Apparatus and method for estimating target component

Provided is an apparatus for estimating a target component, the apparatus including a temperature controller configured to modulate temperature of an object, a measurer configured to measure a spectrum for each temperature of the object that changes based on the modulation, and a processor configured to obtain effective optical pathlength vectors corresponding to a temperature change based on the spectrum for each temperature of the object, obtain a representative effective optical pathlength based on the obtained effective optical pathlength vectors, and obtain a target component estimation model based on the obtained representative effective optical pathlength.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from Korean Patent Application No. 10-2021-0028122, filed on Mar. 3, 2021, in the Korean Intellectual Property Office, the entirety of which is incorporated herein by reference.

BACKGROUND

Example embodiments of the present disclosure relate to an apparatus and method for estimating a component of an object based on an absorbance spectrum.

2. Description of Related Art

Recently, research is conducted on methods of measuring bio-information, such as blood glucose, in a non-invasive manner using Raman spectroscopy or near-infrared spectroscopy (NIRS).

When temperature of an object is changed during spectrum measurement in a scattering medium, an absorption coefficient and a scattering coefficient are optically changed at the same time, and optical pathlength distribution is changed for each wavelength due to scattering. The change in absorption coefficient is mainly caused by a difference in refractive-index change of a medium and a scatterer due to a temperature increase, and the change in scattering coefficient is mainly caused by a change in absorption coefficient of water according to temperature.

In such changing environments, quantitative analysis of the object based on the general Beer-Lambert law has a limitation in that it assumes optical pathlength distribution at all wavelengths is equal.

SUMMARY

One or more example embodiments provide to an apparatus and method for estimating a component of an object based on an absorbance spectrum.

According to an aspect of an example embodiment, there is provided an apparatus for estimating a target component, the apparatus including a temperature controller configured to modulate temperature of an object, a measurer configured to measure a spectrum for each temperature of the object that changes based on the modulation, and a processor configured to obtain effective optical pathlength vectors corresponding to a temperature change based on the spectrum for each temperature of the object, obtain a representative effective optical pathlength based on the obtained effective optical pathlength vectors, and obtain a target component estimation model based on the obtained representative effective optical pathlength.

The temperature controller may include a heater configured to provide thermal energy to the object, and a temperature sensor configured to measure the temperature change of the object.

The temperature controller may be further configured to perform modulation one or more times within a predetermined temperature range.

The measurer may include one or more light sources configured to emit light of one or more wavelengths to the object, and a detector configured to detect light scattered or reflected from the object.

The object may include at least one of water not containing the target component, a solution that mimics a scattering coefficient of the target component, and human skin.

The processor may be further configured to obtain a change in absorbance with respect to the temperature change based on the spectrum corresponding to the temperature change, and obtain the effective optical pathlength vectors corresponding to the temperature change based on a relationship between the obtained change in absorbance and a change in optical pathlength.

The processor may be further configured to obtain the change in absorbance corresponding to each wavelength based on the temperature change by Monte Carlo (MC) simulation.

Based on a relationship of a sum of a first value, obtained by multiplying the change in absorbance, a change in an absorption coefficient, and an optical pathlength, and a second value obtained by multiplying the absorption coefficient and the change in optical pathlength, the processor may be further configured to obtain the effective optical pathlength vectors corresponding to the temperature change.

The processor may be further configured to obtain the first value at an isosbestic wavelength based on a change in absorbance, and obtain the effective optical pathlength vectors for each wavelength based on the change in absorbance at another wavelength and a change in the first value and a change in the second value.

The processor may be further configured to obtain the second value for each wavelength based on a lookup table or a pre-defined model equation.

The processor may be further configured to obtain a value, including an average value of the effective optical pathlength vectors for each wavelength, as the representative effective optical pathlength.

The processor may be further configured to obtain the target component estimation model based on at least one of least squares and net analyte signal (NAS) based on the obtained representative effective optical pathlength.

The processor may be further configured to obtain the target component including at least one of blood glucose, calories, alcohol, triglyceride, protein, cholesterol, uric acid, and carotenoid, based on the spectrum of the object being measured.

According to another aspect of an example embodiment, there is provided a method of estimating a target component, the method including modulating temperature of an object, obtaining a spectrum for each temperature that changes based on the modulation, obtaining effective optical pathlength vectors corresponding to a temperature change based on the spectrum for each temperature, obtaining a representative effective optical pathlength based on the obtained effective optical pathlength vectors, and obtaining a target component estimation model based on the obtained representative effective optical pathlength.

The modulating of the temperature of the object may include performing modulation one or more times within a predetermined temperature range.

The obtaining of the effective optical pathlength vectors corresponding to the temperature change may include obtaining a change in absorbance with respect to the temperature change based on the spectrum corresponding to the temperature change, and obtaining the effective optical pathlength vectors corresponding to the temperature change based on a relationship between the obtained change in absorbance and a change in optical pathlength.

The obtaining of the effective optical pathlength vectors corresponding to the temperature change may include obtaining the effective optical pathlength vectors corresponding to the temperature change, based on a relationship of a sum of a first value, obtained by multiplying the change in absorbance, a change in an absorption coefficient, and an optical pathlength, and a second value obtained by multiplying the absorption coefficient and the change in optical pathlength.

The obtaining of the effective optical pathlength vectors corresponding to the temperature change may include obtaining the first value at an isosbestic wavelength based on the change in absorbance, and obtaining the effective optical pathlength vectors for each wavelength based on a change in absorbance at another wavelength and a change in the first value and a change in the second value.

The obtaining of the representative effective optical pathlength may include obtaining a value, including an average value of the effective optical pathlength vectors for each wavelength, as the representative effective optical pathlength.

The method may further include obtaining the target component based on the measured spectrum and the target component estimation model based on the spectrum being measured from the object.

DETAILED DESCRIPTION

Details of example embodiments are included in the following detailed description and drawings. Advantages and features of the present disclosure, and a method of achieving the same will be more clearly understood from the following example embodiments described in detail with reference to the accompanying drawings. Throughout the drawings and the detailed description, unless otherwise described, the same drawing reference numerals will be understood to refer to the same elements, features, and structures.

It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. Also, the singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that when an element is referred to as “comprising” another element, the element is intended not to exclude one or more other elements, but to further include one or more other elements, unless explicitly described to the contrary. Expressions such as “at least one of,” when preceding a list of elements, modify the entire list of elements and do not modify the individual elements of the list. For example, the expression, “at least one of a, b, and c,” should be understood as including only a, only b, only c, both a and b, both a and c, both b and c, or all of a, b, and c. In the following description, terms such as “unit” and “module” indicate a unit for processing at least one function or operation and they may be implemented by using hardware, software, or a combination thereof.

Hereinafter, example embodiments of an apparatus and method for estimating a target component will be described in detail with reference to the accompanying drawings.

Various example embodiments of the apparatus for estimating a target component may be mounted in various information processing devices, such as a portable wearable device, a smart device, and the like. Examples of various information processing devices may include various types of wearable devices, such as a smart watch worn on the wrist, a smart band-type wearable device, a headphone-type wearable device, a hairband-type wearable device, etc., or a mobile device, such as a smartphone, a tablet PC, etc., or a system of a specialized medical institution, and the like. However, the information processing devices are not limited thereto.

FIG.1is a block diagram illustrating an apparatus for estimating a target component according to an example embodiment.

Referring toFIG.1, the apparatus100for estimating a target component includes a measurer110, a temperature controller120, and a processor130.

The measurer110may calibrate a target component estimation model, or may measure a spectrum from an object for estimating a target component. In this case, the object for estimating a target component may be body tissue containing a target component to be analyzed, e.g., human skin tissue, such as an upper portion of the wrist which is adjacent to the radial artery, or veins or capillaries are located, fingers, and the like. Further, in the case where a target component estimation model is calibrated, the object may include water containing no target component, or a solution that mimics a scattering coefficient of a target component, human skin, and the like. In this case, the target component may include at least one of blood glucose, calories, alcohol, triglyceride, protein, cholesterol, uric acid, and carotenoid, but is not limited thereto.

The measurer110may include one or more light sources111for emitting light onto the object, and a detector112for detecting light scattered or reflected from the object. The light source111may be formed as a light emitting diode (LED), a laser diode (LD), a phosphor, or a combination thereof. In addition, the light sources may be formed as an array of a plurality of light sources such that the respective light sources may emit light of different wavelengths. The detector112may be formed as a photo diode, a photo transistor (PTr), or an array thereof. However, embodiments are not limited thereto. For example, the detector112may be formed as an image sensor, for example, complementary metal-oxide semiconductor (CMOS) image sensor, and the like.

The temperature controller120may modulate temperature of the object while the measurer110measures a spectrum from the object. The temperature controller120may include a heater121and a temperature sensor122. The heater121may provide thermal energy to the object for temperature modulation of the object, and the temperature sensor122may measure temperature of the object which is changed by the thermal energy provided by the heater121.

The heater121and the temperature sensor122may interact with each other to modulate temperature of the object at least once within a predetermined temperature range. For example, when performing modulation a plurality of number of times, the temperature controller120may perform a second modulation successively as soon as a first modulation ends, so that each time modulation is performed, temperature of the object may increase continuously within a predetermined temperature range.

Further, temperature modulation may be performed at a calibration time and/or a target component estimation time. For example, the temperature modulation may be performed only at a calibration time of the object, without being performed at the target component estimation time.

According to the example embodiment, the measurer110and the temperature controller120may be integrally formed with one hardware device, but embodiments are not necessarily limited thereto. For example, the measurer110and the temperature controller120may be formed as separate devices.

The processor130may be electrically connected to the measurer110and the temperature controller120, and may control the measurer110and the temperature controller120.

The processor130may control temperature controller120, may obtain a spectrum for each temperature from an object for calibration, which is measured by the measurer110according to the modulation performed by the temperature controller120, and may generate and/or calibrate a target component estimation model based on the obtained spectrum for each temperature.

For example, the processor130may obtain effective optical pathlength vectors from the object according to a temperature change based on the spectrum for each temperature, and may obtain a representative optical pathlength from the obtained effective optical pathlength vectors.

For example, as illustrated inFIG.2, as the temperature controller120applies a temperature change to the object, the absorbance of the object changes.

A change in absorbance ∂A with respect to a temperature change has a relationship with a first value leff*∂μaand a second value μa*∂leff, as represented by the following Equation 1, in which leffdenotes the effective optical pathlength, μadenotes the absorption coefficient, ∂leffdenotes a change in effective optical pathlength, and ∂μadenotes a change in absorption coefficient.
∂A=leff∂μa+μa∂leff[Equation 1]

FIG.3Ais a diagram illustrating a variation in the first value leff*∂μafor each wavelength according to a temperature change by Monte Carlo (MC) simulation.FIG.3Bis a diagram illustrating a variation in the second value μa*∂lefffor each wavelength according to a temperature change by Monte Carlo (MC) simulation.

For example, the processor130may obtain effective optical pathlength vectors according to a temperature change by using the first value leff*∂μa. Referring toFIGS.3A and3B, a simulation result shows that the first value leff*∂μais several hundred times greater than the second value μa*∂leff, such that the second value may be ignored, and thus Equation 1 may be approximated, as shown in the following Equation 2.
∂A≈leff∂μa[Equation 2]

In Equation 2, an absorption coefficient change ∂μacorresponds to intrinsic properties of a material that never change, such that by using a variation in absorbance which corresponds to the calculated first value leff*∂μa, the effective optical pathlength vector may be obtained.

In another example embodiment, the second value μa*∂leffmay be obtained for each wavelength based on a lookup table or a pre-defined model equation. A change in absorbance with respect to a temperature change may be expressed in Equation 1, in which ∂μa=0 at an isosbestic wavelength shown inFIG.3A, and thus may be approximated as shown in the following Equation 3.
∂A≈leff∂μa+μa∂leff≈μa∂leff[Equation 3]

The processor130may first perform fitting of the second value μa*∂leffby using the absorbance change ∂A at the isosbestic wavelength, and may obtain the effective optical pathlength vector based on the absorbance change and the change in the first value leff*∂μaand the second value μa*∂leff. Referring toFIG.3A, the isosbestic wavelength may be, for example, approximately 1780 nm.

Referring back toFIG.1, the processor130may determine a representative effective optical pathlength based on the obtained effective optical pathlength vectors for each wavelength.

For example, the processor130may determine an average value of the effective optical pathlength vectors for each wavelength to be the representative effective optical pathlength. However, the representative effective optical pathlength is not limited thereto, and the processor130may also determine a statistical value, such as a median value, a maximum value, a minimum value, etc., or a value obtained by combining the vectors using a pre-defined combination equation, to be the representative effective optical pathlength.

FIG.4is a diagram illustrating effective optical pathlength vectors for each wavelength and an average value thereof.

For example, the processor130may determine, as the representative effective optical pathlength, a value42corresponding to an average value of effective optical pathlength vectors41for each wavelength according to a temperature change in a range of 30° C. to 35° C. and effective optical pathlength vectors43for each wavelength according to a temperature change in a range of 25° C. to 35° C.

Referring back toFIG.1, the processor130may generate a target component estimation model by using the determined representative effective optical pathlength.

By applying the determined representative effective optical pathlength to a model based on at least one of least squares and net analyte signal (NAS), the processor130may generate or calibrate a target component estimation model, but the processor130is not limited thereto.

FIGS.5A and5Bare diagrams illustrating a change in residual component before and after calibration of the target component estimation model using the representative effective optical pathlength.

Quantitative analysis of the object based on the Beer-Lambert law has a limitation in that it assumes optical pathlength distribution at all wavelengths is equal.
S=εwLCw+εkLCk+εcLCc+εfLCf+εgLCg+Residual  [Equation 4]

That is, as represented by Equation 4, a linear regression equation using least squares, as a general model for estimating a target component, is a quantification method based on the assumption that a spectrum has linearity by combining an intrinsic absorption coefficient ε, derived from physical properties and a molecular structure of components included in a spectrum, a fixed optical pathlength L, and concentrations of the respective components.

As illustrated inFIG.5A, if a linear regression equation is applied based on the assumption that an optical pathlength distribution is equal, it can be seen that a blood glucose measurement error may not be explained by the combination using the linear regression equation, but the blood glucose measurement error occurs in a range of, for example, 10 mg/dl to 1000 mg/dl, which is a range much greater than a normal blood glucose range of 80 mg/dl to 180 mg/dl for ordinary people.
S=εw,leffCw+εk,leffCk+εc,leffCc+εf,leffCf+εg,leffCg+Residual

Equation 5 is an example of a target component estimation model used for calibrating an optical path with the obtained representative effective optical pathlength leff, as described above according to an example embodiment.

As illustrated inFIG.5B, when a target component estimation model is generated by least squares by obtaining the effective optical pathlength vectors, rather than a uniform optical pathlength distribution as shown in Equation 5, a variation in residual component is small, such that a target component may be more estimated accurately.

Referring back toFIG.1, upon receiving a request for estimating a target component from a user or an external device, the processor130may control the measurer110, and upon receiving a spectrum for measurement from the measurer110, the processor130may estimate a target component by using a target component estimation model.

If there is a pre-defined calibration cycle or a user's request, the processor130may determine whether to perform calibration again by analyzing a target component estimation result, and upon determining that calibration is required, the processor130may guide a user to perform calibration again, and may control the measurer110and the temperature controller120to perform calibration by modulation for each temperature, as described above.

FIG.6is a block diagram illustrating an apparatus for estimating a target component according to another example embodiment.

Referring toFIG.6, the apparatus600for estimating a target component includes the measurer110, the light source111, the detector112, the temperature controller120, the heater121, the temperature sensor122, the processor130, an output interface610, a storage620, and a communication interface630. In this case, the measurer110, the light source111, the detector112, the temperature controller120, the heater121, the temperature sensor122, and the processor130are described above in detail with reference toFIG.1, such that the following description will focus on non-overlapping parts.

The output interface610may output a variety of information processed by the processor130. The output interface610may include a visual output module such as a display and the like, an audio output module such as a speaker and the like, or a haptic module using vibrations, tactile sensation, and the like. For example, the output interface610may generate a target component estimation model and/or may output a calibration result, and may output an estimation result of a target component estimated by using the generated and/or calibrated target component estimation model.

The storage620may store user characteristic information, driving condition information of the light source111and the detector112of the measurer110, and the like. Further, the storage620may store processing results of the processor130, for example, effective optical pathlength vector information according to a temperature change, information on the determined representative effective optical pathlength, the generated and/or calibrated target component estimation model, the estimation result of the target component estimated by using the generated and/or calibrated target component estimation model.

The storage620may include at least one storage medium of a flash memory type memory, a hard disk type memory, a multimedia card micro type memory, a card type memory (e.g., an SD memory, an XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read only memory (ROM), an electrically erasable programmable read only memory (EEPROM), a programmable read only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk, and the like, but is not limited thereto.

The communication interface630may communicate with an external device through wired or wireless communications to receive a variety of information from the external device. The external device may include an information processing device such as a smartphone, a tablet personal computer (PC), a laptop computer, a desktop computer, and the like, but is not limited thereto, and may have a function of estimating a component of an object.

For example, the communication interface630may receive, from an external device, a request for generating and/or calibrating a target component estimation model for estimating a target component of an object, and may transmit the request to the processor130. Further, the communication interface630may receive, from the external device, a request for estimating a target component, which is to be performed by using the generated and/or calibrated target component estimation model, and may transmit the request to the processor130.

In this case, in response to the request, the processor130may control the measurer110. Further, by receiving reference information, such as driving conditions of the light source111and the detector112and the like, from the external device, the communication interface630may transmit the reference information to the processor130. In this case, the processor130may store the received reference information in the storage62. In addition, the communication interface630may transmit processing results of the processor130to the external device.

The communication interface630may communicate with the external device by using various wired or wireless communication techniques, such as Bluetooth communication, Bluetooth Low Energy (BLE) communication, near field communication (NFC), WLAN communication, Zigbee communication, infrared data association (IrDA) communication, Wi-Fi Direct (WFD) communication, ultra-wideband (UWB) communication, Ant+ communication, WIFI communication, radio frequency identification (RFID) communication, 3G communication, 4G communication, 5G communication, and the like. However, embodiments of the communication techniques are not limited thereto.

FIG.7is a flowchart illustrating a method of estimating a target component according to an example embodiment.

Referring toFIG.7, the apparatus for estimating a target component may modulate temperature of an object for calibration in operation710. The object for calibration of a target component estimation model may include water containing no target component, or a solution that mimics a scattering coefficient of a target component, human skin, and the like.

Temperature modulation of the object may be performed at least one or more times within a predetermined temperature range by interaction between the heater121and the temperature sensor122.

For example, if modulation is performed a plurality of number of times, the apparatus for estimating a target component may perform a second modulation successively as soon as a first modulation ends, so that each time modulation is performed, temperature of the object may increase continuously within a predetermined temperature range.

Then, the apparatus for estimating a target component may measure a spectrum for each temperature which changes according to the temperature modulation in operation720. For example, the apparatus for estimating a target component may measure a spectrum for each temperature by using one or more light sources111emitting light onto an object, and the detector112detecting light scattered or reflected from the object. The light source111may be formed as a light emitting diode (LED), a laser diode (LD), a phosphor, or a combination thereof. In addition, the light source may be formed as an array of a plurality of light sources such that the respective light sources may emit light of different wavelengths. The detector112may be formed as a photo diode, a photo transistor (PTr), or an array thereof. Alternatively, the detector112may be formed as an image sensor, e.g., complementary metal-oxide semiconductor (CMOS) image sensor and the like.

Subsequently, the apparatus for estimating a target component may obtain effective optical pathlength vectors according to a temperature change based on the measured spectrum for each temperature in operation730.

For example, the apparatus for estimating a target component may calculate a change in absorbance with respect to a temperature change based on a spectrum according to the temperature change, and may obtain effective optical pathlength vectors according to the temperature change based on a relationship between the calculated change in absorbance and a change in optical pathlength.

In addition, as described above using Equation 1, based on a relationship of a sum of a first value, obtained by multiplying the change in absorbance, a change in absorption coefficient, and an optical pathlength, and a second value obtained by multiplying an absorption coefficient and a change in optical pathlength, the apparatus for estimating a target component may obtain effective optical pathlength vectors according to a temperature change.

For example, when a variation in the first value for each wavelength according to a temperature change and a variation in the second value for each wavelength according to a temperature change are calculated by Monte Carlo (MC) simulation, the simulation result shows that the first value is several hundred times greater than the second value, such that the second value may be ignored, and thus the effective optical pathlength vectors may be obtained by using the calculated variation in absorbance which corresponds to the first value.

In another example, the apparatus for estimating a target component may calculate a first value at an isosbestic wavelength based on the change in absorbance, and may obtain effective optical pathlength vectors for each wavelength based on a change in absorbance at another wavelength and the change in the first and second values. Here, the second value may be obtained based on a lookup table or a pre-defined model equation.

When the second value may be obtained for each wavelength based on the lookup table or the pre-defined model equation, the apparatus for estimating a target component may first perform fitting of the second value using the change in absorbance at the isosbestic wavelength and may obtain the effective optical pathlength vectors based on the change in absorbance at another wavelength and the change in the first and second values.

Then, the apparatus for estimating a target component may determine a representative optical pathlength based on the obtained effective optical pathlength vectors for each wavelength in operation740. For example, the apparatus for estimating a target component may determine an average value of the effective optical pathlength vectors to be the representative effective optical pathlength. However, the representative effective optical pathlength is not limited thereto, and the apparatus for estimating a target component may determine a statistical value, such as a median value, a maximum value, a minimum value, etc., or a value obtained by combining the vectors using a pre-defined combination equation, to be the representative effective optical pathlength.

Subsequently, the apparatus for estimating a target component may generate a target component estimation model by using the determined representative effective optical pathlength in operation750. For example, by applying the determined representative effective optical pathlength to a model based on at least one of least square and net analyte signal (NAS), the apparatus for estimating a target component may generate and/or calibrate a target component estimation model. However, the target component estimation model is not limited thereto.

As described above, in the case where a target component estimation model is generated by obtaining the effective optical pathlength vectors, rather than using a uniform optical pathlength distribution, a variation in residual component is relatively small, such that a target component may be estimated more accurately.

Next, once the spectrum is measured from the object for measurement, the apparatus for estimating a target component may estimate a target component by using the measured spectrum and the target component estimation model in operation760.

FIG.8is a diagram schematically illustrating an example of an electronic device including an apparatus for estimating a target component according to an example embodiment.

As illustrated inFIG.8, an example embodiment of the electronic device may be a smart watch-type wearable device800, but is not particularly limited to a specific type, and may include various types of wearable devices, such as a smartphone, a tablet PC, a smart band, smart earphones, a smart ring, a smart necklace, and the like. The wearable device800ofFIG.8may have a function of generating and/or calibrating the aforementioned target component estimation model, and a function of estimating a target component by using the generated and/or calibrated target component estimation model.

Referring toFIG.8, the wearable device800includes a main body810and a strap820.

The strap820, which is connected to both ends of the main body810, may be flexible so as to be wrapped around a user's wrist. The strap820may be composed of a first strap and a second strap which are separated from each other. One ends of the first strap and the second strap are connected to the main body810, and the other ends thereof may be connected to each other via a connecting means. In this case, the connecting means may be formed as magnetic connection, Velcro connection, pin connection, and the like, but is not limited thereto. Further, the strap820is not limited thereto, and may be integrally formed as a non-detachable band. In this case, air may be injected into the strap820, or the strap820may be provided with an air bladder to have elasticity according to a change in pressure applied to the wrist, and may transmit the change in pressure of the wrist to the main body810.

In this case, a battery may be embedded in the main body810or the strap820to supply power to the wearable device800.

In addition, the apparatuses100or600for estimating a target component may be mounted in the main body810. The apparatuses100or600for estimating a target component may include the measurer110, the temperature controller120, and the processor130, as described above with reference toFIGS.1to6. The measurer110may include the light source111for emitting light of one or more wavelengths onto an object (e.g., a user's wrist or finger), and the detector112for detecting light scattered or reflected from the object. The temperature controller120may include the heater121for providing thermal energy to the object, and the temperature sensor122for measuring a change in temperature of the object. The processor130controls the temperature controller120, and obtains a spectrum for each temperature from the object for calibration which is measured by the measurer110according to modulation performed by the temperature controller120, and may generate and/or calibrate a target component estimation model based on the obtained spectrum for each temperature. For example, by obtaining effective optical pathlength vectors from the object according to the temperature change based on the spectrum for each temperature and by obtaining a representative optical pathlength from the obtained effective optical pathlength vectors, the processor130may generate a target component estimation model by using the representative optical pathlength. Once the spectrum for estimating a target component is obtained from the object, the processor130may estimate a target component by using the target component estimation model.

A display, provided on a front surface of the main body810, may display various application screens, including time information, received message information, and the like. A rear surface of the main body810may come into contact with the upper part of a user's wrist.

Further, the wearable device800may further include a manipulation interface815and a display unit814which are mounted in the main body810. The manipulation interface815may receive and transmit a user command, and may have a power button to input a command to turn on/off the wearable device800. The display unit814may display additional information, such as a component estimation result, warning, alarm, etc., by various visual methods to provide the information to the user.

Embodiments can be realized as a computer-readable code written on a computer-readable recording medium. The computer-readable recording medium may be any type of recording device in which data is stored in a computer-readable manner.

Examples of the computer-readable recording medium include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disc, an optical data storage, and a carrier wave (e.g., data transmission through the Internet). The computer-readable recording medium can be distributed over a plurality of computer systems connected to a network so that a computer-readable code is written thereto and executed therefrom in a decentralized manner. Functional programs, codes, and code segments needed for realizing the present invention can be readily deduced by programmers of ordinary skill in the art.

While example embodiments have been illustrated and described above, it will be apparent to those skilled in the art that modifications and variations could be made without departing from the scope as defined by the appended claims and their equivalents.