Air-injection type garment having piezoelectric element designed to measure ballistocardiogram

Abstract

According to one embodiment, a wearable device for the care of a psychological state of a user on the basis of biometric information of the user may comprise an outer cover consisting of a front part located on a chest portion of the user and a rear part located on a back portion of the user. The outer cover may include a first surface facing the body of the user and a second surface facing away from the body. The wearable device may comprise: an air-tube positioned between the first surface and the second surface and an air injection port provided at one end of the air-tube so that air is injected into or discharged from the air-tube; a driving unit allowing air to be injected into or discharged from the air-tube through the air injection port; a silicone tube extending from the driving unit and connected to the air injection port; a sensor module positioned inside the silicone tube and having a piezoelectric element for sensing pressure; and a processor positioned inside the silicone tube and operatively connected to the driving unit and the sensor module.

Description

Inflatable garments equipped with piezoelectric elements designed to measure cardiograms

[0001] The present disclosure relates to an inflatable garment designed to care for a psychological state based on biometric information, and more particularly, to an inflatable garment having a sensor module including a piezoelectric element designed to measure ballistic cardiography.

[0002] As the importance of managing and stabilizing a person's psychological state or stress has increased in recent years, the need for methods and devices to relieve anxiety and stress has emerged.

[0003] In this regard, as a method and technology for judging or estimating a user's psychological state or stress, a wearable device equipped with a biometric sensor designed to acquire biometric information acquires the user's biometric information, and a method and technology for judging or estimating the user's psychological state based on the acquired biometric information of the user have been proposed.

[0004] To assess or infer a user's psychological state or stress, biometric data is required. To assess these psychological states or stress more precisely, noise in the biometric data, which serves as the basis for assessing these states or stress, must be minimized. In other words, a method is needed to acquire the user's biometric data in real time while minimizing noise.

[0005] In response to the above-mentioned needs, a method of acquiring a user's biometric information in real time was previously adopted through a sensor in contact with the user's body (i.e., a contact-type biometric sensor). However, the user may experience uncomfortable sensations such as discomfort, a foreign body sensation, or a feeling of restriction due to the contact of the biometric sensor. Accordingly, a method of acquiring a user's biometric information using a non-contact biometric sensor has been proposed as a method of acquiring the user's biometric information without making contact with the user's body.

[0006] However, even when biometric information is acquired using a non-contact biometric sensor, noise caused by the user's body movements, etc., is still greater in the sensing data of the non-contact biometric sensor than in the sensing data of the contact biometric sensor.

[0007] Therefore, there is a need for an air-injected compression garment that can collect the user's biometric information while minimizing noise that may be generated from the user's movements and relieving discomfort by not directly contacting the user's body, such as a non-contact biometric sensor.

[0008] According to one embodiment, a wearable device for caring for a user's psychological state based on the user's biometric information may include an outer shell having a front part positioned on the user's chest area and a rear part positioned on the user's back area. The outer shell may include a first side facing the user's body and a second side facing away from the body. The wearable device may include an air tube positioned between the first side and the second side, an air inlet provided at one end of the air tube so that air is injected into or extracted from the air tube, a driving unit for injecting or extracting air into or from the air tube through the air inlet, a silicone tube extending from the driving unit and connected to the air inlet, a sensor module positioned inside the silicone tube and having a piezoelectric element for detecting pressure, and a processor positioned inside the silicone tube and operatively connected to the driving unit and the sensor module.

[0009] According to one embodiment, the processor obtains air pressure data representing the air pressure inside the air tube through the piezoelectric element of the sensor module, and determines the user's condition based on the air pressure data, wherein the user's condition may include at least one of the user's psychological condition, stress level, or type of physical symptom. Based on the determined user's condition, the driving unit may be controlled to inject air into the air tube.

[0010] According to one embodiment of the present disclosure, an inflatable garment equipped with a bio-module including a piezoelectric element can determine a user's psychological state or stress, and non-intrusively collect bio-information necessary for caring for the same, without contacting the user's skin. Furthermore, based on air pressure data measured through a piezoelectric element located inside a sealed silicone tube, the user's cardiogram can be measured, and by acquiring the user's bio-information based on the measured air pressure data, noise included in the bio-information can be reduced compared to when the bio-information is acquired using a conventional non-contact bio-sensor. In other words, the user's psychological state or stress can be more precisely determined using the bio-information with reduced noise, and by applying air pressure to the user based on the determined air pressure, the user's anxiety or stress can be effectively reduced.

[0011] In addition, various effects may be provided directly or indirectly through the present disclosure.

[0012] FIG. 1 illustrates a system for caring for a user's psychological state using a wearable device according to one embodiment.

[0013] FIG. 2 illustrates a wearable device having a sensor module for measuring cardiogram according to one embodiment.

[0014] FIG. 3 illustrates an internal configuration diagram of a sensor module for measuring heart rate according to one embodiment.

[0015] FIG. 4 illustrates a block diagram of a sensor module for measuring heart rate according to one embodiment.

[0016] FIG. 5 illustrates a cross-sectional view illustrating a fabric of a wearable device according to one embodiment.

[0017] FIG. 6 illustrates a conceptual diagram of a technology for obtaining a user's biometric information using a sensor module provided in a wearable device according to one embodiment.

[0018] FIG. 7 illustrates a conceptual diagram of a technology for correcting biometric information using user movement information according to one embodiment.

[0019] FIG. 8 illustrates a flowchart of a method for a wearable device to care for a psychological state using biometric information according to one embodiment.

[0020] FIG. 9 illustrates a flowchart of operations between a wearable device and a server for caring for a psychological state using biometric information according to one embodiment.

[0021] In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components.

[0022] Hereinafter, various embodiments of the present invention will be described with reference to the attached drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.

[0023] Below, with reference to the attached drawings, embodiments of the present invention are described in detail so that those skilled in the art can easily implement the invention. However, the present invention can be implemented in various different forms and is not limited to the embodiments described herein. In addition, in the drawings, parts irrelevant to the description are omitted for clarity of description, and similar parts are designated with similar reference numerals throughout the specification.

[0024] Throughout the specification, when a part is said to "include" a component, this does not exclude other components, but rather implies the inclusion of other components, unless otherwise specifically stated. Furthermore, terms such as "part," "unit," and "module" used in the specification mean a unit that processes at least one function or operation, which may be implemented using hardware, software, or a combination of hardware and software.

[0025] Throughout the specification, when a part is said to be "connected" to another part, this includes not only the case where it is "directly connected" but also the case where it is "electrically connected" with another element in between. Furthermore, when a part is said to "include" a component, this should be understood to mean that, unless specifically stated to the contrary, it may include other components rather than excluding them, and does not preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0026] FIG. 1 illustrates a system for caring for a user's psychological state using a wearable device (101) according to one embodiment.

[0027] Referring to FIG. 1, a system for managing a user's psychological state may include a wearable device (101), a network (102), and a server (103). However, the system is not limited to the components illustrated in FIG. 1, and some components may be omitted or added. For example, the system may further include a user terminal (e.g., a smartphone).

[0028] According to one embodiment, the wearable device (101) may be referred to as an air-injected garment including an air tube (e.g., the air tube (110) of FIG. 2), an air inlet (e.g., the air inlet (204) of FIG. 2), and a driving unit to provide deep touch pressure (DTP) to a user wearing the wearable device (101). For example, the wearable device (101) may refer to an air-injected garment designed to provide deep touch pressure to a user wearing the wearable device (101) so as to relieve psychological anxiety and reduce stress by the user feeling psychologically anxious or under high stress when the user wears the wearable device (101). Here, deep touch pressure refers to pressure that stimulates the parasympathetic nerve by applying appropriate pressure to the user's body, thereby allowing the user to feel as if someone is hugging him / her, thereby relieving anxiety or reducing stress and providing a sense of psychological stability. The above users may include individuals who require psychological stability or who seek relief from stress (e.g., children or individuals with developmental disabilities). However, the above users are not limited to the examples described above, and may include infants, toddlers, children, adolescents, people with disabilities, or the elderly.

[0029] According to one embodiment, the driving unit may function as an air pump, and may inject air into the air tube (e.g., the air tube (110) of FIG. 2) through the air inlet (e.g., the air inlet (204) of FIG. 2) or extract air from the air tube. For example, when it is determined that a user wearing the wearable device (101) feels anxious or stressed, the driving unit may inject air into the air tube through the air inlet. After air is injected into the air tube, when it is determined that the user's anxiety or stress has been relieved, the driving unit may extract air from the air tube. The criterion for determining that the user feels anxious or stressed may be determined based on the user's biometric information acquired through a sensor module (e.g., the sensor module (201)) provided in the wearable device (101). For example, if a value included in the biometric information exceeds a set value and / or a set range, the wearable device (101) may determine that the user is anxious or stressed.

[0030] According to one embodiment, the wearable device (101) may include a communication module capable of performing data communication with an external electronic device. For example, the wearable device (101) may perform data communication with a server (103) via a network (102). The network (102) may refer to wireless communication and may include a mobile hotspot and / or Wi-Fi.

[0031] According to one embodiment, the wearable device (101) may transmit biometric information (e.g., ballistic cardiogram information, heart rate variability information, or respiration information) acquired using a sensor module (e.g., sensor module (201) of FIG. 2) provided in the wearable device (101) to a server (103) via a network (102). The wearable device (101) may receive an artificial intelligence model updated by the server (103) from the server (103).

[0032] According to one embodiment, the server (103) can perform data communication with the wearable device (101) and drive an artificial intelligence model to produce data values ​​for controlling the driving unit of the wearable device (101). For example, the server (103) can train an artificial intelligence model using learning data and obtain output data by inputting input data into the artificial intelligence model.

[0033] According to one embodiment, when data communication is established with the wearable device (101), the server (103) can receive the user's biometric information from the wearable device (101). When data communication is established with the wearable device (101), the server (103) can transmit an updated artificial intelligence model to the wearable device (101).

[0034] According to one embodiment, although not shown in FIG. 1, the wearable device (101) can perform data communication with a user terminal (e.g., a smart phone) via a network (102). For example, the wearable device (101) can transmit biometric information acquired through a sensor module (e.g., a sensor module (201) of FIG. 2) provided in the wearable device (101) to the user terminal. The user terminal can transmit command data for controlling the wearable device (101) to the wearable device (101) via the network (102).

[0035] FIG. 2 illustrates a wearable device (101) having a sensor module (201) for measuring heart rate according to one embodiment.

[0036] Referring to FIG. 2, a wearable device (101) may include a sensor module (201) having a piezoelectric element capable of measuring a ballistocardiogram (BCG), an air tube (110) including a first adhesive portion (110-1) of a first type and a second adhesive portion (110-2) of a second type, an air inlet (204), and a driving portion (not shown).

[0037] According to one embodiment, although not illustrated in FIG. 2, the wearable device (101) is not limited to a sensor module having a piezoelectric element capable of measuring cardiograms, and may further include a biosensor capable of measuring various bio-information. For example, the biosensor may include a biosensor measuring electrodermal activity (EDA), a biosensor measuring photoplethysmography (PPG), a biosensor measuring blood volume pulse (BVP), or a biosensor measuring heat (or body temperature).

[0038] According to one embodiment, the wearable device (101) is described below assuming a vest-shaped garment equipped with an air tube (110). However, the form of the wearable device (101) is not limited to a vest-shaped garment, and may include a garment form capable of applying deep pressure to the user. According to one embodiment, the wearable device (101) may include a front portion that contacts the user's chest area and a rear portion that contacts the user's back area when worn by the user.

[0039] According to one embodiment, the wearable device (101) may include a first adhesive portion (110-1) of a first shape and a second adhesive portion (110-2) of a second shape so that the air injected through the driving portion can evenly pressurize the entire chest and back of the user wearing the wearable device (101). The first adhesive portion (110-1) of the first shape may be formed by bonding in a circular or dot shape, and the second adhesive portion (110-2) of the second shape may be formed by bonding in a line of a predetermined length. According to one embodiment, the air tube (110) provided in the wearable device (101) may form a passage through which the injected air passes by alternately arranging the first adhesive portion (110-1) and the second adhesive portion (110-2). According to one embodiment, the first adhesive portion (110-1) and the second adhesive portion (110-2) may be alternately arranged to form a single adhesive line. A plurality of adhesive lines may be arranged in a horizontal direction parallel to each other across the front and rear of the wearable device (101). The second adhesive portion (110-2) may be formed straight or in a curved shape having a certain curvature.

[0040] According to one embodiment, the sensor module (201) may include a piezoelectric element inside a housing and may include a silicone tube (202) that seals the piezoelectric element. For example, the piezoelectric element or the PCB may be placed inside the silicone tube (202) so as to seal the entire PCB (Printed Circuit Board) (or BCB signal board) on which the piezoelectric element is placed.

[0041] According to one embodiment, the air tube (110) provided in the wearable device (101) may be configured in a sealed form except for the air inlet (204) through which air is injected or extracted. The end of the silicone tube (202) extended from the sensor module (201) may be connected (203) to the air inlet (204) of the wearable device (101). The silicone tube (202) extended from the sensor module (201) may be configured in a sealed form except for the end connected to the air inlet (204). That is, by connecting (203) the end of the silicone tube (202) extended from the sensor module (201) to the air inlet (204) provided in the wearable device (101), the pressure of the air sealed inside the air tube (110) and the silicone tube (202) may be applied to the piezoelectric element. The piezoelectric element can detect changes in air pressure of the wearable device (101) according to the heartbeat and / or breathing of the user wearing the wearable device (101). The processor of the wearable device (101) (e.g., the processor (401) of FIG. 4) can obtain ballistic cardiogram data using data (e.g., air pressure change data) detected through the piezoelectric element.

[0042] FIG. 3 illustrates an internal configuration diagram of a sensor module (201) for measuring a heart rate according to one embodiment.

[0043] Referring to FIG. 3, the sensor module (201) may include a piezoelectric element, and a silicone tube (202) may be arranged to seal (302) (or seal) the piezoelectric element. The wearable device (101) may be arranged with an air tube (110) that forms a sealed (303) (or seal) space so that air can be injected throughout the entire front and rear portions. The air tube (110) may include a first type of air adhesive portion (110-1) and a second type of air adhesive portion (110-2).

[0044] According to one embodiment, when the silicone tube (202) of the sensor module (201) and the air inlet (204) of the wearable device (101) are connected, the piezoelectric element of the sensor module (201) can detect changes in air pressure inside the air tube (101) according to the heartbeat and / or breathing of a user wearing the wearable device (101).

[0045] According to one embodiment, a processor of a sensor module (201) (e.g., processor (401) of FIG. 4) may amplify and filter a micro output voltage of a piezoelectric element using an amplifier (e.g., BCG Analog Front End (AFE) 403)) to generate an analog voltage so that the processor can perform an analog-digital converter (ADC).

[0046] FIG. 4 illustrates a block diagram of a sensor module (201) for measuring heart rate according to one embodiment.

[0047] Referring to FIG. 4, the sensor module (201) may include a processor (401), a piezoelectric element (402), a BCG AFE (403), an RGB (404), a data storage unit (405), a communication unit (406), a motion sensor (407), an artificial intelligence model unit (408), a data processing unit (409), a noise removal unit (410), and a battery (410). However, the sensor module (201) is not limited to the components illustrated in FIG. 4, and some components may be omitted or added. For example, the sensor module (201) may omit the RGB (404). As another example, the sensor module (201) may further include a pressure detection sensor capable of detecting air pressure of an air tube (110) of a wearable device (101). The artificial intelligence model unit (408) may include a first artificial intelligence model (408-1) and a second artificial intelligence model (408-2). The first artificial intelligence model (408-1) may refer to an artificial intelligence model used to process (or preprocess) air pressure data or biosignals acquired through a piezoelectric element (402). The second artificial intelligence model (408-2) may refer to an artificial intelligence model used to classify a user's symptoms through data (e.g., HRV data) processed from data (e.g., BCG data) output through the first artificial intelligence model (408-1).

[0048] According to one embodiment, although not shown in FIG. 4, the sensor module (201) may be operatively and / or electrically connected to a driving unit designed to inject or extract air into or from an air tube (110) of the wearable device (101). For example, the sensor module (201) may be disposed inside a housing of the driving unit.

[0049] In one embodiment, although not shown in FIG. 4, the battery (411) may be operatively and / or electrically connected to the driving unit. For example, the battery (411) may supply power to the driving unit. The battery (411) may be a separate component from the sensor module (201) and may supply power to both the sensor module (201) and the driving unit.

[0050] According to one embodiment, although not shown in FIG. 4, the sensor module (201) may be positioned inside the silicone tube, and the sensor module (20!) positioned inside the silicone tube may be placed inside the housing of the driving unit.

[0051] According to one embodiment, the processor (401) may be a dedicated processor for controlling a specific system, and may refer to a microcontroller (Micro Controller Unit, MCU). The processor (401) may be operatively and / or electrically connected to a piezoelectric element (402), a BCG AFE (403), an RGB (404), a data storage unit (405), a communication unit (406), a motion sensor (407), an artificial intelligence model unit (408), a data processing unit (409), a noise removal unit (410), and a battery (410). The processor (401) can analyze and process various data obtained from the piezoelectric element (402), BCG AFE (403), RGB (404), data storage unit (405), communication unit (406), motion sensor (407), artificial intelligence model unit (408), data processing unit (409), noise removal unit (410), and battery (410), and can control the corresponding devices according to the results of the analysis and processing.

[0052] According to one embodiment, the processor (401) can obtain air pressure data within the air tube (110) of the wearable device (101) through the piezoelectric element (402).

[0053] According to one embodiment, the circuit may be one that amplifies and filters the micro output voltage of the piezoelectric element (402) through the BCG AFE (403) to generate an analog voltage so that the processor (401) can perform an analog-digital converter (ADC).

[0054] According to one embodiment, RGB (404) may be a three-color LED indicating the operating status of the processor (401).

[0055] According to one embodiment, the data storage unit (405) may be a circuit that stores air pressure data acquired through the piezoelectric element (402) and output data output through the artificial intelligence model unit (408).

[0056] According to one embodiment, the communication unit (406) may support data communication with an external device (e.g., a server (103)). For example, a wearable device (101) may transmit a biosignal to the server (103) via the communication unit (406).

[0057] According to one embodiment, the motion sensor (407) may be a sensor that obtains movement data of a user wearing the wearable device (101). For example, the motion sensor (407) may include a three-axis motion sensor, an acceleration sensor, a geomagnetic sensor, and / or a gyro sensor.

[0058] According to one embodiment, the motion sensor (407) can perform I2C communication with the processor (401). The motion sensor (407) can provide user movement data acquired through the motion sensor (407) to the processor (401) through I2C communication.

[0059] According to one embodiment, the artificial intelligence model unit (408) may include a first artificial intelligence model (408-1) and a second artificial intelligence model (408-2). The first artificial intelligence model (408-1) may refer to an artificial intelligence model used to process (or preprocess) air pressure data or biosignals (e.g., BCG signals) acquired through the piezoelectric element (402). That is, the first artificial intelligence model (408-1) may refer to an artificial intelligence model that performs a raw data filter to extract filtered data (filtered signals) from raw data processed (e.g., Fourier transform and wavelet transform) using a template matching technique. The first artificial intelligence model (408-1) may be referred to as a similar signal matching model as an artificial intelligence model that utilizes a template matching technique. The above raw data may refer to air pressure data obtained through a piezoelectric element (402) or data that has been preprocessed through a data processing unit (409). The above preprocessing may refer to Fourier transform and wavelet transform.

[0060] According to one embodiment, the first artificial intelligence model (408-1) may be trained to output filtered data when raw data is input by utilizing a normal BCG data set (or a normal biometric data set). The normal BCG data set or the normal biometric data set may be data that serves as a basis for template data. The first artificial intelligence model (408-1) may be trained to output the filtered data differently for each user. The normal biometric data set may include at least one of a normal heart rate data set, a normal heart rate variability data set, a normal respiration rate data set, a normal skin conductance data set, a normal photoplethysmography data set, a normal pulse data set, or a normal body temperature data set.

[0061] According to one embodiment, the first artificial intelligence model (408-1) may mean an artificial intelligence model that uses as input data at least one of air pressure data acquired through a piezoelectric element (402) or data obtained by preprocessing the air pressure data through a data processing unit (409), and a normal BCG data set (or a normal biological data set) as output data.

[0062] According to one embodiment, the second artificial intelligence model (408-2) may refer to an artificial intelligence model used to classify a user's symptoms through data (e.g., heart rate variability (HRV) data) processed from data (e.g., ballistic cardiogram (BCG) data) output by the first artificial intelligence model.

[0063] According to one embodiment, the second artificial intelligence model (408-2) can be trained to output the type of symptom when data (e.g., heart rate variability (HRV) data) processed from data (e.g., BCG data) output by the first artificial intelligence model is input by utilizing heart rate variability data according to symptoms.

[0064] In one embodiment, the symptoms may include stress-related symptoms, breathing-related symptoms, pain-related symptoms, depression-related symptoms, or heart disease-related symptoms.

[0065] In one embodiment, the stress-related symptoms may reduce heart rate variability. For example, anxiety, tension, irritability, and physical tension can cause irregular heartbeats and reduced heart rate variability. The second AI model (408-2) can be trained using heart rate variability data that shows reduced heart rate variability and irregular heartbeats in response to stress-related symptoms.

[0066] In one embodiment, the respiratory symptoms, such as changes in breathing patterns, airway obstruction, asthma, and dyspnea, may reduce heart rate variability. The second artificial intelligence model (408-2) may be trained using heart rate variability data with reduced heart rate variability according to respiratory symptoms.

[0067] In one embodiment, the pain-related symptoms, such as chronic pain, neuralgia, and myalgia, can cause irregular heartbeats and reduced heart rate variability. The second artificial intelligence model (408-2) can be trained using heart rate variability data that shows reduced heart rate variability and irregular heartbeats in response to pain-related symptoms.

[0068] In one embodiment, the above depression-related symptoms, such as depression, lethargy, fatigue, and gloomy mood, may be associated with decreased heart rate variability. The second artificial intelligence model (408-2) may be trained using heart rate variability data in which heart rate variability is decreased according to depression-related symptoms.

[0069] According to one embodiment, the heart disease-related symptoms, such as heart failure, arrhythmia, and myocardial infarction, which affect heart health, can reduce the variability of heart rate variability. The second artificial intelligence model (408-2) can be trained using heart rate variability data with reduced variability in heart rate variability according to heart disease-related symptoms.

[0070] According to one embodiment, the data processing unit (409) may be a circuit that processes sensing data (e.g., BCG raw data) acquired through a sensor (e.g., a piezoelectric element (402) of a sensor module (201)) into input data to be input into an artificial intelligence model (e.g., a first artificial intelligence model (408-1)). For example, the data processing unit (409) may perform a preprocessing operation to process air pressure data representing air pressure in an air tube (110) of a wearable device (101) acquired through a piezoelectric element (402) into input data to be input into the first artificial intelligence model unit (408-1) of the artificial intelligence model unit (408). The preprocessing process may mean a Fourier transform and a wavelet transform. The data processing unit (409) can first apply Fourier transform to the air pressure data acquired through the piezoelectric element (402) and secondarily apply wavelet transform to generate transformed data. Data obtained by applying the Fourier transform to the raw data may be first transformed data, and data obtained by applying the wavelet transform to the first transformed data may be second transformed data.

[0071] According to one embodiment, the noise removal unit (410) can remove noise included in sensing data detected through the piezoelectric element (402) based on movement data acquired from a motion sensor (407) (e.g., a 3-axis motion sensor, an acceleration sensor, a geomagnetic sensor, and / or a gyro sensor). For example, the noise removal unit (410) can remove noise data included in the sensing data by superimposing signals of the x, y, and z axes measured from an acceleration sensor on sensing data (e.g., air pressure data) measured through the piezoelectric element (402) and removing the amplified signals.

[0072] According to one embodiment, the battery (411) may supply power to operate the sensor module (201). The battery (411) may be capable of being charged by wires and / or wirelessly. When the battery (411) is in a charged state, the communication unit (406) may be activated. For example, the processor (401) may activate the communication unit (406) in response to identifying that the battery (411) is in a charged state to perform data communication with an external device (e.g., server (103)). That is, the processor (401) may transmit sensing data to the server (103) through the communication unit (406) when the battery (411) is in a charged state. The above sensing data may include sensing data (e.g., air pressure data) detected through a piezoelectric element (402) or data processed through a data processing unit (409) (e.g., data subjected to preprocessing including Fourier transform and wavelet transform and data input to and output from the preprocessing and the first artificial intelligence model (408-1)).

[0073] FIG. 5 illustrates a cross-sectional view for explaining the fabric of a wearable device (101) according to one embodiment.

[0074] Referring to FIG. 5, the front and rear parts of the wearable device (101) may be composed of a fabric having four layers. That is, the layers of the wearable device (101) may be composed of a first layer (501), a second layer (502), a third layer (503), and a fourth layer (504) that are sequentially laminated from top to bottom. The layers of the wearable device (101) may be composed of a first layer (501) made of clothing fabric, a second layer (502) made of a urethane film, a third layer (503) made of a urethane film, and a fourth layer (504) made of clothing fabric. The urethane film of the second layer (502) and the urethane film of the third layer (503) may be bonded to each other through a high-frequency fusion process. The clothing fabric of the first layer (501) and the urethane film of the second layer (502) can be bonded to each other through a binding process. The urethane film of the third layer (503) and the clothing fabric of the fourth layer (504) can be bonded to each other through a binding process.

[0075] According to one embodiment, a process for forming four layers constituting the fabric of a wearable device (101) may include a first process of bonding a first layer (501) and a second layer (502) through a binding process to create a first laminate, and a second process of bonding a third layer (503) and a fourth layer (504) through a binding process to create a second laminate, and a second process of bonding the first laminate and the second laminate to each other through a high-frequency fusion method. In the second process, the urethane film surfaces of the first laminate and the second laminate may be bonded to each other.

[0076] According to one embodiment, the clothing fabric may include cotton, polyester, rayon, nylon, wool, cashmere, silk, linen, or spandex.

[0077] In one embodiment, the urethane film may refer to a thin film made using an artificial fiber called polyurethane. Urethane has excellent durability and elasticity, and can be flexible yet strong, and can be transparent.

[0078] FIG. 6 illustrates a conceptual diagram of a technology for obtaining a user's biometric information using a sensor module (201) provided in a wearable device (101) according to one embodiment.

[0079] FIG. 6 (a) shows a wearable device (101) equipped with a sensor (e.g., sensor module (201)) for obtaining biometric information of a user in real time when the user wears the device, FIG. 6 (b) shows the appearance of contraction or expansion of blood vessels according to the biometric state (e.g., heartbeat state or breathing state) of the user when the user wears the wearable device (101), and FIG. 6 (c) can show a graph showing biometric information of a user wearing the wearable device (101) obtained in real time through a sensor of the wearable device (101).

[0080] According to one embodiment, when a user wearing a wearable device (101) exhales or his / her heart contracts (601), the user's blood vessels may maintain their original state or become dilated. When a user wearing a wearable device (101) inhales or his / her heart contracts (602), the user's blood vessels may become contracted compared to their original state.

[0081] According to one embodiment, the wearable device (101) can obtain the user's biometric information through the sensor module (201). The wearable device (101) can obtain raw data (611) representing the user's biometric information through the sensor module (201). The wearable device (101) can process the raw data through the data processing unit (409) to generate ballistic cardiogram (BCG) data (612) and respiration data (613). For example, the wearable device (101) can apply Fourier transform and wavelet transform to the raw data through the data processing unit (409). The wearable device (101) can input the transformed data obtained by applying the Fourier transform and wavelet transform to the first artificial intelligence model (408-1) and output filtered data. The above filtered data may include ballistic data (612) or respiratory data (613).

[0082] In one embodiment, the Fourier transform may refer to a process of separating frequencies corresponding to biological rhythms (e.g., heart rhythm or breathing rhythm) from raw data measured through the piezoelectric element (402).

[0083] According to one embodiment, wavelet transform may mean a process of creating various bands from low frequency to high frequency by increasing or decreasing the length of a signal in the time axis direction and a process of calculating a correlation coefficient with raw data by changing the time scale of a wavelet function modeled according to a specific rule.

[0084] FIG. 7 illustrates a conceptual diagram of a technology for correcting biometric information using user movement information according to one embodiment.

[0085] Referring to FIG. 7, the wearable device (101) can remove noise included in air pressure data acquired through the piezoelectric element (402) by using the noise removal unit (410). For example, the wearable device (101) can preprocess raw data (e.g., air pressure data) acquired through the piezoelectric element (402) of the sensor module (201), and can acquire biometric data (e.g., ballistic cardiogram (BCG) data and respiration data) through the preprocessing process. The biometric data acquired through the preprocessing process may be data including noise generated due to the user's movement. For example, the graph (701) of FIG. 7 (a) represents biometric data acquired through the preprocessing process, and the noise may be included in the first portion (711). The graph (702) of (a) of FIG. 7 represents the user's movement data acquired through the motion sensor (407), and the second part (721) may include movement data generated due to the user's movement.

[0086] According to one embodiment, the graph (701) of FIG. 7 (b) may represent data from which noise of the biometric data displayed in the graph (701) is removed by using the motion data displayed in the graph (702). For example, due to the motion data displayed in the second part (721) of the graph (702), the noise data displayed in the first part (711) of the graph (701) may have noise removed, as in the third part (731) of the graph (703), by removing the amplified signal by overlapping the biometric data of the graph (701) and the motion data of the graph (702).

[0087] FIG. 8 illustrates a flowchart of a method for a wearable device (101) to care for a psychological state using biometric information according to one embodiment.

[0088] The operations of the wearable device (101) described below may be performed in different order or simultaneously.

[0089] In operation 801, the wearable device (101) can obtain biometric information of a user wearing the wearable device (101) in real time through the sensor module (201). For example, the wearable device (101) can detect air pressure within an air tube (110) of the wearable device (101) through the piezoelectric element (402) of the sensor module (201), thereby obtaining air pressure data. The air pressure may vary depending on the user's heartbeat or breathing state.

[0090] According to one embodiment, the wearable device (101) can process the air pressure data into biometric data through the data processing unit (409) and the artificial intelligence model unit (408). The air pressure data can be understood as raw data or ballistic cardiogram data. The wearable device (101) can process the raw data through the data processing unit (409). For example, the wearable device (101) can first apply a Fourier transform to the raw data through the data processing unit (409) and secondarily apply a wavelet transform to the raw data.

[0091] According to one embodiment, the wearable device (101) can obtain ballistic cardiogram data and respiratory data by applying puree transformation and wavelet transformation to the raw data. The ballistic cardiogram data and the respiratory data may refer to the second transformed data.

[0092] According to one embodiment, the wearable device (101) may perform a Fourier transform by separating a first frequency corresponding to a heart rhythm to obtain ballistic cardiogram data (or second transformed data) from the raw data. The wearable device (101) may perform a wavelet transform using a wavelet function modeled according to a heart rhythm on the first transformed data obtained by performing the Fourier transform.

[0093] According to one embodiment, the wearable device (101) may perform a Fourier transform by separating a second frequency corresponding to a respiratory rhythm to obtain respiratory data (or second transformed data) from the raw data. The wearable device (101) may perform a wavelet transform using a wavelet function modeled according to the respiratory rhythm on the second transformed data obtained by performing the Fourier transform.

[0094] According to one embodiment, the wearable device (101) inputs the second transformed data to which the Fourier transform and wavelet transform are applied into the first artificial intelligence model (408-1) of the artificial intelligence model unit (408), and based on the output data, acquires biometric data in real time. The biometric data is data to which the template matching technique of the first artificial intelligence model is applied, and may be referred to as data to which a raw data filter is applied. The biometric data may include ballistic cardiogram data or respiratory data to which the raw data filter is applied.

[0095] In operation 802, the wearable device (101) can detect an abnormal condition of a user wearing the wearable device (101) based on biometric data acquired in real time (e.g., ballistic cardiogram data or respiration data) or data processed from the biometric data (e.g., heart rate data or heart rate variability data). For example, the wearable device (101) can detect an abnormal condition of the user based on whether a numerical value of the biometric data exceeds a specified range.

[0096] According to one embodiment, the wearable device (101) can detect an abnormal state of the user based on whether the heart rate data exceeds a specified range. The specified range may be a first value or a second value, and the specified range may mean a normal heart rate range by age. For example, since the normal average heart rate for adults aged 20 or older is approximately 70 to 75 beats per minute, the first value may be 70 beats per minute, and the second value may be 75 beats per minute. However, the present invention is not limited to the above-described values. If the heart rate data exceeds the specified range, the wearable device (101) may determine that the user is feeling anxious or stressed.

[0097] According to one embodiment, the wearable device (101) can detect an abnormal state of the user based on whether the heart rate variability data exceeds a specified range. The specified range may be a third value or a fourth value, and the specified range may refer to a normal heart rate variability range by age. For example, when the psychological state is stable or the stress level is low, the heart rate variability may be high, in which case the difference value between the third value and the fourth value, which is a specified range, may be a first difference value. When the psychological state is unstable or the stress level is high, the heart rate variability may be low, in which case the difference value between the third value and the fourth value, which is a specified range, may be a second difference value. The first difference value may be greater than the second difference value. When the heart rate variability data exceeds a specified range, the wearable device (101) may determine that the user is feeling anxious or stressed.

[0098] According to one embodiment, the wearable device (101) can detect an abnormal state of the user based on whether the breathing data exceeds a specified range. The specified range may be a fifth value or a sixth value, and the specified range may refer to a normal breathing rate range. The fifth value may be 12 breaths per minute, and the sixth value may be 20 breaths per minute. If the breathing data exceeds the specified range, the wearable device (101) can determine that the user is feeling anxious or stressed.

[0099] In operation 803, when the wearable device (101) detects that the user is feeling anxious or stressed, i.e., is in an abnormal state, the wearable device (101) may automatically control the driving unit to inject air into the air tube (110) of the wearable device (101). The wearable device (101) may transmit at least one of status information indicating that the user is in an abnormal state or information related to controlling the driving unit to an external device (e.g., a server (103) or a user terminal).

[0100] According to one embodiment, the wearable device (101) can determine at least one of the type of the user's psychological state, the level of stress, or the type of symptom based on the user's biometric information.

[0101] In one embodiment, the types of psychological states may include anger, sadness, joy, comfort, anxiety, etc. The stress level may be divided into intervals and categorized into predetermined stages. For example, the stress level may be categorized from Level 1 to Level 5, and it may be understood that stress increases from Level 1 to Level 5. The types of symptoms may include stress-related symptoms, respiratory symptoms, pain-related symptoms, depression-related symptoms, and heart disease-related symptoms.

[0102] According to one embodiment, the wearable device (101) may select an air injection method corresponding to the type of the user's psychological state, the level of stress, and the type of symptom determined above. The air injection method may include at least one of air injection strength, air injection speed, maintenance time after air injection, or air compression area. The wearable device (101) may control the driving unit of the wearable device (101) to inject air into the air tube (110) based on the selected air injection method. For example, when the user's psychological state is determined to be anxious, the wearable device (101) may inject air into the air tube (110) at a first pressure and a first speed, and maintain the air injection state for a first duration. For another example, if the user's psychological state is determined to be angry, the wearable device (101) may inject air into the air tube (110) at a second pressure greater than the first pressure and at a second speed faster than the first speed, and maintain the air injection state for a second duration longer than the first duration. The air injection method is not limited to the above example.

[0103] In operation 804, the wearable device (101) can detect that the user's psychological state is stable based on the user's biometric information acquired in real time. For example, the wearable device (101) can determine that the user's psychological state is stable if the numerical value of the user's biometric data falls within a specified range. For example, the wearable device (101) can determine that the user's psychological state is stable if at least one of the ballistic cardiogram data, heart rate data, heart rate variability data, or respiration data falls within a specified range.

[0104] According to one embodiment, when the user's psychological state is determined to be stable, the wearable device (101) may control the driving unit of the wearable device (101) to extract air from the air tube (110). The wearable device (101) may transmit at least one of state information indicating that the user's psychological state is stable or information related to controlling the driving unit to an external device (e.g., a server (103) or a user terminal).

[0105] FIG. 9 illustrates a flowchart of operations between a wearable device (101) and a server (103) for caring for a psychological state using biometric information according to one embodiment.

[0106] The operations of the wearable device (101) and the server (103) described below may be performed in different order or simultaneously.

[0107] In operation 901, the wearable device (101) can acquire biometric data through a sensor (e.g., a sensor module (201)) provided in the wearable device (101). The biometric data may include at least one of ballistic cardiogram data, heart rate data, heart rate variability data, respiration data, skin conductance data, photoplethysmography data, pulse data, or thermal data. The biometric data may refer to data processed from raw data (or pressure data or air pressure data) detected through a piezoelectric element (402) of the sensor module (201) of the wearable device (101).

[0108] According to one embodiment, the wearable device (101) can obtain air pressure data (or pressure data or raw data) representing the air pressure within the air tube (110) of the wearable device (101) through the piezoelectric element (402) of the sensor module (201). The wearable device (101) can obtain biometric data by processing and handling the air pressure data through the data processing unit (409) and the artificial intelligence model unit (408).

[0109] In operation 902, the wearable device (101) may store the biometric data in the data storage unit (405). The wearable device (101) may perform an operation of removing noise included in the biometric data through the noise removing unit (410). According to one embodiment, the wearable device (101) may remove noise included in the sensing data (e.g., pressure data, air pressure data) detected through the piezoelectric element (402) through the noise removing unit (410). For example, the wearable device (101) may remove noise in the sensing data by overlapping the sensing data with movement data detected through the motion sensor (407) and removing an amplified signal. The movement data may include x, y, and z-axis data of an acceleration sensor.

[0110] In operation 903, the wearable device (101) may transmit the biometric data to the server (103) via the communication unit (406). Although not illustrated in FIG. 9, the wearable device (101) may transmit not only the biometric data, but also at least one of raw data, preprocessed data of the raw data (e.g., first converted data, second converted data), and filtered data processed using the first artificial intelligence model to the server (103). The raw data may refer to pressure data or air pressure data. In order for the wearable device (101) to transmit the biometric data to the server (103) via the communication unit (406), it may be assumed that the sensor module (201) or the driving unit of the wearable device (101) is in a charged state. For example, only when the sensor module (201) or driving unit of the wearable device (101) is in a charged state, the wearable device (101) can perform data communication with the server (103) through the communication unit (406).

[0111] According to one embodiment, the wearable device (101) may transmit the user's personal information (e.g., age, gender, height, weight, degree of disability, etc.) to the server (103) through the communication unit (406).

[0112] In operation 904, the server (103) can store biometric data and personal information received from the wearable device (101) in a database. The server (103) can receive biometric data and personal information from the wearable device (101) only when it is connected to the wearable device (101) via wireless communication.

[0113] In operation 905, the server (103) may train an artificial intelligence model using the received biometric data and personal information. The artificial intelligence model may be substantially identical to an artificial intelligence model (e.g., a first artificial intelligence model (408-1) and a second artificial intelligence model (408-2)) operated in the wearable device (101).

[0114] According to one embodiment, the server (103) may learn and operate a third artificial intelligence model that performs a raw data filter to extract filtered data from raw data processed (e.g., Fourier transform and wavelet transform) using a template matching technique. The third artificial intelligence model may be learned and operated in substantially the same manner as the first artificial intelligence model (408-1). The third artificial intelligence model may be referred to as a signal matching model as an artificial intelligence model that utilizes a template matching technique. For example, the third artificial intelligence model may be learned to output filtered data when raw data is input using a normal BCG data set (or a normal biometric data set).

[0115] According to one embodiment, the server (103) may extract features from raw data received from a wearable device (101), and then perform vector quantization to generate a template (or template data) for utilizing a template matching technique.

[0116] According to one embodiment, the server (103) may learn and operate a fourth artificial intelligence model that uses the user's personal information and biometric information to determine the user's psychological state, stress level, and type of symptom. The fourth artificial intelligence model may be learned and operated in substantially the same manner as the second artificial intelligence model (408-2). The fourth artificial intelligence model may be referred to as a personalized learning model as an artificial intelligence model used to classify the user's symptoms. For example, the fourth artificial intelligence model may be trained to output the type of symptom when inputting data (e.g., heart rate variability data, respiration data) processed from data (e.g., ballistic cardiogram data) output from the third artificial intelligence model.

[0117] In operation 906, the server (103) can transmit the generated template (or template data) to the wearable device (101). The server (103) can update the learned artificial intelligence model (e.g., the third artificial intelligence model and the fourth artificial intelligence model) using the data (e.g., raw data, biometric data, etc.) received from the wearable device (101), and transmit the updated artificial intelligence model to the wearable device (101).

[0118] In operation 907, operation 907 may be substantially the same as operation 802. The wearable device (101) may detect an abnormal state of the user by utilizing the updated artificial intelligence model received from the server (103). The wearable device (101) inputs preprocessed raw data (e.g., second transformed data) into the updated first artificial intelligence model (408-1), outputs filtered data, processes the filtered data, inputs it into the updated second artificial intelligence model (408-2), and determines whether the user's state is abnormal based on the output value.

[0119] In operation 908, operation 908 may be substantially the same as operation 803. In response to detecting that the user's condition is abnormal, the wearable device (101) may automatically inject air into the air tube (110) and transmit the user's current condition to an external device (e.g., a server (103) or a user terminal).

[0120] In operation 909, operation 909 may be substantially identical to the stable state detection operation of operation 804. The wearable device (101) may determine that the user's state is stable when the numerical value of the biometric information falls within a specified range.

[0121] In operation 910, operation 909 may be substantially identical to the air extraction and current state transmission operations of operation 804. When the wearable device (101) determines that the user's state is stable, it extracts air from the air tube (110) and transmits at least one of information indicating that the user's state is stable or information indicating that the driving unit is controlled to extract air from the air tube (110) to an external device (e.g., a server (103) or a user terminal).

[0122] According to one embodiment, a wearable device for caring for a user's psychological state based on the user's biometric information may include an outer shell having a front part positioned on the user's chest area and a rear part positioned on the user's back area. The outer shell may include a first side facing the user's body and a second side facing away from the body. The wearable device may include an air tube positioned between the first side and the second side, an air inlet provided at one end of the air tube so that air is injected into or extracted from the air tube, a driving unit for injecting or extracting air into or from the air tube through the air inlet, a silicone tube extending from the driving unit and connected to the air inlet, a sensor module positioned inside the silicone tube and having a piezoelectric element for detecting pressure, and a processor positioned inside the silicone tube and operatively connected to the driving unit and the sensor module.

[0123] According to one embodiment, the processor obtains air pressure data representing the air pressure inside the air tube through the piezoelectric element of the sensor module, and determines the user's condition based on the air pressure data, wherein the user's condition may include at least one of the user's psychological condition, stress level, or type of physical symptom. Based on the determined user's condition, the driving unit may be controlled to inject air into the air tube.

[0124] According to one embodiment, the wearable device may further include a data processing unit that processes data acquired through the piezoelectric element. The processor may apply a first Fourier transform to the air pressure data through the data processing unit, wherein the first Fourier transform may be a transform that separates a first frequency corresponding to a heart rhythm from the air pressure data. The processor may apply a second Fourier transform to the air pressure data through the data processing unit, wherein the second Fourier transform may be a transform that separates a second frequency corresponding to a respiratory rhythm from the air pressure data. Based on the first Fourier transform and the second Fourier transform, the user's ballistic cardiogram data and respiratory data may be acquired from the air pressure data.

[0125] According to one embodiment, the processor performs a first wavelet transform using a first wavelet function modeled according to a heart rhythm on air pressure data to which the first Fourier transform has been applied through the data processing unit, and performs a second wavelet transform using a second wavelet function modeled according to the respiratory rhythm on air pressure data to which the second Fourier transform has been applied through the data processing unit, and based on the first wavelet transform and the second wavelet transform, the user's ballistic cardiogram data and the respiratory data can be obtained from the air pressure data.

[0126] According to one embodiment, the wearable device may further include an artificial intelligence model unit that drives a first artificial intelligence model trained to extract filtered data from the ballistic cardiogram data and the respiration data, which have undergone the first wavelet transformation and the second wavelet transformation using a template matching technique. The processor inputs the ballistic cardiogram data and the respiration data into the first artificial intelligence model of the artificial intelligence model unit to obtain filtered ballistic cardiogram data and filtered respiration data, and the first artificial intelligence model is trained to output the filtered ballistic cardiogram data and the filtered respiration data when the ballistic cardiogram data and the respiration data are input, using a normal ballistic cardiogram data set and a normal respiration data set as learning data.

[0127] According to one embodiment, the artificial intelligence model unit further includes a second artificial intelligence model trained to determine the user's condition by using the filtered cardiographic data and the filtered respiration data, and the processor inputs the filtered cardiographic data and the filtered respiration data into the second artificial intelligence model to determine the user's condition, and controls the driving unit to inject air into the air tube based on the user's condition, and the type of the physical symptom included in the user's condition may include at least one of a stress-related symptom, a respiration-related symptom, a pain-related symptom, a depression-related symptom, or a heart disease-related symptom. The second artificial intelligence model may be trained to output the user's condition when the user's biometric data is input by using biometric data according to the user's condition.

[0128] According to one embodiment, the wearable device may further include a motion sensor. The processor may obtain movement data of the user through the motion sensor, and based on the movement data, remove noise included in the filtered ballistic cardiogram data and the filtered respiration data.

[0129] According to one embodiment, the wearable device may further include a motion sensor. The processor may obtain movement data of the user through the motion sensor and, based on the movement data, remove noise included in the air pressure data.

[0130] According to one embodiment, the first side and the second side are formed of clothing fabric, a first urethane film is positioned between the first side and the second side, a second urethane film is positioned between the first urethane film and the second side, the first side and the first urethane film are bonded through binding, the second side and the second urethane film are bonded through binding, and the first urethane film and the second urethane film can be bonded through high-frequency fusion.

[0131] According to one embodiment, the clothing fabric may include at least one of cotton, polyester, rayon, nylon, wool, cashmere, silk, linen, or spandex.

[0132] According to one embodiment, the wearable device may further include a battery and a communication unit. The processor may activate the communication unit only when the battery is charging, and transmit the air pressure data to an external server through the communication unit.

Claims

1. A wearable device for caring for a user's psychological state based on the user's biometric information, An outer shell comprising a front portion positioned on the chest area of ​​the user and a back portion positioned on the back area of ​​the user, wherein the outer shell includes a first side facing the body of the user and a second side facing away from the body; An air tube positioned between the first surface and the second surface, and an air inlet provided at one end of the air tube so that air is injected into or extracted from the air tube; A driving unit that injects or extracts air into the air tube through the air inlet; A silicone tube extending from the driving unit and connected to the air inlet, wherein one end of the silicone tube forms an opening and is connected to the air inlet, and the other end of the silicone tube is formed in a sealed structure; A sensor module having a piezoelectric element for detecting pressure, wherein the piezoelectric element is located at a specified distance from the other end of the silicone tube formed into the sealed structure and is located inside the silicone tube; a processor positioned inside the silicon tube and operatively connected to the driving unit and the sensor module; The above processor: Obtaining air pressure data representing the air pressure inside the air tube through the piezoelectric element of the sensor module, Based on the above air pressure data, determine the user's status, A wearable device that controls the driving unit to inject air into the air tube based on the determined user's condition.

2. In claim 1, A wearable device wherein the user's condition includes at least one of the user's psychological condition, stress level, or physical symptoms.

3. In claim 1, Further comprising a data processing unit for processing data obtained through the piezoelectric element; The above processor: Through the above data processing unit, a first Fourier transform is applied to the air pressure data, Through the above data processing unit, a second Fourier transform is applied to the air pressure data, A wearable device that obtains the user's ballistic heart data and respiration data from the air pressure data based on the first Fourier transform and the second Fourier transform.

4. In claim 3, The above first Fourier transform is a transform that separates the first frequency corresponding to the heart rhythm from the air pressure data, A wearable device, wherein the second Fourier transform is a transform that separates a second frequency corresponding to a breathing rhythm from the air pressure data.

5. In claim 4, The above processor: Through the above data processing unit, a first wavelet transform is performed on the air pressure data to which the first Fourier transform has been applied using a first wavelet function modeled according to the heart rhythm, Through the above data processing unit, a second wavelet transform is performed on the air pressure data to which the second Fourier transform has been applied using a second wavelet function modeled according to the breathing rhythm, A wearable device that obtains the user's ballistic cardiogram data and the respiration data from the air pressure data based on the first wavelet transform and the second wavelet transform.

6. In claim 5, Further comprising an artificial intelligence model unit that drives a first artificial intelligence model learned to extract filtered data from the ballistic cardiogram data and the respiratory data that have undergone the first wavelet transformation and the second wavelet transformation using a template matching technique; The above processor: By inputting the ballistic cardiogram data and the respiratory data into the first artificial intelligence model of the artificial intelligence model section, filtered ballistic cardiogram data and filtered respiratory data are obtained, A wearable device in which the first artificial intelligence model is trained to output the filtered ballistic cardiogram data and the filtered respiration data when the ballistic cardiogram data and the respiration data are input by using a normal ballistic cardiogram data set and a normal respiration data set as learning data.

7. In claim 6, The above artificial intelligence model unit further includes a second artificial intelligence model learned to determine the user's condition using the filtered cardiogram data and the filtered respiratory data, The above processor: By inputting the filtered cardiogram data and the filtered respiratory data into the second artificial intelligence model, the user's condition is determined, Based on the user's condition, the driving unit is controlled to inject air into the air tube, The types of physical symptoms included in the above user's condition include at least one of stress-related symptoms, breathing-related symptoms, pain-related symptoms, depression-related symptoms, or heart disease-related symptoms. A wearable device in which the second artificial intelligence model is trained to output the user's status when the user's biometric data is input by utilizing biometric data according to the user's status.

8. In claim 6, Including more motion sensors, The above processor: Obtaining the user's movement data through the above motion sensor, A wearable device that removes noise included in the filtered ballistic heart data and the filtered respiratory data based on the movement data.

9. In claim 1, Including more motion sensors, The above processor: Obtaining the user's movement data through the above motion sensor, A wearable device that removes noise included in the air pressure data based on the movement data.

10. In claim 1, The first side and the second side are formed of clothing fabric, A first urethane film is positioned between the first surface and the second surface, and a second urethane film is positioned between the first urethane film and the second surface. The first surface and the first urethane film are bonded through binding, The above second surface and the above second urethane film are bonded through binding, A wearable device wherein the first urethane film and the second urethane film are bonded through high-frequency fusion.

11. In claim 10, A wearable device, wherein the clothing fabric comprises at least one of cotton, polyester, rayon, nylon, wool, cashmere, silk, linen, or spandex.

12. In claim 1, battery; and Including the Department of Communications; The above processor: Activate the communication unit only when the battery is charging, A wearable device that transmits the air pressure data to an external server through the communication unit.

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