection of at least one of a plurality of modes based on the request information (S210). At this time, as described above, the mode information may be provided differently depending on the wearing position of the user terminal (100), and thus, in step S210, an operation of detecting the wearing position of the user terminal (100) may be further performed.

[0076] Next, when at least one mode among a plurality of modes is selected based on the mode information provided by step S210, the user terminal (100) configures a vibration pattern based on selection information for the selected at least one mode (S220).

[0077] Next, the user terminal (100) provides a vibration stimulus according to the vibration pattern configured by step S220 (S230).

[0078] FIG. 5 is a diagram illustrating a series of operations for generating analysis information based on accumulated stored vibration driving history according to one embodiment of the present disclosure.

[0079] Referring to FIG. 5, the user terminal (100) records the vibration driving history before the provision of vibration stimulation ends and stores it as a customized training mode corresponding to the user (S240), and analyzes the accumulated and stored vibration driving history according to a preset cycle to generate analysis information (S250).

[0080] Next, the user terminal (100) transmits the analysis information generated by step S250 to at least one preset guardian terminal (S260).

[0081] FIG. 6 is a diagram illustrating a series of operations for improving the performance of an artificial intelligence-based pre-learning model according to one embodiment of the disclosure.

[0082] As described above, the user terminal (100) may have stored an artificial intelligence-based pre-learning model as a model for which learning has been completed by the service providing device (200), and the performance of this pre-learning model may be improved through the user terminal (100).

[0083] Referring to FIG. 6, the user terminal (100) compares the user's vibration driving history with a preset threshold to check the accumulated number of times it is stored (S270), and if the accumulated number of times it is stored is equal to or greater than the preset threshold, learning is performed on a pre-stored pre-learning model according to the check result to improve its performance (S280).

[0084] At this time, learning by step S280 can be performed using the accumulated stored vibration driving history as learning data.

[0085] FIG. 7 is a drawing showing an example of displaying and providing mode information on a display module of a skin vibration stimulation device according to one embodiment of the disclosure.

[0086] Referring to FIG. 7, the user terminal (100) can display and provide mode information (191) including multiple modes on the display module (190).

[0087] At this time, each of the plurality of modes may be configured to have different vibration intensities and / or times, and the user may configure a vibration pattern by selecting at least one of the plurality of modes. That is, as an example, the user terminal (100) may configure a vibration pattern through at least one mode selected from the plurality of modes.

[0088] Meanwhile, as another embodiment, the user terminal (100) may pre-configure a vibration pattern composed of at least one mode and provide it as pattern information instead of mode information.

[0089] FIG. 8 is a drawing showing an example of providing a control bar by displaying it on a display module of a skin vibration stimulation device according to one embodiment of the present disclosure.

[0090] Referring to FIG. 8, the user terminal (100) may display a control bar (192) on the display module (190) while providing the vibration stimulus, which allows the user to adjust at least one of the intensity and / or time of the vibration stimulus currently provided.

[0091] However, this is only one embodiment, and the control bar (192) may be configured to select the intensity and / or time of the vibration stimulus in other forms, or may be configured to be directly input, and the form is not limited thereto.

[0092] As previously explained, cognitive behavioral therapy techniques can be applied by having the user recognize his / her own condition and select a mode to perform training through the user terminal (100).

[0093] Meanwhile, the present disclosure performs prediction (inference) for a predetermined purpose using a model implemented in an artificial neural network manner. In the following, the artificial neural network will be examined.

[0094] A model in this specification may refer to any form of computer program that operates based on a network function, an artificial neural network, and / or a neural network. Throughout this specification, the terms model, neural network, network function, and neural network may be used interchangeably. A neural network is a network in which one or more nodes are interconnected through one or more links to form input node and output node relationships within the neural network. The characteristics of a neural network can be determined based on the number of nodes and links within the neural network, the correlation between the nodes and links, and the weight value assigned to each link. A neural network may be composed of a set of one or more nodes. A subset of the nodes constituting the neural network may constitute a layer.

[0095] A deep neural network (DNN) may refer to a neural network that includes multiple hidden layers in addition to an input layer and an output layer. As shown in FIG. 4 as an example of this concept, a deep neural network is composed of one or more, preferably two or more, hidden layers in the middle.

[0096] These deep neural networks may include convolutional neural networks (CNNs), vision transformers, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, generative pre-trained transformers (GPTs), autoencoders, generative adversarial networks (GANs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), Q-networks, U-networks, Siamese networks, generative adversarial networks (GANs), transformers, etc.

[0097] Alternatively, depending on the embodiment, the deep neural network may be a model trained using transfer learning. Here, transfer learning refers to a learning method in which a large amount of unlabeled training data is pre-trained using a semi-supervised learning or self-learning method to obtain a pre-trained model (or base part) having a first task using techniques such as MLM and NSP as shown in FIGS. 11 and 12, and then the pre-trained model is trained using labeled training data using a supervised learning method to fine-tune it to be suitable for a second task, thereby implementing a target model. One of the models trained using this transfer learning method includes, but is not limited to, BERT (Bidirectional Encoder Representations from Transformers).

[0098] The description of the aforementioned deep neural network is merely an example, and the present disclosure is not limited thereto. In the case of the aforementioned convolutional neural network, it is composed of a feature extraction unit (feature learning) that extracts features from an image, and a classification unit (classification) that performs classification using the extracted features. The feature extraction unit may include, but is not limited to, a convolution layer that extracts features from an image using a kernel, a ReLU layer as one of the activation functions, and a pooling layer for reducing the dimensionality of the data. In addition, the classification unit may include, but is not limited to, a flatten layer that lines up the features extracted from the feature extraction unit, and a fully connected layer and a softmax function that actually perform classification.

[0099] Neural networks can learn through at least one of the following methods: supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning, or reinforcement learning. Neural network learning can be the process of applying knowledge to the neural network to perform a specific action.

[0100] Neural networks can be trained to minimize output errors. This process involves repeatedly inputting training data into the neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network to reduce the error. In supervised learning, labeled data is used for each training data, while unsupervised learning uses unlabeled data. The amount of change in the connection weights of each updated node can be determined by the learning rate. The neural network's calculation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate can vary depending on the number of iterations in the neural network's training cycle. Additionally, to prevent overfitting, methods such as increasing the learning data, regularization, dropout that disables some nodes, and batch normalization layers can be applied.

[0101] Meanwhile, the model disclosed in one embodiment may borrow at least a portion of a transformer. The transformer may be composed of an encoder that encodes embedded data and a decoder that decodes the encoded data. The transformer may have a structure that receives a series of data, performs encoding and decoding steps, and outputs a series of data of different types. In one embodiment, the series of data may be processed into a form operable by the transformer. The process of processing the series of data into a form operable by the transformer may include an embedding process. Expressions such as data tokens, embedding vectors, and embedding tokens may refer to data embedded in a form operable by the transformer.

[0102] To encode and decode a series of data, a transformer can utilize an attention algorithm to process the encoders and decoders within the transformer. An attention algorithm can refer to an algorithm that, for a given query, calculates the similarity for one or more keys, reflects this similarity in the values ​​corresponding to each key, and then weights and sums the values ​​to which the similarity is reflected to calculate an attention value.

[0103] Depending on how the query, key, and value are configured, various types of attention algorithms can be categorized. For example, if attention is obtained by setting the query, key, and value all to the same value, this could be a self-attention algorithm. If attention is obtained by reducing the dimensionality of the embedding vector to process a series of input data in parallel and then generating individual attention heads for each segmented embedding vector, this could be a multi-head attention algorithm.

[0104] In one embodiment, the transformer may be composed of modules that perform multiple multi-head self-attention algorithms or multi-head encoder-decoder algorithms. In one embodiment, the transformer may also include additional components other than attention algorithms, such as embedding, normalization, and softmax. Methods for constructing a transformer using attention algorithms may include methods disclosed in Vaswani et al., Attention Is All You Need, 2017 NIPS, which is incorporated herein by reference.

[0105] A transformer can be applied to various data domains, such as embedded natural language, segmented image data, and audio waveforms, to transform a series of input data into a series of output data. To transform data with various data domains into a series of data that can be input to a transformer, the transformer can embed the data. The transformer can process additional data that expresses the relative positional relationship or phase relationship between the series of input data. Alternatively, vectors expressing the relative positional relationship or phase relationship between the input data can be additionally reflected in the series of input data to embed the series of input data. In one example, the relative positional relationship between the series of input data may include, but is not limited to, word order within a natural language sentence, the relative positional relationship between each segmented image, and the time order of segmented audio waveforms. The process of adding information expressing the relative positional relationship or phase relationship between the series of input data may be referred to as positional encoding.

[0106] The above-described program may include codes coded in a computer language, such as C, C++, JAVA, or machine language, that can be read by the processor (CPU) of the computer through the device interface of the computer, so that the computer reads the program and executes the methods implemented as a program. Such codes may include functional codes related to functions that define functions necessary for executing the methods, and may include control codes related to execution procedures necessary for the processor of the computer to execute the functions according to a predetermined procedure. In addition, such codes may further include memory reference-related codes regarding which location (address address) of the internal or external memory of the computer should reference additional information or media necessary for the processor of the computer to execute the functions. In addition, if the processor of the computer needs to communicate with any other computer or server located remotely in order to execute the functions, the code may further include communication-related code regarding how to communicate with any other computer or server located remotely using the communication module of the computer, and what information or media to send and receive during communication.

[0107] The above storage medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, examples of the storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. That is, the program can be stored in various recording media on various servers that the computer can access or in various recording media on the user's computer. In addition, the medium can be distributed across network-connected computer systems, so that computer-readable code can be stored in a distributed manner.

[0108] The steps of a method or algorithm described in connection with the embodiments of the present disclosure may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in a random access memory (RAM), a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, a CD-ROM, or any other form of computer-readable recording medium well known in the art to which the present disclosure pertains.

[0109] While the embodiments of the present disclosure have been described above with reference to the attached drawings, those skilled in the art will appreciate that the present disclosure can be implemented in other specific forms without altering the technical spirit or essential features thereof. Therefore, the embodiments described above should be understood to be illustrative in all respects and not restrictive.

Claims

1. Communication module that communicates with external devices; A storage module storing at least one process for providing vibration-based stimulation to a user's skin; A vibration module that generates vibration using at least one vibrator based on a vibration pattern; and A control module for performing an operation to provide vibration-based stimulation to the skin of the user based on at least one of the above processes, The above control module, When request information is input from the user, mode information configured to select at least one of a plurality of modes based on the request information is provided, and when at least one mode of the plurality of modes is selected, the vibration pattern is configured based on selection information for the selected at least one mode, and a vibration stimulus is provided according to the configured vibration pattern. Device for providing skin vibration stimulation.

2. In paragraph 1, The above control module, Provides visual display of biometric data for the user while providing the vibration stimulation so that the user can recognize his / her condition; Characterized in that while providing the vibration stimulation, a control bar is displayed to enable the user to adjust the vibration stimulation currently provided. Device for providing skin vibration stimulation.

3. In paragraph 2, The above control module, The vibration driving history before the provision of the above vibration stimulus is terminated is recorded and stored as a training mode corresponding to the user. At least one of the above modes, characterized by being composed of multiple vibrations of different intensities and durations, Device for providing skin vibration stimulation.

4. In paragraph 3, The above control module, Generate analysis information by analyzing the vibration driving history according to the preset cycle, Transmit the above analysis information to at least one of the preset guardian terminal and medical staff terminal, The above vibration driving history is, It is characterized in that it includes biometric information about the user's biometric data that changes according to the provided vibration stimulus and the user's control information while providing the selection information and the vibration stimulus according to the selection information. Device for providing skin vibration stimulation.

5. In paragraph 4, The above control module, Characterized in that the analysis information is generated by analyzing the correlation between the above-mentioned control information and the above-mentioned bio-information based on an artificial intelligence-based pre-learning model. Device for providing skin vibration stimulation.

6. In paragraph 5, The above pre-learning model is, It is learned by using the vibration driving history of multiple different users as learning data, The above control module, When the vibration driving history for the user is accumulated and stored a preset number of times or more, the vibration driving history for the user is used as learning data to perform learning on the pre-learning model, thereby improving the performance to suit the user. Device for providing skin vibration stimulation.

7. In paragraph 1, The above control module, Characterized in that it detects the user's wearing position and provides the mode information mapped to the identified wearing position. Device for providing skin vibration stimulation.

8. In a method for providing skin vibration stimulation, performed by a control module of a device, A step of providing mode information configured to enable selection of at least one of a plurality of modes based on request information input from a user; When at least one mode among the above multiple modes is selected, a step of configuring a vibration pattern based on selection information for the at least one selected mode; and characterized by including a step of providing a vibration stimulus according to a configured vibration pattern, Method for providing skin vibration stimulation.

9. In paragraph 8, The step of providing the above vibration stimulation is: A step of providing biometric data for the user by visually displaying it while providing the vibration stimulation so that the user can recognize his / her condition; and A step of providing a control bar that allows the user to adjust the vibration stimulus currently provided while providing the vibration stimulus, characterized in that the step includes: Method for providing skin vibration stimulation.

10. In paragraph 8, After the step of providing the above vibration stimulation, It further includes a step of recording the vibration driving history before the provision of the vibration stimulus is terminated and storing it as a customized training mode corresponding to the user. At least one of the above modes, characterized by being composed of multiple vibrations of different intensities and durations, Method for providing skin vibration stimulation.

11. In paragraph 10, After the step of providing the above vibration stimulation, It further includes a step of analyzing the vibration driving history according to a preset cycle to generate analysis information and transmitting the analysis information to at least one of the preset guardian terminal and medical staff terminal. The above vibration driving history is, It is characterized in that it includes biometric information about the user's biometric data that changes according to the provided vibration stimulus and information about the user's control while providing the selection information and the vibration stimulus according to the selection information. Method for providing skin vibration stimulation.

12. In paragraph 11, The above analysis information is, It is characterized in that it is generated by analyzing the correlation between the above-mentioned control information and the above-mentioned bio-information based on an artificial intelligence-based pre-learning model. Method for providing skin vibration stimulation.

13. In paragraph 12, The above pre-learning model is, It is characterized in that the vibration driving history for multiple different users is used as learning data, and when the vibration driving history for the user is accumulated and stored a preset number of times or more, the vibration driving history for the user is used as learning data to perform learning on the pre-learning model, so that the performance is improved to suit the user. Method for providing skin vibration stimulation.

14. In paragraph 8, The step of providing the above mode information is: Characterized in that it detects the user's wearing position and provides the mode information mapped to the identified wearing position. Method for providing skin vibration stimulation.

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