Sleep state feedback system for providing feedback based on sleep patterns of glaucoma patient and sleep state feedback method using same

Abstract

A sleep state feedback system of the present disclosure may comprise: a server for storing glaucoma diagnosis information; a mobile device carried by a glaucoma patient; and a wearable device worn by the glaucoma patient during sleep. The wearable device may estimate sleep postures of a glaucoma patient by using a sensing unit and generate sleep pattern data on the basis of the sleep postures and sleep duration. The server may generate feedback data on the basis of the sleep pattern data and the glaucoma diagnosis information. The mobile device may provide, to the glaucoma patient, at least one of a wake-up alarm and sleep coaching on the basis of the feedback data.

Description

A sleep state feedback system that provides feedback based on the sleep pattern of a glaucoma patient and a sleep state feedback method using the same

[0001] The present disclosure relates to a sleep state feedback technology, and more particularly, to a sleep state feedback system that provides feedback to a glaucoma patient based on the patient's sleep pattern, and a sleep state feedback method using the same.

[0002] Glaucoma is a leading cause of irreversible blindness worldwide, progressively affecting the optic nerve. Glaucoma is one of the leading causes of blindness and is a chronic, incurable eye disease. The number of patients with glaucoma is rapidly increasing each year, a global trend. Therefore, accurate diagnosis of glaucoma is a crucial issue.

[0003] Intraocular pressure (IOP) is considered one of the most important indicators of the progression of glaucoma in patients. Existing methods for measuring IOP, such as the Goldmann applanation tonometer, rebound tonometer, and non-contact tonometer, have limitations in that they can only measure static IOP. Furthermore, errors can vary depending on the measurement environment and individual.

[0004] For example, existing methods for measuring intraocular pressure (IOP) have limitations in continuously monitoring the intraocular pressure of glaucoma patients. In particular, they cannot accurately monitor the intraocular pressure of sleeping glaucoma patients. Therefore, a system capable of monitoring the sleep state and managing intraocular pressure in sleeping glaucoma patients is needed.

[0005] One purpose of the present disclosure is to provide a sleep state feedback system and a sleep state feedback method using the same, which can improve the quality of sleep of glaucoma patients and help manage intraocular pressure by monitoring the sleeping posture and sleeping time of glaucoma patients and providing feedback on sleep patterns based on the monitoring.

[0006] Another object of the present disclosure is to provide a sleep state feedback system and a sleep state feedback method using the same, which provide personalized feedback tailored to the individual needs of a glaucoma patient by combining glaucoma diagnosis information with sleep state to provide a recommendation including at least one of a wake-up alarm and sleep coaching.

[0007] However, the problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

[0008] A sleep state feedback system according to the present disclosure for achieving the above-described technical problem may include a server storing glaucoma diagnosis information, a mobile device carried by a glaucoma patient, and a wearable device worn by the glaucoma patient while sleeping. The wearable device may estimate a sleeping posture of the glaucoma patient using a sensing unit and generate sleep pattern data based on the sleeping posture and sleeping time. The server may generate feedback data based on the sleep pattern data and the glaucoma diagnosis information. The mobile device may provide at least one of a wake-up alarm and sleep coaching to the glaucoma patient based on the feedback data.

[0009] In one embodiment, the glaucoma diagnosis information may include at least one of intraocular pressure data, optic nerve damage data, visual field examination data, corneal thickness data, and glaucoma type data.

[0010] In one embodiment, the wearable device may include a sensing unit including a first sensing unit, a second sensing unit, and a third sensing unit. Each of the first sensing unit, the second sensing unit, and the third sensing unit may include at least one pressure sensor and at least one position sensor.

[0011] In one embodiment, the second sensing unit may be arranged on the right side of the first sensing unit. The third sensing unit may be arranged on the left side of the first sensing unit. The second sensing unit and the third sensing unit may sense a change in pressure applied to the eye of the glaucoma patient and a change in the position of the eye of the glaucoma patient relative to the first sensing unit, within a predetermined distance from the eye of the glaucoma patient.

[0012] In one embodiment, the sensing unit may sense the pressure change applied to the right eye of the glaucoma patient based on a first relative pressure difference between a first pressure sensed by the first sensing unit and a second pressure sensed by the second sensing unit, and may sense the pressure change applied to the left eye of the glaucoma patient based on a second relative pressure difference between the first pressure and a third pressure sensed by the third sensing unit.

[0013] In one embodiment, the sensing unit can sense a change in the position of the eye of the glaucoma patient based on a first relative position difference between the first sensing unit and the second sensing unit, and a second relative position difference between the first sensing unit and the third sensing unit.

[0014] In one embodiment, the sleep pattern data may include at least one of real-time data, daily data, weekly data, and monthly data on the sleep status of the glaucoma patient generated based on the sleeping posture and the sleeping time.

[0015] In one embodiment, the server may determine whether the sleep state of the glaucoma patient is above a reference risk level based on the sleep pattern data and the glaucoma diagnosis information, and if the sleep state of the glaucoma patient is above the reference risk level, transmit the feedback data including a wake-up request signal to the mobile device.

[0016] In one embodiment, the server may analyze the sleep state of the glaucoma patient based on the sleep pattern data and the glaucoma diagnosis information, and transmit the feedback data including at least one of sleep posture coaching, sleep condition recommendation, and bedding change recommendation corresponding to the sleep state to the mobile device.

[0017] In addition, a method for providing feedback based on a sleep pattern of a glaucoma patient according to the present disclosure performed by a system may include an operation of estimating a sleeping posture of a glaucoma patient using a sensing unit of a wearable device of the system; an operation of generating sleep pattern data based on the sleeping posture and sleeping time by the wearable device; an operation of generating feedback data based on the sleep pattern data and glaucoma diagnosis information by a server of the system; and an operation of providing at least one of a wake-up alarm and sleep coaching to the glaucoma patient by a mobile device of the system based on the feedback data.

[0018] In one embodiment, the glaucoma diagnosis information may include at least one of intraocular pressure data, optic nerve damage data, visual field examination data, corneal thickness data, and glaucoma type data.

[0019] In one embodiment, the wearable device may include a sensing unit including a first sensing unit, a second sensing unit, and a third sensing unit. Each of the first sensing unit, the second sensing unit, and the third sensing unit may include at least one pressure sensor and at least one position sensor.

[0020] In one embodiment, the second sensing unit may be arranged on the right side of the first sensing unit. The third sensing unit may be arranged on the left side of the first sensing unit. The second sensing unit and the third sensing unit may sense a change in pressure applied to the eye of the glaucoma patient and a change in the position of the eye of the glaucoma patient relative to the first sensing unit, within a predetermined distance from the eye of the glaucoma patient.

[0021] In one embodiment, the operation of estimating a sleeping posture of a glaucoma patient using the sensing unit of the wearable device may sense the pressure change applied to the right eye of the glaucoma patient based on a first relative pressure difference between a first pressure sensed by the first sensing unit and a second pressure sensed by the second sensing unit, and sense the pressure change applied to the left eye of the glaucoma patient based on a second relative pressure difference between the first pressure and a third pressure sensed by the third sensing unit.

[0022] In one embodiment, the operation of estimating a sleeping posture of a glaucoma patient using the sensing unit of the wearable device may sense a change in the position of the eye of the glaucoma patient based on a first relative position difference between the first sensing unit and the second sensing unit, and a second relative position difference between the first sensing unit and the third sensing unit.

[0023] In one embodiment, the sleep pattern data may include at least one of real-time data, daily data, weekly data, and monthly data on the sleep status of the glaucoma patient generated based on the sleeping posture and the sleeping time.

[0024] In one embodiment, the operation of the server generating feedback data based on the sleep pattern data and the glaucoma diagnosis information may include determining whether the sleep state of the glaucoma patient is above a reference risk level based on the sleep pattern data and the glaucoma diagnosis information, and, if the sleep state of the glaucoma patient is above the reference risk level, transmitting the feedback data including a wake-up request signal to the mobile device.

[0025] In one embodiment, the operation of the server generating feedback data based on the sleep pattern data and the glaucoma diagnosis information may include analyzing the sleep state of the glaucoma patient based on the sleep pattern data and the glaucoma diagnosis information, and transmitting the feedback data including at least one of sleep posture coaching, sleep condition recommendation, and bedding change recommendation corresponding to the sleep state to the mobile device.

[0026] In addition, a computer program stored in a computer-readable recording medium for implementing the present disclosure may be further provided.

[0027] In addition, a computer-readable recording medium recording a computer program for implementing the present disclosure may be further provided.

[0028] According to the aforementioned problem solving means of the present disclosure, the sleep state feedback system of the present disclosure and the sleep state feedback method using the same can monitor the sleeping posture and sleeping time of a glaucoma patient and provide feedback on the sleep pattern based on the monitoring, thereby improving the sleep quality of the glaucoma patient and helping to manage intraocular pressure.

[0029] In addition, the sleep state feedback system of the present disclosure and the sleep state feedback method using the same can provide personalized feedback tailored to the individual needs of a glaucoma patient by combining glaucoma diagnosis information with sleep state to provide a recommendation including at least one of a wake-up alarm and sleep coaching.

[0030] The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

[0031] Fig. 1 is a diagram showing the configuration of the sleep state feedback system of the present disclosure.

[0032] Figure 2 is a block diagram showing the configuration of a mobile device of the present disclosure.

[0033] Figure 3 is a drawing showing the configuration of a wearable device of the present disclosure.

[0034] Figure 4 is a drawing showing the configuration of a wearable device when a glaucoma patient wears the wearable device.

[0035] FIG. 5 is a diagram showing the types of sleeping postures estimated by the wearable device of the present disclosure.

[0036] FIG. 6 is a diagram illustrating a mobile device of the present disclosure providing a weather alarm based on feedback data.

[0037] FIG. 7 is a diagram illustrating a mobile device of the present disclosure providing sleep coaching based on feedback data.

[0038] Figure 8 is a flowchart showing the sleep state feedback method of the present disclosure.

[0039] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and any content that is common in the technical field to which this disclosure pertains or that overlaps between embodiments is omitted. The terms "part, module, element, block" used in the specification may be implemented in software or hardware, and depending on the embodiments, multiple "parts, modules, elements, blocks" may be implemented as a single component, or a single "part, module, element, block" may include multiple components.

[0040] Throughout the specification, when a part is said to be "connected" to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection via a wireless communication network.

[0041] Additionally, when a part is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise specifically stated.

[0042] Throughout the specification, when we say that an element is "on" another element, this includes not only cases where the element is in contact with the other element, but also cases where another element exists between the two elements.

[0043] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0044] Singular expressions include plural expressions unless the context clearly indicates otherwise.

[0045] The identification codes for each step are used for convenience of explanation and do not describe the order of each step. Each step may be performed in a different order than specified unless the context clearly indicates a specific order.

[0046] The operating principle and embodiments of the present disclosure are described below with reference to the attached drawings.

[0047] As used herein, the term "device according to the present disclosure" encompasses a variety of devices capable of performing computational processing and providing results to a user. For example, the device according to the present disclosure may include a computer, a server device, and a portable terminal, or may be any one of them.

[0048] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0049] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0050] The above portable terminal may include, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as a PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminal, a smart phone, and a wearable device such as a watch, a ring, a bracelet, an anklet, a necklace, glasses, contact lenses, or a head-mounted device (HMD).

[0051] The artificial intelligence-related functions according to the present disclosure are operated via a processor and memory. The processor may be comprised of one or more processors. In this case, one or more processors may be a general-purpose processor such as a CPU, an AP, a DSP (Digital Signal Processor), a graphics-only processor such as a GPU or a VPU (Vision Processing Unit), or an artificial intelligence-only processor such as an NPU. One or more processors control the processing of input data according to predefined operating rules or artificial intelligence models stored in memory. Alternatively, if one or more processors are artificial intelligence-only processors, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0052] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is trained using a learning algorithm using a plurality of learning data, thereby creating a predefined operation rules or artificial intelligence model set to perform a desired characteristic (or purpose). This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and / or system. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0053] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values, and performs neural network operations through operations between the operation results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers may be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model is reduced or minimized during the learning process. The artificial neural network may include a deep neural network (DNN), and examples thereof include, but are not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a RecuREnt neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional RecuREnt deep neural network (BRDNN), or deep Q-networks.

[0054] According to an exemplary embodiment of the present disclosure, a processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that imitates human neurons (biological neurons) to enable machines to learn. Artificial intelligence methodologies can be categorized into supervised learning, in which input data and output data are provided together as training data depending on the learning method, so that the solution (output data) to the problem (input data) is determined; unsupervised learning, in which only input data is provided without output data, so that the solution (output data) to the problem (input data) is not determined; and reinforcement learning, in which a reward (Reward) is provided from an external environment whenever an action (Action) is taken in the current state (State), and learning is performed in a direction to maximize this reward. In addition, artificial intelligence methodologies can be categorized according to the architecture, which is the structure of the learning model. The architectures of widely used deep learning technologies can be categorized into convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs).

[0055] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0056] Figure 1 is a drawing showing the configuration of the sleep state feedback system (10) of the present disclosure.

[0057] Referring to FIG. 1, a sleep state feedback system (10) may include a server (100) that stores glaucoma diagnosis information, a mobile device (200) carried by a glaucoma patient, and a wearable device (300) worn by the glaucoma patient during sleep.

[0058] The server (100), the mobile device (200), and the wearable device (300) can communicate with each other through a network (400). For example, the network (400) refers to a connection structure that enables information exchange between each node, such as terminals and servers, and examples of such a network (400) include, but are not limited to, a 3GPP (3rd Generation Partnership Project) network, an LTE (Long Term Evolution) network, a 5G network, a WIMAX (World Interoperability for Microwave Access) network, the Internet, a LAN (Local Area Network), a Wireless LAN (Wireless Local Area Network), a WAN (Wide Area Network), a PAN (Personal Area Network), a Wi-Fi network, a Bluetooth network, a satellite broadcasting network, an analog broadcasting network, a DMB (Digital Multimedia Broadcasting) network, etc.

[0059] The sleep state feedback system (10) of the present disclosure can improve the sleep quality of glaucoma patients and help manage intraocular pressure by monitoring the sleeping posture and sleeping time of glaucoma patients and providing feedback on sleep patterns based on the monitoring.

[0060] In addition, the sleep state feedback system (10) of the present disclosure can provide personalized feedback tailored to the individual needs of a glaucoma patient by combining glaucoma diagnosis information with sleep state to provide recommendations including at least one of a wake-up alarm and sleep coaching.

[0061] The wearable device (300) can be worn on a part of the user's body (e.g., the wrist) to collect the user's biometric information. For example, the biometric information can include heart rate information, respiration information, blood oxygen concentration information, pulse information, blood pressure information, electrocardiogram information, etc. In addition, the wearable device (300) can include a plurality of sensors. The plurality of sensors can include an acceleration sensor, a G-sensor, a 3-axis acceleration sensor, a 6-axis motion sensor, an electromyography sensor, a temperature sensor, an optical sensor, etc. The wearable device (300) can obtain the user's movement information, electromyography signal, biometric signal, etc. The movement information can include a movement distance, angle, intensity, direction, acceleration, angular velocity, quaternion information, etc. For example, the biometric signal can include at least one of heart rate information, respiration information, blood oxygen concentration information, blood pressure information, pulse information, body temperature information, electrocardiogram information, blood flow image information, and body composition analysis information. For example, the wearable device (300) can estimate the sleeping posture of a glaucoma patient using the sensing unit. For example, the wearable device (300) can generate sleep pattern data based on the sleeping posture and sleeping time. For example, the wearable device (300) can transmit the sleep pattern data to the server (100) via the network (400).

[0062] The server (100) transmits and receives data, content, and various communication signals with a mobile device (200) and a wearable device (300) via a network (400), and may include any type of server (100), terminal, or device having the function of storing and processing data. The server (100) may store glaucoma diagnosis information. For example, the server (100) may generate feedback data based on the sleep pattern data and the glaucoma diagnosis information. For example, the server (100) may transmit the feedback data to the mobile device (200) via the network (400).

[0063] The mobile device (200) may be any type of wireless communication device, such as a smartphone, a smart pad, a tablet PC, a wearable device, a PCS (Personal Communication System), a GSM (Global System for Mobile communication), a PDC (Personal Digital Cellular), a PHS (Personal Handyphone System), a PDA (Personal Digital Assistant), an IMT (International Mobile Telecommunication)-2000, a CDMA (Code Division Multiple Access)-2000, a W-CDMA (W-Code Division Multiple Access), a Wibro (Wireless Broadband Internet) terminal, or a stationary terminal, such as a desktop computer or a smart TV. For example, the mobile device (200) may provide at least one of a wake-up alarm and sleep coaching to the glaucoma patient based on the feedback data.

[0064] FIG. 2 is a block diagram showing the configuration of a mobile device (200) of the present disclosure.

[0065] Referring to FIG. 2, the mobile device (200) may include a processor (210), a memory (220), a communication module (230), a display module (240), an audio output module (250), and an input module (260). In one embodiment, the mobile device (200) may omit at least one of these components (e.g., an audio output module (250)), or may have one or more other components added (e.g., a sensor module, a battery).

[0066] The processor (210) may control at least one other component (e.g., hardware or software component) of the mobile device (200) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (210) may store commands or data received from other components (e.g., communication module (230)) in the memory (220), process the commands or data stored in the memory (220), and store result data in the memory (220).

[0067] According to one embodiment, the processor (210) may include a main processor (e.g., a central processing unit or an application processor) or an auxiliary processor (e.g., a graphics processing unit, a neural network processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or in conjunction therewith.

[0068] The memory (220) can store various data used by at least one component (e.g., the processor (210) or the communication module (230)) of the mobile device (200). The data can include, for example, input data or output data for a program (e.g., an application) and commands related thereto. The memory (220) can include at least one instruction executable by the processor (210). The memory (220) can include volatile memory or non-volatile memory.

[0069] The communication module (230) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the mobile device (200) and another electronic device (e.g., wearable device (300), another wearable device (300), server (100)), and the performance of communication through the established communication channel. The communication module (230) may include a communication circuit for performing a communication function. The communication module (230) may operate independently from the processor (210) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (230) may include a wireless communication module (e.g., a Bluetooth communication module, a cellular communication module, a Wi-Fi communication module, or a GNSS communication module) or a wired communication module (e.g., a LAN communication module or a power line communication module) that performs wireless communication. The communication module (230) may transmit a control command to, for example, a wearable device (300), and receive at least one of sensor data including body movement information of a user wearing the wearable device (300), status data of the wearable device (300), or control result data corresponding to the control command from the wearable device (300).

[0070] The display module (240) can visually provide information to an external device (e.g., a user) of the mobile device (200). The display module (240) may include, for example, an LCD or OLED display, a holographic device, or a projector device. The display module (240) may further include a control circuit for controlling display operation. In one embodiment, the display module (240) may further include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0071] The audio output module (250) can output audio signals to the outside of the mobile device (200). The audio output module (250) can include a speaker that plays a guide audio signal (e.g., a start-up sound, an operation error notification sound), music content, or a guide voice based on the status of the wearable device (300). If it is determined that the wearable device (300) is not properly worn on the user's body, for example, the audio output module (250) can output a guide voice to notify the user of an abnormal wearing or to induce normal wearing. The audio output module (250) can also output an alarm voice to wake up a sleeping glaucoma patient, for example.

[0072] The input module (260) can receive commands or data to be used in a component of the mobile device (200) (e.g., a processor (210)) from an external source (e.g., a user) of the mobile device (200). The input module (260) can include an input component circuit and can receive user input. The input module (260) can include, for example, a key (e.g., a button) or a touch screen.

[0073] In one embodiment, the mobile device (200) may provide at least one of a wake-up alarm and sleep coaching to the glaucoma patient based on feedback data received from the server (100) via the network (400).

[0074] FIG. 3 is a drawing showing the configuration of a wearable device (300) of the present disclosure, and FIG. 4 is a drawing showing the configuration of a wearable device (300) when a glaucoma patient wears the wearable device (300).

[0075] Referring to FIGS. 3 and 4, the wearable device (300) of the present disclosure can estimate a sleeping posture of a glaucoma patient using a sensing unit. The wearable device (300) can collect biometric information of a user using the sensing unit. For example, the wearable device (300) may include a body unit (310) and a sensing unit (320). The body unit may be implemented in a shape and material that a user (e.g., a glaucoma patient) can wear on a part of the body. For example, the body unit may be a hair band. The sensing unit may include at least one sensor that collects biometric information.

[0076] The wearable device (300) can be worn on a part of the user's body (e.g., the wrist) and collect the user's biometric information. For example, the wearable device (300) can estimate the sleeping posture of a glaucoma patient using the sensing unit (320). For example, the wearable device (300) can generate sleep pattern data based on the sleeping posture and sleeping time.

[0077] As shown in FIG. 3, the wearable device (300) may include the sensing unit (320) including a first sensing unit (321), a second sensing unit (322), and a third sensing unit (323). Each of the first sensing unit (321), the second sensing unit (322), and the third sensing unit (323) may include at least one pressure sensor and at least one position sensor.

[0078] As shown in FIG. 4, the first sensing unit (321) may include a first pressure sensor (321a) and a first position sensor (321b). The second sensing unit (322) may include a second pressure sensor (322a) and a second position sensor (322b). The third sensing unit (323) may include a third pressure sensor (323a) and a third position sensor (323b).

[0079] The first sensing unit (321) may be positioned at the center of the body. The second sensing unit (322) may be positioned on the left side of the first sensing unit. The third sensing unit (323) may be positioned on the right side of the first sensing unit. For example, when a user wears the wearable device (300), the first sensing unit (321) may be positioned at the center of the user's forehead. For example, when a user wears the wearable device (300), the second sensing unit (322) may be positioned within a predetermined distance (e.g., 0.5 cm to 1 cm) from the user's left eyebrow and left eyeball. For example, when a user wears the wearable device (300), the third sensing unit (323) may be positioned within a predetermined distance (e.g., 0.5 cm to 1 cm) from the user's right eyebrow and right eyeball.

[0080] The second sensing unit (322) and the third sensing unit (323) can sense a change in pressure applied to the eye of the glaucoma patient and a change in the position of the eye of the glaucoma patient based on the first sensing unit (321) within a predetermined distance from the eye of the glaucoma patient.

[0081] For example, the sensing unit may sense the pressure change applied to the left eye of the glaucoma patient based on a first relative pressure difference between the first pressure sensed by the first sensing unit (321) and the second pressure sensed by the second sensing unit (322). For example, the sensing unit may sense the pressure change applied to the right eye of the glaucoma patient based on a second relative pressure difference between the first pressure sensed by the first sensing unit (321) and the third pressure sensed by the third sensing unit (323).

[0082] For example, the sensing unit can sense a change in the position of the eye of the glaucoma patient based on a first relative position difference between the first sensing unit (321) and the second sensing unit (322), and a second relative position difference between the first sensing unit (321) and the third sensing unit (323).

[0083] FIG. 5 is a diagram showing the types of sleeping postures estimated by the wearable device (300) of the present disclosure.

[0084] Referring to FIGS. 3 to 5, the wearable device (300) can estimate the sleeping posture of a glaucoma patient based on changes in pressure applied to the patient's eye and changes in the position of the patient's eye. For example, the wearable device (300) can learn the sleeping posture of a glaucoma patient using an artificial intelligence model and estimate the sleeping posture of the glaucoma patient based on data sensed by the sensing unit.

[0085] For example, a wearable device (300) may include an artificial intelligence model. The artificial intelligence model may be a single artificial intelligence model or may be implemented as multiple artificial intelligence models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include a statistical learning algorithm that mimics biological neurons in machine learning and cognitive science. A neural network may refer to a model in which artificial neurons (nodes) that form a network by combining synapses change the binding strength of synapses through learning, thereby having problem-solving capabilities. Neurons of a neural network may include a combination of weights or biases. A neural network may include one or more layers composed of one or more neurons or nodes. For example, a device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer a desired result (output) from an arbitrary input (input) by changing the weights of neurons through learning.

[0086] At least one processor included in the wearable device (300) may generate a neural network, train a neural network, perform a calculation based on received input data, generate an information signal based on the calculation result, or retrain a neural network. The models of the neural network may include various types of models such as CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (RecuREnt Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, etc., but are not limited thereto. The processor may perform calculations according to the models of the neural network. It may include one or more processors. For example, the neural network may include a deep neural network.

[0087] Neural networks include CNN (Convolutional Neural Network), RNN (RecuREnt Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated RecuREnt Unit), AE (Auto Encoder), VAE (Variational Auto Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Depp Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics) Network), GAN (Generative Adversarial Network), LSM (Liquid State Machine), ELM (Extreme Learning It will be understood by those skilled in the art that any neural network may be included, including but not limited to, a Machine (Echo State Network), an ESN (Echo State Network), a DRN (Deep Residual Network), a DNC (Differentiable Neural Computer), an NTM (Neural Turning Machine), a CN (Capsule Network), a KN (Kohonen Network), and an AN (Attention Network).

[0088] According to an exemplary embodiment of the present disclosure, at least one processor included in the wearable device (300) may be configured to perform a CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, etc., R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (RecuREnt Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT for natural language processing, SP-BERT, MRC / QA, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics for vision processing, Visual Understanding, Video Synthesis, ResNet for data intelligence. Various artificial intelligence structures and algorithms can be used, including but not limited to Anomaly Detection, Prediction, Time-Series Forecasting, Optimization, Recommendation, and Data Creation.

[0089] As shown in FIG. 5, the wearable device (300) can classify the sleeping posture of a glaucoma patient into at least one of the first to sixth sleeping postures using the sensing unit. For example, the wearable device (300) can classify the sleeping posture of a glaucoma patient into a first sleeping posture of curled up on the side based on data sensed by the sensing unit. For example, the wearable device (300) can classify the sleeping posture of a glaucoma patient into a second sleeping posture of stretching out on the side based on data sensed by the sensing unit. For example, the wearable device (300) can classify the sleeping posture of a glaucoma patient into a third sleeping posture of bending over on the side based on data sensed by the sensing unit. For example, the wearable device (300) can classify the sleeping posture of a glaucoma patient into a fourth sleeping posture of lying down on the back based on data sensed by the sensing unit. For example, the wearable device (300) can classify the sleeping position of a glaucoma patient as a fifth sleeping position, lying face down, based on data sensed by the sensing unit. For example, the wearable device (300) can classify the sleeping position of a glaucoma patient as a sixth sleeping position, lying down with arms and legs spread, based on data sensed by the sensing unit.

[0090] The wearable device (300) may generate sleep pattern data based on the sleeping posture and sleeping time. The sleep pattern data may include at least one of real-time data, daily data, weekly data, and monthly data regarding the sleep status of the glaucoma patient, generated based on the sleeping posture and sleeping time.

[0091] A wearable device (300) can generate real-time data by monitoring the real-time sleeping posture and sleeping time of a sleeping glaucoma patient. The real-time data can include the glaucoma patient's degree of movement, sleeping posture, and sleeping time.

[0092] The wearable device (300) can generate daily data by summarizing the glaucoma patient's sleep patterns throughout the day. For example, the daily data may include at least one of the following: the number of times the glaucoma patient falls asleep and wakes up during the day, the total sleep time, and the ratio of deep sleep to light sleep.

[0093] The wearable device (300) can generate weekly data by synthesizing the sleep patterns of a glaucoma patient over a week. For example, weekly data may include sleep trends over a wider period than daily data. For example, weekly data can be used to understand the sleep habits of a glaucoma patient.

[0094] The wearable device (300) can generate monthly data by analyzing the sleep patterns of a glaucoma patient over a month. For example, the monthly data can be used to identify long-term sleep trends and sleep habits of glaucoma patients. For example, the monthly data can include changes in sleep patterns due to seasonal changes and changes in sleep habits over a long period of time.

[0095] In one embodiment, the server (100) may store glaucoma diagnosis information. For example, the server (100) may generate feedback data based on the sleep pattern data and the glaucoma diagnosis information. For example, the glaucoma diagnosis information may include at least one of intraocular pressure data, optic nerve damage data, visual field test data, corneal thickness data, and glaucoma type data. The intraocular pressure data may include intraocular pressure information of a glaucoma patient. The optic nerve damage data may include the degree of progression of optic nerve damage of a glaucoma patient. The visual field test data may include the extent and location of visual field loss of a glaucoma patient. The corneal thickness data may include the corneal thickness of a glaucoma patient and the normal intraocular pressure range according to the corneal thickness. The glaucoma type data may include whether the glaucoma patient has open-angle glaucoma or closed-angle glaucoma.

[0096] In one embodiment, the server (100) determines whether the sleep state of the glaucoma patient is above a reference risk level based on the sleep pattern data and the glaucoma diagnosis information, and if the sleep state of the glaucoma patient is above the reference risk level, the server (100) can transmit the feedback data including a wake-up request signal to the mobile device (200).

[0097] In one embodiment, the server (100) may analyze the sleep state of the glaucoma patient based on the sleep pattern data and the glaucoma diagnosis information, and transmit the feedback data including at least one of sleep posture coaching, sleep condition recommendation, and bedding change recommendation corresponding to the sleep state to the mobile device (200).

[0098] FIG. 6 is a diagram showing a mobile device (200) of the present disclosure providing a weather alarm based on feedback data.

[0099] Referring to FIG. 6, the mobile device (200) may provide a wake-up alarm to the glaucoma patient based on the feedback data. For example, the mobile device (200) may provide a wake-up alarm if the glaucoma patient's sleep state is above the reference risk level.

[0100] The mobile device (200) can determine the sleeping posture, movement during sleep, depth of sleep, and sleep duration of a glaucoma patient from sleep pattern data.

[0101] The mobile device (200) can determine the intraocular pressure level and optic nerve condition of a glaucoma patient based on glaucoma diagnostic information. The mobile device (200) can also set baseline risk values ​​for safe sleeping postures and intraocular pressure levels that may affect the progression of glaucoma.

[0102] The mobile device (200) may provide a wake-up alarm if the sleep state of a glaucoma patient exceeds a preset risk level. For example, the mobile device (200) may provide a wake-up alarm if the glaucoma patient's intraocular pressure increases or if the sleeping posture is potentially harmful to the glaucoma patient. For example, the mobile device (200) may determine an appropriate wake-up time for the glaucoma patient by considering the patient's sleep cycle. For example, the mobile device (200) may provide a wake-up alarm using at least one of vibration, sound, and light.

[0103] After activating the wake-up alarm, the mobile device (200) may provide recommendations to the awakened glaucoma patient. For example, the mobile device (200) may display recommendations including at least one of posture, sleep environment changes, and consultation with a medical professional to lower intraocular pressure.

[0104] FIG. 7 is a diagram showing a mobile device (200) of the present disclosure providing sleep coaching based on feedback data.

[0105] Referring to FIG. 7, the mobile device (200) may provide sleep coaching to the glaucoma patient based on the feedback data. For example, the mobile device (200) may analyze the sleep state of the glaucoma patient and provide the glaucoma patient with at least one of sleep posture coaching, sleep condition recommendation, and bedding change recommendation corresponding to the sleep state.

[0106] For example, a mobile device (200) can identify a glaucoma patient's sleep pattern by combining sleep pattern data and glaucoma diagnostic information. For example, the mobile device (200) can identify at least one of increased intraocular pressure due to a specific posture, frequent movement during sleep, and decreased sleep quality.

[0107] The mobile device (200) can recommend a sleeping position that minimizes intraocular pressure and improves sleep quality. For example, the sleeping position may include at least one of head height, neck angle, and a supine position. As shown in Fig. 7(a), the mobile device (200) can recommend a neck angle that is conducive to sleep. For example, since excessive neck bending can increase intraocular pressure, the mobile device (200) can recommend an angle that naturally connects the neck and spine to a glaucoma patient.

[0108] The mobile device (200) may recommend an optimized sleep environment, including at least one of appropriate room temperature, humidity, and lighting. For example, the mobile device (200) may recommend at least one activity, habit, or pre-sleep routine that aids sleep.

[0109] As shown in Fig. 7(b), the mobile device (200) can recommend a pillow of an appropriate height and firmness to optimize intraocular pressure. For example, a low pillow may cause an uncomfortable neck angle for a glaucoma patient, and a high pillow may place excessive pressure on the patient's head and neck. For example, the mobile device (200) can recommend a personalized pillow height that allows the glaucoma patient's head to be positioned higher than the heart while maintaining appropriate intraocular pressure. Furthermore, the mobile device (200) can recommend a mattress that optimizes intraocular pressure and improves sleep quality.

[0110] Figure 8 is a flowchart showing the sleep state feedback method of the present disclosure.

[0111] Referring to FIG. 8, the sleep state feedback method may include an operation (810) of estimating a sleeping posture of a glaucoma patient using a sensing unit of a wearable device (300), an operation (820) of generating sleep pattern data based on the sleeping posture and sleeping time by the wearable device (300), an operation (830) of generating feedback data based on the sleep pattern data and glaucoma diagnosis information by the server (100), and an operation (840) of providing at least one of a wake-up alarm and sleep coaching to the glaucoma patient based on the feedback data by the mobile device (200).

[0112] According to an example, in operation 810, the wearable device (300) can estimate the sleeping posture of a glaucoma patient based on changes in pressure applied to the eye of the glaucoma patient and changes in the position of the eye of the glaucoma patient. For example, the wearable device (300) can learn the sleeping posture of the glaucoma patient using an artificial intelligence model and estimate the sleeping posture of the glaucoma patient based on data sensed by the sensing unit.

[0113] According to an example, in operation 820, the wearable device (300) may generate sleep pattern data based on the sleeping posture and sleeping time. The sleep pattern data may include at least one of real-time data, daily data, weekly data, and monthly data regarding the sleep status of the glaucoma patient generated based on the sleeping posture and the sleeping time.

[0114] For example, in operation 830, the server (100) may generate feedback data based on sleep pattern data and glaucoma diagnosis information. For example, the glaucoma diagnosis information may include at least one of intraocular pressure data, optic nerve damage data, visual field test data, corneal thickness data, and glaucoma type data.

[0115] In one embodiment, the server (100) determines whether the sleep state of the glaucoma patient is above a reference risk level based on the sleep pattern data and the glaucoma diagnosis information, and if the sleep state of the glaucoma patient is above the reference risk level, the server (100) can transmit the feedback data including a wake-up request signal to the mobile device (200).

[0116] In one embodiment, the server (100) may analyze the sleep state of the glaucoma patient based on the sleep pattern data and the glaucoma diagnosis information, and transmit the feedback data including at least one of sleep posture coaching, sleep condition recommendation, and bedding change recommendation corresponding to the sleep state to the mobile device (200).

[0117] For example, in operation 840, the mobile device (200) may provide at least one of a wake-up alarm and sleep coaching to the glaucoma patient based on the feedback data. For example, the mobile device (200) may provide a wake-up alarm if the sleep state of the glaucoma patient is above the reference risk level. For example, the mobile device (200) may analyze the sleep state of the glaucoma patient and provide the glaucoma patient with at least one of sleep posture coaching, sleep condition recommendation, and bedding change recommendation corresponding to the sleep state.

[0118] In this way, the sleep state feedback method of the present disclosure can improve the sleep quality of glaucoma patients and help manage intraocular pressure by monitoring the sleeping posture and sleeping time of glaucoma patients and providing feedback on sleep patterns based on the monitoring.

[0119] Additionally, the sleep state feedback method of the present disclosure can provide personalized feedback tailored to the individual needs of glaucoma patients by combining glaucoma diagnosis information with sleep state to provide recommendations including at least one of a wake-up alarm and sleep coaching.

[0120] However, since this has been described above, a duplicate explanation will be omitted.

[0121] The devices described above may be implemented as hardware components, software components, and / or a combination of hardware components and software components. For example, the devices and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used alone; however, one of ordinary skill in the art will recognize that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, a processing unit may include multiple processors, or a processor and a controller. Other processing configurations, such as parallel processors, are also possible.

[0122] The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program commands, data files, data structures, etc., alone or in combination. The program commands recorded on the medium may be those specially designed and configured for the embodiment or may be those known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, 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 commands, such as ROMs, RAMs, and flash memories. Examples of the program commands include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.

[0123] Although the embodiments described above have been described with limited drawings, those skilled in the art will recognize that various modifications and variations are possible based on the above teachings. For example, appropriate results can be achieved even if the described techniques are performed in a different order than described, and / or components of the described systems, structures, devices, circuits, etc. are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents. Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the following claims.

Claims

1. In a system that provides feedback based on the sleep patterns of glaucoma patients, A server that stores glaucoma diagnosis information; Mobile devices carried by glaucoma patients; and Including a wearable device worn by the above glaucoma patient during sleep, The above wearable device, Estimating the sleeping posture of glaucoma patients using the sensing unit, Generate sleep pattern data based on the above sleeping position and sleeping time, The above server, Generate feedback data based on the above sleep pattern data and the above glaucoma diagnosis information, The above mobile device, Providing at least one of a wake-up alarm and sleep coaching to the glaucoma patient based on said feedback data; Sleep state feedback system.

2. In paragraph 1, The above glaucoma diagnosis information is, Including at least one of intraocular pressure data, optic nerve damage data, visual field examination data, corneal thickness data, and glaucoma type data. Sleep state feedback system.

3. In paragraph 1, The above wearable device, A sensing unit comprising a first sensing unit, a second sensing unit, and a third sensing unit, Each of the first sensing unit, the second sensing unit, and the third sensing unit, comprising at least one pressure sensor and at least one position sensor, Sleep state feedback system.

4. In paragraph 3, The second sensing unit is arranged on the left side of the first sensing unit, The third sensing unit is arranged to the right of the first sensing unit, The second sensing unit and the third sensing unit, Sensing a change in pressure applied to the eye of the glaucoma patient and a change in position of the eye of the glaucoma patient based on the first sensing unit within a predetermined distance from the eye of the glaucoma patient. Sleep state feedback system.

5. In paragraph 4, The above sensing part, Sense the pressure change applied to the left eye of the glaucoma patient based on the first relative pressure difference between the first pressure sensed by the first sensing unit and the second pressure sensed by the second sensing unit, Sense the pressure change applied to the right eye of the glaucoma patient based on the second relative pressure difference between the first pressure and the third pressure sensed by the third sensing unit. Sleep state feedback system.

6. In paragraph 4, The above sensing part, Sensing a change in the position of the eye of the glaucoma patient based on a first relative position difference between the first sensing unit and the second sensing unit, and a second relative position difference between the first sensing unit and the third sensing unit. Sleep state feedback system.

7. In paragraph 1, The above sleep pattern data is, At least one of real-time data, daily data, weekly data, and monthly data on the sleep status of the glaucoma patient generated based on the sleeping posture and the sleeping time. Sleep state feedback system.

8. In paragraph 1, The above server, Based on the above sleep pattern data and the above glaucoma diagnosis information, it is determined whether the sleep state of the glaucoma patient is above the standard risk level, If the sleep state of the glaucoma patient is higher than the standard risk level, the feedback data including the wake-up request signal is transmitted to the mobile device. Sleep state feedback system.

9. In paragraph 1, The above server, Analyzing the sleep state of the glaucoma patient based on the above sleep pattern data and the glaucoma diagnosis information, Transmitting the feedback data including at least one of sleep posture coaching, sleep condition recommendation, and bedding change recommendation corresponding to the sleep state to the mobile device; Sleep state feedback system.

10. A method for providing feedback based on the sleep pattern of a glaucoma patient, performed by a system, An operation of estimating the sleeping posture of a glaucoma patient using the sensing unit of the wearable device of the above system; An operation of the wearable device to generate sleep pattern data based on the sleeping position and sleeping time; An operation of the server of the above system to generate feedback data based on the sleep pattern data and glaucoma diagnosis information; and The mobile device of the system comprises an action of providing at least one of a wake-up alarm and sleep coaching to the glaucoma patient based on the feedback data. Sleep state feedback method.

11. In paragraph 10, The above glaucoma diagnosis information includes at least one of intraocular pressure data, optic nerve damage data, visual field examination data, corneal thickness data, and glaucoma type data. The above wearable device, A sensing unit comprising a first sensing unit, a second sensing unit, and a third sensing unit, Each of the first sensing unit, the second sensing unit, and the third sensing unit, comprising at least one pressure sensor and at least one position sensor, Sleep state feedback method.

12. In paragraph 11, The second sensing unit is arranged on the left side of the first sensing unit, The third sensing unit is arranged to the right of the first sensing unit, The second sensing unit and the third sensing unit, Sensing a change in pressure applied to the eye of the glaucoma patient and a change in position of the eye of the glaucoma patient based on the first sensing unit within a predetermined distance from the eye of the glaucoma patient. Sleep state feedback method.

13. In paragraph 12, The operation of estimating the sleeping posture of a glaucoma patient using the sensing part of the above wearable device is as follows. The pressure change applied to the left eye of the glaucoma patient is sensed based on a first relative pressure difference between the first pressure sensed by the first sensing unit and the second pressure sensed by the second sensing unit, and the pressure change applied to the right eye of the glaucoma patient is sensed based on a second relative pressure difference between the first pressure and the third pressure sensed by the third sensing unit. The operation of estimating the sleeping posture of a glaucoma patient using the sensing part of the above wearable device is as follows. Sensing a change in the position of the eye of the glaucoma patient based on a first relative position difference between the first sensing unit and the second sensing unit, and a second relative position difference between the first sensing unit and the third sensing unit. Sleep state feedback method.

14. In paragraph 10, The above sleep pattern data includes at least one of real-time data, daily data, weekly data, and monthly data on the sleep status of the glaucoma patient generated based on the sleeping posture and the sleeping time. The operation of the above server generating feedback data based on the above sleep pattern data and glaucoma diagnosis information is as follows: Based on the sleep pattern data and the glaucoma diagnosis information, determining whether the sleep state of the glaucoma patient is above the standard risk level, and if the sleep state of the glaucoma patient is above the standard risk level, transmitting the feedback data including the wake-up request signal to the mobile device. Sleep state feedback method.

15. In paragraph 10, The operation of the above server generating feedback data based on the above sleep pattern data and glaucoma diagnosis information is as follows: Based on the sleep pattern data and the glaucoma diagnosis information, the sleep state of the glaucoma patient is analyzed, and the feedback data including at least one of sleep posture coaching, sleep condition recommendation, and bedding change recommendation corresponding to the sleep state is transmitted to the mobile device. Sleep state feedback method.

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