PHYSIOLOGICAL INFORMATION ESTIMATION METHOD, COMPUTER PROGRAM, AND USER TERMINAL

Abstract

To provide a physiological information estimation method.SOLUTION: A method includes the steps of: receiving, by photodiodes, a first set of optical data associated with light with which a first substance is irradiated by plural LEDs (Light Emitting Diodes); receiving, by the photodiodes, a second set of optical data associated with light with which a second substance is irradiated by the plural LEDs; calculating first correction data associated with the plural LEDs based on the first set of optical data and the second set of optical data; calculating second correction data associated with the plural LEDs based on the first correction data and profile data associated with the plural LEDs; and estimating physiological information associated with the second substance based on the first correction data and the second correction data.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present disclosure relates to a physiological information estimation method, a computer program, and a user terminal, and more particularly to a method, a computer program, and a user terminal for accurately estimating physiological information by correcting optical data of a body. [Background technology]

[0002] Hydration is essential for human survival, but it is difficult to monitor continuously and portable using optical methods. Dehydration of 10% body water loss can lead to severe motor and cognitive decline, and further loss can even lead to death. Thus, body tissue water is an important biomarker in other fields such as breast cancer research, dermatology, and critical care medicine. Conventional techniques estimate water content using near-infrared (NIR) optical techniques such as diffuse optical spectroscopy and spatial frequency domain imaging (SFDI).

[0003] Additionally, conventional techniques have demonstrated that narrowband diffuse reflectance spectroscopy (nb-DRS) can calculate the moisture ratio (or RH2O), which closely tracks the absolute moisture content. This can be demonstrated by analyzing diffuse reflectance in the far-infrared wavelength range of 900-1000 nm using a broadband lamp and a spectrometer. Here, RH2O can be defined as the ratio of water (H2O) and lipids (FAT) as shown in the following formula 1.

[0004]

number

[0005] Meanwhile, nb-DRS is suitable for miniaturization as a wearable moisture monitor because of its low hardware requirements. For the light source, a multi-wavelength LED is used as a small size, low power and cheap alternative to a broadband lamp. However, when converting the light source to a photodiode using a spectrometer to sense the light source, there is a limitation that high resolution spectral data is lost because the photodiode cannot process wavelength information. In addition, there is a problem that LEDs have a non-negligible full-width at half maximum (FWHM), which potentially generates spectral cross-talk. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Korean Patent Publication No. 10-2023-0146347 Summary of the Invention [Problem to be solved by the invention]

[0007] The present disclosure provides a physiological information estimation method, a computer program, and an apparatus (system) for solving the above problems. [Means for solving the problem]

[0008] The present disclosure can be embodied in numerous ways, including as a method, an apparatus (system), or a computer program stored on a readable storage medium.

[0009] According to one embodiment of the present disclosure, a method for estimating physiological information may include receiving, by a photodiode, a first set of light data associated with light irradiated from a plurality of LEDs (Light Emitting Diodes) onto a first material; receiving, by the photodiode, a second set of light data associated with light irradiated from the plurality of LEDs onto a second material; calculating first correction data associated with the plurality of LEDs based on the first set of light data and the second set of light data; calculating second correction data associated with the plurality of LEDs based on the first correction data and profile data associated with the plurality of LEDs; and estimating physiological information associated with the second material based on the first correction data and the second correction data.

[0010] A computer readable computer program is provided for carrying out a method according to an embodiment of the present disclosure on a computer.

[0011] According to an embodiment of the present disclosure, a user terminal is provided, the user terminal including a communication module, 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 including instructions for receiving a first set of light data associated with light irradiated from the multiple LEDs onto a first material via a photodiode, receiving a second set of light data associated with light irradiated from the multiple LEDs onto a second material via a photodiode, calculating first compensation data associated with the multiple LEDs based on the first set of light data and the second set of light data, calculating second compensation data associated with the multiple LEDs based on the first compensation data and profile data associated with the multiple LEDs, and estimating physiological information associated with the second material based on the first compensation data and the second compensation data. Effect of the Invention

[0012] According to some embodiments of the present disclosure, physiological information can be estimated based on optical data acquired from a medical device. Additionally, the estimated physiological information can be provided to a user via a user terminal. In this manner, the present disclosure can provide physiological information to a user without the support of a specialist such as a doctor. Furthermore, the present disclosure can increase user convenience through a simple method of use and increase consumer accessibility through personalization.

[0013] Some embodiments of the present disclosure provide a more accurate estimation of physiological information using multiple wavelength LEDs and photodiodes while reducing crosstalk, which can facilitate simplification and miniaturization of spectroscopic techniques. Additionally, the methods of the present disclosure can be useful in narrowband LED-based devices, such as wearable moisture monitors.

[0014] The effects of the present disclosure are not limited to these, and other effects not mentioned should be clearly understood by a person having ordinary skill in the art to which the present disclosure pertains (hereinafter referred to as a "person skilled in the art") from the description in the claims. [Brief description of the drawings]

[0015] BRIEF DESCRIPTION OF THE DRAWINGS Examples of the present disclosure will now be described with reference to the accompanying drawings, in which like reference numerals refer to like elements, without limitation, and in which: FIG. [Figure 1] FIG. 1 is a schematic diagram illustrating an example of a medical device for estimating physiological information according to an embodiment of the present disclosure. [Diagram 2] FIG. 1 is a schematic diagram showing a configuration in which an information processing system, a medical device, and multiple user terminals are communicatively coupled to estimate physiological information according to one embodiment of the present disclosure. [Diagram 3] 2 is a block diagram showing an internal configuration of a user terminal and an information processing system according to an embodiment of the present disclosure. FIG. [Figure 4] FIG. 1 is a diagram illustrating an example of a physiological information estimation method according to an embodiment of the present disclosure. [Diagram 5]FIG. 2 shows an example of an LED spectrum and a chromophore absorption spectrum according to one embodiment of the present disclosure. [Figure 6] FIG. 13 is a diagram illustrating an example of a simulation result according to an embodiment of the present disclosure. [Figure 7] FIG. 13 shows an example of emulsion phantom results according to one embodiment of the present disclosure. [Figure 8] 1 is a flowchart illustrating an example of a method according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] <Summary of the Invention> According to one embodiment of the present disclosure, the first material may be a material for which reflectance information of light irradiated onto the first material is known in advance, and the second material may be a material to be tested.

[0017] According to one embodiment of the present disclosure, calculating the second correction data may include calculating normalized weighted data for the plurality of LEDs based on profile data associated with the plurality of LEDs.

[0018] According to one embodiment of the present disclosure, the step of calculating the second correction data may further include a step of calculating simulated diffuse reflectance data for the plurality of LEDs based on the normalized weighted value data and a diffusion model.

[0019] According to one embodiment of the present disclosure, the diffusion model may be a model that predicts the diffuse reflectance for a medium illuminated by light based on the concentration and extinction coefficient of the chromophore.

[0020] According to one embodiment of the present disclosure, the step of calculating the second correction data may further include a step of calculating the second correction data based on the first correction data and simulated diffuse reflectance data for the multiple LEDs.

[0021] According to one embodiment of the present disclosure, the step of calculating the second correction data may further include a step of calculating the second correction data by adjusting a scale of the simulated diffuse reflectance data based on a portion of the first correction data and a portion of the simulated diffuse reflectance data.

[0022] According to one embodiment of the present disclosure, the physiological information may include information regarding water (H2O) and information regarding fat (fat).

[0023] <Detailed Description of the Invention> Hereinafter, specific contents for carrying out the present disclosure will be described in detail with reference to the accompanying drawings. However, in the following description, detailed descriptions of known functions and configurations will be omitted if there is a risk of unnecessarily obscuring the gist of the present disclosure.

[0024] In the accompanying drawings, the same or corresponding components are given the same reference numerals. In addition, in the following description of the embodiments, duplicated descriptions of the same or corresponding components may be omitted. However, even if the description of a component is omitted, it should not be intended that such a component is not included in a certain embodiment.

[0025] The advantages and features of the disclosed embodiments and the methods of achieving them will become apparent from the following examples, taken in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below, and may be embodied in various different forms. However, the embodiments are provided only to complete the disclosure and to allow those skilled in the art to accurately recognize the category of the invention.

[0026] The terms used in this disclosure are briefly explained, and the disclosed embodiments are specifically described. The terms used in this disclosure are selected as currently widely used general terms as much as possible while taking into consideration the functions in this disclosure, but these may change depending on the intentions or precedents of engineers engaged in related fields, the emergence of new technologies, etc. In addition, in certain cases, there may be terms arbitrarily selected by the applicant, but the meanings of these will be described in detail in the description of the invention. Therefore, the terms used in this disclosure should be defined based on the meanings of the terms and the overall content of this disclosure, rather than simply the names of the terms.

[0027] In this disclosure, unless otherwise clearly specified in the context, singular expressions include plural expressions, and plural expressions include singular expressions. Throughout the specification, when a part "comprises" a certain element, this does not exclude other elements, and means that other elements may also be included, unless otherwise specified to the contrary.

[0028] In addition, the term "module" or "unit" as used in the specification means a software or hardware component, and the "module" or "unit" performs a certain function. However, the term "module" or "unit" is not limited to software or hardware. The "module" or "unit" may be configured to be in an addressable storage medium or to execute one or more processors. Thus, by way of example, a "module" or "unit" may include components such as software components, object-oriented software components, class components, and task components, as well as at least one of a process, a function, an attribute, a procedure, a subroutine, a segment of program code, a driver, firmware, microcode, a circuit, data, a database, a data structure, a table, an array, or a variable. The components and "modules" or "units" may be combined into a smaller number of components and "modules" or "units" or functions provided therein may be further separated into additional components and "modules" or "units".

[0029] According to one embodiment of the present disclosure, a "module" or "unit" may be embodied with a processor and a memory. A "processor" should be broadly construed 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, and the like. In some environments, a "processor" may also refer to an application specific semiconductor (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), and the like. A "processor" may also 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 in conjunction with a DSP core, or any other such configuration. Additionally, a "memory" should be broadly construed to include any electronic component capable of storing electronic information. "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. Memory is said to be in electronic communication with a processor if the processor can read / write information from and write information to the memory. Memory that is integrated into a processor is in electronic communication with the processor.

[0030] In the present disclosure, a "system" may include at least one of a server device and a cloud device, but is not limited thereto. For example, the system may be composed of one or more server devices. As another example, the system may be composed of one or more cloud devices. As yet another example, the system may be operated by being composed of all of the server device and the cloud device.

[0031] In this disclosure, "display" may refer to any display device associated with a computing device, for example, any display device capable of displaying any information / data controlled by or provided by a computing device.

[0032] In the present disclosure, "each of a plurality of A's" can refer to each of all of the components included in the plurality of A's, or each of some of the components included in the plurality of A's.

[0033] Light emitting diodes (LEDs) are commonly used in tissue spectroscopy due to their small size, low cost, and simplicity. However, LEDs are often estimated as single wavelength elements, even though they have a relatively wide spectral bandwidth. In addition, when LEDs are used together with photodiodes, the wavelength information of the light sensed by the photodiode may be lost. This may cause calculation errors when calculating the concentration of chromophores using LEDs and photodiodes. In particular, such errors occur significantly when analyzing water and fat in the 900-1000 nm range, where the spectral bandwidth of LEDs can cover most of the analysis area, which may lead to serious cross-talk.

[0034] The present disclosure provides a Spectral Correction (SC) method for correcting the spectral bandwidth of LEDs. The disclosed method can be applied to narrowband techniques that use LEDs with wide and overlapping spectra. Specifically, the present disclosure uses narrowband diffuse reflectance spectroscopy (nb-DRS), a technique that can quantify the water fraction (RH2O) in turbid media. Generally, nb-DRS requires a broadband light source and a spectrometer. However, the present disclosure reduces the hardware to five LEDs and one photodiode and uses a spectral correction method to correct the spectral crosstalk. The effectiveness of the disclosed spectral correction method can be confirmed through simulations, emulsion phantoms, and human tissue tests.

[0035] Simulation results show that the disclosed spectral correction method can correct the error of RH2O calculated by the spectral bandwidth of the LEDs. Furthermore, emulsion phantom results show that if the disclosed spectral correction method is not applied, an average error of 8.7% (maximum error 14%) can occur. In contrast, if the disclosed spectral correction method is applied, the average error can be reduced to 2.2% (maximum error 6.4%). Thus, despite the use of multiple LEDs with very wide and overlapping spectra, the disclosed spectral correction method can restore the performance of the narrowband scheme to a similar level to a larger pooled broadband system.

[0036] The present disclosure provides an algorithm for spectral correction to mitigate crosstalk of narrowband techniques even when multiple LEDs with wide and overlapping spectra are used. The effectiveness of the present disclosure is demonstrated through simulations, emulsion phantoms with a wide range of RH2O values, and human tissue tests (e.g., human abdominal tissue and thenar tissue). The spectral correction method of the present disclosure can facilitate the development of tissue moisture monitors using nb-DRS and can be applied to other modalities.

[0037] FIG. 1 is a schematic diagram illustrating an example of a medical device 100 for estimating physiological information according to an embodiment of the present disclosure. As illustrated in the figure, the medical device 100 may include a communication unit capable of transmitting and receiving with a user terminal 120. The medical device 100 may also include a plurality of photodiodes 112_1-112_20 and a plurality of light source groups 114_1-114_4. The medical device 100 may acquire light data related to a body using the plurality of photodiodes 112_1-112_20 and the plurality of light source groups 114_1-114_4. The user terminal 120 may receive the light data related to the body and estimate physiological information of a user based on the received light data. In FIG. 1, the medical device 100 includes 20 photodiodes 112_1-112_20 and four light source groups 114_1-114_4, but is not limited thereto. That is, the number of photodiodes and light source groups included in the medical device 100 may be changed as necessary.

[0038] 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. In this case, 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 bladder region.

[0039] In one embodiment, the light sources included in each of the light source groups 114_1 to 114_4 may be configured to emit light of different wavelengths. Here, the light sources may be LEDs (Light Emitting Diodes). Also, the light sources included in each of the light source groups 114_1 to 114_4 may emit continuous wave light.

[0040] In one embodiment, the photodiodes 112_1 to 112_20 can detect light and generate light data. Specifically, the photodiodes 112_1 to 112_20 can detect the intensity of diffused light, which is light diffused from the body. The photodiodes 112_1 to 112_20 can detect diffused light associated with the light sources irradiated by the light sources included in the light source groups 114_1 to 114_4. Each of the photodiodes 112_1 to 112_20 can detect the diffused light and measure a voltage value corresponding to the intensity of the diffused light.

[0041] In one embodiment, the user terminal 120 can send an optical data detection request to the medical device 100. In response to the optical data detection request, the medical device 100 can operate the light source groups 114_1-114_4 and detect the photodiodes 112_1-112_20. Alternatively, the medical device 100 can periodically detect optical data and transmit it to the user terminal 120 without receiving an optical data detection request from the user terminal 120.

[0042] In one embodiment, the medical device 100 can transmit a plurality of optical data detected from a plurality of photodiodes 112_1 to 112_20 to the user terminal 120. A processor included in the user terminal 120 can estimate physiological information based on the plurality of optical data. Here, the physiological information can include information on water (H2O) and information on fat (fat). A method of estimating physiological information based on a plurality of optical data will be described in detail below with reference to FIG. 4. Alternatively, the medical device 100 can estimate physiological information directly based on the optical data without transferring the optical data to the user terminal 120.

[0043] With this configuration, physiological information can be estimated based on the optical data acquired from the medical device. Additionally, the estimated physiological information can be provided to the user via the user terminal. In this manner, the invention according to the present disclosure can provide the physiological information to the user without the support of a specialist such as a doctor. Furthermore, the invention according to the present disclosure increases the convenience of the user through its simple usage method, and increases the accessibility of consumers as a personal device.

[0044] 2 is a schematic diagram illustrating a configuration in which an information processing system 230, a medical device 240, and a plurality of user terminals 210_1, 210_2, and 210_3 are connected to each other so as to be able to communicate with each other in order to estimate physiological information according to an embodiment of the present disclosure. As shown in the figure, the plurality of user terminals 210_1, 210_2, and 210_3 may be connected to the information processing system 230 and the medical device 240 that can provide a physiological information estimation service via a network 220. Here, the plurality of user terminals 210_1, 210_2, and 210_3 may include terminals of users who will receive the physiological information estimation service.

[0045] According to one embodiment, the information processing system 230 may include one or more server devices and / or databases capable of storing, providing and executing computer-executable programs (e.g., downloadable applications) and data related to providing the physiological information estimation service, and one or more distributed computing devices and / or distributed databases based on a cloud computing service.

[0046] The physiological information estimation service provided by the information processing system 230 may be provided to a user via a physiological information estimation service application or the like installed in each of the multiple user terminals 210_1, 210_2, 210_3. For example, the information processing system 230 may provide information related to physiological information estimation received from the user terminals 210_1, 210_2, 210_3 and / or the medical device 240 or perform corresponding processing via the physiological information estimation service application or the like.

[0047] According to an embodiment, the information processing system 230 can estimate physiological information based on the light data, where the light data can be data measured by the medical device 240. The information processing system 230 can receive the light data directly from the medical device 240 or receive the light 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.

[0048] A plurality of user terminals 210_1, 210_2, and 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, and 210_3, the information processing system 230, and the medical device 240. The network 220 can be composed of a wired network such as Ethernet (registered trademark), PLC (Power Line Communication), telephone line communication device, and RS-serial communication, a mobile communication network, a wireless network such as 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 can include not only a communication method utilizing a communication network (e.g., a mobile communication network, a wired Internet, a wireless Internet, a broadcast network, a satellite network, etc.) that can include the network 220, but also a short-distance wireless communication between the user terminals 210_1, 210_2, and 210_3.

[0049] 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 are not limited thereto, and the user terminals 210_1, 210_2, and 210_3 may be any computing device capable of wired and / or wireless communication and capable of installing and executing a physiological information estimation service application or a web browser. For example, the user terminal may include an AI speaker, a smartphone, a mobile phone, a navigation system, a desktop computer, a laptop computer, a digital broadcasting terminal, a PDA (Personal Digital Assistant), 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. Also, while FIG. 2 shows three user terminals 210_1, 210_2, and 210_3 communicating with the information processing system 230 and medical device 240 via the network 220, this is not limited thereto, and a different number of user terminals may be configured to communicate with the information processing system 230 and medical device 240 via the network 220.

[0050] FIG. 3 is a block diagram showing the internal configuration of a user terminal 210 and an information processing system 230 according to an embodiment of the present disclosure. The user terminal 210 may refer to any computing device capable of executing a physiological information estimation service application and the like and capable of wired / wireless communication, and may include, for example, a mobile phone terminal 210_1, a tablet terminal 210_2, and a PC terminal 210_3 of FIG. 2. As shown in the figure, the user terminal 210 may include a memory 312, a processor 314, a communication module 316, and an input / output interface 318. Similarly, the information processing system 230 may 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 may be configured to communicate information and / or data via the network 220 using the respective communication modules 316 and 336. In addition, the input / output device 320 may be configured to input information and / or data to the user terminal 210 and output information and / or data generated from the user terminal 210 via the input / output interface 318.

[0051] The memories 312 and 332 may include any non-transitory computer-readable recording medium. According to an embodiment, the memories 312 and 332 may include a permanent mass storage device such as a read only memory (ROM), a disk drive, a solid state drive (SSD), and a flash memory. As another example, a permanent mass storage device such as a ROM, an SSD, a flash memory, and a disk drive may be included in the user terminal 210 or the information processing system 230 as a permanent storage device separate from the memory. In addition, the memories 312 and 332 may store an operating system and at least one program code (e.g., code for a physiological information estimation service application installed and operated in the user terminal 210).

[0052] Such software components can be loaded from a computer-readable recording medium other than the memory 312, 332. Such another computer-readable recording medium can include a recording medium directly connectable to such a user terminal 210 and information processing system 230, and can include computer-readable recording media such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, and a memory card. As another example, the software components, etc. can be loaded into the memory 312, 332 via the communication module 316, 336 instead of a computer-readable recording medium. For example, at least one program can be loaded into the memory 312, 332 based on a computer program to be installed by a file provided via the network 220 by a developer or a file distribution system that distributes an installation file of an application.

[0053] The processor 314, 334 may be configured to process computer program instructions by performing basic arithmetic, logic, and input / output operations. The instructions may be provided to the processor 314, 334 by the memory 312, 332 or the communication module 316, 336. For example, the processor 314, 334 may be configured to execute instructions received by program code stored in a storage device, such as the memory 312, 332.

[0054] The communication modules 316, 336 may provide configurations and functions for the user terminal 210 and the information processing system 230 to communicate with each other via the network 220, and may provide configurations and functions for the user terminal 210 and / or the information processing system 230 to communicate with other user terminals or other systems (e.g., another cloud system, etc.). As an example, a request or data (e.g., optical data and a physiological information estimation request, etc.) generated by the processor 314 of the user terminal 210 via a program code stored in a storage device such as the memory 312 may 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 command provided by the processor 334 of the information processing system 230 may be received by the user terminal 210 via the communication module 316 of the user terminal 210 via the communication module 336 and the network 220.

[0055] The input / output interface 318 may be a means for interfacing with the input / output device 320. As an example, the input device may include a device 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 a device such as a display, a speaker, a haptic feedback device, etc. As another example, the input / output interface 318 may be a means for interfacing with a device having a configuration or function for performing input and output integrated into one, such as a touch screen. For example, when the processor 314 of the user terminal 210 processes the instructions of a computer program loaded in the memory 312, a service screen configured using information and / or data provided by the information processing system 230 or another user terminal may be displayed on the display via the input / output interface 318. In FIG. 3, the input / output device 320 is illustrated as not being included in the user terminal 210, but is not limited thereto, and may be configured integrally with the user terminal 210. Furthermore, the input / output interface 338 of the information processing system 230 may be a means for interfacing with a device (not shown) for input or output that may be coupled to the information processing system 230 or may be included in the information processing system 230. 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 also be configured to be included in the processors 314, 334.

[0056] The user terminal 210 and the information processing system 230 may include more components than those shown in FIG. 3. However, it is not necessary to explicitly show most of the conventional technical components. According to an embodiment, the user terminal 210 may be embodied to include at least a part of the above-mentioned input / output device 320. The user terminal 210 may further include other components such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, and a database. For example, if the user terminal 210 is a smartphone, it may include components that are generally included in a smartphone, and may be embodied to further include 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, a button using a touch panel, an input / output port, and a vibrator for vibration.

[0057] When a program for a physiological information estimation service application, etc. is being run, the processor 314 can receive text, images, videos, voice, and / or actions, etc., input or selected by an input device, such as a touch screen, keyboard, camera including an audio sensor and / or image sensor, microphone, etc., connected to the input / output interface 318, and can store the received text, images, videos, voice, and / or actions, etc. in the memory 312 or provide them to the information processing system 230 via the communication module 316 and the network 220.

[0058] The processor 314 of the user terminal 210 may 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 multiple external systems. The information and / or data processed by the processor 314 may 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 may transfer and output information and / or data to the input / output device 320 via the input / output interface 318. For example, the processor 314 may display the received information and / or data on a screen of the user terminal 210.

[0059] The processor 334 of the information processing system 230 may be configured to manage, process and / or store information and / or data received from the plurality of user terminals 210 and / or the plurality of external systems. The information and / or data processed by the processor 334 may be provided to the user terminal 210 via the communication module 336 and the network 220.

[0060] 4 is a diagram illustrating an example of a physiological information estimation method according to an embodiment of the present disclosure. In one embodiment, a first set of optical data 410 associated with light irradiated from a plurality of LEDs onto a first material may be received by a photodiode. Here, the photodiode and the plurality of LEDs may be part of the photodiode and LEDs included in the medical device 100 of FIG. 1. Also, the first material may be a material (or a control material) for which reflectance information of light irradiated onto the first material is known in advance. Additionally, the first set of optical data 410 may be a reference value expressed as a vector for the number of the plurality of LEDs.

[0061] In one embodiment, a second set of light data 420 associated with irradiating a second material from the plurality of LEDs may be received by a photodiode. Here, the second material may be a material under test. For example, the second material may include abdominal tissue, thenar tissue, etc. of a subject. Additionally, the second set of light data 420 may be a measurement represented as a vector equal to the number of LEDs.

[0062] In one embodiment, first correction data 430 associated with a plurality of LEDs may be calculated based on the first set of light data 410 and the second set of light data 420. Here, the first correction data 430 may be actual correction values ​​expressed as a vector equal to the number of the plurality of LEDs. The first correction data 430 may be expressed as the following Equation 2.

[0063]

number

[0064] In one embodiment, normalized weight data 450 for the plurality of LEDs can be calculated based on profile data 440 related to the plurality of LEDs. Here, profile data 440 related to the plurality of LEDs can include data related to light intensity according to wavelengths of a plurality of LED spectra. In general, since an LED irradiates light of a certain band, not light of a single wavelength, light intensity for wavelengths of the LED spectrum is not constant. Thus, light intensity for wavelengths of the spectrum for each of the plurality of LEDs can be measured using a spectrometer. Then, normalized weight data 450 can be calculated as shown in the following Equation 3.

[0065]

number

[0066] In one embodiment, simulated diffuse reflectance data 470 for the plurality of LEDs can be calculated based on the normalized weight data 450 and the diffusion model 460. Here, the diffusion model 460 may be a model that predicts the diffuse reflectance of a medium illuminated by light based on constants required for the diffusion model, such as a chromophore concentration, an absorption coefficient, a refractive index, and a scattering coefficient. The simulated diffuse reflectance data 470 may be a theoretical value expressed by a vector as many as the number of the plurality of LEDs. The simulated diffuse reflectance data 470 can be expressed as the following equation 4.

[0067]

number

[0068] In one embodiment, the second correction data 480 can be calculated based on the first correction data 430 and the simulated diffuse reflectance data 470. The first correction data 430 is corrected using the first material, while the simulated diffuse reflectance data 470 is corrected using the light intensity according to the wavelength of the LED spectrum measured from a spectrometer, so the scales of the first correction data 430 and the simulated diffuse reflectance data 470 can be different. In this case, the second correction data 480 can be normalized by adjusting the scale based on a part of the first correction data 430 and a part of the simulated diffuse reflectance data 470. As a result, the second correction data 480 can be expressed as the following equation 5.

[0069]

number

[0070] In one embodiment, physiological information associated with the second material can be estimated based on the first correction data 430 and the second correction data 480. Here, the physiological information associated with the second material can include a concentration of a chromophore in the second material. The physiological information associated with the second material can be estimated using a least squares method, as shown in the following equation (6).

[0071]

number

[0072]

number

[0073] The pseudo code of the above-mentioned physiological information estimation method can be expressed as shown in Table 1 below.

[0074] [Table 1]

[0075] This configuration allows for more accurate estimation of physiological information while still utilizing multiple wavelength LEDs and photodiodes, reducing crosstalk, which can facilitate simplification and miniaturization of spectroscopic techniques. Additionally, the disclosed method can be usefully used in narrowband LED-based devices, such as wearable moisture monitors.

[0076] FIG. 5 is a diagram showing an example of an LED spectrum and a chromophore absorption spectrum according to an embodiment of the present disclosure. A first graph 510 is an example showing an LED spectrum. To apply the nb-DRS method of the present disclosure, five LEDs having a spectrum as shown in the first graph 510 are used. Here, the peak wavelengths of the LEDs may be 907 nm, 929 nm, 944 nm, 949 nm, and 976 nm, and the FWHMs of the LEDs may be 29 nm, 38 nm, 56 nm, 35 nm, and 53 nm. In addition, in the first graph 510, the vertical dotted lines may indicate the wavelength analysis region of the nb-DRS, and each LED may be labeled and displayed according to its peak wavelength. Additionally, the vertical axis of the first graph 510 may indicate the relative light intensity of each LED according to the normalized weighted value data shown in FIG. 4.

[0077] The second graph 520 is an example showing the chromophore absorption spectrum for human breast tissue containing 12.6 μM oxyhemoglobin (HbO2), 27.9 μM deoxyhemoglobin (HbR), 45.1% water (H2O) and 29.8% fat (FAT). As shown in the second graph 520, compared to the typical absorption spectrum of tissue containing oxyhemoglobin, deoxyhemoglobin, water and fat chromophores, the spectrum of the LEDs used in the present disclosure can be broad enough to overlap with the absorption peaks of fat and water as well as between the LEDs in the wavelength analysis range of nb-DRS. In particular, the spectrum of the 944 nm, 949 nm and 976 nm LEDs can be broad enough to overlap with the absorption peaks of fat and water.

[0078] 6 is a diagram illustrating an example of a simulation result according to an embodiment of the present disclosure. The graph shown in FIG. 6 illustrates an example of a simulation result of estimating physiological information (e.g., RH2O) when the spectral correction method (or SC) of the present disclosure is applied and when it is not applied. Here, the dotted line may indicate an identity line. Also, when the spectral correction method is not applied, the wavelength of each LED may be assumed as a peak wavelength.

[0079] The set of simulated broadband reflectance spectra covers a range of RH2O values ​​and 1 mm -1 The broadband reflectance can be generated using a steady-state diffusion model with a constant scattering falloff of 100 nm, where the diffusion model can be a model that predicts the diffuse reflectance for an illuminated medium based on the concentration and extinction coefficient of the chromophore. An LED profile with a wavelength equal to the central wavelength of the device can be simulated using a Gaussian curve with FWHM values ​​that increase from 10 nm to 60 nm. The simulated broadband reflectance can then be converted to a simulated LED reflectance using a Gaussian curve.

[0080] As shown in FIG. 6, as the width (or FWHM) of the spectrum of the LED increases, the error of the simulation result estimating the physiological information when the spectral correction method is applied and when it is not applied may increase. Specifically, the reflectance spectrum shown in FIG. 6 can be simulated using an LED with a FWHM in the range of 10 nm to 60 nm. Then, the physiological information can be estimated by using nb-DRS for each of the cases where the spectral correction method is applied and not applied. As shown in the figure, when the LED FWHM is 10 nm, the performances of each of the cases where the spectral correction method is applied and not applied are similar with an error range of less than 1% when compared with the identity line. However, as the LED FWHM increases, the error of each of the cases where the spectral correction method is applied and not applied may increase. For example, when the LED FWHM is 60 nm, when the spectral correction method is not applied, the mean absolute error of the estimate of the physiological information and the identity line may suddenly increase to 13.4. In contrast, when the spectral correction method is applied, the estimates of physiological information can be maintained to the same level of accuracy (<1%) for the FWHM of all simulated LEDs.

[0081] FIG. 7 illustrates an example of emulsion phantom results according to an embodiment of the present disclosure. Graph 700 in FIG. 7 illustrates emulsion phantom results measured by nb-DRS using a broadband light source, an LED without a spectral correction method, and an LED with a spectral correction method. Here, the dotted line indicates an identity line, and the emulsion phantom may be the first material (or control material) described above in FIG. 4. The diluted volume added at each step was maintained at 100 ml, but the total emulsion phantom volume may increase over time. As a result, a nonlinear dilution trend can be observed in which RH2O increases more in the early stages of the emulsion phantom experiment and changes to a smaller amount of RH2O as the experiment approaches the later stages.

[0082] Emulsion phantoms can be made with minor modifications to maintain the phantom in a liquid state instead of solid gelatin. An oil and water emulsion phantom can be made by adjusting the initial oil to water ratio to 90:10 using 2% by weight soy lecithin as an emulsifier. The solution can then be mixed using a high-speed vacuum blender. The phantom can then be diluted by adding 100 mL of water in steps. By keeping water as a continuous medium, the phantom can be diluted without separation of its components. Using this method, the RH2O values ​​of a wide range of phantoms can be measured without constructing new phantoms step by step. After a 15-step dilution process, the final expected RH2O can be 74.3%. After all steps are completed, the phantom can be homogenized using a hand-held mixer.

[0083] As shown in graph 700, three methods are compared using an emulsion phantom to test the spectral correction method. First, the method using a broadband light source and a spectrometer detector is the most accurate with an average and standard deviation error of 1.4±0.7%, with a maximum error of 2.2%.

[0084] Secondly, in the case of the method using LED and photodiode detector, the effect of spectral crosstalk can be recognized not only by accuracy but also by sensitivity. For example, 929 nm LED is considered to be sensitive to fat because its peak wavelength coincides with the lipid absorption peak. However, in reality, due to the wide spectral bandwidth of LED, both water and fat can affect the reflectance measurement using LED. This means that without the spectral correction method of the present disclosure, the dynamic range is poor. This point can be confirmed by simulation and emulsion phantom experiment. When the spectral correction method is not applied, the error in phantom emulsion can be 8.7±4.4%, and the maximum error can be 14.1%.

[0085] Finally, when the spectral correction method is applied using the same data set, the error for the phantom emulsion can be improved to 2.2 ± 1.7%, with a maximum error of 6.4%. Also, when the spectral correction method is applied, it can be seen that the sensitivity is recovered with a similar slope to the method using a broadband light source.

[0086] 8 is a flow chart illustrating an example of a method 800 according to an embodiment of the present disclosure. In one embodiment, the method 800 may be performed by at least one processor. The method 800 may begin by the processor receiving, via a photodiode, a first set of light data associated with light irradiated from a plurality of LEDs onto a first material (S810), where the first material may be a material for which reflectance information of the light irradiated onto the first material is previously known.

[0087] The processor may then receive (S820) a second set of light data associated with light emitted from the plurality of LEDs by the photodiodes onto a second material, where the second material may be a material under test, and may calculate (S830) first correction data associated with the plurality of LEDs based on the first set of light data and the second set of light data.

[0088] The processor may then calculate second correction data associated with the plurality of LEDs based on the first correction data and profile data associated with the plurality of LEDs (S840). Specifically, the processor may calculate normalized weighted data for the plurality of LEDs based on the profile data associated with the plurality of LEDs. The processor may also calculate simulated diffuse reflectance data for the plurality of LEDs based on the normalized weighted data and a diffusion model. Here, the diffusion model may be a model that predicts a diffuse reflectance for a medium illuminated by light based on a concentration and an extinction coefficient of a chromophore. Additionally, the processor may calculate second correction data based on the first correction data and the simulated diffuse reflectance data for the plurality of LEDs. Additionally or alternatively, the processor may calculate the second correction data by adjusting a scale of the simulated diffuse reflectance data based on a portion of the first correction data and a portion of the simulated diffuse reflectance data.

[0089] The processor can then estimate physiological information associated with the second substance based on the first and second corrected data (S850), where the physiological information can include information related to water (H2O) and information related to fat (fat).

[0090] Physiological information was measured on two healthy human subjects (a 31-year-old male and a 30-year-old female). The abdominal and thenar regions may be representative examples of fat and lean tissue, respectively. That is, the abdominal and thenar tissues may be the second material described above in FIG. 4. In this case, the light source-detector distance of the LED and photodiode combination may be shortened to improve the signal-to-noise ratio. Hemoglobin may be included in the fitting of the chromophore.

[0091] The present disclosure can clearly distinguish abdominal tissue and thenar tissue of the human body together with the method using a broadband light source as shown in Table 2, which shows the experimental results for the human tissue of two subjects. Because there is a lot of fat in the abdominal tissue, the RH2O of the abdominal tissue can be lower by 18.9% to 37.8% in the case of the method using a broadband light source and by 22.7% to 37.8% in the case of the method using an LED to which the spectrum correction method is applied.

[0092] In contrast, the thenar tissue can be much slimmer at about 77% as seen in the results of the broadband light source method. The LED method with the spectral correction method applied sees a range between 69% and 80.7%. This discrepancy is due to the difference in source-detector separation (15mm vs. 11mm) resulting in slightly different volume measurements. Both methods are able to clearly distinguish between both tissue types, with abdominal tissue having a much lower RH2O than thenar tissue.

[0093] However, if no spectral correction method is applied, it is difficult to clearly distinguish between the two tissue types. The phantom emulsion experiment only contains water and fat, but living human tissue contains hemoglobin factors, which may exacerbate the spectral crosstalk. Thus, complex tissues are computationally complex due to the presence of other factors such as hemoglobin and other unaccounted for chromophores. In this case, adding more LEDs and including other chromophores such as myoglobin and melanin can improve accuracy.

[0094] [Table 2]

[0095] The present disclosure provides a method to improve the accuracy of RH2O measurements using nb-DRS despite the use of broad spectrum LEDs that overlap in a narrow wavelength range. Specifically, the accuracy of RH22 measurements can be improved by utilizing known LED spectra in the chromophore calculation. Results are confirmed through simulations, emulsion phantoms, and human tissue experiments using nb-DRS. The present disclosure facilitates the simplification and miniaturization of spectroscopy techniques, and is particularly useful for narrow band LED based devices.

[0096] The above-mentioned method may be provided as a computer program stored in a computer-readable recording medium for execution by a computer. The medium may be a medium for continuously storing a computer-executable program or a medium for temporarily storing the program for execution or download. The medium may be various recording means or storage means in the form of a single or multiple hardware components combined, and may be distributed over a network without being limited to a medium directly connected to a computer system. Examples of the medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and ROMs, RAMs, flash memories, etc., configured to store program instructions. Other examples of the medium include recording media or storage media managed by app stores that distribute applications, or sites, servers, etc. that supply or distribute various other software.

[0097] The disclosed methods, operations, or techniques can be embodied in a variety of ways. For example, such techniques can be embodied in hardware, firmware, software, or a combination thereof. Those skilled in the art will appreciate that the various exemplary logical blocks, modules, circuits, and algorithmic steps described in the present disclosure can be embodied in electronic hardware, computer software, or a combination of both. To clearly illustrate such interchangeability between hardware and software, the various exemplary components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is embodied as hardware or software will vary depending on the particular application and design requirements imposed on the overall system. Those skilled in the art may also embody the described functionality in various ways for each particular application, but such embodying should not be interpreted as departing from the scope of the present disclosure.

[0098] In a hardware implementation, the processing units used to perform the techniques may be embodied within one or more ASICs, DSPs, digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described in this disclosure, computers, or any combination thereof.

[0099] Thus, the various example logic blocks, modules and circuits described in this disclosure may be embodied or performed by any combination of general purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate and transistor logic, discrete hardware components, or any combination designed to perform the functions described herein. A general purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be embodied by a combination of computing devices, such as a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

[0100] In a firmware and / or software implementation, the techniques may be embodied as instructions stored on a computer-readable medium, 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 PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. The instructions may be executable by one or more processors to cause the processors to perform certain aspects of the functions described in this disclosure.

[0101] If embodied as software, the techniques can be stored on or transferred via a computer-readable medium as one or more instructions or code. A computer-readable medium includes any medium that facilitates transfer of a computer program from one place to another, and includes both computer storage media and communication media. A storage medium can be any available medium that can be accessed 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 transport or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Additionally, any connection can be appropriately made as a computer-readable medium.

[0102] For example, if the software is transferred from a website, server, or other remote source using coaxial cable, fiber optic cable, lead wire, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, lead wire, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of medium. As used herein, disk and disc include CDs, laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically while discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media, etc.

[0103] A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium may be coupled to the processor such that the processor reads information from, and writes information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0104] Although the embodiments described above are described as utilizing aspects of the presently disclosed subject matter on one or more stand-alone computer systems, the present disclosure is not limited thereto and may be embodied in any computing environment, such as a network or distributed computing environment. Moreover, aspects of the subject matter in the present disclosure may be embodied in multiple processing chips or devices, and storage may be shared across multiple devices. Such devices may include PCs, network servers, and handheld devices.

[0105] Although the present disclosure has been described in the present specification by way of some embodiments, various modifications and alterations can be made within the scope of the present disclosure that can be understood by those skilled in the art to which the present disclosure pertains. In addition, such modifications and alterations should be understood to fall within the scope of the claims attached hereto. [Explanation of symbols]

[0106] 100, 240 Medical Devices 112_1 First photodiode 112_2 Second photodiode 112_20 20th 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, 210 User terminals

Claims

1. 1. A method for estimating physiological information, performed by at least one processor, comprising: receiving, by a photodiode, a first set of light data associated with light irradiated from the plurality of LEDs onto the first material; receiving, by the photodiode, a second set of light data associated with light emitted from the plurality of LEDs onto a second material; calculating first correction data associated with the plurality of LEDs based on the first set of light data and the second set of light data; calculating second correction data associated with the plurality of LEDs based on the first correction data and profile data associated with the plurality of LEDs; and estimating physiological information associated with the second substance based on the first corrected data and the second corrected data.

2. The first material is a material for which reflectance information of light irradiated onto the first material is previously known, The physiological information estimation method according to claim 1 , wherein the second substance is a test subject substance.

3. 2. The physiological information estimating method of claim 1, wherein the step of calculating the second correction data includes the step of calculating normalized weighted value data for the plurality of LEDs based on profile data associated with the plurality of LEDs.

4. 4. The physiological information estimating method of claim 3, wherein the step of calculating the second correction data further comprises the step of calculating simulated diffuse reflectance data for the plurality of LEDs based on the normalized weight data and a diffusion model.

5. The physiological information estimation method according to claim 4 , wherein the diffusion model is a model for predicting a diffuse reflectance of a medium irradiated with light based on a concentration and an extinction coefficient of a chromophore.

6. 5. The physiological information estimating method of claim 4, wherein the step of calculating the second correction data further comprises the step of calculating the second correction data based on the first correction data and simulated diffuse reflectance data for the plurality of LEDs.

7. 5. The physiological information estimating method of claim 4, wherein the step of calculating the second correction data further includes the step of calculating the second correction data by adjusting a scale of the simulated diffuse reflectance data based on a portion of the first correction data and a portion of the simulated diffuse reflectance data.

8. The physiological information is moisture (H ２ 2. The physiological information estimation method according to claim 1, further comprising information on body fat (fat) and information on the body fat.

9. A computer readable computer program for carrying out the method according to any one of claims 1 to 8 on a computer.

10. A user terminal, A communication module; 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, by a photodiode, a first set of light data associated with light irradiated from the plurality of LEDs onto the first material; receiving, by the photodiode, a second set of light data associated with light emitted from the plurality of LEDs onto a second material; calculating first correction data associated with the plurality of LEDs based on the first set of light data and the second set of light data; calculating second correction data associated with the plurality of LEDs based on the first correction data and profile data associated with the plurality of LEDs; A user terminal including instructions for estimating physiological information associated with the second substance based on the first correction data and the second correction data.

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