Method and system for managing digital voiding diary

Abstract

To provide a method and a system (apparatus) for managing a digital voiding diary.SOLUTION: A method for managing a digital voiding diary is implemented by at least one processor. The method may include the steps of: receiving a plurality of optical datasets associated with a specific user detected by a plurality of photodiodes within a medical device at each of a plurality of time points, the plurality of photodiodes being configured to detect light intensity associated with light radiated to the skin located above the bladder of the specific user; estimating the bladder urine volume for each of the plurality of time points on the basis of the plurality of optical datasets; and recording the estimated bladder urine volume of the specific user for each of the plurality of time points.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] (Cross - reference to related applications) This application claims priority based on Korean Patent Application No. 10 - 2023 - 0158122, filed on November 15, 2023, the content of which is incorporated herein by reference.

[0002] The present disclosure relates to a digital urination log management method and system, and specifically, to a digital urination log management method and system based on estimated bladder urine volume and physiological measurement information.

Background Art

[0003] With the advent of an aging society, urinary disorders are prevalent in 60% of people aged 40 and above. In particular, in order to prescribe medications for patients with urinary disorders such as overactive bladder, underactive bladder, benign prostatic hyperplasia, nocturia, recurrent cystitis, and urinary incontinence, patients are required to create a urination log within 72 hours.

[0004] A urination log can be handwritten by a patient to record the number of urinations and urine volume for each time period of urination within 72 hours. Specifically, the patient must record the urination time and urine volume even when waking up or during sleep (including the case of waking up to urinate during sleep) within 72 hours starting 3 days before visiting the hospital. Subsequently, the patient revisits the hospital, and based on this, the doctor can analyze the total daily urine volume, the average number of daily urinations, the average number of nocturia episodes, the average percentage of nocturnal urine volume, and the functional bladder capacity to diagnose the patient's urinary disorder. Creating a urination log can support an accurate assessment of symptoms and diagnosis of urinary disorders at the start of treatment. Since changes during the treatment of urinary disorders can be objectively confirmed, the urination log is highly regarded for the evaluation of treatment response and prognosis.

[0005] However, despite such importance, it is difficult for patients to manually write down their urination time and volume in a urination log when they go out. Even though patients come to the hospital for treatment due to frequent urination or urgent urination, they may experience significant stress in creating a urination log within 72 hours. Even though patients come to the hospital due to nocturia (or urination during sleep), if they have to measure the urine volume with a urination cup and manually write it in the urination log every time they urinate during sleep in order to manually write the urination log, there is a risk of sleep deprivation. The creation of a urination log, which is essential for accurate diagnosis of patients or for starting treatment of urinary disorders, instead causes patients to have difficulty and experience stress in creating the urination log. Due to the difficulty and stress of creating a urination log, patients may interrupt the creation of the urination log or create it inaccurately. In particular, for patients with urinary disorders with a high proportion of elderly patients, it may exacerbate the difficulty and stress in creating a urination log.

[0006] Therefore, doctors who make diagnoses / prescriptions based on urination logs may have the problem that the reliability of the data for manually written urination logs within 72 hours is low. Also, in the doctor's clinical environment, there may be a problem of insufficient time to analyze all the results of the urination log.

Summary of the Invention

Problems to be Solved by the Invention

[0007] The present disclosure provides a digital urination log management method and system (device) for solving the above problems.

Means for Solving the Problems

[0008] The present disclosure can be embodied in various ways including a method, a device (system), and / or a computer program stored in a computer-readable storage medium, and a computer-readable storage medium storing the computer program.

[0009] According to an embodiment of the present disclosure, in a digital urination diary management method performed by at least one processor, at each of a plurality of time points, receiving a plurality of optical data sets related to a specific user detected by a plurality of photodiodes in a medical device, wherein the plurality of photodiodes are configured to detect the light intensity related to the light irradiated on the skin located on the bladder of the specific user, and based on the plurality of optical data sets, estimating the urine volume in the bladder for each of the plurality of time points, and recording the estimated urine volume in the bladder of the specific user for each of the plurality of time points.

[0010] According to an embodiment of the present disclosure, the method can further include outputting the total daily urine volume of the specific user based on the urine volume in the bladder of the specific user for each of the plurality of recorded time points.

[0011] According to an embodiment of the present disclosure, the method can further include displaying the urine volume during waking up of the specific user together with a first visual object based on the urine volume in the bladder of the specific user for each of the plurality of recorded time points, and displaying the urine volume during sleeping of the specific user together with a second visual object based on the urine volume in the bladder of the specific user for each of the plurality of recorded time points.

[0012] According to an embodiment of the present disclosure, the method can further include outputting the average of the total daily urine volume and the average of the number of urinations per day of the specific user based on the urine volume in the bladder of the specific user for each of the plurality of recorded time points.

[0013] According to an embodiment of the present disclosure, the method can further include outputting the number of urinations during sleeping per day or the ratio of the urine volume during sleeping per day of the specific user based on the urine volume in the bladder of the specific user for each of the plurality of recorded time points.

[0014] According to an embodiment of the present disclosure, the method may further include estimating a functional bladder volume of a specific user based on the intravesical urine volume of the specific user at each of a plurality of recorded time points, and outputting the estimated functional bladder volume of the specific user.

[0015] According to an embodiment of the present disclosure, the step of estimating the urine volume includes estimating a plurality of sets of optical characteristic values for at least a part of the body of a specific user based on a plurality of optical data sets, and estimating the intravesical urine volume for each of a plurality of time points using a urine volume estimation model based on the estimated sets of optical characteristic values. The urine volume estimation model is a deep learning-based model or a machine learning-based model that has learned a plurality of learning data sets, and the plurality of learning data sets may include pairs of the actual urine volume of the specific user and the sets of optical characteristic values associated with the actual urine volume.

[0016] According to an embodiment of the present disclosure, the plurality of learning data sets include a first learning data set and a second learning data set. The first learning data set includes a pair of the first actual urine volume of the specific user and the first set of learning optical characteristic values associated with the first actual urine volume. The second learning data set includes a pair of the second actual urine volume of the specific user and the second set of learning optical characteristic values associated with the second actual urine volume, and the second actual urine volume may be greater than the first actual urine volume.

[0017] To execute the method according to an embodiment of the present disclosure on a computer, a computer program stored in a computer-readable recording medium can be provided.

[0018] According to an embodiment of the present disclosure, there is provided a user terminal including a communication unit, a memory, and at least one processor coupled to the memory and configured to execute at least one computer-readable program included in the memory. The at least one program is configured to receive, at multiple time points, a plurality of optical data sets related to a specific user detected by a plurality of photodiodes in a medical device. Here, the plurality of photodiodes are configured to detect the light intensity related to the light irradiated on the skin located on the bladder of the specific user, and based on the plurality of optical data sets, estimate the urine volume in the bladder for each of the multiple time points, and may include instructions for recording the urine volume in the bladder of the specific user for each of the estimated multiple time points.

Advantages of the Invention

[0019] According to some embodiments of the present disclosure, physiological information can be provided to a user without the support of an expert such as a doctor. In addition, the convenience of the user can be enhanced by a simple usage method, and the accessibility of the user can be increased for personal use.

[0020] According to some embodiments of the present disclosure, system parameters for a plurality of photodiodes included in a medical device can be corrected identically. After the medical device performs the generation of calibration parameters once, additional calibration may not be required. That is, the performance of calibration using a phantom becomes unnecessary, and the convenience of the user can be enhanced.

[0021] According to some embodiments of the present disclosure, the convenience of the user can be enhanced by providing rapid calculation and high accuracy.

[0022] According to some embodiments of the present disclosure, physiological information regarding a plurality of regions can be provided using a plurality of light sources and a plurality of photodiodes. Physiological information can be provided not only for local regions of the body but also for extensive parts of the body. Further, by providing physiological information regarding a plurality of regions, the state of organs contained in the body (for example, the amount of urine stored in the bladder, the position of the bladder, etc.) can be specifically grasped.

[0023] According to some embodiments of the present disclosure, in the case of patients who do not feel the urge to urinate, etc., physiological information regarding their own bladder and / or the amount of urine in the bladder can be received in real time or periodically. The patient can use the provided information to monitor the amount of urine stored in their own bladder and urinate at an appropriate time.

[0024] According to some embodiments of the present disclosure, the urine volume estimation model can be provided for users by learning learning obesity information. Further, the urine volume estimation model utilizes a machine learning model or a deep learning model for which app development support is relatively good, so the invention according to the present disclosure can be easy for mobile app development for wearable devices. Also, the machine learning model or the deep learning model is easy to re-learn, and the invention according to the present disclosure can perform individual bladder urine volume estimation. Further, the urine volume estimation model is easy to maintain and improve, and is excellent in model expandability and model versatility.

[0025] According to some embodiments of the present disclosure, when the amount of the learning dataset is not large, a plurality of additional learning datasets are generated by a teacher model, and data augmentation is performed. By the urine volume prediction model learning more data through data augmentation, an automated bladder urine volume prediction method designed based on medical knowledge and diagnosis can be realized.

[0026] According to some embodiments of the present disclosure, users can be easily guided on how to link a user terminal and a medical device. Subsequently, the user can use the medical device linked to the user terminal. Also, by inputting information about the user, medical devices, personal artificial intelligence models, etc. can be optimized for the user.

[0027] According to some embodiments of the present disclosure, the invention according to the present disclosure can provide a digitized urination log to patients and doctors by digitizing the urination log. Specifically, by accurately and quickly calculating the urination time and urination volume, when a patient is wearing a medical device, the urination time and urination volume can be automatically recorded. That is, since the patient does not have to measure with a urination cup and write the urination log by hand, high user accessibility can be provided.

[0028] According to some embodiments of the present disclosure, the digital urination log management method can reduce the analysis time of medical staff by providing an automatic analysis function for the diagnosis of urination disorders, thereby enhancing the accessibility for those in need. Also, the invention according to the present disclosure provides data automatically analyzed regarding the average of the total daily urination volume, the average number of daily urination times, the number of nocturia times by date, the average number of nocturia times in a day, the ratio of nocturia volume by date, the average ratio of nocturia volume by date, the average nocturia volume by date, the functional bladder capacity, etc., enabling doctors to perform accurate and prompt diagnosis / prescription for patients.

[0029] The effects of the present disclosure are not limited to this, and other effects not mentioned should be clearly understood by those with ordinary knowledge in the technical field to which the present disclosure belongs (referred to as "ordinary technicians") from the description of the claims.

Brief Description of the Drawings

[0030] Embodiments of the present disclosure will be described based on the following attached drawings. Here, similar reference numerals indicate similar elements, but are not limited thereto.

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Modes for Carrying Out the Invention

[0031] Hereinafter, specific contents for implementing the present disclosure will be described in detail based on the accompanying drawings. However, in the following description, specific descriptions of well-known functions and configurations may be omitted if there is a risk of unnecessarily obscuring the gist of the present disclosure.

[0032] In the accompanying drawings, the same or corresponding components are given the same reference numerals. Also, in the following description of the embodiments, duplicate descriptions of the same or corresponding components may be omitted. However, even if the description of a component is omitted, such a component should not be construed as not being included in a certain embodiment.

[0033] The advantages and features of the disclosed embodiments, and the methods for achieving them, will become clear by referring to the embodiments described later based on the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and can be embodied in various different forms. However, this embodiment is only provided to make the present disclosure complete and to enable an ordinary technician to accurately recognize the category of the invention.

[0034] Briefly explain the terms used in this disclosure and specifically describe the embodiments of the disclosure. When selecting the terms used in this disclosure, while considering their functions in this disclosure, we have chosen the most commonly used general terms as much as possible. However, these terms may change due to the intentions or precedents of those skilled in the relevant fields, the emergence of new technologies, etc. In addition, in certain cases, there may be terms arbitrarily selected by the applicant, and their meanings will be described in detail in the description part of the invention. Therefore, the terms used in this disclosure should be defined based not on the simple names of the terms but on the meanings they carry and the overall content of this disclosure.

[0035] In this disclosure, unless specifically specified in the context, singular expressions include plural expressions, and plural expressions can include singular expressions. Throughout the specification, if a certain part states that a certain component "includes", this means that, unless there is a contrary description, it does not exclude other components and can further include other components.

[0036] Also, the terms "module" or "unit" used in the specification mean software or hardware components, and the "module" or "unit" performs a certain role. However, the "module" or "unit" is not limited in meaning to software or hardware. The "module" or "unit" may be configured to be in an addressable storage medium or configured to reproduce one or more processors. Therefore, as an example, the "module" or "unit" can include at least one of components such as software components, object-oriented software components, class components, task components, and components such as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. The components and the "module" or "unit" can be combined with a smaller number of internal provided functions and other components and "module" or "unit", or can be further separated into additional components and "module" or "unit".

[0037] According to an embodiment of the present disclosure, a "module" or a "unit" may be implemented by a processor and a memory. The "processor" should be broadly interpreted to include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc. In some environments, the "processor" may also refer to an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field-programmable gate array (FPGA), etc. The "processor" may refer to a combination of processing devices such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors coupled with a DSP core, or any other such configuration combination. Also, the "memory" should be broadly interpreted to include any electronic component capable of storing electronic information. The "memory" may refer to various types of processor-readable media such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or optical data storage devices, registers, etc. When a processor can read information from / into the memory, the memory is said to be in electronic communication with the processor. The memory integrated with the processor is in electronic communication with the processor.

[0038] Also, terms such as first, second, A, B, (a), (b), etc. used in the following embodiments, etc. are only used to distinguish one component from another component, and the essence, order, procedure, etc. of the component are not limited by the terms.

[0039] In addition, in the following examples and the like, if a certain component is "connected", "coupled", or "joined" to another component, it should be understood that they can be directly connected or joined to each other, but other components can also be "connected", "coupled", or "joined" between the respective components.

[0040] In the present disclosure, "each of a plurality of A" can refer to each of all the components included in the plurality of A, or can refer to each of some of the components included in the plurality of A.

[0041] In addition, "comprises, comprising" used in the following examples and the like does not exclude the presence or addition of one or more other components, steps, operations, and / or elements.

[0042] In the present disclosure, "diffuse reflectance" can refer to the ratio of the light intensity of a light source and the light intensity of diffused light measured at a specific distance from the light source. Here, the diffused light can refer to the light diffused from an object irradiated with light. For example, when irradiating the body with light, the diffuse reflectance can refer to the ratio of the light intensity of the light source and the light intensity of the diffused light measured at a specific distance from the light source. Specifically, the diffuse reflectance can be expressed as in Equation 1 below.

[0043]

Equation

[0044] In the present disclosure, "system parameters" can refer to coefficients related to the light detection of a photodiode. The system parameters can include a proportionality coefficient and an intercept coefficient. The proportionality coefficient and the intercept coefficient of the system parameters can be understood from the following description.

[0045] When the optical data detected by the photodiode is a voltage value, it can be expressed as in Equation 2 below.

[0046]

Number

[0047] Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0048] FIG. 1 is a schematic diagram showing an example of a medical device 100 for estimating physiological information according to an embodiment of the present disclosure. As shown in the figure, the medical device 100 can include a communication unit for transmitting and receiving data to and from the user terminal 120. Further, the medical device 100 can include a plurality of photodiodes 112_1 to 112_20 and a plurality of light source groups 114_1 to 114_4. The medical device 100 can acquire optical data related to the body using the plurality of photodiodes 112_1 to 112_20 and the plurality of light source groups 114_1 to 114_4. The user terminal 120 can receive optical data related to the body and estimate the physiological information of the user based on the received optical data. In FIG. 1, the medical device 100 is shown as including 20 photodiodes 112_1 to 112_20 and 4 light source groups 114_1 to 114_4, but it is not limited thereto. That is, the number of photodiodes and the number of light source groups included in the medical device 100 can be changed as needed.

[0049] In one embodiment, a plurality of photodiodes 112_1 to 112_20 and a plurality of light source groups 114_1 to 114_4 can be arranged on one surface of the medical device 100. At this time, the medical device 100 can be attached to the body so that the one surface faces the body. As an example, the medical device 100 can be attached to the body so that the one surface faces the site where the bladder is located.

[0050] In one embodiment, each of the plurality of light source groups 114_1 to 114_4 can include, but is not limited to, six light sources having different wavelengths from each other. For example, the first light source group 114_1 can include the first to sixth light sources. The second light source group 114_2 can include the seventh to twelfth light sources. The third light source group 114_3 can include the thirteenth to eighteenth light sources. The fourth light source group 114_4 can include the nineteenth to twenty-fourth light sources. Each of the first to twenty-fourth light sources can be an LD (Laser Diode), an LED (Light-Emitting Diode), or an OLED (Organic Light-Emitting Diode). Also, each of the first light source to the twenty-fourth light source can emit light that is a continuous wave.

[0051] In one embodiment, the plurality of light sources included in each of the plurality of light source groups 114_1 to 114_4 can be configured to emit light of different wavelengths from each other. For example, each of the first to sixth light sources included in the first light source group 114_1 can emit light of different wavelengths from each other. Also, each of the seventh to twelfth light sources included in the second light source group 114_2 can emit light of different wavelengths from each other. Also, each of the thirteenth to eighteenth light sources included in the third light source group 114_3 can emit light of different wavelengths from each other. Also, each of the nineteenth to twenty-fourth light sources included in the fourth light source group 114_4 can emit light of different wavelengths from each other.

[0052] Here, the light sources of different light source groups from each other can emit light of the same wavelength. For example, the first light source, the seventh light source, the thirteenth light source, and the nineteenth light source can emit light of the same wavelength. Similarly, the second light source, the eighth light source, the fourteenth light source, and the twentieth light source can emit light of the same wavelength. Also, the third light source, the ninth light source, the fifteenth light source, and the twenty-first light source can emit light of the same wavelength. Also, the fourth light source, the tenth light source, the sixteenth light source, and the twenty-second light source can emit light of the same wavelength. Also, the fifth light source, the eleventh light source, the seventeenth light source, and the twenty-third light source can emit light of the same wavelength. Also, the sixth light source, the twelfth light source, the eighteenth light source, and the twenty-fourth light source can emit light of the same wavelength.

[0053] In one embodiment, the plurality of photodiodes 112_1 to 112_20 can detect light and generate optical data. Specifically, the plurality of photodiodes 112_1 to 112_20 can detect the light intensity of the diffused light that is the light diffused from the body. Also, the plurality of photodiodes 112_1 to 112_20 can detect the diffused light associated with the light sources irradiated by the light sources included in the plurality of light source groups 114_1 to 114_4. Also, each photodiode can detect the diffused light and measure the voltage value corresponding to the light intensity of the diffused light. At this time, with one light source turned on, one photodiode can detect the diffused light.

[0054] In one embodiment, the user terminal 120 can send a request for detecting optical data to the medical device 100. The medical device 100 can perform the operation of the plurality of light source groups 114_1 to 114_4 and the detection of the plurality of photodiodes 112_1 to 112_20 in response to the request for detecting optical data. The process for the operation of the plurality of light source groups 114_1 to 114_4 and the detection of the plurality of photodiodes 112_1 to 112_20 will be described in detail later with reference to FIG. 10. In contrast, the medical device 100 may periodically detect optical data and transfer it to the user terminal 120 without receiving a request for detecting optical data from the user terminal 120.

[0055] In one embodiment, the medical device 100 can send the plurality of optical data detected via the plurality of photodiodes 112_1 to 112_20 to the user terminal 120. The processor included in the user terminal 120 can estimate physiological information based on the plurality of optical data. Here, the physiological information is information regarding water (H 2 O), information regarding fat, oxygenated hemoglobin (HbO 2) information, information regarding deoxygenated hemoglobin (HHb), bladder monitoring information (such as notification of urination time, notification of catheterization time, amount of urine in the bladder, etc.) can be included. A method for estimating physiological information based on a plurality of optical data will be described in detail later with reference to FIGS. 4 to 12. In contrast, the medical device 100 may directly estimate physiological information based on the optical data without transferring the optical data to the user terminal 120.

[0056] With such a configuration, physiological information can be estimated based on the optical data acquired from the medical device 100. Additionally, the estimated physiological information can be provided to the user via the user terminal 120. Thus, the invention according to the present disclosure can provide physiological information to the user without the support of experts such as doctors. Further, the invention according to the present disclosure can enhance the convenience of the user by a simple usage method and improve the accessibility of the user as an individual.

[0057] FIG. 2 is a schematic diagram showing a configuration in which an information processing system 230, a medical device 240, and a plurality of user terminals 210_1, 210_2, 210_3 according to an embodiment of the present disclosure are connected so as to be communicable with each other. As shown in the figure, the plurality of user terminals 210_1, 210_2, 210_3 can be connected to an information processing system 230 and a medical device 240 that can provide a physiological information estimation service and / or a digital urination diary management service via a network 220. Here, the plurality of user terminals 210_1, 210_2, 210_3 can include terminals of users for whom the physiological information estimation service and / or the digital urination diary management service is provided.

[0058] According to one embodiment, the information processing system 230 can include a computer-executable program (e.g., a downloadable application) related to the provision of a physiological information estimation service, the provision of a digital urination log management service, etc., one or more server devices and / or databases capable of storing, providing, and executing data, and one or more distributed computing devices and / or distributed databases of a cloud computing service infrastructure.

[0059] The physiological information estimation service and / or the digital urination log management service provided by the information processing system 230 can be provided to a user via, for example, a physiological information estimation service application installed on each of the plurality of user terminals 210_1, 210_2, 210_3. For example, the information processing system 230 can provide information related to physiological information estimation and / or digital urination log management received from the user terminals 210_1, 210_2, 210_3 and / or the medical device 240 via a physiological information estimation service application, a digital urination log management service application, etc., and perform corresponding processing. As an example, the digital urination log management service provided by the information processing system 230 can be accessed from the web. Also, the digital urination log management service provided by the information processing system 230 can be extended to PHR (Personal Health Record), EMR (Electronic Medical Record), EHR (Electronic Health Record), etc.

[0060] According to one embodiment, the information processing system 230 can estimate physiological information based on optical data. Here, the optical data can be data measured by the medical device 240. The information processing system 230 can directly receive optical data from the medical device 240 or receive optical data via the user terminals 210_1, 210_2, 210_3. The information processing system 230 can provide the physiological information estimation result to the user terminals 210_1, 210_2, 210_3 and / or the medical device 240.

[0061] According to one embodiment, the information processing system 230 can estimate the amount of urine in the bladder at each of a plurality of time points based on a plurality of optical data sets. Here, the plurality of optical data sets can be data measured by the medical device 240. The information processing system 230 can directly receive the plurality of optical data sets from the medical device 240, or can receive the optical data sets via the user terminals 210_1, 210_2, 210_3. Further, the information processing system 230 can record the amount of urine in the bladder of a specific user at each of the plurality of estimated time points. At this time, the information processing system 230 can provide the recorded amount of urine in the bladder of the specific user to the user terminals 210_1, 210_2, 210_3 and / or the medical device 240.

[0062] The plurality of user terminals 210_1, 210_2, 210_3 can communicate with the information processing system 230 and the medical device 240 via the network 220. The network 220 can be configured to enable communication between the plurality of user terminals 210_1, 210_2, 210_3, the information processing system 230, and the medical device 240. The network 220 can be composed of a wired network such as, for example, Ethernet (registered trademark), PLC (Power Line Communication), a telephone line communication device, and RS-serial communication, a mobile communication network, a WLAN (Wireless LAN), Wi-Fi (registered trademark), Bluetooth (registered trademark), and ZigBee (registered trademark), or a combination thereof, depending on the installation environment. The communication method is not limited, and it includes not only a communication method that utilizes a communication network (for example, a mobile communication network, a wired Internet, a wireless Internet, a broadcast network, a satellite network, etc.) including the network 220, but also short-range wireless communication between the user terminals 210_1, 210_2, 210_3.

[0063] In FIG. 2, the mobile phone terminal 210_1, the tablet terminal 210_2, and the PC terminal 210_3 are shown as examples of user terminals, but the present invention is not limited thereto. The user terminals 210_1, 210_2, and 210_3 can perform wired and / or wireless communication, and can be any computing device on which a physiological information estimation service application or a web browser is installed and can be executed. For example, the user terminal can include an Al speaker, a smartphone, a mobile phone, a navigation device, a desktop computer, a laptop computer, a digital broadcast terminal, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a tablet PC, a game console, a wearable device, an IoT (internet of things) device, a VR (virtual reality) device, an AR (augmented reality) device, a set-top box, and the like. Further, in FIG. 2, three user terminals 210_1, 210_2, and 210_3 are shown as communicating with the information processing system 230 and the medical device 240 via the network 220, but the present invention is not limited thereto, and different numbers of user terminals may be configured to communicate with the information processing system 230 and the medical device 240 via the network 220.

[0064] FIG. 3 is a block diagram showing the internal configuration of a user terminal and an information processing system according to an embodiment of the present disclosure. The user terminal 210 can execute a physiological information estimation service application, a digital urination log management service application, etc., and can refer to any computing device capable of wired / wireless communication. For example, it can include the mobile phone terminal 210_1, the tablet terminal 210_2, and the PC terminal 210_3 shown in FIG. 2. As shown in the figure, the user terminal 210 can include a memory 312, a processor 314, a communication module 316, and an input / output interface 318. Similarly, the information processing system 230 can include a memory 332, a processor 334, a communication module 336, and an input / output interface 338. As shown in FIG. 3, the user terminal 210 and the information processing system 230 can be configured such that information and / or data can be communicated via the network 220 using their respective communication modules 316, 336. Also, the input / output device 320 can be configured to input information and / or data to the user terminal 210 or output information and / or data generated from the user terminal 210 via the input / output interface 318.

[0065] The memories 312 and 332 can include any non-transitory computer-readable recording medium. According to one embodiment, the memories 312 and 332 can include permanent mass storage devices such as ROM (read only memory), disk drives, SSDs (solid state drives), and flash memories. As another example, permanent mass storage devices such as ROM, SSDs, flash memories, and disk drives can be included in the user terminal 210 or the information processing system 230 as another separate permanent storage device distinct from the memory. Also, the operating system and at least one program code (for example, code for a physiological information estimation service application installed and driven on the user terminal 210, a digital urination diary management service application, etc.) can be stored in the memories 312 and 332.

[0066] Such software components and the like can be loaded from a computer-readable recording medium different from the memories 312 and 332. Such another computer-readable recording medium can include a recording medium directly connectable to such user terminals 210 and information processing systems 230, and can include, for example, computer-readable recording media such as flexible drives, disks, tapes, DVD / CD-ROM drives, and memory cards. As another example, software components and the like can be loaded into the memories 312 and 332 via the communication modules 316 and 336 instead of a computer-readable recording medium. For example, at least one program can be loaded into the memories 312 and 332 based on a computer program installed by a file provided by a file distribution system that distributes developer or application installation files via the network 220.

[0067] The processors 314 and 334 can be configured to process the instructions of a computer program by executing basic arithmetic, logic, and input / output operations. The instructions can be provided to the processors 314 and 334 by the memories 312 and 332 or the communication modules 316 and 336. For example, the processors 314 and 334 can be configured to execute instructions received by program code stored in a recording device such as the memories 312 and 332.

[0068] The communication modules 316 and 336 can provide the configuration and functions for the user terminal 210 and the information processing system 230 to communicate with each other via the network 220, and the user terminal 210 and / or the information processing system 230 can provide the configuration and functions for communicating with other user terminals or other systems (e.g., another cloud system, etc.). As an example, a request or data (e.g., optical data, a plurality of optical data sets, a physiological information estimation request, a urination diary, a urination analysis result, etc.) generated by the processor 314 of the user terminal 210 by program code stored in a recording device such as the memory 312 can be transmitted to the information processing system 230 via the network 220 under the control of the communication module 316. Conversely, a control signal or instruction provided under the control of the processor 334 of the information processing system 230 can be received by the user terminal 210 via the communication module 336 of the information processing system 230 and the network 220 and then via the communication module 316 of the user terminal 210.

[0069] The input / output interface 318 may be means for interfacing with the input / output device 320. As an example, the input device may include devices such as a camera including an audio sensor and / or an image sensor, a keyboard, a microphone, a mouse, etc., and the output device may include devices such as a display, a speaker, a haptic feedback device, etc. As another example, the input / output interface 318 may be means for interfacing with a device in which a configuration or function for performing input and output is integrated into one, such as a touch screen. For example, when the processor 314 of the user terminal 210 processes the instructions of the computer program loaded in the memory 312, a service screen configured using information and / or data provided by the information processing system 230 or other user terminals may be displayed on the display via the input / output interface 318. In FIG. 3, the input / output device 320 is shown as not being included in the user terminal 210, but is not limited thereto, and may be configured integrally with the user terminal 210. Also, the input / output interface 338 of the information processing system 230 may be means for interfacing with the information processing system 230 or with a device (not shown) for input and output that the information processing system 230 may include. In FIG. 3, the input / output interfaces 318, 338 are shown as elements configured separately from the processors 314, 334, but are not limited thereto, and the input / output interfaces 318, 338 may be configured to be included in the processors 314, 334.

[0070] The user terminal 210 and the information processing system 230 can include more components than those shown in FIG. 3. However, it is not necessary to clearly show most of the conventional components. According to one embodiment, the user terminal 210 can be embodied to include at least a part of the input / output device 320 described above. Further, the user terminal 210 can further include other components such as a transceiver, a GPS (Global Positioning System) module, a camera, various sensors, and a database. For example, when the user terminal 210 is a smartphone, it can include the components generally included in a smartphone. For example, it can be embodied such that the user terminal 210 further includes various components such as an acceleration sensor, a gyro sensor, an image sensor, a proximity sensor, a touch sensor, an illuminance sensor, a camera module, various physical buttons, buttons using a touch panel, an input / output port, and a vibrator for vibration.

[0071] When programs for a physiological information estimation service application, a digital urination diary management service application, etc. are operated, the processor 314 can receive text, images, videos, voices, and / or actions input or selected by input devices such as a touch screen, a keyboard, an audio sensor, and / or a camera including an image sensor, a microphone, etc. connected to the input / output interface 318, and can store the received text, images, videos, voices, and / or actions in the memory 312, or provide them to the information processing system 230 via the communication module 316 and the network 220.

[0072] The processor 314 of the user terminal 210 can be configured to manage, process, and / or store information and / or data received from the input / output device 320, other user terminals, the information processing system 230, and / or a plurality of external systems. The information and / or data processed by the processor 314 can be provided to the information processing system 230 via the communication module 316 and the network 220. The processor 314 of the user terminal 210 can transfer and output information and / or data to the input / output device 320 via the input / output interface 318. For example, the processor 314 can display the received information and / or data on the screen of the user terminal 210.

[0073] The processor 334 of the information processing system 230 can be configured to manage, process, and / or store information and / or data received from a plurality of user terminals 210 and / or a plurality of external systems. The information and / or data processed by the processor 334 can be provided to the user terminal 210 via the communication module 336 and the network 220.

[0074] JPEG2025081264000004.jpg96151

[0075] In one embodiment, the light source 420 can be one of the first to twenty-fourth light sources of the medical device 100 shown in FIG. 1. The first to third photodiodes 430_1, 430_2, 430_3 can be part of the plurality of photodiodes 112_1 to 112_20 of the medical device 100 shown in FIG. 1. That is, from the description of the examples of the light source 420, the first photodiode 430_1, the second photodiode 430_2, and the third photodiode 430_3, the process of calculating the normalized diffuse reflectance can be understood based on the optical data detected by the plurality of photodiodes.

[0076] JPEG2025081264000005.jpg127151

[0077] In one embodiment, the measured voltage value can be corrected using calibration parameters. The corrected voltage value can be expressed as shown in Equation 3 below.

[0078]

Equation

[0079] In one embodiment, the system parameters can be different for each photodiode. Specifically, due to factors such as manufacturing process errors and connected circuit devices, the proportionality coefficients of the system parameters for each photodiode can be different from each other. As shown in Equation 3 above, the system parameters of each photodiode can be identically corrected using calibration parameters.

[0080] The normalized diffuse reflectance can represent the relative relationship of the diffuse reflectances of other photodiodes based on the diffuse reflectance of a specific photodiode. Here, the normalized diffuse reflectance can be calculated based on the corrected voltage value. Specifically, the normalized diffuse reflectance can be understood by Equation 4 below. At this time, Equation 4 can be derived from Equation 1, Equation 2, and Equation 3.

[0081]

Equation

[0082] JPEG2025081264000008.jpg54151

[0083] In FIG. 4, the process of calculating the normalized diffuse reflectances related to the light source 420 and the first to third photodiodes 430_1, 430_2, 430_3 is described in detail. A method for estimating physiological information based on the normalized diffuse reflectances related to the light source 420 and the first to third photodiodes 430_1, 430_2, 430_3 will be described in detail later with reference to FIG. 5.

[0084] FIG. 5 is a diagram showing an example of a process for estimating physiological information according to an embodiment of the present disclosure. The first optical data 510_1 may be data generated by the first photodiode. The second optical data 510_2 may be data generated by the second photodiode. The third optical data 510_3 may be data generated by the third photodiode. When measuring voltage values corresponding to the light intensities detected by each photodiode, the plurality of optical data 510_1, 510_2, 510_3 may be the measured voltage values. For example, the first optical data 510_1 may be the measured voltage value of the first photodiode described above with reference to FIG. 4. Similarly, the second optical data 510_2 may be the measured voltage value of the second photodiode described above with reference to FIG. 4. Also, the third optical data 510_3 may be the measured voltage value of the third photodiode described above with reference to FIG. 4.

[0085] In one embodiment, the correction unit 520 can calculate a plurality of corrected optical data 522_1, 522_2, 522_3 using the calibration parameter 512 based on the plurality of optical data 510_1, 510_2, 510_3. Specifically, by correcting each of the plurality of optical data 510_1, 510_2, 510_3, each of the plurality of corrected optical data 522_1, 522_2, 522_3 can be calculated. For example, the corrected first optical data 522_1 may be the corrected voltage value of the first photodiode described above with reference to FIG. 4. Similarly, the corrected second optical data 522_2 may be the corrected voltage value of the second photodiode described above with reference to FIG. 4. Also, the corrected third optical data 522_3 may be the corrected voltage value of the third photodiode described above with reference to FIG. 4. The process of correcting the optical data using the calibration parameter 512 can be understood from the content described above with reference to FIG. 4.

[0086] In one embodiment, the diffuse reflectance calculation unit 530 can calculate a plurality of normalized diffuse reflectances 532_1 and 532_2 based on a plurality of corrected optical data 522_1, 522_2, and 522_3. For example, based on the corrected first optical data 522_1 and the corrected second optical data 522_2, the normalized diffuse reflectance 532_1 of the second photodiode can be calculated. Similarly, based on the corrected first optical data 522_1 and the corrected third optical data 522_3, the normalized diffuse reflectance 532_2 of the third photodiode can be calculated. The process of calculating the normalized diffuse reflectance can be understood from the content described above with reference to FIG. 4.

[0087] In one embodiment, the absorption coefficient and the reduced scattering coefficient can be estimated based on a plurality of normalized diffuse reflectances 532_1 and 532_2. Here, the absorption coefficient can be an optical coefficient of the biological tissue for analyzing the physiological components of the biological tissue according to the degree to which light of each wavelength is absorbed from the biological tissue. Also, the reduced scattering coefficient can be an optical coefficient indicating the structural characteristics of the biological tissue. For example, in the adipose tissue of an obese patient with large fat cells, light scattering occurs relatively less, and in the adipose tissue of a normal-weight patient with small fat cells, light scattering occurs relatively more. As shown in the figure, an initial optical property value estimation model 540 and / or a numerical solver 550 can be used to estimate the absorption coefficient and the reduced scattering coefficient.

[0088] In one embodiment, the initial light characteristic value estimation model 540 can estimate the initial light characteristic value for a specific region based on a plurality of normalized diffuse reflectances 532_1 and 532_2. Here, the specific region can be a body part associated with the second photodiode and the third photodiode. The initial light characteristic value can include an initial light scattering coefficient 542 and an initial light absorption coefficient 544. For example, the initial light characteristic value estimation model 540 can be an artificial neural network model (e.g., a deep learning-based model) that has learned a plurality of light characteristic values and the normalized theoretical diffuse reflectances associated with the plurality of light characteristic values. The learning process of the initial light characteristic value estimation model 540 will be described in detail later with reference to FIG. 9.

[0089] In one embodiment, a numerical solver 550 can estimate the final light characteristic value based on the initial light characteristic value. At this time, the final light characteristic value can include a final light scattering coefficient 554 and a final light absorption coefficient 556. As an example, the numerical solver 550 can utilize the Levenberg-Marquardt algorithm. Specifically, the numerical solver 550 takes the initial light characteristic value and the plurality of normalized diffuse reflectances 532_1 and 532_2 as initial values and can estimate the final light characteristic value based on the diffuse reflectance theoretical formula 552.

[0090] Here, the diffuse reflectance theoretical formula 552 is as shown in Equation 5 below.

[0091]

Equation

[0092] In one embodiment, the numerical solver 550 can input the initial optical characteristic values for a specific region and a plurality of normalized diffuse reflectances 532_1 and 532_2 as initial values, and estimate the final optical characteristic values for the specific region based on the diffuse reflectance theoretical formula 552. Specifically, the initial optical scattering coefficient 542 for the specific region, the initial optical absorption coefficient 544 for the specific region, the normalized diffuse reflectance 532_1 of the second photodiode, and the normalized diffuse reflectance 532_2 of the third photodiode are input to the numerical solver 550 as a set, and the final optical scattering coefficient 554 and the final optical absorption coefficient 556 for the specific region can be estimated.

[0093] In one embodiment, the physiological information estimation unit 560 can estimate the physiological information 564 of a specific region based on the final optical characteristic values. Specifically, the physiological information 564 can be estimated based on the extinction coefficient 562, the final optical scattering coefficient 554, and the final optical absorption coefficient 556. For example, the extinction coefficient 562 can be represented by an extinction coefficient matrix as shown in Table 1 below.

[0094]

Table 1

[0095] JPEG2025081264000011.jpg58151

[0096] In one embodiment, a pseudo inverse matrix of the extinction coefficient matrix as shown in Table 1 can be calculated. Using the inverse matrix of the extinction coefficient matrix, the physiological information 564 based on the wavelength-dependent optical absorption coefficient can be calculated. Specifically, let's examine the process of calculating the physiological information by the following Equation 6 obtained by multiplying the wavelength-dependent optical absorption coefficient and the inverse matrix of the extinction coefficient matrix.

[0097]

Equation

[0098] JPEG2025081264000013.jpg67155

[0099] In FIG. 5, an example of estimating physiological information 564 using light irradiated from one light source is shown, but it is not limited thereto. For example, the body is irradiated with light using a group of light sources having different wavelengths from each other, and a group of photodiodes can detect the light intensity of diffused light. Specifically, a group of light sources including a light source that irradiates light of a first wavelength, a light source that irradiates light of a second wavelength, a light source that irradiates light of a third wavelength, a light source that irradiates light of a fourth wavelength, a light source that irradiates light of a fifth wavelength, and a light source that irradiates light of a sixth wavelength is used. At this time, based on a plurality of light data related to the light of six wavelengths, six final light absorption coefficients 556 for a specific region can be estimated. Then, based on the six final light absorption coefficients 556 for the specific region, using the inverse matrix of the absorption coefficient matrix, the content of oxygenated hemoglobin, the content of deoxygenated hemoglobin, the content of water, and the content of fat for the specific region can be estimated.

[0100] When sorted out, based on three light data, one light absorption coefficient for a specific region can be estimated. When six lights having different wavelengths from each other are used, six light absorption coefficients for a specific region can be estimated based on three light data related to each wavelength, and four content information (the content of oxygenated hemoglobin, the content of deoxygenated hemoglobin, the content of water, the content of fat) for the specific region can be estimated based on the six light absorption coefficients.

[0101] In FIG. 5, an example of estimating physiological information 564 using the light data detected by three photodiodes is shown, but it is not limited thereto. For example, a larger number of photodiodes (for example, 20) than three photodiodes can be used. At this time, it is possible to estimate the physiological information 564 regarding a plurality of regions.

[0102] By the method described with reference to FIGS. 4 and 5, the physiological information estimation process for an example of the medical device 100 shown in FIG. 1 can be understood. The flow of the optical data structure for an example of the medical device 100 shown in FIG. 1 will be described in detail later with reference to FIGS. 10 to 12.

[0103] FIG. 6 is a diagram showing an example of generating calibration parameters using a calibration box 610 according to an embodiment of the present disclosure. In one embodiment, a plurality of photodiode openings 612_1 to 612_20 and a plurality of light source group openings 614_1 to 614_4 may be formed on one surface of the calibration box 610. Each of the plurality of photodiode openings 612_1 to 612_20 can correspond to the position of each of the plurality of photodiodes 112_1 to 112_20 included in the medical device 100 shown in FIG. 1. Also, each of the plurality of light source group openings 614_1 to 614_4 can correspond to the position of each of the plurality of light source groups 114_1 to 114_4 included in the medical device 100 shown in FIG. 1. At this time, the medical device 100 on which the plurality of photodiodes 112_1 to 112_20 and the plurality of light source groups 114_1 to 114_4 are arranged can be placed on the calibration box 610 such that one surface of the medical device 100 faces one surface of the calibration box 610 in which the plurality of openings 612_1 to 612_20 and 614_1 to 614_4 are formed.

[0104] Although the calibration box 610 is shown as having 20 photodiode openings 612_1 to 612_20 and 4 light source group openings 614_1 to 614_4 formed therein, it is not limited thereto. That is, the number of openings formed in the calibration box 610 can be changed according to the number of photodiodes and the number of light source groups included in the medical device 100 shown in FIG. 1.

[0105] In one embodiment, the calibration box 610 can include a standard reflection object inside. The standard reflection object can diffuse (and / or reflect, hereinafter referred to as "diffuse") the irradiated light. Also, the optical information regarding the standard reflection object (for example, diffuse reflectance by wavelength, etc.) can be predefined.

[0106] In one embodiment, before generating the calibration parameters, a LUT (Look Up Table) can be generated using the calibration box 610. At this time, the LUT can include relative relationship information regarding the light intensity of the diffused light among the plurality of apertures 612_1 to 612_20 for photodiodes. Specifically, the LUT can include information regarding the ratio of the light intensity of the diffused light reaching each aperture for the photodiode.

[0107] As an example, the information included in the LUT can be generated as follows. The first light source can be positioned at the aperture 614_1 for the first light source group. Then, a specific photodiode can be positioned at the aperture 612_1 for the first photodiode. Here, in order to remove the influence of the section coefficient of the system parameters on the specific photodiode, when no light is detected by the specific photodiode, an offset can be set so that the measured voltage value of the specific photodiode becomes 0.

[0108] JPEG2025081264000014.jpg96151

[0109] JPEG2025081264000015.jpg64151

[0110]

Table 2

[0111] JPEG2025081264000017.jpg51151

[0112] In one embodiment, the LUT can store, in the form of a table, the light intensity ratio information for each position of the diffused light according to the wavelength of the irradiated light. At this time, the LUT can be stored separately into a table associated with a first wavelength (for example, a first light source) and a table associated with a second wavelength (for example, a second light source). For example, when light of six mutually different wavelengths (for example, a first light source to a sixth light source) is used, six tables can be generated for the six wavelengths, and 20 pieces of light intensity ratio information of diffused light can be generated for each table.

[0113] As can be confirmed from the formula of Equation 1, the light intensity of the diffused light reaching each photodiode can be proportional to the light intensity of the light source. Also, using the information included in the LUT, the light intensity of the diffused light reaching other photodiodes can be proportional to the light intensity of the diffused light reaching a specific photodiode. Overall, the light intensity information of the diffused light reaching each photodiode can be generated by correcting the light intensity information of the light source using the information included in the LUT. An example of the process of correcting the light intensity information of the light source using the information included in the LUT will be described in detail later with reference to FIG. 7.

[0114] In one embodiment, a calibration parameter can be generated based on the light intensity information of the light source corrected using the LUT. Specifically, the calibration parameter can be generated for each of the plurality of photodiodes. Here, the calibration parameter can include a proportionality coefficient and an intercept coefficient.

[0115] The process of generating the calibration parameter will be described based on the plurality of photodiodes 112_1 to 112_20 and the plurality of light source groups 114_1 to 114_4 shown in FIG. 1. The medical device 100 can be arranged in the calibration box 610 such that one surface of the medical device 100 on which the plurality of photodiodes 112_1 to 112_20 and the plurality of light source groups 114_1 to 114_4 are arranged faces one surface of the calibration box 610 in which the plurality of openings 612_1 to 612_20 and 614_1 to 614_4 are formed.

[0116] The first detection process may include a process in which the first photodiode detects diffused light while the standard reflection object is irradiated with light from the first light source. At this time, the first light source included in the first light source group can be preferentially irradiated, but it is not limited thereto, and one of the second to twenty-fourth light sources may be preferentially irradiated. The first detection process may include a process in which the second photodiode 112_2 detects diffused light while the standard reflection object is irradiated with light from the first light source. That is, the first detection process may include a process in which all of the plurality of photodiodes 112_1 to 112_20 detect diffused light while being irradiated with light from the first light source. Here, the light intensity of the first light source may be constant during the first detection process. The second detection process is the same as the first detection process except that the light intensity of the first light source is changed in the first detection process. Similarly, the third to nth detection processes in which the light intensity of the first light source is changed can be continuously performed. Such a detection process can be performed dozens of times. As an example, the light intensity of the first light source can be continuously increased or decreased as the detection process progresses.

[0117] Through continuous detection processes, a measurement value graph according to the light intensity of the light source can be generated for each photodiode. At this time, using the LUT information, a measurement value graph according to the light intensity of the diffused light can be generated for each photodiode. Then, based on the measurement value graph according to the light intensity of the diffused light, a trend line can be generated for each photodiode. Then, based on the equation of the generated trend line, a calibration parameter can be generated. The process of generating the trend line is described in detail in FIG. 7, and the process of generating the calibration parameter based on the equation of the trend line is described in detail in FIG. 8.

[0118] FIG. 7 is a graph showing an example of a process of generating a calibration parameter according to an embodiment of the present disclosure. In FIG. 7, for convenience of explanation, the description is centered around the two photodiodes shown in FIG. 1, that is, the first photodiode 112_1 and the third photodiode 112_3.

[0119] JPEG2025081264000018.jpg74151

[0120] As described above with reference to FIG. 6, the light intensity of the diffused light reaching a specific photodiode can be expressed in proportion to the light intensity of the light source. For example, in the first detection process, when the light intensity of the light source is 16 mW, the relative value of the light intensity of the diffused light reaching the third photodiode 112_3 can be 8 a.u.

[0121] JPEG2025081264000019.jpg83151

[0122] The second graph 720 is a measurement value graph based on the light intensity of the diffused light obtained in the first to fifth detection processes. In an example shown in the figure, as the detection process progresses, the light intensity of the light source can decrease. As a result, the light intensity of the diffused light reaching the third photodiode 112_3 can also decrease in proportion to the light intensity of the light source. Also, the light intensity of the diffused light reaching the first photodiode 112_1 can decrease in proportion to the light intensity of the diffused light reaching the third photodiode 112_3.

[0123] JPEG2025081264000020.jpg64151

[0124] Similar to the description of the trend lines of the first and third photodiodes described above, trend lines of a plurality of photodiodes can be generated. Then, based on the equations of the generated trend lines, calibration parameters for the plurality of photodiodes can be generated. The specific process of generating the calibration parameters will be described in detail later with reference to FIG. 8.

[0125] FIG. 8 is a diagram showing an example in which calibration parameters according to an embodiment of the present disclosure are applied. The first graph 810 is a graph showing trend lines 812, 814, 816, 818 of a plurality of photodiodes before using the calibration parameters. The x-axis is the light intensity of diffused light, and the y-axis is the measured voltage value. Using the first graph 810, the calibration parameters can be generated by the following Equation 7.

[0126] [Number]

[0127] JPEG2025081264000022.jpg51151

[0128] JPEG2025081264000023.jpg77151

[0129] JPEG2025081264000024.jpg51151

[0130] In one example, when using lights of six different wavelengths, each photodiode can generate six trend lines associated with the lights of each wavelength. At this time, six proportionality coefficients of the calibration parameters of the six trend lines can be generated, one for each, for a total of six. Further, the proportionality coefficient and the intercept coefficient of the calibration parameters can be generated for each photodiode. That is, when lights of six different wavelengths and 20 photodiodes are used, 120 proportionality coefficients of the calibration parameters and 20 intercept coefficients of the calibration parameters can be generated.

[0131] JPEG2025081264000025.jpg80151

[0132] With such a configuration, system parameters for a plurality of photodiodes included in a medical device can be corrected identically. Further, as shown in FIG. 5, a normalized diffuse reflectance can be calculated based on the identically corrected system parameters, and physiological information can be estimated based on the normalized diffuse reflectance. Thus, the medical device according to the invention of the present disclosure may become unnecessary for additional calibration after performing generation of calibration parameters once. That is, the invention according to the present disclosure can enhance user convenience by eliminating the need for performing calibration using a phantom.

[0133] JPEG2025081264000026.jpg64151

[0134] In one embodiment, the normalized theoretical diffuse reflectance calculation unit 920 can calculate a first normalized theoretical diffuse reflectance 922_1 and a second normalized theoretical diffuse reflectance 922_2 based on an arbitrary light scattering coefficient 912 and an arbitrary light absorption coefficient 914. Specifically, the normalized theoretical diffuse reflectance calculation unit 920 can calculate a normalized theoretical diffuse reflectance from an arbitrary light scattering coefficient and an arbitrary light absorption coefficient using a diffuse reflectance theoretical formula. The diffuse reflectance theoretical formula can be expressed as the formula of Equation 5 above.

[0135] The normalized theoretical diffuse reflectance calculation unit 920 can calculate a pair of normalized theoretical diffuse reflectances based on a pair of optical characteristic values. For example, the pair of optical characteristic values can include an arbitrary light scattering coefficient 912 and an arbitrary light absorption coefficient 914. Further, the pair of normalized theoretical diffuse reflectances can include a first normalized theoretical diffuse reflectance 922_1 and a second normalized theoretical diffuse reflectance 922_2. At this time, one piece of learning data can include a pair of optical characteristic values and a pair of normalized theoretical diffuse reflectances.

[0136] In one embodiment, the process of calculating the normalized theoretical diffuse reflectance may be the same as the following process. As shown in FIG. 5, the theoretical formula for diffuse reflectance may be an equation related to the light scattering coefficient, the light absorption coefficient, and the distance between the light source and the photodiode. That is, based on an arbitrary light scattering coefficient 912, an arbitrary light absorption coefficient 914, and a first distance between the light source and the first photodiode, a first theoretical diffuse reflectance can be calculated. Similarly, based on an arbitrary light scattering coefficient 912, an arbitrary light absorption coefficient 914, and a second distance between the light source and the second photodiode, a second theoretical diffuse reflectance can be calculated. Also, based on an arbitrary light scattering coefficient 912, an arbitrary light absorption coefficient 914, and a third distance between the light source and the third photodiode, a third theoretical diffuse reflectance can be calculated. Here, the first normalized theoretical diffuse reflectance 922_1 may be a value obtained by dividing the second theoretical diffuse reflectance by the first theoretical diffuse reflectance. Also, the second normalized theoretical diffuse reflectance 922_2 may be a value obtained by dividing the third theoretical diffuse reflectance by the first theoretical diffuse reflectance. At this time, information regarding the first distance, the second distance, and the third distance may be input in advance to the normalized theoretical diffuse reflectance calculation unit 920.

[0137] A plurality of arbitrary light scattering coefficients 912 and a plurality of arbitrary light absorption coefficients 914 may be generated. Based on each of the plurality of pairs of light characteristic coefficients, a plurality of pairs of normalized theoretical diffuse reflectances may be generated. Based on the plurality of light characteristic coefficients and the plurality of pairs of normalized theoretical diffuse reflectances corresponding to the pairs of light characteristic coefficients, a first set of learning data may be generated. As an example, the first set of learning data may include 20,000,000 pairs of light characteristic coefficients and pairs of normalized theoretical diffuse reflectances.

[0138] In one embodiment, the first initial optical characteristic value estimation model 930_1 can be a deep learning-based model or a machine learning-based model learned using a first set of training data. Here, the machine learning-based model can be one of KNN (K-Nearest Neighbors), GB (Gradient Boost), and ANN (Artificial Neural Network). The first initial optical characteristic value estimation model 930_1 learned based on the first set of training data can estimate the initial optical characteristic values (light scattering coefficient and light absorption coefficient) based on the normalized diffuse reflectance of the second photodiode and the normalized diffuse reflectance of the third photodiode.

[0139] In one embodiment, the fourth to sixth distances can be the distances between the light source and the fourth to sixth photodiodes, respectively. Information regarding the fourth distance, the fifth distance, and the sixth distance can be pre-input to the normalized theoretical diffuse reflectance calculation unit 920. At this time, the fourth distance, the fifth distance, and the sixth distance can be different from the first distance, the second distance, and the third distance, respectively. At this time, based on an arbitrary light scattering coefficient 912, an arbitrary light absorption coefficient 914, and the fourth distance between the light source and the fourth photodiode, the fourth theoretical diffuse reflectance can be calculated. Also, based on an arbitrary light scattering coefficient 912, an arbitrary light absorption coefficient 914, and the fifth distance between the light source and the fifth photodiode, the fifth theoretical diffuse reflectance can be calculated. Additionally, based on an arbitrary light scattering coefficient 912, an arbitrary light absorption coefficient 914, and the sixth distance between the light source and the sixth photodiode, the sixth theoretical diffuse reflectance can be calculated. Here, the third normalized theoretical diffuse reflectance can be a value obtained by dividing the fifth theoretical diffuse reflectance by the fourth theoretical diffuse reflectance. Also, the fourth normalized theoretical diffuse reflectance 922_2 can be a value obtained by dividing the sixth theoretical diffuse reflectance by the fourth theoretical diffuse reflectance. The plurality of training data generated by repeating such a process can be the second set of training data. The second initial optical characteristic value estimation model 930_2 can be learned based on the second set of training data.

[0140] For the seventh to ninth distances, by repeating the above process, a third set of training data can be generated. At this time, the third initial light characteristic value estimation model 930_3 can be trained based on the third set of training data. For the tenth to twelfth distances, by repeating the above process, a fourth set of training data can be generated. At this time, the fourth initial light characteristic value estimation model 930_4 can be trained based on the fourth set of training data. In FIG. 9, an example of generating four initial light characteristic value estimation models 930_1 to 930_4 is shown, but the present invention is not limited thereto, and any number of initial light characteristic value estimation models can be generated depending on the number of photodiodes and the arrangement of the photodiodes and the light source.

[0141] When estimating the initial light characteristic values based on the normalized diffuse reflectance related to the first, second, and third distances, the first initial light characteristic value estimation model 930_1 is used. Similarly, when estimating the initial light characteristic values based on the normalized diffuse reflectance related to the fourth, fifth, and sixth distances, the second initial light characteristic value estimation model 930_2 is used. When estimating the initial light characteristic values based on the normalized diffuse reflectance related to the seventh, eighth, and ninth distances, the third initial light characteristic value estimation model 930_3 is used. Similarly, when estimating the initial light characteristic values based on the normalized diffuse reflectance related to the tenth, eleventh, and twelfth distances, the fourth initial light characteristic value estimation model 930_4 is used.

[0142] When the above-described numerical solver inputs arbitrary light scattering coefficients 912 and arbitrary light absorption coefficients 914 as initial values and estimates the final light scattering coefficient and the final light absorption coefficient, there are drawbacks such as long calculation time and low accuracy. When the initial light characteristic value estimation model 930 of the invention according to the present disclosure is used to estimate the initial light scattering coefficient and the initial light absorption coefficient, and then the numerical solver is used to estimate the final light scattering coefficient and the final light absorption coefficient, the calculation time is short and the accuracy can be improved. Thus, the invention according to the present disclosure has an advantage of enhancing the convenience of the user by providing rapid calculation and high accuracy.

[0143] FIG. 10 is a diagram showing an example of a medical device according to an embodiment of the present disclosure. As shown in the figure, a plurality of photodiodes and a plurality of light source groups 1012, 1014, 1022, 1024 can be arranged on one surface of the medical device. An example shown in the figure can be the same as the medical device shown in FIG. 1. The first light source group 1012 can include six light sources. The six light sources can irradiate light with different wavelengths from each other. The second to fourth light source groups 1014, 1022, 1024 can also include six light sources configured to irradiate light with different wavelengths from each other.

[0144] In one embodiment, the medical device can be a device on which calibration has been performed. For example, calibration for the medical device can be performed during the manufacturing process. Also, the medical device is used by being attached to the body. The process in which the medical device attached to the body detects optical data will be described in detail later.

[0145] In one embodiment, the first state 1010 can represent a detection relationship between the first light source group 1012 and the third light source group 1014 and the first set of photodiodes 1016. In the first measurement process, each of the first set of photodiodes 1016 can detect diffused light in a state where the first light source included in the first light source group 1012 irradiates light. At this time, the light intensity of the first light source can be constant during the first measurement process. After the first measurement process is performed, optical data (12 measurement voltage values) related to the first light source can be detected by the first set of photodiodes 1016. Each of the second to sixth measurement processes is the same as the first measurement process except that the second to sixth light sources included in the first light source group 1012 are used instead of the first light source in the first measurement process. Similarly, the 13th to 18th measurement processes related to the 13th to 18th light sources included in the third light source group 1014 can be performed.

[0146] In one embodiment, the second state 1020 can represent the detection relationship between the second light source group 1022 and the fourth light source group 1024 and the second set of photodiodes 1026. In the seventh measurement process, with the seventh light source included in the second light source group 1022 irradiating light, each of the second set of photodiodes 1026 can detect diffused light. At this time, the light intensity of the seventh light source can be constant during the seventh measurement process. After the seventh measurement process is performed, the second set of photodiodes 1026 can detect optical data (12 measured voltage values) related to the seventh light source. Each of the eighth to twelfth measurement processes is the same as the seventh measurement process, except that the eighth to twelfth light sources included in the second light source group 1022 are used instead of the seventh light source in the seventh measurement process. Similarly, the nineteenth to twenty-fourth measurement processes related to the nineteenth to twenty-fourth light sources included in the fourth light source group 1024 can be performed.

[0147] The process of estimating physiological information based on the optical data obtained by the first to twenty-fourth measurement processes will be described in detail later with reference to FIGS. 11 and 12. The physiological information estimated based on the optical data can include the content information of oxygenated hemoglobin, the content information of deoxygenated hemoglobin, the content information of moisture, the content information of fat, the urine volume in the bladder, and the like.

[0148] FIG. 11 is a diagram showing an example of estimating physiological information based on a plurality of optical data according to an embodiment of the present disclosure. In FIG. 11, among the first to twenty-fourth measurement processes described above, the first measurement process, the thirteenth measurement process, the seventh measurement process, and the nineteenth measurement process related to the first wavelength (that is, the first light source, the seventh light source, the thirteenth light source, and the nineteenth light source) are centered on the optical data (measured voltage values) detected. Here, the measured voltage value can mean the voltage value corrected using the calibration parameter. The optical data related to the second wavelength to the sixth wavelength can be processed in the same manner as the optical data related to the first wavelength.

[0149] JPEG2025081264000027.jpg54151

[0150] As described above with reference to FIG. 4, the normalized diffuse reflectance can be calculated by dividing the voltage value of each photodiode by the reference photodiode voltage value. As shown in the figure, in the optical data related to the first light source and the thirteenth light source, the fifth photodiode and the fifteenth photodiode closest to the first light source and the thirteenth light source can be selected as the reference photodiodes. For example, in the first row regarding the plurality of photodiodes related to the first light source, the fifth photodiode can be selected as the reference photodiode, and in the second row, the fifteenth photodiode can be selected as the reference photodiode. As shown in the figure, in the optical data related to the seventh light source and the nineteenth light source, the sixth photodiode and the sixteenth photodiode closest to the seventh light source and the nineteenth light source can be selected as the reference photodiodes. For example, in the first row regarding the plurality of photodiodes related to the seventh light source, the sixth photodiode can be selected as the reference photodiode, and in the second row, the sixteenth photodiode can be selected as the reference photodiode.

[0151] JPEG2025081264000028.jpg45151

[0152] As shown in the figure, the normalized diffuse reflectance 1122 of the first photodiode with respect to the seventh light source can be generated based on the measured voltage value 1112 of the first photodiode with respect to the seventh light source and the measured voltage value 1114 of the sixth photodiode with respect to the seventh light source. Here, the sixth photodiode can be the reference photodiode. Also, the normalized diffuse reflectance 1128 of the seventh photodiode with respect to the thirteenth light source can be generated based on the measured voltage value 1116 of the fifth photodiode with respect to the thirteenth light source and the measured voltage value 1118 of the seventh photodiode with respect to the thirteenth light source. Here, the fifth photodiode can be the reference photodiode.

[0153] In one embodiment, the normalized diffuse reflectance data map 1120 can have fewer data points than the measured voltage data map 1110. Specifically, the normalized diffuse reflectance for a specific reference photodiode is not calculated. For example, the normalized diffuse reflectance for the measured voltage value 1116 of the fifth photodiode with respect to the thirteenth light source is not calculated. Here, the fifth photodiode can be a reference photodiode. In the illustrated example, if the number of data points in the measured voltage data map 1110 is 48 (4×12) and the number of measured voltage values corresponding to the reference photodiode is 8, the number of data points of the normalized diffuse reflectance can be 40.

[0154] The light scattering coefficient can be associated with a specific region. Here, the light scattering coefficient can be the final light scattering coefficient. As an example, the first region can be a body part associated with the first photodiode and the second photodiode. Similarly, the nth region can be a body part associated with the nth photodiode and the (n + 1)th photodiode.

[0155] JPEG2025081264000029.jpg42151

[0156] As an example, the light scattering coefficient of the nth region with respect to the yth light source can be estimated based on the normalized diffuse reflectance of the nth photodiode with respect to the yth light source and the normalized diffuse reflectance of the (n + 1)th photodiode with respect to the yth light source. For example, the light scattering coefficient 1132 of the first region with respect to the seventh light source can be estimated based on the normalized diffuse reflectance 1122 of the first photodiode with respect to the seventh light source and the normalized diffuse reflectance 1124 of the second photodiode with respect to the seventh light source. As another example, the light scattering coefficient 1134 of the sixth region with respect to the thirteenth light source can be estimated based on the normalized diffuse reflectance 1126 of the sixth photodiode with respect to the thirteenth light source and the normalized diffuse reflectance 1128 of the seventh photodiode with respect to the thirteenth light source.

[0157] Similarly, the optical absorption coefficient data map can be calculated based on the normalized diffuse reflectance data map 1120. Here, the optical absorption coefficient can be the final optical absorption coefficient. Since the optical absorption coefficient is part of the physiological information, the optical absorption coefficient data map is used as physiological information.

[0158] In one embodiment, the number of data in the optical scattering coefficient data map 1130 may be less than the number of data in the normalized diffuse reflectance data map 1120. Specifically, one optical scattering coefficient can be estimated based on two normalized diffuse reflectances. For example, when the number of the normalized diffuse reflectance data map 1120 is 40 (4×10), the optical scattering coefficient data map 1130 can be 32 (4×8). Similarly, the number of data in the optical absorption coefficient data map may also be less than the number of data in the normalized diffuse reflectance data map 1120.

[0159] After the first to the 24th measurement processes are performed based on the above-described content, the optical scattering coefficient data map and the optical absorption coefficient data map can be calculated for the detected plurality of optical data. In one embodiment, the optical scattering coefficient data map and the optical absorption coefficient data map can be generated for each wavelength. For example, after performing the second measurement process, the eighth measurement process, the fourteenth measurement process, and the twentieth measurement process related to the second wavelength, the optical scattering coefficient data map and the optical absorption coefficient data map for the second wavelength can be calculated for the detected plurality of optical data. Similarly, after performing a plurality of measurement processes related to the nth wavelength, the optical scattering coefficient data map and the optical absorption coefficient data map for the nth wavelength can be calculated for the detected plurality of optical data. The process of calculating the physiological information data map based on the plurality of optical scattering coefficient data maps will be described in detail later with reference to FIG. 12.

[0160] FIG. 12 is a diagram showing an example of estimating physiological information based on a plurality of light scattering coefficient data maps according to an embodiment of the present disclosure. The plurality of light scattering coefficient data maps 1210 may include light scattering coefficient data maps for each wavelength. In one embodiment, the light scattering coefficient data map 1212_1 for the first wavelength may include all of the light scattering coefficients estimated based on a plurality of measurement processes associated with the first wavelength and a plurality of detected light data. Similarly, each of the light scattering coefficient data maps 1212_2 to 1212_6 for the second to sixth wavelengths is the same as the light scattering coefficient data map 1212_1 for the first wavelength, except that in the light scattering coefficient data map 1212_1 for the first wavelength, a plurality of measurement processes associated with each of the second to sixth wavelengths are performed instead of the first wavelength, and based on a plurality of detected light data. Here, the light scattering coefficient can mean the final light scattering coefficient. In an example shown in the figure, the light scattering coefficient of the x-th region with respect to the y-th light source can be represented by x and y.

[0161] In one embodiment, a physiological information data map can be calculated based on the plurality of light scattering coefficient data maps 1210. Specifically, physiological information can be estimated based on a plurality of light scattering coefficients of a specific region with respect to light sources associated with different wavelengths. For a method of estimating physiological information based on the light scattering coefficient, reference can be made to the content described in FIG. 5.

[0162] For example, physiological information regarding the x-th region can be estimated based on the light scattering coefficients of [(x, y), (x, y + 1), (x, y + 2), (x, y + 3), (x, y + 4), (x, y + 5)]. Here, (x, y) can be the light scattering coefficient of the x-th region with respect to the y-th light source included in the light scattering coefficient data map 1212_1 for the first wavelength. Specifically, based on the light scattering coefficient of the 8th region with respect to the 1st light source, the light scattering coefficient of the 8th region with respect to the 2nd light source, the light scattering coefficient of the 8th region with respect to the 3rd light source, the light scattering coefficient of the 8th region with respect to the 4th light source, the light scattering coefficient of the 8th region with respect to the 5th light source, and the light scattering coefficient of the 8th region with respect to the 6th light source, physiological information regarding the 8th region (for example, content information of oxygenated hemoglobin, content information of deoxygenated hemoglobin, content information of moisture, content information of fat (Fat), etc.) can be estimated. Here, each of the 1st to 6th light sources can irradiate light of each of the 1st to 6th wavelengths.

[0163] In one embodiment, the plurality of physiological information data maps can include a first physiological information data map 1220, a second physiological information data map 1230, a third physiological information data map 1240, and a fourth physiological information data map 1250. Here, each of the first to fourth physiological information data maps 1220, 1230, 1240, 1250 can be one of an oxygenated hemoglobin (HbO 2 ) data map, a deoxygenated hemoglobin (HHb) data map, a moisture (H 2 O) data map, and a fat (Fat) data map.

[0164] In one embodiment, the plurality of physiological information data maps 1220, 1230, 1240, 1250 can have fewer data than the plurality of light scattering coefficient data maps 1210. Specifically, four physiological information can be estimated based on the six data included in the plurality of physiological information data maps. For example, each of the plurality of light scattering coefficient data maps 1210 includes 32 (4×8) data, so that the plurality of light scattering coefficient data maps 1210 can include 192 (4×8×6) data. At this time, the plurality of physiological information data maps 1220, 1230, 1240, 1250 can include 128 (4×8×4) data.

[0165] Using a plurality of light sources and a plurality of photodiodes, physiological information regarding a plurality of regions can be provided. The invention according to the present disclosure can provide physiological information not only regarding local regions of the body but also regarding a wide range of parts of the body. Further, by providing physiological information regarding a plurality of regions, the state of organs contained in the body (for example, the amount of urine stored in the bladder, the position of the bladder, etc.) can be specifically grasped. In the case of a patient who does not feel the urge to urinate, etc., the invention according to the present disclosure can be used to receive physiological information regarding their own bladder in real time or periodically. The patient can monitor the amount of urine stored in their own bladder based on the provided information and urinate at an appropriate time.

[0166] FIG. 13 is a block diagram showing an example of a method for estimating the amount of urine in the bladder according to an embodiment of the present disclosure. In the content described below based on FIGS. 13 to 16, the “measurement cycle” can refer to a series of processes for detecting an optical data set using a medical device (for example, the medical device 100 described with reference to FIG. 1) disposed on the skin located on the bladder of a specific user. That is, the measurement cycle can include the first to 24th measurement processes shown in FIG. 10. The detailed process of the measurement cycle can be understood from the content described above based on FIGS. 1 and 10.

[0167] In one embodiment, the processor can receive an optical data set 1302 associated with a specific user by performing a measurement cycle. For example, the optical data set 1302 can include a measured voltage data map 1110 shown in FIG. 11. For example, the optical data set 1302 can include 48 optical data for each of 6 wavelengths.

[0168] In one embodiment, the optical characteristic value set estimator 1310 can estimate an optical characteristic value set for at least a part of the body of a specific user based on the optical data set 1302. Here, the processor is used by the optical characteristic value set estimator 1310. For example, the optical characteristic value set estimator 1310 can calculate a set of normalized diffuse reflectances related to a plurality of photodiodes based on the optical data set 1302. Further, the optical characteristic value set estimator 1310 can estimate an optical characteristic value set 1312 related to at least a part of the body based on the set of normalized diffuse reflectances. A series of processes performed by the optical characteristic value set estimator 1310 can be understood from the content described with reference to FIGS. 4 to 12.

[0169] As an example, the optical characteristic value set 1312 can include the final light scattering coefficient 554 and the final light absorption coefficient 556 described with reference to FIG. 5. For example, the optical characteristic value set 1312 can include a light scattering coefficient data map 1130 and a light absorption coefficient data map described with reference to FIG. 11. For example, the optical characteristic value set 1312 can include 32 pairs of light scattering coefficients and light absorption coefficients for each of 6 wavelengths.

[0170] In one embodiment, the urine volume estimation model 1320 can estimate the urine volume 1322 in the bladder of a specific user based on the set of optical characteristic values 1312. At this time, the urine volume estimation model 1320 can be a deep learning-based model or a machine learning-based model that has learned a plurality of learning data sets. For example, the machine learning-based model can be an ANN (Artificial Neural Network), KNN (K-Nearest Neighbors), GB (Gradient Boost), Linear regression model, Random Forest model, or Ada Boost model. Also, the learning data set can include pairs of the actual urine volume and the set of optical characteristic values related to the actual urine volume. The process of obtaining the learning data set and the process of the urine volume estimation model 1320 learning a plurality of learning data sets will be described in detail later based on FIGS. 14 and 15.

[0171] In another embodiment, the urine volume estimation model 1320 can estimate the urine volume 1322 in the user's bladder based on the set of optical characteristic values 1312 and the obesity information 1314. Here, the urine volume estimation model 1320 can be a deep learning-based model or a machine learning-based model that has learned a plurality of learning data sets and learning obesity information. At this time, the obesity information can include information about the fat on the body around where the bladder is located. For example, the obesity information can include the body mass index (BMI; Body Max Index), the obesity measured by the abdominal obesity measurement method, the obesity measured by the standard weight method, the body fat index, the abdominal fat thickness measured using ultrasound, and the like. As another example, the obesity information can include the above-described light absorption coefficient. With such a configuration, the urine volume estimation model 1320 can accurately estimate the urine volume even in the case of an obese user by learning the obesity information 1314.

[0172] In one embodiment, the processor can calculate a set of physiological information based on the set of optical characteristic values 1312. The process of calculating the set of physiological information can be understood from the content described based on FIG. 12. Here, the set of physiological information can correspond to the set of optical characteristic values 1312. For example, the set of physiological information can include the light absorption coefficient data included in the light absorption coefficient data map. Also, the set of physiological information can include a plurality of physiological information data maps 1220, 1230, 1240, 1250 calculated based on the plurality of light scattering coefficient data maps 1210 described above based on FIG. 12. At this time, the processor can estimate the urine volume 1322 using the set of physiological information corresponding to the set of optical characteristic values 1312 together with the set of optical characteristic values 1312. However, since the set of physiological information can be calculated based on the set of optical characteristic values 1312, it will be described with reference to the set of optical characteristic values 1312.

[0173] In the case of a user with a lot of fat around the skin where the bladder is located, the light data included in the light data set 1302 may change little even though the urine volume in the bladder increases / decreases. In one embodiment, when the amount of change in the light data included in the light data set 1302 is extremely small even though the urine volume in the bladder increases / decreases, the processor can output a result related to the inability to estimate the urine volume. For example, the processor can output a result related to the inability to estimate the urine volume when the obesity degree information 1314 is equal to or greater than a predetermined obesity degree reference value.

[0174] In one embodiment, when the estimated urine volume 1322 is equal to or greater than a predetermined reference value, the processor can output a message related to a urination advice. For example, the reference value can correspond to the urine volume in the bladder at which a person generally feels the urge to urinate. Specifically, the processor can output visual, auditory, and tactile information via the user terminal / medical device as a message related to the urination advice. For example, the user terminal / medical device can output a pop-up window or a vibration / sound notification that advises urination. With such a configuration, a patient wearing the medical device can urinate at an appropriate time by receiving a message related to the urination advice.

[0175] The measurement cycle can be performed in real-time or periodically. The invention according to the present disclosure can provide a patient with an estimated urine volume 1322 estimated based on an optical data set 1302 detected by the measurement cycle. The intravesical urine volume can be provided to the patient in real-time or periodically. That is, the patient can monitor the urine volume stored in his or her bladder based on the provided information and urinate at an appropriate time.

[0176] FIG. 14 is a graph showing an example of learning data according to an embodiment of the present disclosure. In one embodiment, the processor can receive an optical data set associated with the nth measurement by the nth measurement cycle (where n is 1, 2, 3, 4 or more). The processor can estimate an optical characteristic value of the nth measurement based on the optical data set of the nth measurement. At this time, the nth actual urine volume can be a value obtained by directly measuring the intravesical urine volume of a specific user at the time when the nth measurement cycle is performed. For example, the actual urine volume can be obtained through a bladder irrigation process, a urodynamic study (UDS) process, a clean intermittent catheterization (CIC) process, etc.

[0177] As an example, the actual urine volume can be obtained through a bladder irrigation process. Specifically, the bladder irrigation process can include discharging the urine in the bladder of a specific user using a Foley catheter. At this time, the intravesical urine volume of a specific user can be specifically confirmed using a residual urine scanner (RU scanner). Then, the bladder irrigation process can inject sterile saline into the bladder of a specific user. At this time, the actual urine volume can correspond to the volume of the injected sterile saline. For example, when all the urine in the bladder of a specific user is discharged, the first actual urine volume can be about 0 ml. Then, when 100 ml of sterile saline is injected into the bladder of a specific user, the second actual urine volume can be 100 ml.

[0178] As another example, the actual urine volume can be obtained through the urodynamic examination process. Specifically, the urodynamic examination process can include a bladder irrigation process using a Foley catheter for UDS instead of a regular Foley catheter. The actual urine volume can be obtained through the bladder irrigation process included in the urodynamic examination process. Also, through the urodynamic examination process, various measurement data such as the internal pressure of the bladder, the activity of the bladder muscles, and the connection state between the urethra and the bladder can be obtained. As an example, the training dataset can include such measurement data.

[0179] As yet another example, the actual urine volume can be obtained through the clean intermittent self-catheterization process. Specifically, the clean intermittent self-catheterization process can include using a clean intermittent self-catheter to drain the urine in the bladder. At this time, the urine volume drained using the clean intermittent self-catheter can be measured (for example, measuring the urine volume drained using a urine collection cup). At this time, the actual urine volume can be calculated using the drained urine volume. For example, through the clean intermittent self-catheterization process, all the urine in the bladder can be drained in two times. 200 ml of urine in the bladder can be drained the first time, and 150 ml of urine in the bladder can be drained the second time. At this time, the first actual urine volume can be 350 ml, the second actual urine volume can be 150 ml, and the third actual urine volume can be 0 ml.

[0180] As an example, multiple actual urine volumes can be obtained. For example, the first actual urine volume corresponding to the minimum urine volume of a specific user's bladder can be obtained. Also, the Xth actual urine volume corresponding to the minimum urine volume of a specific user's bladder can be obtained (where X is 2, 3, or more). When X is 3 or more, the second to the (X - 1)th actual urine volumes can be obtained as values between the first actual urine volume and the Xth actual urine volume.

[0181] The graph of FIG. 14 may have the time on the x-axis and the urine volume in the bladder of a specific user on the y-axis. Referring to FIG. 14, the actual urine volumes included in each of the first learning data 1410, the second learning data 1420, the third learning data 1430, and the fourth learning data 1440 may be displayed. Specifically, the first learning data 1410 may include the first actual urine volume at the time when the first measurement cycle was performed. Similarly, the nth learning data may include the nth actual urine volume at the time when the nth measurement cycle was performed. Additionally, the nth learning data may include a pair of the nth actual urine volume and the optical characteristic value of the nth measurement. In FIG. 14, only the first learning data 1410 to the fourth learning data 1440 are shown, but it is not limited thereto. For example, a plurality of learning data can be obtained by more than 4 or less than 4 measurement cycles.

[0182] The minimum urine volume of the bladder of a specific user can correspond to the urine volume at the time when all the urine in the bladder has been discharged. For example, the minimum urine volume of the bladder of a specific user may be about 0 ml. Also, the maximum urine volume of the bladder of a specific user can correspond to the maximum capacity of the bladder. For example, the maximum urine volume of the bladder of a specific user may be about 400 ml to 500 ml. The minimum urine volume and the maximum urine volume of the bladder of a specific user can be different for each individual user. Referring to FIG. 14, the first actual urine volume can correspond to the minimum urine volume of the bladder of a specific user, and the third actual urine volume can correspond to the maximum urine volume of the bladder of a specific user.

[0183] In one embodiment, a teacher model can be generated by learning a plurality of learning data sets. For example, a linear regression model, a random forest model, etc. can be used as the teacher model. Also, the teacher model can estimate a set of optical characteristic values for additional learning by inputting the urine volume for additional learning.

[0184] As an example, the n-th teacher model can be a teacher model that has learned the (n + 1)-th training dataset and the n-th training dataset. Here, for the additional learning of the n-th estimation of urine volume, any value between the (n + 1)-th actual urine volume and the n-th actual urine volume can be selected. Here, the n-th estimation can mean the process of estimating a set of light characteristic values for additional learning of the n-th estimation based on a plurality of urine volumes for additional learning of the n-th estimation between the (n + 1)-th actual urine volume and the n-th actual urine volume using the n-th teacher model. For example, for the plurality of urine volumes for additional learning of the n-th estimation, values at regular intervals can be selected between the (n + 1)-th actual urine volume and the n-th actual urine volume. For example, when the (n + 1)-th actual urine volume is 400 ml and the n-th actual urine volume is 100 ml, the first urine volume for additional learning of the n-th estimation can be 200 ml, and the second urine volume for additional learning of the n-th estimation can be 300 ml.

[0185] For example, the first estimation can include the process of estimating a first set of light characteristic values for additional learning based on the first urine volume for additional learning between the second actual urine volume and the first actual urine volume. Similarly, the first estimation can include the process of estimating a k-th set of light characteristic values for additional learning based on the k-th urine volume for additional learning between the second actual urine volume and the first actual urine volume (k is 1, 2, 3 or more). At this time, the first additional learning dataset of the first estimation can include the first urine volume for additional learning and the first set of light characteristic values for additional learning. Similarly, the k-th additional learning dataset of the first estimation can include the k-th urine volume for additional learning and the k-th set of light characteristic values for additional learning.

[0186] Referring to the graph of FIG. 14, the urine volumes for additional learning included in each of the first additional learning dataset 1412_1, the second additional learning dataset 1412_2, and the third additional learning dataset 1412_3 of the first estimation can be displayed. Specifically, the first urine volume for additional learning to the third urine volume for additional learning of the first estimation can be displayed between the first actual urine volume and the second actual urine volume. As an example, each time point for the first additional learning dataset 1412_1 to the third additional learning dataset 1412_3 of the first estimation can correspond to a method in which the urine volume for additional learning is selected between the second actual urine volume and the first actual urine volume. For example, when a plurality of urine volumes for additional learning are selected as values at regular intervals between the second actual urine volume and the first actual urine volume, each time point for the first additional learning dataset 1412_1 to the third additional learning dataset 1412_3 of the first estimation can be selected as a value at regular intervals between the time point of measuring the second actual urine volume and the time point of measuring the first actual urine volume. For example, if the second actual urine volume is 400 ml, the time point corresponding to the first learning data 1410 is 0 seconds, the first actual urine volume is 0 ml, the time point corresponding to the second learning data 1420 is 4000 seconds, the first urine volume for additional learning of the first estimation is 100 ml, the second urine volume for additional learning of the first estimation is 200 ml, and the third urine volume for additional learning of the first estimation is 300 ml, the time point corresponding to the first additional learning dataset 1412_1 of the first estimation is 1,000 seconds, the time point corresponding to the second additional learning dataset 1412_2 of the first estimation is 2,000 seconds, and the time point corresponding to the third additional learning dataset 1412_3 of the first estimation can be 3,000 seconds.

[0187] Based on the above-described content regarding the first estimation between the second learning data 1420 and the first learning data 1410, the second estimation between the third learning data 1430 and the second learning data 1420 and the third estimation between the fourth learning data 1440 and the third learning data 1430 can be similarly understood.

[0188] In FIG. 14, three additional learning data sets 1412_1 to 1412_3 of the first estimation are shown, but it is not limited thereto. For example, when the number of urine volumes for additional learning is selected to be more than three or less than three, an additional learning data set with a number more than three or less than three can also be generated. Also, in FIG. 14, it shows that four measurement cycles are performed, but it is not limited thereto. For example, the measurement can be performed a number of times more than four or less than four. As another example, multiple measurements can be performed within 72 hours.

[0189] In one embodiment, when two measurement cycles are performed, two learning data sets can be obtained. For example, the first urine volume included in the first learning data set can correspond to the minimum urine volume of a specific user's bladder, and the second urine volume included in the second learning data set can correspond to the maximum urine volume of a specific user's bladder. At this time, by minimizing the data collection for the user, the inconvenience to the user can be minimized, and a urine volume prediction model for individuals can be generated.

[0190] In other embodiments, when multiple measurement cycles (for example, three or more times) are performed, multiple learning data sets can be obtained. At this time, multiple teacher models can be generated based on the multiple learning data sets, and multiple additional learning data sets can be generated based on the multiple teacher models. The urine volume prediction model can improve the accuracy of estimating the urine volume in the bladder by learning the multiple learning data sets and the multiple additional learning data sets.

[0191] FIG. 15 is a block diagram showing an example of a urine volume estimation model 1550 according to an embodiment of the present disclosure. In one embodiment, the urine volume estimation model 1550 can learn a plurality of learning data sets 1512, 1514. Here, the n-th learning data set can include a pair of the n-th actual urine volume and the n-th set of light characteristic values. That is, the plurality of learning data sets 1512, 1514 can include a plurality of actual urine volumes 1512 and a plurality of sets of light characteristic values 1514. The method of obtaining the plurality of learning data sets can be understood from the plurality of learning data 1410, 1420, 1430, 1440 described above with reference to FIG. 14.

[0192] In one embodiment, the urine volume estimation model 1550 can further learn a single or a plurality of additional learning data sets 1532, 1534. Here, the k-th additional learning data can include a pair of the k-th urine volume for additional learning and the k-th set of light characteristic values for additional learning. That is, the plurality of additional learning data sets 1532, 1534 can include a plurality of urine volumes 1532 for additional learning and a plurality of sets of light characteristic values 1534 for additional learning. The method of obtaining the plurality of additional learning data sets can be understood from the plurality of additional learning data sets 1412_1 to 1412_3, 1422_1, 1422_2, 1432_1 to 1432_4 described above with reference to FIG. 14. In FIG. 15, a plurality of pairs of the urine volume for additional learning and the set of light characteristic values for additional learning are shown, but it is not limited thereto, and there may be one pair of the urine volume for additional learning and the set of light characteristic values for additional learning.

[0193] In one embodiment, the urine volume estimation model 1550 can learn by applying a weighting value 1520 to the plurality of learning data sets 1512, 1514. Specifically, the weighting value 1520 can be information for the urine volume estimation model 1550 to adjust the learning ratio of the plurality of additional learning data sets 1532, 1534 and the plurality of learning data sets 1512, 1514. For example, the weighting value 1520 can be determined in advance before the data learning of the urine volume estimation model 1550. Additionally or alternatively, the weighting value 1520 can be applied to the plurality of additional learning data sets and adjusted during the learning process of the urine volume estimation model 1550.

[0194] In one embodiment, the urine volume estimation model 1550 can further learn the obesity information 1540 for learning. At this time, the obesity information 1540 for learning can be the obesity information of the body to be measured for a plurality of actual urine volumes 1512. In FIG. 15, a single obesity information 1540 for learning is shown, but the present invention is not limited thereto. For example, when the measurement of a plurality of actual urine volumes 1512 is performed on a plurality of bodies, the urine volume estimation model 1550 can learn a plurality of obesity information for learning.

[0195] The urine volume estimation model 1550 can learn with emphasis on a plurality of actual urine volumes 1512 by applying weight values 1520 to a plurality of learning data sets 1512 and 1514 for learning. Thereby, the urine volume estimation model 1550 can accurately estimate the urine volume. In addition, the urine volume estimation model 1550 can be provided for individual users by learning the obesity information 1540 for learning. In addition, the urine volume estimation model 1550 uses a machine learning model or a deep learning model for which app development support is relatively good, so that the invention according to the present disclosure can be easy for mobile app development for wearable devices. In addition, the machine learning model or the deep learning model is easy to re-learn, and the invention according to the present disclosure can estimate the intravesical urine volume for individuals. In addition, the urine volume estimation model 1550 is easy to maintain and improve, and is excellent in model expandability and model versatility.

[0196] FIG. 16 is a graph showing a plurality of examples of a urine volume estimation model according to an embodiment of the present disclosure. Referring to FIG. 16, a urine volume estimation graph 1600 can display the urine volume estimation results of a plurality of urine volume estimation models. Specifically, the urine volume estimation graph 1600 can display a first graph 1640, a second graph 1650, and a third graph 1660. In FIG. 16, the third graph 1660 can be displayed as a dotted line graph. The urine volume estimation graph 1600 can also display a first actual urine volume included in the first learning data set 1610 and a second actual urine volume included in the second learning data set 1630. The urine volume estimation graph 1600 can also display a first comparison actual urine volume included in the first comparison data set 1622, a second comparison actual urine volume included in the second comparison data set 1624, and a third comparison actual urine volume included in the third comparison data set 1626. The urine volume estimation graph 1600 can display an index on the x-axis and the urine volume in the bladder (actual urine volume and / or estimated urine volume) on the y-axis. At this time, the index can be an indicator for representing the passage of time. For example, the index at the time when the first measurement cycle is performed can be designated as 0, and the index at the time when the second measurement cycle is performed can be designated as 4.

[0197] In one embodiment, the first learning dataset 1610 can include pairs of a first actual urine volume and a first set of optical characteristic values. Also, the second learning dataset 1630 can include pairs of a second actual urine volume and a second set of optical characteristic values. In one embodiment, the first comparison dataset 1622 can include pairs of a first comparison actual urine volume and a first set of comparison optical characteristic values. Similarly, the second comparison dataset 1624 includes pairs of a second comparison actual urine volume and a second set of comparison optical characteristic values, and the third comparison dataset 1626 can include pairs of a third comparison actual urine volume and a third set of comparison optical characteristic values. Here, the plurality of learning datasets 1610, 1630 and the plurality of comparison datasets 1622, 1624, 1626 can be obtained by performing a measurement cycle. For example, the plurality of learning datasets 1610, 1630 and the plurality of comparison datasets 1622, 1624, 1626 can be obtained from a specific user wearing a medical device (e.g., the medical device 100 shown in FIG. 1) on the skin located on the bladder.

[0198] As an example, the first actual urine volume can be the actual urine volume in the bladder of a specific user at the time when the first measurement is performed. Also, the second actual urine volume can be the actual urine volume in the bladder of a specific user at the time when the second measurement of the bladder of the specific user is performed. Referring to FIG. 16, the first actual urine volume can be about 100 ml, and the second actual urine volume can be about 300 ml. The method for measuring the actual urine volume can be understood from the above description based on FIG. 14.

[0199] Similarly, the method for measuring the first to third comparison actual urine volumes is the same as the method for measuring the first actual urine volume and the second actual urine volume. At this time, the first to third comparison actual urine volumes can be actual urine volumes not used for learning the urine volume learning model. Also, the first to third sets of comparison optical characteristic values can be sets of optical characteristic values not used for learning the urine volume learning model.

[0200] As an example, the first to third comparison actual urine volumes can be selected between the first actual urine volume and the second actual urine volume. For example, the first to third comparison actual urine volumes can be values having the same interval between the first actual urine volume and the second actual urine volume. Referring to FIG. 16, when the first actual urine volume is 100 ml and the second actual urine volume is 300 ml, the first comparison actual urine volume can be 150 ml, the second comparison actual urine volume can be 200 ml, and the third comparison actual urine volume can be 250 ml. In FIG. 16, three comparison actual urine volumes are selected, but a number of comparison actual urine volumes more or less than three may also be selected.

[0201] In FIG. 16, the "comparison measurement cycle" can mean a measurement cycle for estimating a comparison light characteristic value set. At this time, the index at the time when the first comparison measurement cycle is performed can be referred to as 1, the index at the time when the second comparison measurement cycle is performed can be referred to as 2, and the index at the time when the third comparison measurement cycle is performed can be referred to as 3. As an example, the nth comparison measurement cycle can be performed corresponding to the time when the nth comparison actual urine volume is measured. Also, based on the nth comparison light data set detected by the nth comparison measurement cycle, the nth comparison light characteristic value set can be estimated.

[0202] In one embodiment, the first urine volume estimation model may be a model trained with a first learning dataset 1610 and a second learning dataset 1630. For example, an ANN model may be used for the first urine volume estimation model. The first graph 1640 can display the urine volume estimation result of the first urine volume estimation model. Also, the teacher model may be a model trained with the first learning dataset 1610 and the second learning dataset 1630. For example, a Random Forest model or a Linear Regression model may be used for the teacher model. The second graph 1650 can display the urine volume estimation result of the teacher model. Also, the second urine volume estimation model may be a model trained with the first learning dataset 1610, the second learning dataset 1630, and a plurality of additional learning datasets. At this time, the plurality of additional learning datasets may be generated by the teacher model. For example, an ANN model may be used for the second urine volume estimation model. The third graph 1660 can display the urine volume estimation result of the second urine volume estimation model.

[0203] Comparing the urine volume estimation results of the first urine volume estimation model, the urine volume estimation results of the second urine volume estimation model, and the urine volume estimation results of the teacher model, it can be represented as shown in Table 3 below. Referring to FIG. 16, the urine volume estimation graph 1600 may represent Table 3 below.

[0204]

Table 3

[0205] In Table 3, the urine volume estimation model can include a first urine volume estimation model, a second urine volume estimation model, and a teacher model. The urine volume estimation model can estimate the urine volume when a set of optical characteristic values (or a set of optical characteristic values for comparison) corresponding to each index is input. For example, when the first set of optical characteristic values for comparison is input to the first urine volume estimation model, a urine volume estimation result corresponding to about 39.1 ml can be generated. At this time, the estimation error of the first urine volume estimation model can be 110.9 ml, which is obtained by subtracting 39.1 ml, the estimation result of the first urine volume estimation model, from 150 ml, the actual urine volume for the first comparison. Similarly, when the first set of optical characteristic values for comparison is input to the second urine volume estimation model, a urine volume estimation result corresponding to about 115.6 ml can be generated. At this time, the estimation error of the second urine volume estimation model can be 34.4 ml, which is obtained by subtracting 115.6 ml, the estimation result of the first urine volume estimation model, from 150 ml, the actual urine volume for the first comparison. It can be confirmed that the second urine volume estimation model that has learned a plurality of learning data sets and a plurality of additional learning data sets has a smaller urine volume estimation error than the first urine volume estimation model that has learned a plurality of learning data sets from Table 3 or the urine volume estimation graph 1600.

[0206] When the amount of the learning data set is not large, a plurality of additional learning data sets are generated by the teacher model for data augmentation. By having the urine volume prediction model learn more data through data augmentation, an automated intravesical urine volume prediction method designed based on medical knowledge and diagnosis can be implemented.

[0207] FIG. 17 is a diagram showing an example of an interface for managing a digital urination log according to an embodiment of the present disclosure. In one embodiment, a medical device (e.g., medical device 100 shown in FIG. 1) is used by a specific user to manage a digital urination log. The specific user can perform the linkage between the user terminal he / she is using and the medical device before using the medical device. Also, in order to provide information optimized for the specific user, the user terminal can receive the personal information of the specific user and the like. Then, based on the information obtained from the linked medical device, an artificial intelligence model (e.g., urine volume prediction model) for the specific user can be generated.

[0208] In one embodiment, the first interface 1710 can display a message guiding a specific user about the linkage between the medical device and the user terminal. For example, the first interface 1710 can display a guide on how to link the medical device and the user terminal. As an example, once the linkage between the medical device and the user terminal is completed, the next step can be proceeded with.

[0209] In one embodiment, the second interface 1720 can display a message requesting an input for the information of a specific user. Specifically, the second interface 1720 can display a message requesting the information of the specific user necessary for creating a urination log. For example, the second interface 1720 can display a message requesting an input for the personal information of the specific user (e.g., name, gender, age, weight, height, information related to obesity, etc.). The user can input his / her personal information via the second interface 1720.

[0210] In one embodiment, the third interface 1730 can display a message guiding the procedure after the input for the information of a specific user is completed. For example, the third interface 1730 can display a message indicating that the input for the information of the specific user is completed. Additionally or alternatively, the third interface 1730 can summarize and display the input information.

[0211] In one embodiment, the fourth interface 1740 can display messages related to generating an artificial intelligence model for a specific user. For example, the fourth interface 1740 can include an input object 1742. When an input to the input object 1742 is received, a medical device linked to the user terminal can receive a plurality of optical data sets for a specific user multiple times. Then, by learning the received plurality of optical data sets, an artificial intelligence model for a specific user can be generated. At this time, the artificial intelligence model can also learn personal information related to the specific user.

[0212] With such a configuration, the user can easily receive a guide regarding the linkage method between the user terminal and the medical device. Then, the user uses the medical device linked to the user terminal. Also, by inputting information about the user, the medical device and the artificial intelligence model for the user can be optimized for the user.

[0213] FIG. 18 is a diagram showing an example of a urination log according to an embodiment of the present disclosure. The processor can output information related to the urine volume of a specific user as a urination log. The urine volume can be calculated based on the estimated urine volume (for example, the urine volume 1322 shown in FIG. 13). Specifically, the processor can receive a plurality of optical data related to a specific user measured from a plurality of photodiodes in the medical device at each of a plurality of time points. Also, the processor can estimate the intravesical urine volume for each of the plurality of time points based on the plurality of optical data sets. Then, the processor can record the estimated intravesical urine volume of the specific user for the plurality of time points. Here, the specific process of estimating the intravesical urine volume for each of the plurality of time points can be understood from the content described above with reference to FIGS. 13 to 16. At this time, the urine volume can be calculated based on the recorded intravesical urine volume of the specific user for the plurality of time points.

[0214] In one embodiment, the amount of urine in the bladder of a specific user at a first time point and the amount of urine in the bladder of the specific user at a second time point can be recorded. At this time, the urine output can be calculated based on the difference between the urine volume at the first time point and the urine volume at the second time point. As an example, the urine output calculated based on the urine volume at the second time point and the urine volume at the first time point can be recorded as the urine output at the second time point.

[0215] The urine output can be classified into the urine output during sleep and the urine output during waking up. For example, the urine output during sleep can be the urine output at the time when the specific user is sleeping, and can be calculated based on the amount of urine in the bladder recorded at the time when the specific user is sleeping. Similarly, the urine output during waking up can be the urine output at the time when the specific user is waking up, and can be calculated based on the amount of urine in the bladder recorded at the time when the specific user is waking up.

[0216] Referring to FIG. 18, the first interface 1810 can display at least a part of the urination log. Here, the first interface 1810 can display the urine output during waking up together with the first visual object 1812. Also, the first interface 1810 can display the urine output during sleep together with the second visual object 1814.

[0217] In one embodiment, the processor can receive or estimate information regarding the sleep / wake-up of a specific user. For example, the user can input information regarding sleep / wake-up via the user terminal. As another example, the processor can estimate information regarding sleep / wake-up based on an optical dataset (for example, the optical dataset 1302 shown in FIG. 13). As still another example, the processor can estimate information regarding sleep / wake-up based on information generated by sensors (such as motion sensors and gyroscopes, etc.) included in the medical device. As still another example, the processor can estimate information regarding sleep / wake-up based on time information, etc.

[0218] The processor can calculate the total daily urine output based on the urine output at multiple time points. Here, each of the multiple time points can include information regarding year, month, day, hour, minute, and second. Specifically, the total daily urine output can be calculated based on the urine output at the time points corresponding to the same date among the multiple time points. Referring to FIG. 18, the first interface 1810 can display the total urine output by date via the third visual object 1816.

[0219] In one embodiment, the processor can calculate the urine output at periodic time points. At this time, the processor can periodically receive a plurality of optical data sets detected by a plurality of photodiodes in the medical device, and calculate the urine output at periodic time points based on the received plurality of optical data sets. Here, the period for calculating the urine output can be determined in advance.

[0220] As an example, the processor can calculate the urine output for each urination time point of a specific user. At this time, the processor can determine the urination time point based on the sensor or optical data set included in the medical device. Alternatively, the processor can receive an input of the urination time point via the user terminal. Referring to FIG. 18, the first interface 1810 can display the first input object 1818. The processor can receive the optical data set by receiving an input to the first input object 1818. Thereafter, the processor can calculate the urine output at the time point when the input to the first input object 1818 is received based on the received optical data set. At this time, the time point when the input to the first input object 1818 is received can correspond to the urination time point.

[0221] The processor can output information related to the urine output of a specific user in various ways as a urine diary. Referring to FIG. 18, the second interface 1820 can display information related to the urine output for a specific date. Here, by receiving an input for the second input object 1822 by the processor, it can be converted from the first interface 1810 to the second interface 1820. The second interface 1820 can display at least a part of the urine diary for the time when the specific user is awake on a specific date, together with the fourth visual object 1824. Also, the second interface 1820 can display the total urine output and the number of urinations for the time when the specific user is awake on a specific date via the fifth visual object 1826. Further, the second interface 1820 can display the total urine output and the number of urinations for the time when the specific user is asleep on a specific date via the sixth visual object 1828.

[0222] For the diagnosis and prescription of urination disorders such as overactive bladder, underactive bladder, benign prostatic hyperplasia, nocturia, recurrent cystitis, urinary incontinence, etc., the creation of a urine diary may be essential. The invention according to the present disclosure can provide a digitalized urine diary to patients and doctors by digitalizing the urine diary. Specifically, by accurately and quickly calculating the urination time and urine output, when a patient wears a medical device, the urination time and urine output can be automatically recorded. That is, since the patient does not have to measure with a urine cup and write the urine diary by hand, high user accessibility can be provided.

[0223] FIG. 19 is a diagram showing an example of a urination log according to an embodiment of the present disclosure. The processor can calculate at least a part of the urination log based on the recorded urine volume (for example, the urine volume 1322 shown in FIG. 13). Specifically, the processor can calculate a urination analysis result obtained by analyzing the urination record. For example, as the urination analysis result, the processor can calculate the average of the total daily urine volume, the average of the number of urinations per day, the number of nocturia per date, the average of the number of nocturia per day, the ratio of the nocturia volume per date, the average of the ratio of the nocturia volume per date, the average of the nocturia volume per date, the functional bladder volume, and the like. Here, the functional bladder volume can mean the maximum urine volume of the bladder of a specific user. For example, the functional bladder volume can mean the maximum urine volume in the created urination log.

[0224] In one embodiment, the processor can output the urination analysis result. Referring to FIG. 19, the first interface 1910 can display the urination analysis result. The first interface 1910 can display the average of the total daily urine volume together with the first visual object 1912. The first interface 1910 can display the average of the number of urinations per day together with the second visual object 1914. The first interface 1910 can display the average of the number of nocturia per day together with the third visual object 1916. The first interface 1910 can display the average of the ratio of the nocturia volume together with the fourth visual object 1918. The first interface 1910 can display the functional bladder volume together with the fifth visual object 1919.

[0225] The second interface 1920 can display the urine volume by date. For example, the processor can record the urine volume in the bladder of a specific user at a plurality of time points within a certain period (for example, 72 hours). At this time, the second interface 1920 can display the urine volume by date within a certain period in a bar graph.

[0226] The third interface 1930 can display the percentage of nocturia volume by date. For example, the processor can calculate the percentage of nocturia volume by date based on the nocturia volume by date and the nocturia frequency volume by date. At this time, in the third interface 1930, the calculated percentage of nocturia volume by date can be displayed as a bar graph.

[0227] With such a configuration, the digital urination diary management method can reduce the analysis time of medical staff by providing an automatic analysis function for the diagnosis of urination disorders, thereby enhancing the accessibility for the users. Further, the invention according to the present disclosure provides automatically analyzed data regarding the average of the total urination volume per day, the average of the number of urinations per day, the number of nocturia per date, the average of the number of nocturia per day, the percentage of nocturia volume by date, the average of the percentage of nocturia volume by date, the average of the nocturia volume by date, the functional bladder volume, etc., so that a doctor can perform an accurate and prompt diagnosis / prescription for a patient.

[0228] FIG. 20 is a flowchart for explaining a digital urination diary management method 2000 according to an embodiment of the present disclosure. The method 2000 can be performed by at least one processor of a control unit (or, at least one processor) of a medical device, a user terminal, and / or an information processing system. The method 2000 starts with a step (S2010) in which a processor receives a plurality of optical data sets related to a specific user detected by a plurality of photodiodes in a medical device at a plurality of time points.

[0229] In one embodiment, the processor can estimate the amount of urine in the bladder for each of a plurality of time points based on a plurality of optical data sets (S2020). Specifically, the processor can estimate a plurality of sets of optical characteristic values for at least a part of the body of a specific user based on the plurality of optical data sets. Also, based on the estimated sets of optical characteristic values, the amount of urine in the bladder for each of the plurality of time points can be estimated using a urine volume estimation model. Here, the urine volume estimation model is a deep learning-based model or a machine learning-based model that has learned a plurality of learning data sets, and the plurality of learning data sets can include pairs of the actual urine volume of a specific user and sets of optical characteristic values related to the actual urine volume.

[0230] In one embodiment, the plurality of learning data sets include a first learning data set and a second learning data set. The first learning data set includes a pair of the first actual urine volume of a specific user and a first set of learning optical characteristic values related to the first actual urine volume. The second learning data set includes a pair of the second actual urine volume of a specific user and a second set of learning optical characteristic values related to the second actual urine volume, and the second actual urine volume can be greater than the first actual urine volume.

[0231] In one embodiment, the processor can record the amount of urine in the bladder of a specific user for each of the plurality of estimated time points (S2030). Also, the processor can output the total urine output of the specific user by date based on the amount of urine in the bladder of the specific user for each of the plurality of recorded time points.

[0232] In one embodiment, the processor can display the urine output during waking up of a specific user together with a first visual object based on the amount of urine in the bladder of the specific user for each of the plurality of recorded time points. Also, based on the amount of urine in the bladder of the specific user for each of the plurality of recorded time points, the urine output during sleeping of the specific user can be displayed together with a second visual object.

[0233] In one embodiment, the processor can output the average of the total daily urine output and the average number of daily urinations of a specific user based on the amount of urine in the bladder of the specific user at each of the plurality of recorded time points.

[0234] In one embodiment, the processor can output the number of nocturnal urinations by date or the ratio of the nocturnal urine output by date of a specific user based on the amount of urine in the bladder of the specific user at each of the plurality of recorded time points.

[0235] In one embodiment, the processor can estimate the functional bladder volume of a specific user based on the amount of urine in the bladder of the specific user at each of the plurality of recorded time points. Further, the processor can output the estimated functional bladder volume of the specific user.

[0236] The above-described method can be provided as a computer program stored in a computer-readable recording medium for execution by a computer. The medium can continuously store a computer-executable program, temporarily store it for execution or download, or both. Further, the medium can be various recording or storage means in the form of a single or multiple hardware components combined, and is not limited to a medium directly connected to a certain computer system, and can be distributed and exist on a network. Examples of the medium include magnetic media such as hard disks, flexible disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and those configured to store program instruction words, including ROM, RAM, flash memory, and the like. Further, examples of other media include application stores that distribute applications and recording media or storage media managed by sites, servers, and the like that supply or distribute other various software.

[0237] The methods, operations, or techniques of this disclosure can be implemented in a variety of means. For example, such techniques can be implemented in hardware, firmware, software, or combinations thereof. It should be understandable to those of ordinary skill in the art that the various exemplary logical blocks, modules, circuits, and algorithm steps described by the disclosure of this application can be implemented in electronic hardware, computer software, or combinations of both. For the purpose of clearly explaining such mutual substitution between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described from their functional perspectives. Whether such functions are implemented as hardware or as software varies depending on the design requirements added to the specific application and the overall system. Those of ordinary skill in the art can also implement the functions described in various ways for each specific application, but such implementation should not be construed as departing from the scope of this disclosure.

[0238] In the implementation of hardware, the processing unit used to perform the technique can be embodied within one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described in this disclosure, computers, or combinations thereof.

[0239] Accordingly, the various illustrative logical blocks, modules, and circuits described by the present disclosure can be implemented or performed in any combination of a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gates or transistor logic, discrete hardware components, or the like designed to perform the functions described in this application. The general-purpose processor can be a microprocessor, but alternatively, the processor can be any conventional processor, controller, microcontroller, or state machine. The processor can also be embodied in a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors associated with a DSP core, or any other configuration.

[0240] In a firmware and / or software implementation, the techniques can be embodied in instructions stored on a computer-readable medium such as RAM (random access memory), ROM (read-only memory), NVRAM (non-volatile random access memory), PROM (programmable read-only memory), EPROM (erasable programmable read-only memory), EEPROM (electrically erasable PROM), flash memory, CD (compact disc), magnetic or optical data storage device, and the like. The instructions can be executable by one or more processors and can cause the processors to perform a particular manner of the functions described by the present disclosure.

[0241] When implemented in software, the techniques can be stored on a computer-readable medium as one or more instructions or code, or transferred via a computer-readable medium. A computer-readable medium includes any medium that facilitates transfer of a computer program from one location to another, and includes both computer storage media and communication media. A storage media can be any available media accessible by a computer. By way of non-limiting example, such computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to transfer or store the desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection can properly be termed a computer-readable medium.

[0242] For example, when software is transferred from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, the coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line, or wireless technologies such as infrared, radio, and microwave are included within the definition of the medium. As used herein, disk and disc include CD, laser disk, optical disk, DVD (digital versatile disc), flexible disk, and Blu-ray disk, where disk typically magnetically reproduces data, while disc optically reproduces data using a laser. Combinations thereof must also be included within the scope of computer-readable media and the like.

[0243] The software module can also reside in any form of storage medium known in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or the like. Exemplary storage media can be connected to the processor such that the processor can read information from or write information to the storage media. Alternatively, the storage media can be integrated with the processor. The processor and the storage media can also be present in an ASIC. The ASIC can also be present in the user terminal. Alternatively, the processor and the storage media can also be present as individual components in the user terminal.

[0244] Although the embodiments described above have been described as utilizing aspects of the presently disclosed subject matter in one or more stand-alone computer systems, the present disclosure is not limited thereto and can be implemented by any computing environment such as a network or a distributed computing environment. Furthermore, aspects of the subject matter of the present disclosure can also be implemented in multiple processing chips or devices, and storage can similarly be affected across multiple devices. Such devices can also include a PC, a network server, and a portable device.

[0245] In this specification, although the present disclosure has been described by some embodiments, various modifications and changes are possible within the scope that does not deviate from the present disclosure that can be understood by those of ordinary skill in the technical field to which the invention of the present disclosure belongs. Also, such modifications and changes should be understood to fall within the scope of the claims appended to this specification.

Description of Reference Numerals

[0246] 100 Medical device 112_1 First photodiode 112_2 Second photodiode 112_20 Twentieth photodiode 114_1 First light source group 114_2 Second light source group 114_3 Third light source group 114_4 Fourth light source group 120 User terminal

Claims

1. A digital urination diary management method, performed by at least one processor, comprising: receiving, for each of a plurality of time points, a plurality of light data sets associated with a particular user detected by a plurality of photodiodes within a medical device, the plurality of photodiodes being configured to detect light intensities associated with light projected onto skin located over the particular user's bladder; estimating a bladder volume for each of the plurality of time points based on the plurality of optical data sets; and recording the specific user's bladder urine volume for each of the estimated multiple time points.

2. The urination diary management method according to claim 1 , further comprising a step of outputting the specific user's total urination volume by date based on the specific user's intrabladder urine volume for each of the recorded multiple time points.

3. displaying, with a first visual object, a morning urination volume of the particular user based on the particular user's bladder volume for each of the recorded time points; The urination diary management method of claim 1 , further comprising a step of displaying the specific user's urination volume during sleep together with a second visual object based on the specific user's bladder urine volume for each of the recorded multiple time points.

4. The urination diary management method of claim 1, further comprising a step of outputting an average of the specific user's total daily urination volume and an average of the specific user's daily urination frequency based on the specific user's bladder urine volume for each of the recorded multiple time points.

5. The urination diary management method of claim 1, further comprising a step of outputting the number of times the specific user urinates while asleep by date, or the percentage of the amount of urine voided while asleep by date, based on the amount of urine in the bladder of the specific user for each of the recorded multiple time points.

6. estimating a functional bladder capacity of the specific user based on the specific user's bladder urine volume for each of the recorded plurality of time points; The urination diary management method according to claim 1 , further comprising a step of outputting the estimated functional bladder volume of the specific user.

7. The step of estimating the urine volume includes: estimating a set of light characteristic values ​​for at least a portion of the particular user's body based on the plurality of light data sets; and estimating a bladder urine volume for each of the plurality of time points using a urine volume estimation model based on the estimated set of light characteristic values; The urine volume estimation model is a deep learning-based model or a machine learning-based model that has learned a plurality of learning data sets, The urination diary management method according to claim 1 , wherein the plurality of learning data sets include pairs of actual urine volumes of the specific user and sets of light characteristic values ​​associated with the actual urine volumes.

8. the plurality of training data sets include a first training data set and a second training data set; The first learning data set includes a pair of a first actual urine volume of the specific user and a first learning light characteristic value set associated with the first actual urine volume; The second learning data set includes a pair of a second actual urine volume of the specific user and a second learning light characteristic value set associated with the second actual urine volume; The urination diary management method according to claim 7 , wherein the second actual urine volume is greater than the first actual urine volume.

9. A computer program stored on a computer-readable recording medium for executing the method according to any one of claims 1 to 8 on a computer.

10. A user terminal, The Communications Department and Memory, at least one processor coupled to the memory and configured to execute at least one computer readable program contained in the memory; The at least one program comprises: receiving, for each of a plurality of time points, a plurality of light data sets associated with a particular user detected by a plurality of photodiodes within the medical device, where the plurality of photodiodes are configured to detect light intensities associated with light projected onto skin located over the particular user's bladder; estimating a bladder volume for each of the plurality of time points based on the plurality of optical data sets; A user terminal including instructions for recording the specific user's bladder urine volume for each of the estimated multiple time points.

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