Biofeedback-based bruxism relief system and method

Abstract

A biofeedback-based bruxism relief system and method are disclosed. a user recognition unit that receives a bruxism diagnosis request signal generated from at least one of a pre-designated user terminal and a wearable device attached to the user's skin, extracts a unique number assigned to the wearable device included in the bruxism diagnosis request signal, and retrieves user information corresponding to the unique number from a pre-designated database; a data collection unit that receives electromyogram data collected from at least one sensor provided in the wearable device; a bruxism diagnosis unit that diagnoses the occurrence of bruxism by determining whether the electromyogram data collected from the data collection unit satisfies a pre-designated condition; a biofeedback generation unit that applies a vibration stimulus to the user's skin via a vibration motor provided in the wearable device if the bruxism diagnosis unit determines that bruxism has occurred; and a user monitoring unit that monitors the user's bruxism symptoms based on the bruxism diagnosis data derived by the bruxism diagnosis unit and the vibration stimulus data generated by the biofeedback generation unit.

Description

[Technical Field]

[0001] The present invention relates to a bruxism diagnosis and alleviation system, and more particularly to a biofeedback-based bruxism alleviation system and method that can diagnose bruxism symptoms by analyzing a user's temporal muscle signals during sleep and provide the user with biofeedback signals including vibration stimulation to alleviate bruxism symptoms. [Background technology]

[0002] Recently, various sleep diseases and disorders have been identified based on physiological and physical signals emitted from the subject's body during sleep, and based on this, diagnostic and treatment directions for sleep problems have been established, and specific treatment methods such as drug therapy, cognitive behavioral correction, and surgery have been proposed.

[0003] For reference, electroencephalograms are used as basic data to understand the progression and structure of sleep, measuring eye movements is useful for determining sleep stages, and electromyograms are essential because muscle tension changes depending on the sleep stage. In particular, leg electromyograms are measured to diagnose a sleep disorder called periodic limb movement disorder, and jaw electromyograms are necessary to identify teeth grinding.

[0004] In particular, teeth grinding can damage teeth through strong jaw movements in addition to chewing and softening movements. This involves grinding your teeth with 2 to 10 times more force than when chewing food, and occurs mainly during sleep, but can also occur during the day.

[0005] Teeth grinding is known to occur in about 10% of the Korean population, and is often caused by stress and occurs when the person is sensitive and in an unstable psychological state. Other known causes include malocclusion (when the teeth do not fit properly), alcohol use, genetics, taking certain medications, and central nervous system disorders.

[0006] Grinding your teeth puts strong pressure on certain parts of your teeth, which can damage the periodontal tissue and cause wear, which can wear down the surface of the teeth and damage the tissue around the teeth, making your teeth sensitive when eating cold foods and, in severe cases, causing your teeth to become loose.

[0007] Teeth grinding can sometimes be treated professionally at a hospital, with psychological therapy, the wearing of bite stabilizers between the upper and lower teeth, and medication.

[0008] In recent years, there has been a demand for devices that can easily diagnose and prevent teeth grinding at home while sleeping. One such method for diagnosing teeth grinding is described in Korean Patent Publication No. 10-1621500, which discloses a teeth grinding and teeth clenching analysis device and method that is worn on the subject's head and detects and outputs tilt signals associated with changes in sleeping posture and electromyogram signals generated by the subject's teeth grinding and teeth clenching, and outputs a preset sleep posture correction signal when it receives a treatment signal from an analysis terminal. However, there are problems with this device, such as limitations in accurately diagnosing the symptoms of teeth grinding and teeth clenching, and in that it does not have a significant awakening effect during sleep, making it ineffective in treating teeth grinding and teeth clenching.

[0009] Therefore, in order to solve the above problems, research into a biofeedback-based bruxism alleviation system and method is required. Summary of the Invention [Problem to be solved by the invention]

[0010] The present invention aims to provide a biofeedback-based teeth grinding alleviation system and method that can more effectively detect whether a user's teeth grinding events have occurred while sleeping by collecting electromyogram data of the muscles around the user's face and head via a wearable device attached to the user's skin and determining whether the user is sleeping and whether a teeth grinding event has occurred based on the collected electromyogram data.

[0011] Another object of the present invention is to provide a biofeedback-based Bruxism relief system and method that can more effectively prevent and alleviate bruxism symptoms by receiving input of the minimum intensity of vibration stimulation provided via a wearable device through a user terminal and gradually increasing the intensity of the stimulation from the minimum vibration stimulation input through the user terminal to provide an optimized biofeedback stimulation signal to the user.

[0012] Another object of the present invention is to provide a biofeedback-based teeth grinding alleviation system and method that can collect and store information on a user's sleep state and teeth grinding events, collect data for each user, build teeth grinding learning data based on the collected data, and quickly calculate user-customized biofeedback data.

[0013] The problems that the present invention aims to solve are not limited to the problems mentioned above, and other problems that the present invention aims to solve that are not described here will be clearly understood by those having ordinary skill in the art to which the present invention pertains from the following description. [Means for solving the problem]

[0014] A biofeedback-based teeth grinding alleviation system according to one embodiment of the present invention includes a user recognition unit that receives a teeth grinding diagnosis request signal generated from at least one of a pre-designated user terminal and a wearable device attached to the user's skin, extracts a unique number assigned to the wearable device included in the teeth grinding diagnosis request signal, and retrieves user information corresponding to the unique number from a pre-designated database (DB); a data collection unit that receives electromyogram data collected from at least one sensor provided in the wearable device, and a teeth grinding diagnosis unit that diagnoses the occurrence of teeth grinding by determining whether the electromyogram data collected from the data collection unit meets pre-set conditions; a biofeedback generation unit that applies vibration stimulation to the user's skin via a vibration motor provided in the wearable device if the teeth grinding diagnosis unit determines that teeth grinding has occurred; and a user monitoring unit that monitors the user's teeth grinding symptoms based on the teeth grinding diagnosis data derived from the teeth grinding diagnosis unit and the vibration stimulation data generated by the biofeedback generation unit.

[0015] The data collection unit includes a first sensor that collects first electromyogram data for a predetermined time period and a second sensor that is spaced a predetermined interval from the first sensor and collects second electromyogram data. The bruxism diagnosis unit includes a sleep state determination unit that compares the first electromyogram data collected through the first sensor with a predetermined first reference voltage V_th1 to determine the user's sleep state, and a bruxism occurrence detection unit that, when the sleep state determination unit determines that the user is asleep, compares the second electromyogram data collected through the second sensor with a predetermined second reference voltage V_th2 to determine whether a bruxism event has occurred.

[0016] The bruxism occurrence detection unit generates a window mask for a predetermined time range and superimposes the window mask on the second electromyogram data to determine whether a bruxism event has occurred. The determination of whether a bruxism event has occurred is based on the time during which the second electromyogram data exceeds V_th2 within the window mask. The biofeedback generation unit generates a first pattern of vibration stimulation at T_winend, which is the time when the window mask ends, when the bruxism occurrence detection unit determines that a bruxism event has occurred, and provides the first pattern of vibration stimulation to the user via the wearable device. The biofeedback generation unit also includes a user stimulus provision unit that provides the first pattern of vibration stimulation to the user via the wearable device, and a user stimulus correction unit that corrects the second electromyogram data collected via the data collection unit after the first pattern of vibration stimulation is generated and the first pattern of vibration stimulation collected via the data collection unit before the first pattern of vibration stimulation is generated. The first pattern of vibration stimulation is characterized in that an initial value of the vibration stimulation is determined based on a minimum stimulation intensity set from the user terminal, and the intensity of the vibration stimulation is corrected by one level via the user stimulus correction unit.

[0017] In addition, the bruxism diagnosis unit determines whether to use the electromyogram data collected from the first sensor and the second sensor based on the bruxism diagnosis mode input from the user terminal. The bruxism diagnosis mode includes a first diagnosis mode that determines the user's sleep state through the sleep state determination unit, and if it is determined that the user is asleep, determines whether a bruxism event of the user has occurred through the bruxism occurrence detection unit, and a second diagnosis mode that counts a preset sleep waiting time, and if the sleep waiting time is exceeded, automatically determines that the user is asleep and determines whether a bruxism event of the user has occurred through the bruxism occurrence detection unit.

[0018] The user monitoring unit also includes a user data generation unit that measures the user's daily sleeping time and generates daily user data including at least one of the number of times teeth grinding occurred during the daily sleeping time, the duration of teeth grinding, the number of times vibration stimulation was generated, and the maximum intensity of vibration stimulation; a bruxism diagnosis learning unit that learns the number of times teeth grinding occurred during the daily sleeping time, the duration of teeth grinding, and the maximum intensity of vibration stimulation included in the daily user data through a preset artificial intelligence learning algorithm and optimizes the bruxism diagnosis criteria of the bruxism diagnosis unit based on the result data derived through the artificial intelligence learning algorithm; and a user data transmission unit that transmits the daily user data generated by the user data generation unit to the user terminal based on a request signal received from the user terminal. [Effects of the Invention]

[0019] According to the present invention, electromyogram data of the muscles around the user's face and head is collected via a wearable device attached to the user's skin, and the collected electromyogram data is used to determine whether the user is asleep and whether a teeth grinding event has occurred, thereby more effectively detecting whether a user has a teeth grinding event while sleeping.

[0020] In addition, by receiving input of the minimum intensity of vibration stimulation provided via the wearable device through the user terminal and gradually increasing the intensity of the stimulation from the minimum vibration stimulation input through the user terminal, an optimized biofeedback stimulation signal can be provided to the user, thereby more effectively preventing and alleviating bruxism symptoms.

[0021] In addition, by collecting and storing information on the user's sleep state and teeth grinding events, data for each user can be collected, and based on this, teeth grinding learning data can be constructed, enabling the user's customized biofeedback data to be quickly calculated. [Brief explanation of the drawings]

[0022] [Figure 1]FIG. 1 is a block diagram of a biofeedback-based bruxism reduction system according to one embodiment of the present invention. [Figure 2] FIG. 1 is a block diagram of a biofeedback-based bruxism reduction system according to one embodiment of the present invention. [Figure 3] FIG. 2 is a diagram illustrating a data collection section of a biofeedback-based bruxism reduction system according to an embodiment of the present invention. [Figure 4] 1 is a diagram illustrating a bruxism diagnosis unit of a biofeedback-based bruxism alleviation system according to an embodiment of the present invention; FIG. [Figure 5] 1 is a diagram illustrating a biofeedback generating unit of a biofeedback-based bruxism alleviation system according to an embodiment of the present invention; FIG. [Figure 6] 1 is a diagram illustrating a biofeedback generating unit of a biofeedback-based bruxism alleviation system according to an embodiment of the present invention; FIG. [Figure 7] 1 is a diagram illustrating a biofeedback generating unit of a biofeedback-based bruxism alleviation system according to an embodiment of the present invention; FIG. [Figure 8] 1 is a diagram illustrating a biofeedback generating unit of a biofeedback-based bruxism alleviation system according to an embodiment of the present invention; FIG. [Figure 9] FIG. 1 is a diagram illustrating a user monitoring portion of a biofeedback-based bruxism reduction system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0023] The specific details of the present invention, including the problems to be solved, means for solving the problems, and effects of the invention, are included in the following examples and drawings. The advantages and features of the present invention, as well as methods for achieving them, will become apparent by referring to the following detailed description of the embodiments together with the accompanying drawings.

[0024] The scope of the present invention is not limited to the examples described below, and can be modified and implemented in various ways by those skilled in the art without departing from the technical gist of the present invention.

[0025] The present invention will be described in detail below with reference to the accompanying FIG.

[0026] Figure 1 is a block diagram of a biofeedback-based teeth grinding relief system according to one embodiment of the present invention. Figures 1 and 2 are block diagrams of a biofeedback-based teeth grinding relief system according to one embodiment of the present invention. Figure 3 is a diagram for explaining a data collection unit of a biofeedback-based teeth grinding relief system according to one embodiment of the present invention. Figure 4 is a diagram for explaining a teeth grinding diagnosis unit of a biofeedback-based teeth grinding relief system according to one embodiment of the present invention. Figures 5 to 8 are diagrams for explaining a biofeedback generation unit of a biofeedback-based teeth grinding relief system according to one embodiment of the present invention. Figure 9 is a diagram for explaining a user monitoring unit of a biofeedback-based teeth grinding relief system according to one embodiment of the present invention. [Example]

[0027] 1 and 2, a biofeedback-based teeth grinding alleviation system 100 according to an embodiment of the present invention collects electromyogram data required for diagnosing teeth grinding via a wearable device 10 attached to the user's skin or one side of the user's head, and the electromyogram data collected via the wearable device 10 is transmitted to the biofeedback-based teeth grinding alleviation system 100, which analyzes the electromyogram data to diagnose the user's teeth grinding symptoms and generates a biofeedback signal capable of alleviating the teeth grinding symptoms in accordance with the diagnosis result. In this case, the biofeedback signal generated by the biofeedback-based teeth grinding alleviation system 100 can control a vibration motor provided in the wearable device 10 to provide vibration stimulation to the user.

[0028] This allows biofeedback such as vibration stimulation to be provided to the user, thereby regulating the function of the user's autonomic nervous system and encouraging the user to actively alleviate their bruxism symptoms.

[0029] Furthermore, data relating to the user's bruxism symptoms generated through the biofeedback-based bruxism reduction system 100 can be transmitted to a user terminal 20 linked to the wearable device 10. Furthermore, the data transmitted to the user terminal 20 can be stored in a pre-designated DB 30 based on a request signal received from the user terminal 20.

[0030] More specifically, the biofeedback-based bruxism relief system 100 may include a user recognition component 110 , a data collection component 120 , a bruxism diagnosis component 130 , a biofeedback generation component 140 , and a user monitoring component 150 .

[0031] The user recognition unit 110 receives a bruxism diagnosis request signal generated from at least one of a pre-designated user terminal 20 and a wearable device 10 attached to the user's skin, extracts a unique number assigned to the wearable device 10 included in the bruxism diagnosis request signal, and retrieves user information corresponding to the unique number from a pre-designated DB 30.

[0032] Furthermore, the data collection unit 120 receives electromyogram data collected from at least one sensor provided in the wearable device 10, and the bruxism diagnosis unit 130 diagnoses whether or not bruxism is occurring by determining whether or not the electromyogram data collected from the data collection unit 120 satisfies a preset condition. If the bruxism diagnosis unit 130 determines that bruxism is occurring, the biofeedback generation unit 140 applies a vibration stimulus to the user's skin via a vibration motor provided in the wearable device 10. The user monitoring unit 150 can monitor the bruxism symptoms of the user based on the bruxism diagnosis data derived from the bruxism diagnosis unit 130 and the vibration stimulus data generated by the biofeedback generation unit 140.

[0033] More specifically, as shown in FIG. 3, the data collecting unit 120 may include a first sensor 121 that collects first electromyogram data for a predetermined time and a second sensor 122 that is spaced apart from the first sensor 121 by a predetermined distance and collects second electromyogram data.

[0034] As an example, when the wearable device 10 is provided in a form that is attached to one side of the user's head, it may be attached near the user's temple.

[0035] When the wearable device 10 is attached to one side of the user's temple, electromyogram data of the user's face can be collected through the first sensor 121 and the second sensor 122 provided on the wearable device 10.

[0036] For example, the first sensor 121 may collect the user's eye edge electromyogram signal, and the second sensor 122 may collect the user's temporal electromyogram signal.

[0037] Meanwhile, referring to FIG. 4, the bruxism diagnosis unit 130 may include a sleep state determination unit 131 that determines the sleep state of the user by comparing the first electromyogram data collected through the first sensor 121 with a preset first reference voltage V_th1, and a bruxism occurrence detection unit 132 that, when the sleep state determination unit 131 determines that the user is asleep, compares the second electromyogram data collected through the second sensor 122 with a preset second reference voltage V_th2 to determine whether a bruxism event has occurred.

[0038] At this time, the bruxism diagnosis unit 130 determines whether to use the data collected from the first sensor 121 and the second sensor 122 based on a bruxism diagnosis mode input to the user terminal 20. The bruxism diagnosis mode may include a first diagnosis mode in which the sleep state of the user is determined through the sleep state determination unit 131, and if it is determined that the user is asleep, whether a bruxism event of the user has occurred through the bruxism occurrence detection unit 132, and a second diagnosis mode in which a preset sleep waiting time is counted, and if the sleep waiting time is exceeded, it is automatically determined that the user is asleep and whether a bruxism event of the user has occurred through the bruxism occurrence detection unit 132.

[0039] More specifically, when the bruxism diagnosis mode is set to the first diagnosis mode, electromyogram data is collected using both the first sensor 121 and the second sensor 122, but while the first electromyogram data is collected through the first sensor 121, the second sensor 122 can be switched to a power-saving mode.

[0040] In addition, the sleep state determination unit 131 of the bruxism diagnosis unit 130 may compare the first electromyogram data collected through the first sensor 121 with the V_th1 to analyze the eye edge movement of the user.

[0041] As an example, the sleep state determination unit 131 generates eye edge movement data corresponding to the section where the first electromyogram data exceeds V_th1, and compares the eye edge movement data with the eye edge movement learning data during sleep stored in the DB30 to determine the user's sleep state.

[0042] If the sleep state determination unit 131 determines that the user is asleep, the first sensor 121 may be switched to a power-saving mode, and data collection via the first sensor 121 may be stopped.

[0043] In addition, when the bruxism diagnosis mode is set to the second diagnosis mode, the first sensor 121 switches to a power saving mode, and the bruxism diagnosis unit 130 can determine whether the user is experiencing bruxism symptoms using only the second electromyogram data collected through the second sensor 121.

[0044] That is, in the first diagnostic mode, the user's sleeping time can be accurately monitored, and in the process of determining the sleeping state through the sleep state determination unit 131, the teeth grinding occurrence detection unit 132 is switched to a power-saving mode, and when the determination of the user's sleeping state is completed, the teeth grinding occurrence detection unit 132 switches the teeth grinding sleep state determination unit 131 to a power-saving mode, thereby improving the battery power efficiency of the wearable device 10, and in the second diagnostic mode, the data processing process of the sleep state determination unit 131 is omitted, thereby improving the speed of the data calculation process.

[0045] Meanwhile, the teeth grinding occurrence detection unit 132 collects second electromyogram data relating to the movement of the user's temporalis muscle via the second sensor 122, and can determine whether or not a teeth grinding event has occurred by comparing the second electromyogram data with V_th2.

[0046] In more detail, as shown in FIG. 5, the bruxism occurrence detector 132 generates a window mask 510 for a preset time range and superimposes the window mask 510 on the second electromyogram data to determine whether the bruxism event has occurred.

[0047] Based on the time during which the second electromyogram data exceeds V_th2 within the window mask 510, it is possible to determine whether the bruxism event has occurred.

[0048] For example, the time range of the window mask 510 may be initially set to a system setting of 2 seconds, and may be optimized through artificial intelligence learning of the user monitor.

[0049] As shown in FIG. 6, the biofeedback generation unit 140 may include a user stimulus providing unit 141 that generates one pattern of vibration stimulus at T_winend, which is the time when the window mask 510 ends, and provides the first pattern of vibration stimulus to the user through the wearable device 10 when the bruxism occurrence detection unit 132 determines that the bruxism event has occurred, and a user stimulus correcting unit 142 that corrects the first pattern of vibration stimulus when the bruxism event is detected through the bruxism diagnosis unit 130 within a predetermined time range after the first pattern of vibration stimulus ends.

[0050] For example, if the vibration stimulation of the first pattern is ended and the teeth grinding diagnosis unit 130 determines that a teeth grinding event has occurred within 5 seconds, the user stimulation correction unit 142 can modify the first pattern so that a stimulation of higher intensity than the intensity of the vibration stimulation of the first pattern is provided to the user.

[0051] In this case, the initial value of the vibration stimulus for the first pattern is determined based on the minimum stimulus intensity set from the user terminal 20, and the intensity of the vibration stimulus can be corrected by one level via the user stimulus correction unit 142.

[0052] As an example, if the intensity of the vibration stimulus is divided into levels 1 to 10 and the initial value of the vibration stimulus is input at level 3 via the user terminal 20, the initial value of the first pattern can be set to level 3 at the same time as the user starts (or restarts) the wearable device 10.

[0053] As shown in FIG. 7, the teeth grinding diagnosis unit 130 determines that the teeth grinding event has occurred and provides the user with a first pattern of vibration stimulation 521 set to an initial value (level 3) via the biofeedback generation unit 140. If the first pattern of vibration stimulation 521 is terminated and the teeth grinding event occurs again within 5 seconds, the user's teeth grinding symptoms cannot be alleviated with the level 3 vibration stimulation, so the user stimulation correction unit 142 can correct the first pattern of vibration stimulation to level 4.

[0054] As a result, for the bruxism event that occurs within 5 seconds after the first pattern vibration stimulus 521 has ended, the first pattern vibration stimulus 522 corrected to the four levels can be provided to the user via the wearable device 10.

[0055] Similarly, if the bruxism event occurs again within 5 seconds after the first pattern vibration stimulation 522 ends, the 4-level vibration stimulation cannot alleviate the user's bruxism symptoms, so the user stimulation correction unit 142 can correct the first pattern vibration stimulation to 5-level.

[0056] At this time, the vibration stimulation of the first pattern can be corrected up to level 10, which is the maximum level of the vibration stimulation. If a bruxism event occurs again within 5 seconds after the vibration stimulation of the first pattern has been corrected to level 10, the user stimulation correction unit 142 can interrupt the correction of the vibration stimulation of the first pattern and send a wake-up notification signal to the user terminal 20 to wake up the user.

[0057] Furthermore, depending on the settings of the user terminal, a wake-up notification signal can be sent to the user terminal after the first pattern has been corrected up to level 10, or after two corrections, the correction of the vibration stimulation of the first pattern can be interrupted and a wake-up notification signal can be immediately sent to the user terminal 20 to induce the user to wake up.

[0058] On the other hand, as shown in FIG. 8, if the bruxism event does not occur within the preset time (5 seconds) after the first pattern of vibration stimulation is applied to the user, the user stimulation correction unit 142 does not correct the first pattern of vibration stimulation, and the user stimulation generation unit 141 can apply the first pattern of vibration stimulation set to the initial value to the user via the wearable device 10 for any bruxism event that occurs after the preset time (5 seconds).

[0059] As another example, the user stimulus correction unit 142 may include a fixed pattern correction mode in which the vibration stimulus intensity of the first pattern is fixedly increased by one level at a time to provide the same magnitude of stimulus to the user for a predetermined time, and a variable pattern correction mode in which the vibration stimulus intensity of the first pattern is corrected so as to be variable within the predetermined time.

[0060] For example, if the minimum stimulation intensity is input as level 3 and the maximum stimulation intensity is input as level 6 from the user terminal 20, the fixed pattern correction mode can be applied so that the vibration stimulation of the first pattern is varied to any value within level 3 to level 6 for the preset unit time (e.g., 0.5 seconds) for the preset time and provided to the user.

[0061] Therefore, by providing the user with vibration stimuli that vary from weak to strong rather than fixed size vibration stimuli, more effective regulation of the autonomic nervous system can be induced.

[0062] Meanwhile, the bruxism diagnosis unit 130 can determine whether or not bruxism is occurring from the second electromyogram data collected through the data collection unit 120 even while the vibration stimulus generated through the biofeedback generation unit 140 is being applied to the user.

[0063] Therefore, all of the electromyographic data related to the user's bruxism can be collected without missing data, providing more accurate bruxism diagnosis and customized biofeedback.

[0064] Meanwhile, referring to FIG. 9, the user monitoring unit 150 may include a user data generating unit 151 that measures the user's daily sleeping time and generates daily user data including at least one of the number of times teeth grinding occurred during the daily sleeping time, the degree of teeth grinding, the duration of tooth brushing, the number of times vibration stimulation occurred, and the maximum intensity of vibration stimulation; a bruxism diagnosis learning unit 152 that learns the number of times teeth grinding occurred during the daily sleeping time, the duration of the teeth grinding, and the maximum intensity of vibration stimulation included in the daily user data through a preset artificial intelligence learning algorithm and optimizes the bruxism diagnosis criteria of the bruxism diagnosis unit based on the result data derived through the artificial intelligence learning algorithm; and a user data transmitting unit 153 that transmits the daily user data generated by the user data generating unit 151 to the user terminal 20 based on a request signal received from the user terminal 20.

[0065] As an example, the bruxism diagnosis learning unit 152 can optimize the time range of the window mask using any one of the mean, median, and mode of the bruxism duration included in the daily user data.

[0066] Furthermore, the degree of bruxism of the user can be calculated based on the second electromyogram data, and the magnitude of V_th2 can be corrected based on the degree of bruxism.

[0067] Therefore, if the degree of teeth grinding is classified into "strong," "average," and "weak," and the degree of teeth grinding of the user is calculated to be "weak," the magnitude of V_th2 can be reduced to enable an optimized teeth grinding diagnosis for the user and alleviation of teeth grinding symptoms through biofeedback.

[0068] As another example, the teeth grinding diagnosis learning unit 152 verifies the teeth grinding diagnosis of the teeth grinding diagnosis unit 140, and uses a correct classification dataset that ``judges teeth grinding as teeth grinding'' and ``judges non-teeth grinding as not teeth grinding'' and a misclassification dataset that ``judges teeth grinding as not teeth grinding'' and ``judges non-teeth grinding as teeth grinding'' to verify the teeth grinding diagnosis of the teeth grinding diagnosis unit 140 and improve the accuracy of the teeth grinding diagnosis of the teeth grinding diagnosis unit 140.

[0069] According to the present invention, electromyogram data of the muscles around the user's face and head is collected through a wearable device attached to the user's skin, and the presence or absence of a bruxism event is determined based on the collected electromyogram data. This provides a biofeedback-based bruxism alleviation system and method that can more effectively detect the presence or absence of a bruxism event occurring in a user's sleep.

[0070] In addition, a biofeedback-based teeth grinding alleviation system and method can be provided that receives input of the minimum intensity of vibration stimulation provided via a wearable device through a user terminal and gradually increases the intensity of the stimulation from the minimum vibration stimulation input through the user terminal, thereby providing an optimized biofeedback stimulation signal to the user and more effectively preventing and alleviating teeth grinding symptoms.

[0071] In addition, by collecting and storing information on the user's sleep state and teeth grinding events, it is possible to provide a biofeedback-based teeth grinding alleviation system and method that can collect data for each user, build teeth grinding learning data based on this, and quickly calculate user-customized biofeedback data.

[0072] Furthermore, a method for controlling a biofeedback-based bruxism relief system according to an embodiment of the present invention may be recorded on a computer-readable medium containing program instructions for performing various computer-implemented operations. The computer-readable medium may include, alone or in combination, program instructions, data files, data structures, and the like. The program instructions may be specially designed and constructed for the present invention, or may be known and available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code, such as produced by a compiler, but also high-level language code that can be executed by a computer using an interpreter, for example.

[0073] As described above, although one embodiment of the present invention has been described using limited embodiments and drawings, the present invention is not limited to the above-described embodiments, and various modifications and variations can be made from such descriptions by those skilled in the art to which the present invention pertains. Therefore, one embodiment of the present invention should be understood only by the scope of the claims set forth below, and all equivalent or similar modifications are within the scope of the present invention. [Explanation of symbols]

[0074] 10: Wearable devices 20: User terminal 30:DB 110: User recognition section 120: Data collection section 121: First sensor 122: Second sensor 130: Teeth Grinding Diagnostic Department 131: Sleep state determination unit 132: Bruxism detection unit 140: Biofeedback generation unit 141: User stimulus generation unit 142: User stimulus correction unit 150: User monitoring unit 151: User data generation unit 152: Teeth Grinding Learning and Diagnosis Department 153: User data transfer unit

Claims

1. a user recognition unit that receives a bruxism diagnosis request signal generated from at least one of a pre-designated user terminal and a wearable device attached to the user's skin, extracts a unique number assigned to the wearable device included in the bruxism diagnosis request signal, and retrieves user information corresponding to the unique number from a pre-designated DB; a data collection unit that receives electromyogram data collected from at least one sensor provided in the wearable device; a bruxism diagnosis unit that determines whether or not the electromyogram data collected from the data collection unit satisfies a preset condition to diagnose whether or not bruxism occurs; a biofeedback generation unit that applies vibration stimuli to the user's skin via a vibration motor provided in the wearable device when the bruxism diagnosis unit determines that bruxism has occurred; and A biofeedback-based teeth grinding relief system including a user monitoring unit that monitors the user's teeth grinding symptoms based on the teeth grinding diagnosis data derived from the teeth grinding diagnosis unit and the vibration stimulation data generated by the biofeedback generation unit.

2. The data collection unit a first sensor for collecting first electromyogram data for a predetermined period of time; a second sensor spaced apart from the first sensor by a predetermined distance and configured to collect second electromyogram data; The bruxism diagnosis unit includes: a sleep state determination unit that determines a sleep state of the user by comparing first electromyogram data collected through the first sensor with a preset first reference voltage V_th1; 2. The biofeedback-based teeth grinding alleviation system of claim 1, further comprising a teeth grinding occurrence detection unit that, when the sleep state determination unit determines that the user is asleep, compares the second electromyogram data collected via the second sensor with a preset second reference voltage V_th2 to determine whether a teeth grinding event has occurred in the user.

3. The bruxism occurrence detection unit is generating a window mask for a preset time range, and superimposing the window mask on the second electromyogram data to determine whether the bruxism event has occurred; determining whether or not the bruxism event has occurred based on the time during which the second electromyogram data exceeds V_th2 within the window mask; The biofeedback generation unit a user stimulus providing unit that generates a first pattern of vibration stimulus at T_windend, which is a time when the window mask ends, when the bruxism occurrence detecting unit determines that the bruxism event has occurred, and provides the first pattern of vibration stimulus to the user through the wearable device; a user stimulus correction unit that corrects the vibration stimulus of the first pattern when the bruxism event is detected through the bruxism diagnosis unit within a preset time range after the vibration stimulus of the first pattern is terminated, The first pattern is an initial value of the vibration stimulus is determined based on a minimum stimulus intensity set from the user terminal; The biofeedback-based bruxism alleviation system according to claim 2 , wherein the intensity of the vibration stimulus is corrected by one level via the user stimulus correction unit.

4. The bruxism diagnosis unit includes: Whether or not to use the electromyogram data collected from the first sensor and the second sensor is determined based on a bruxism diagnosis mode input from the user terminal. The bruxism diagnosis mode includes: a first diagnosis mode in which the sleep state of the user is determined through the sleep state determination unit, and if it is determined that the user is asleep, whether or not a bruxism event of the user has occurred is determined through the teeth grinding occurrence detection unit; and a second diagnostic mode that counts a preset sleep waiting time, automatically determines that the user is asleep if the sleep waiting time is exceeded, and determines whether a bruxism event has occurred in the user via the bruxism occurrence detector.

5. The user monitoring unit a user data generating unit that measures the user's daily sleeping time and generates daily user data including at least one of the number of times teeth grinding occurred during the daily sleeping time, the duration of teeth grinding, the degree of teeth grinding, the number of times vibration stimulation was generated, and the maximum intensity of vibration stimulation; a bruxism diagnosis learning unit that learns the number of times bruxism occurred during the daily sleep time, the duration of bruxism, and the maximum intensity of the vibration stimulation, which are included in the daily user data, through a set artificial intelligence learning algorithm, and optimizes the bruxism diagnosis criteria of the bruxism diagnosis unit based on the result data derived through the artificial intelligence learning algorithm; 2. The biofeedback-based bruxism alleviation system of claim 1, further comprising: a user data transmission unit that transmits the daily user data generated by the user data generation unit to the user terminal based on a request signal received from the user terminal.

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