Patent Description:
Diabetes is a chronic disease that causes various complications and can be difficult to manage. Accordingly, people with diabetes are advised to check their blood glucose regularly to prevent complications. In particular, when insulin is administered to control blood glucose levels, the blood glucose levels should be closely monitored to avoid hypoglycemia and control insulin dosage. An invasive method of finger pricking is generally used to measure blood glucose levels. However, while the invasive method may provide high reliability in measurement, it may cause pain and inconvenience as well as an increased risk of disease and infection due to the use of inj ection. Recently, research has been conducted regarding a method of non-invasively measuring blood glucose accurately by using a spectrometer without blood sampling.

The document <CIT> discloses prior art spectral analysis methods.

Provided are an apparatus for measuring a spectrum, and a method of correcting a temperature change of a light source in a spectrum.

In accordance with an aspect of the disclosure, an apparatus for measuring a spectrum is defined in claim <NUM>.

The processor may obtain a light source temperature drift vector component from the measured analysis spectrum by regression analysis using the obtained light source temperature drift vector, and remove the obtained light source temperature drift vector component from the measured analysis spectrum.

The processor may correct a slope and an offset in the measured analysis spectrum.

The plurality of temperature correction spectra and the analysis spectrum may be absorption spectra.

The apparatus for measuring a spectrum may include a reference photodetector configured to detect light emitted by the light source array.

The processor may determine whether a driving condition change is effectively applied based on an intensity of the light detected by the reference photodetector.

In accordance with an aspect of the disclosure, a method of correcting a temperature change of a light source in a spectrum is defined in claim <NUM>.

The adjusting of the measured analysis spectrum to reduce the effect of the temperature change of the light source may include obtaining a light source temperature drift vector component from the measured analysis spectrum by regression analysis using the obtained light source temperature drift vector, and removing the obtained light source temperature drift vector component from the analysis spectrum.

The method may include correcting a slope and an offset in the measured analysis spectrum.

The method of correcting a temperature change of a light source in a spectrum may further include receiving a light signal emitted by the light source.

The method of correcting a temperature change of a light source in a spectrum may further include determining whether a driving condition change is effectively applied based on an intensity of the detected light.

Throughout the drawings and the detailed description, unless otherwise described, the same drawing reference numerals may refer to the same elements, features, and structures.

It should be noted that wherever possible, the same reference symbols may refer to the same elements, features, structures, etc. In the following description, a detailed description of known functions and configurations incorporated herein may be omitted so as to not obscure the subject matter of the present disclosure.

Process steps described herein may be performed differently from a specified order. That is, each step may be performed in a specified order, at substantially simultaneously, in a reverse order, or in a different order.

Further, the terms used throughout the specification may be defined in consideration of the functions according to exemplary embodiments, and can be varied according to a purpose of a user or manager, precedent, etc. Therefore, definitions of the terms should be made on the basis of the overall context of the disclosure.

It will be understood that, although terms such as "first," "second," etc. may be used herein to describe various elements, these elements might not be limited by these terms. These terms may be used to distinguish one element from another. The singular form of terms may include the plural form of the term unless expressly stated otherwise. In the present disclosure, it should be understood that the terms, such as "including," "having," etc., may indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof disclosed in the specification, and might not preclude the possibility that one or more other features, numbers, steps, actions, components, parts, or combinations thereof may exist or may be added.

Further, components described in the specification are discriminated according to functions mainly performed by the components. That is, two or more components may be integrated into a single component. Furthermore, a single component may be separated into two or more components. Moreover, each component may additionally perform some or all of a function executed by another component in addition to the main function thereof. Some or all of the main function of each component may be carried out by another component.

<FIG> is a diagram illustrating an example of an apparatus for measuring a spectrum according to an embodiment. The spectrum measuring apparatus <NUM> of <FIG> is an apparatus for measuring an in vivo spectrum of an object, and correcting an effect of a temperature change of a light source on the measured in vivo spectrum, and may be embedded in an electronic device or may be enclosed in a housing to be provided as a separate device. In this case, examples of the electronic device may include a cellular phone, a smartphone, a tablet personal computer (PC), a laptop computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, an MP3 player, a digital camera, a wearable device, and the like; and examples of the wearable device may include a wristwatch-type wearable device, a wristband-type wearable device, a ring-type wearable device, a waist belt-type wearable device, a necklace-type wearable device, an ankle band-type wearable device, a thigh band-type wearable device, a forearm band-type wearable device, and the like. However, the electronic device is not limited to the above examples, and the wearable device is neither limited thereto.

Referring to <FIG>, the spectrum measuring apparatus <NUM> includes a light source array <NUM>, a photodetector <NUM>, and a processor <NUM>.

The light source array <NUM> may include a plurality of light sources which emit light of different wavelengths towards an object. In an embodiment, each light source may emit light of a predetermined wavelength (e.g., near-infrared (NIR) light, mid-infrared (MIR) light, and the like) towards the object. However, wavelengths of light to be emitted by each light source may vary according to a purpose of measurement or the type of an analyte. Further, each light source may be a single light-emitting body, or may be formed as an array of a plurality of light-emitting bodies. In an embodiment, each light source may include a light emitting diode (LED), a laser diode, a fluorescent body, and the like.

The photodetector <NUM> may receive a light signal which is reflected by an object, scattered by the object, or transmitted towards the object. The photodetector <NUM> may convert the received light signal into an electrical signal, and may transmit the electrical signal to the processor <NUM>. In an embodiment, the photodetector <NUM> may include a photo diode (PD), a photo transistor (PTr), a charge-coupled device (CCD), and the like. The photodetector <NUM> may be a single device, or may be formed as an array of a plurality of devices.

There may be various numbers and arrangements of the light sources and the photodetector, and the number and arrangement thereof may vary according to a type and a purpose of use of an analyte, the size and shape of the electronic device in which the spectrum measuring apparatus <NUM> is mounted, and the like. In addition, the spectrum measuring apparatus <NUM> may further include various optical elements (e.g., a filter, a mirror, a lens, etc.).

The processor <NUM> may process various signals and perform operations associated with measuring an in vivo spectrum, correcting an effect of a temperature change of the light source, and the like.

The processor <NUM> may measure the in vivo spectrum of the object by reconstructing a spectrum based on an intensity of light received by the photodetector <NUM>. Here, the in vivo spectrum may be an absorption spectrum, but is not limited thereto, and may be a reflection spectrum or a transmission spectrum.

The method of reconstructing a spectrum by the processor <NUM> will be described in more detail with reference to FIGS.

By using the light source array <NUM> and the photodetector <NUM>, the processor <NUM> may measure a plurality of in vivo spectra based on a temperature change of each light source of the light source array <NUM> (hereinafter referred to as a "temperature correction spectrum"). Further, the processor <NUM> may measure an in vivo spectrum for analysis (hereinafter referred to as an "analysis spectrum").

The processor <NUM> induces a temperature change of each light source of the light source array <NUM> by changing driving conditions of the light source array <NUM>. In this case, the driving conditions to be changed include at least one of an intensity of an applied current, a pulse width, and a cooling delay. Further, the processor <NUM> may measure a plurality of temperature correction spectra by measuring the in vivo spectra of the object while changing the driving conditions of the light source array <NUM>. The processor <NUM> measures a first temperature correction spectrum by driving the light source array <NUM> based on a first driving condition; and induces a temperature change of the light source array <NUM> by changing the first driving condition to a second driving condition, and measures a second temperature correction spectrum by driving the light source array <NUM> based on the second driving condition. Further, the processor <NUM> may measure a plurality of first temperature correction spectra, and a plurality of second temperature correction spectra by repeatedly changing the driving conditions during a predetermined period of time. In this case, the predetermined period of time may be <NUM> seconds, but this is merely an example, and the predetermined period of time is not limited thereto and may be set to various values.

In an embodiment, the processor <NUM> may measure a plurality of temperature correction spectra by changing a cooling delay of the light source array <NUM>. For example, the processor <NUM> may measure a first temperature correction spectrum by driving each light source of the light source array <NUM> based on a first cooling delay. Further, upon completing measurement of the first temperature correction spectrum, the processor <NUM> may change the first cooling delay to a second cooling delay; and based on the cooling delay being changed to the second cooling delay, the processor <NUM> may drive each light source of the light source array <NUM> based on the second cooling delay, to measure a second temperature correction spectrum. In addition, based on completing measurement of the second temperature correction spectrum, the processor <NUM> may change the second cooling delay to the first cooling delay; and based on the cooling delay being changed to the first cooling delay, the processor <NUM> may drive each light source of the light source array <NUM> based on the first cooling delay, to measure the first temperature correction spectrum. By repeatedly changing the cooling delay of each light source, from the first cooling delay to the second cooling delay and from the second cooling delay to the first cooling delay, during a predetermined period of time, the processor <NUM> may measure the plurality of first temperature correction spectra and the plurality of second temperature correction spectra.

In another example, the processor <NUM> may measure a plurality of temperature correction spectra by changing a pulse width of the light source array <NUM>. For example, the processor <NUM> may measure a first temperature correction spectrum by driving each light source of the light source array <NUM> based on a first pulse width. Further, based on completing measurement of the first temperature correction spectrum, the processor <NUM> may change the first pulse width to a second pulse width; and based on the first pulse width being changed to the second pulse width, the processor <NUM> may drive each light source based on the second pulse width, to measure a second temperature correction spectrum. In addition, based on completing measurement of the second temperature correction spectrum, the processor <NUM> may change the second pulse width to the first pulse width; and based on the second pulse width being changed to the first pulse width, the processor <NUM> may drive each light source with the first pulse width, to measure the first temperature correction spectrum. By repeatedly changing the pulse width of each light source, from the first pulse width to the second pulse width and from the second pulse width to the first pulse width, during a predetermined period of time, the processor <NUM> may measure the plurality of first temperature correction spectra and the plurality of second temperature correction spectra.

In yet another example, the processor <NUM> may measure a plurality of temperature correction spectra by changing an intensity of an applied current of the light source array <NUM>. For example, the processor <NUM> may measure a first temperature correction spectrum by applying a current having a first intensity to each light source of the light source array <NUM>. Further, based on completing measurement of the first temperature correction spectrum, the processor <NUM> may change the first intensity of the applied current to a second intensity; and based on the intensity of the applied current being changed from the first intensity to the second intensity, the processor <NUM> may measure a second temperature correction spectrum by applying a current having the second intensity to each light source of the light source array <NUM>. In addition, based on completing measurement of the second temperature correction spectrum, the processor <NUM> may change the second intensity of the applied current to the first intensity; and based on the intensity of the applied current being changed from the second intensity to the first intensity, the processor <NUM> may measure the first temperature correction spectrum by applying the current having the first intensity to each light source of the light source array <NUM>. By repeatedly changing the intensity of the applied current, from the first intensity to the second intensity and from the second intensity to the first intensity, during a predetermined period of time, the processor <NUM> may measure the plurality of first temperature correction spectra and the plurality of second temperature correction spectra.

In still another example, the processor <NUM> may measure a plurality of temperature correction spectra by changing two or more of the cooling delay, the pulse width, and the intensity of the applied current of the light source array <NUM>.

Further, in the case where the processor <NUM> measures the plurality of temperature correction spectra by changing the pulse width and/or the intensity of the applied current of the light source array <NUM>, a change in temperature of the light source array <NUM>, a change in the pulse width, and/or a change in the intensity of the applied current may all affect the temperature correction spectra. In this case, the processor <NUM> may perform preprocessing to reduce the effect of the change in the pulse width and/or the effect of the change in the intensity of the applied current from the measured temperature correction spectrum. Information associated with the effect of the change in the pulse width and/or the effect of the change in the intensity of the applied current may be pre-stored in an internal or external memory of the processor <NUM>.

The processor <NUM> obtains a light source temperature drift vector by analyzing the measured plurality of temperature correction spectra. The processor <NUM> calculates a difference spectrum between the first temperature correction spectrum, measured in association with the first driving condition, and the second temperature correction spectrum measured in association with the second driving condition; and extracts a principal component spectrum vector of the calculated difference spectrum as the light source temperature drift vector. The processor extracts the principal component spectrum vector from the difference spectrum by using one of the following dimension reduction algorithms : Principal Component Analysis (PCA), Independent Component Analysis (ICA), Non-negative Matrix Factorization (NMF), Singular Value Decomposition (SVD). In an embodiment, the processor <NUM> may preprocess the difference spectrum by using various preprocessing methods such as multiplicative scatter correction (MSC), Standard normal variate (SNV), Orthogonal Signal Correction (OSC), Savitzky-Golay (SG), and the like.

By using the obtained light source temperature drift vector, the processor <NUM> reduces the effect of the temperature change of the light source array <NUM> from the analysis spectrum. The processor <NUM> adjusts an analysis spectrum based on the light source temperature drift vector. For example, the processor <NUM> may obtain a light source temperature drift vector component from the analysis spectrum by regression analysis, and may reduce the effect of the temperature change of the light source array <NUM> from the analysis spectrum by removing the obtained light source temperature drift vector component from the analysis spectrum.

In an embodiment, the processor <NUM> may correct a slope and an offset in the analysis spectrum. For example, the processor <NUM> may correct the slope and the offset in the analysis spectrum by MIN-MAX normalization, multiplicative scatter correction (MSC), and the like.

When a predetermined period of time elapses after obtaining the light source temperature drift vector, the processor <NUM> may re-measure a plurality of temperature correction spectra, and may re-obtain a light source temperature drift vector based on the re-measured plurality of temperature correction spectra. That is, by periodically updating the light source temperature drift vector, the processor <NUM> may properly reflect a change in optical characteristics (e.g., scattering coefficient) of an object.

<FIG> is a diagram illustrating an example of an LED-PD structure according to an embodiment. The LED-PD structure of <FIG> may be an example of a structure of the light source array <NUM> and the photodetector <NUM> of <FIG>.

Referring to <FIG>, the LED-PD structure may be formed with an LED array of n number of LEDs and a photo diode (PD). The LED array may be disposed outside of the photo diode (PD) to surround the photo diode (PD). For example, the LED array may be arranged in a concentric circle around the photo diode (PD).

The LEDs may be configured to emit light of predetermined and different peak wavelengths λ<NUM>, λ<NUM>, λ<NUM>,. , and λn, respectively. The LEDs may be driven sequentially according to a predetermined control signal to emit light of a predetermined peak wavelength towards an object OBJ; and the photo diode (PD) may detect light reflected by the object OBJ.

<FIG> are diagrams explaining an example of reconstructing a spectrum by a processor according to an embodiment.

Referring to <FIG> and <FIG>, the light source array <NUM> is composed of an LED array having n number of LEDs; and the LEDs may be configured to emit light of predetermined peak wavelengths λ<NUM>, λ<NUM>, λ<NUM>,. , and λn, respectively, based on light source driving conditions (e.g., an intensity of an applied current, a pulse width, a cooling delay, etc.).

Referring to <FIG>, the processor <NUM> may sequentially drive each light source based on a predetermined driving sequence, light source driving conditions, and the like, to emit light; and the photodetector (PD) may detect light reflected by the object OBJ. In this case, the processor <NUM> may drive a subset of the light sources, and may divide the light sources into groups to drive each group of the light sources in a time-division manner.

Referring to <FIG>, the processor <NUM> may reconstruct a spectrum by receiving the light signal detected by the photodetector (PD).

Referring to Equation <NUM> above, α denotes a parameter for spectrum reconstruction, E denotes a unit matrix, A denotes a light source spectrum measured for each light source according to driving conditions, p denotes the intensity of the light signal detected by the photodetector, and yα denotes the reconstructed spectrum. In this case, the light source spectrum may refer to a spectrum of light emitted by each light source, and information associated with the light source spectrum may be pre-stored in an internal or an external database.

<FIG> are exemplary diagrams explaining an example of obtaining a light source temperature drift vector according to an embodiment. More specifically, <FIG> is an exemplary diagram illustrating an intensity of a light signal emitted by a light source based on a change in driving conditions according to an embodiment; <FIG> is an exemplary diagram illustrating an intensity of a light signal reflected by an object based on light being emitted with the intensity of <FIG> towards the object according to an embodiment; <FIG> is an exemplary diagram illustrating a difference spectrum between a first temperature correction spectrum and a second temperature correction spectrum which are measured by changing driving conditions according to an embodiment; and <FIG> is an exemplary diagram illustrating a light source temperature drift vector which is extracted from the difference spectrum of <FIG> according to an embodiment.

Referring to <FIG> and <FIG>, the processor <NUM> may measure a first temperature correction spectrum by driving the light source array <NUM> with a <NUM> microsecond (µs) cooling delay at a measurement time <NUM>. Based on completing measurement of the first temperature correction spectrum, the processor <NUM> may change the cooling delay from <NUM> to <NUM>; and at a measurement time <NUM> after the <NUM> cooling delay, the processor <NUM> may drive the light source array <NUM> to measure a second temperature correction spectrum. In this case, temperature of the light source array <NUM> at the measurement time <NUM> is increased by ΔT as compared to temperature of the light source at the measurement time <NUM>. Based on completing measurement of the second temperature correction spectrum, the processor <NUM> may change the cooling delay from <NUM> to <NUM>; and at a measurement time <NUM> after the <NUM> cooling delay, the processor <NUM> may drive the light source array <NUM> to measure the first temperature correction spectrum. In this case, temperature of the light source array <NUM> at the measurement time <NUM> is decreased by ΔT as compared to the temperature of the light source array <NUM> at the measurement time <NUM>. In this manner, by repeatedly changing the cooling delay during a predetermined period time, the processor <NUM> may measure a plurality of first temperature correction spectra and a plurality of the second temperature correction spectra.

An example of the intensity of the light signal emitted by the light source array <NUM> based on the cooling delay being repeatedly changed is illustrated in <FIG>; and an example of the intensity of the light signal reflected by the object at that time is illustrated in <FIG>. As illustrated in <FIG> and <FIG>, it can be seen that as the cooling delay is repeatedly changed, the intensity of the light signal and the intensity of light reflected by the sample at that time are changed.

Referring to <FIG>, the processor <NUM> may calculate the difference spectrum by subtracting the second temperature correction spectrum from the first temperature correction spectrum. In this case, if the temperature of the light source array <NUM> is decreased by ΔT, the difference spectrum may indicate a change in the spectrum of the sample.

While <FIG> illustrates an example of the difference spectrum calculated by subtracting the second temperature correction spectrum from the first temperature correction spectrum, the difference spectrum is not limited thereto. That is, in contrast with <FIG>, the difference spectrum may be calculated by subtracting the first temperature correction spectrum from the second temperature correction spectrum. In this case, if the temperature of the light source is increased by ΔT, the difference spectrum may indicate a change in the spectrum of the sample.

Referring to <FIG>, the processor <NUM> may extract, as a light source temperature drift vector, a principal component spectrum vector from the difference spectrum by using the aforementioned various dimension reduction algorithms. While <FIG> illustrates an example of extracting one principal component spectrum vector, this is merely an example, and the principal component spectrum vector is not limited thereto and the number of the principal component spectrum vectors is not specifically limited. The processor <NUM> may preprocess the difference spectrum by using various preprocessing methods such as multiplicative scatter correction (MSC), Standard normal variate (SNV), Orthogonal Signal Correction (OSC), Savitzky-Golay (SG), and the like.

<FIG> is an exemplary diagram illustrating a result obtained by correcting an effect of a temperature change of a light source by using a method of correcting a temperature change of a light source according to an embodiment. In <FIG>, reference numeral <NUM> shows a difference spectrum which indicates a difference between an analysis spectrum and a preceding analysis spectrum when measuring a plurality of analysis spectra while maintaining a cooling delay at <NUM>; reference numeral <NUM> shows a difference spectrum preprocessed by multiplicative scatter correction (MSC); reference numeral <NUM> shows a light source temperature drift vector extracted from the preprocessed difference spectrum; and reference numeral <NUM> shows a result obtained by removing the light source temperature drift vector component from the preprocessed difference spectrum.

As shown by reference numeral <NUM> of <FIG>, a biological spectrum having a high signal-to-noise ratio may be measured using the method of correcting a light source temperature change according to an embodiment.

<FIG> is a diagram illustrating another example of an apparatus for measuring a spectrum. The spectrum measuring apparatus <NUM> of <FIG> may be embedded in an electronic device, or may be enclosed in a housing to be provided as a separate device.

Referring to <FIG>, the spectrum measuring apparatus <NUM> includes the light source array <NUM>, the photodetector <NUM>, a reference photodetector <NUM>, and a processor <NUM>. Here, the light source array <NUM> and the photodetector <NUM> may be substantially similar to the light source array <NUM> and the photodetector <NUM> described above with reference to <FIG>, and redundant description thereof may be omitted. Further, the processor <NUM> performs a function similar to that of the processor <NUM> of <FIG>, such that detailed description of redundant functionality may be omitted.

The reference photodetector <NUM> may receive a light signal which is emitted by the light source array <NUM>. The reference photodetector <NUM> may convert the received light signal into an electrical signal, and may transmit the electrical signal to the processor <NUM>. In an embodiment, the reference photodetector <NUM> may include a photo diode (PD), a photo transistor (PTr), a charge-coupled device (CCD), and the like. The reference photodetector <NUM> may be a single device, or may be formed as an array of a plurality of devices.

The processor <NUM> may determine whether a driving condition change is effectively applied based on an intensity of the light signal received by the reference photodetector <NUM>.

As illustrated in <FIG>, the intensity of the light signal received by the reference photodetector <NUM> may be repeatedly increased or decreased based on a driving condition change. Accordingly, in an embodiment, if an increment or a decrement of an intensity of the light signal, compared to a preceding value of the reference photodetector <NUM>, is greater than or equal to a predetermined threshold value, then the processor <NUM> may determine that the driving condition change is effectively applied. Alternatively, if an increment or a decrement of an intensity of the light signal, compared to a preceding value of the reference photodetector <NUM>, is less than a predetermined threshold value, then the processor <NUM> may determine that the driving condition change is not effectively applied.

The processor <NUM> may use a temperature correction spectrum, measured in the case where the driving condition change is effectively applied, in obtaining a light source temperature drift vector, and may discard a temperature correction spectrum measured in the case where the driving condition change is not effectively applied.

In this manner, the accuracy of obtaining the light source temperature drift vector may be improved.

<FIG> is a diagram illustrating an example of a method of correcting a temperature change of a light source in a spectrum according to an embodiment. The method of correcting a light source temperature change of <FIG> may be performed by the spectrum measuring apparatuses <NUM> and <NUM> of <FIG> and <FIG>, respectively.

Referring to <FIG>, the spectrum measuring apparatus may measure a plurality of temperature correction spectra according to a temperature change of each light source in operation <NUM>.

In an embodiment, the spectrum measuring apparatus may measure the plurality of temperature correction spectra by measuring in vivo spectra of an object based on changing driving conditions of a light source array. In this case, the driving conditions to be changed may include at least one of an intensity of an applied current, a pulse width, and a cooling delay. According to the invention, the spectrum measuring apparatus measures a first temperature correction spectrum by driving the light source array based on a first driving condition; and induces a temperature change of the light source array by changing the first driving condition to a second driving condition, and measures a second temperature correction spectrum by driving the light source array based on the second driving condition. Further, the spectrum measuring apparatus may measure a plurality of first temperature correction spectra and a plurality of second temperature correction spectra by repeatedly changing the driving conditions during a predetermined period of time. In this case, the predetermined period of time may be <NUM> seconds, but this is merely an example, and the predetermined period of time is not limited thereto and may be set to various values.

The spectrum measuring apparatus may obtain a light source temperature drift vector by analyzing the measured plurality of temperature correction spectra in operation <NUM>. In the invention, the spectrum measuring apparatus calculates a difference spectrum between the first temperature correction spectrum, measured based on the first driving condition, and the second temperature correction spectrum measured based on the second driving condition; and extracts a principal component spectrum vector of the calculated difference spectrum as the light source temperature drift vector. Further, in an embodiment, before extracting the light source temperature drift vector, the spectrum measuring apparatus may preprocess the difference spectrum by using various preprocessing methods as described above.

The spectrum measuring apparatus may measure an analysis spectrum in operation <NUM>.

The spectrum measuring apparatus may reduce (e.g., eliminate, mitigate, lessen, prevent, etc.) the effect of the temperature change of the light source array from the analysis spectrum by using the obtained light source temperature drift vector in operation <NUM>. For example, the spectrum measuring apparatus obtain a light source temperature drift vector component from the analysis spectrum by regression analysis, and may reduce the effect of the temperature change of the light source array from the analysis spectrum by removing the obtained light source temperature drift vector component from the analysis spectrum.

In an embodiment, before reducing the effect of the temperature change of the light source array, the spectrum measuring apparatus may correct a slope and an offset in the analysis spectrum. For example, the spectrum measuring apparatus may correct the slope and the offset in the analysis spectrum by MIN-MAX normalization, multiplicative scatter correction (MSC), and the like.

Further, in an embodiment, the spectrum measuring apparatus may receive a light signal emitted by the light source array, and may determine whether a driving condition change is effectively applied based on an intensity of the received light signal. For example, if an increment or a decrement of an intensity of the light signal, compared to a preceding value of the reference photodetector, is greater than or equal to a predetermined threshold value, then the spectrum measuring apparatus may determine that the driving condition change is effectively applied. Alternatively, if an increment or a decrement of an intensity of the light signal, compared to a preceding value of the reference photodetector, is less than a predetermined threshold value, then the spectrum measuring apparatus may determine that the driving condition change is not effectively applied. Further, the spectrum measuring apparatus may use a temperature correction spectrum, measured in the case where the driving condition change is effectively applied, in obtaining a light source temperature drift vector, and may discard a temperature correction spectrum measured in the case where the driving condition change is not effectively applied.

Moreover, in an embodiment, based on a predetermined period of time elapsing after obtaining the light source temperature drift vector, the spectrum measuring apparatus may re-measure a plurality of temperature correction spectra, and may re-obtain a light source temperature drift vector based on the re-measured plurality of temperature correction spectra. That is, by periodically updating the light source temperature drift vector, the spectrum measuring apparatus may properly reflect a change in optical characteristics (e.g., scattering coefficient) of an object.

<FIG> and <FIG> are diagrams explaining a concept of a Net Analyte Signal (NAS) algorithm.

Referring to <FIG> and <FIG>, the Net Analyte Signal (NAS) algorithm may generate an analyte concentration estimation model by identifying a spectrum change factor, which is relatively less relevant to a change in an analyte concentration, using in vivo spectra S<NUM>, S<NUM>,. , and Sn measured during a training interval as training data. Further, the NAS algorithm may estimate analyte concentrations Cn+<NUM>, Cn+<NUM> and Cm by using in vivo spectra Sn+<NUM>, Sn+<NUM>,. , and Sm measured during an estimation interval following the training interval, and the concentration estimation model generated using training data corresponding to the training interval. In this case, the training interval may be an interval (e.g., a fating interval if an analyte is glucose) in which the concentration of an in vivo analyte is substantially constant. As used herein, a concentration of an in vivo analyte being "substantially constant" may refer to a change in the concentration of the in vivo analyte being less than a predetermined threshold. As an example, and referring to <FIG>, the glucose concentration may be substantially constant in the training interval because a change in the concentration is not greater than substantially five millimolar (mM). It should be understood that a threshold change value for "substantially constant" may vary depending on the underlying value that remains "substantially constant.

That is, the NAS algorithm may generate a concentration estimation model based on the in vivo spectra measured during the training interval, and then may estimate an analyte concentration by applying the generated concentration estimation model to the in vivo spectra measuring during the estimation interval.

<FIG> is a block diagram illustrating an example of an apparatus for estimating a concentration of an analyte. The concentration estimating apparatus <NUM> of <FIG> is an apparatus for estimating an analyte concentration by analyzing an in vivo spectrum of an object, and may be embedded in an electronic device or may be enclosed in a housing to be provided as a separate device.

Referring to <FIG>, the concentration estimating apparatus <NUM> includes the light source array <NUM>, the photodetector <NUM>, and a processor <NUM>. Here, the light source array <NUM> and the photodetector <NUM> may be substantially similar to the light source array <NUM> and the photodetector <NUM> described above with reference to <FIG>, such that redundant description thereof may be omitted.

The processor <NUM> may control the overall operation of the concentration estimating apparatus <NUM>.

By using the light source array <NUM> and the photodetector <NUM>, the processor <NUM> may measure a plurality of in vivo spectra during an interval in which an analyte concentration of an object is substantially constant (hereinafter referred to as "training spectra").

The processor <NUM> may generate a concentration estimation model based on the measured plurality of training spectra. In this case, examples of the analyte may include glucose, triglyceride, urea, uric acid, lactate, protein, cholesterol, ethanol, and the like, but the analyte is not limited thereto. In the case where an in vivo analyte is glucose, an analyte concentration may indicate a blood glucose level; and an interval in which an analyte is substantially constant may be a fasting interval in which glucose is not consumed by the object. Hereinafter, for convenience of explanation, the following description will be given using glucose as an example of an analyte.

In an embodiment, the processor <NUM> may generate a concentration estimation model by using the NAS algorithm and the plurality of training spectra measured during the empty-stomach interval. More specifically, the processor <NUM> may identify a spectrum change factor, which is relatively less relevant to a change in the analyte concentration, by using the plurality of training spectra measured during the empty-stomach interval as training data. For example, the processor <NUM> may extract a principal component spectrum vector from the plurality of training spectra, measured during the empty-stomach interval, by using various dimension reduction algorithms such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Non-negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), and the like. In addition, the processor <NUM> may generate the concentration estimation model based on a result of the training, i.e., the extracted principal component spectrum vector. In this case, the generated concentration estimation model may be represented by the following Equations <NUM> and <NUM> shown below. <MAT><MAT>.

Referring to Equations <NUM> and <NUM> above, Cm denotes the analyte concentration, C<NUM> denotes a reference analyte concentration (e.g., analyte concentration measured during the fasting interval), ΔC denotes a variation in concentration compared to C<NUM>, Sm denotes an analysis spectrum vector, Spc,i denotes the principal component spectrum vector, ai denotes a contribution of each principal component spectrum vector to the in vivo spectrum vector for estimation, εg denotes a spectrum vector of an analyte per unit concentration (e.g., <NUM>) (hereinafter referred to as a pure component spectrum vector), and L denotes a light path length, in which εg may be obtained experimentally.

By using the light source array <NUM> and the photodetector <NUM>, the processor <NUM> may measure a plurality of temperature correction spectra based on a temperature change of each light source of the light source array <NUM>. Further, the processor <NUM> may measure an analysis spectrum for analysis to estimate the analyte concentration of the object.

The processor <NUM> may induce a temperature change of each light source of the light source array <NUM> by changing driving conditions of the light source array <NUM>. In this case, the driving conditions to be changed may include at least one of an intensity of an applied current, a pulse width, and a cooling delay. Further, the processor <NUM> may measure a plurality of temperature correction spectra by measuring the in vivo spectra of the object based on changing the driving conditions of the light source array <NUM>. In an embodiment, the processor <NUM> may measure a first temperature correction spectrum by driving the light source array <NUM> based on a first driving condition; and may induce a temperature change of the light source array <NUM> by changing the first driving condition to a second driving condition, and may measure a second temperature correction spectrum by driving the light source array <NUM> based on the second driving condition. Further, the processor <NUM> may measure a plurality of first temperature correction spectra and a plurality of second temperature correction spectra by repeatedly changing the driving conditions during a predetermined period of time. In this case, the predetermined period of time may be <NUM> seconds, but this is merely an example, and the predetermined period of time is not limited thereto and may be set to various values.

The processor <NUM> may obtain a light source temperature drift vector by analyzing the measured plurality of temperature correction spectra. The processor <NUM> may calculate a difference spectrum between the first temperature correction spectrum, measured based on the first driving condition, and the second temperature correction spectrum measured based on the second driving condition; and may extract a principal component spectrum vector of the calculated difference spectrum as the light source temperature drift vector. In this case, the processor <NUM> may extract the principal component spectrum vector from the difference spectrum by using various dimension reduction algorithms described above. Further, the processor <NUM> may preprocess the difference spectrum by using the aforementioned various dimension reduction algorithms.

The processor <NUM> may update the concentration estimation model by using the obtained light source temperature drift vector. For example, the processor <NUM> may update Equation <NUM> to Equation <NUM>.

Referring to Equation <NUM> shown above, SLED denotes the light source temperature drift vector, and αLED denotes the contribution of the SLED to the in vivo spectrum vector for estimation.

Based on updating the concentration estimation model and obtaining the analysis spectrum for estimating the analyte concentration, the processor <NUM> may estimate the analyte concentration by using the analysis spectrum and the updated concentration estimation model. For example, the processor <NUM> may calculate ΔC by applying a regression analysis algorithm (e.g., least square method) to Equation <NUM>, and may estimate the analyte concentration using Equation <NUM> shown elsewhere herein. In the process of calculating ΔC by applying the regression analysis algorithm, ai and aLED may also be calculated.

Based on a predetermined period of time elapsing after updating the concentration estimation model, the processor <NUM> may re-update the updated concentration estimation model by re-measuring a plurality of temperature correction spectra, and re-obtaining a light source temperature drift vector based on the re-measured plurality of temperature correction spectra. That is, by periodically updating the light source drift vector, the processor <NUM> may properly reflect a change in optical characteristics (e.g., scattering coefficient) of an object.

<FIG> is a diagram illustrating an example of a method of estimating a concentration of an analyte. The concentration estimating method of <FIG> may be performed by the concentration estimating apparatus <NUM> of <FIG>.

Referring to <FIG>, the concentration estimating apparatus may measure a plurality of temperature correction spectra according to a temperature change of each light source in operation <NUM>.

The concentration estimating apparatus may measure the plurality of temperature correction spectra by measuring the in vivo spectra of an object based on changing driving conditions of the light source array. In this case, the driving conditions to be changed may include at least one of an intensity of an applied current, a pulse width, and a cooling delay. For example, the concentration estimating apparatus may measure a first temperature correction spectrum by driving the light source array based on a first driving condition; and may induce a temperature change of the light source array by changing the first driving condition to a second driving condition, and may measure a second temperature correction spectrum by driving the light source array based on the second driving condition. Further, the concentration estimating apparatus may measure a plurality of first temperature correction spectra and a plurality of second temperature correction spectra by repeatedly changing the driving conditions during a predetermined period of time. In this case, the predetermined period of time may be <NUM> seconds, but this is merely an example, and the predetermined period of time is not limited thereto and may be set to various values.

The concentration estimating apparatus may obtain a light source temperature drift vector by analyzing the measured plurality of temperature correction spectra in operation <NUM>. The concentration estimating apparatus may calculate a difference spectrum between the first temperature correction spectrum, measured based on the first driving condition, and the second temperature correction spectrum measured based on the second driving condition; and may extract a principal component spectrum vector of the calculated difference spectrum as the light source temperature drift vector. In this case, the concentration estimating apparatus may extract the principal component spectrum vector from the difference spectrum by using various dimension reduction algorithms described above. Further, before extracting the light source temperature drift vector, the concentration estimating apparatus may preprocess the difference spectrum by using one or more of the aforementioned various dimension reduction algorithms.

The concentration estimating apparatus may update the concentration estimation model by using the obtained light source temperature drift vector in operation <NUM>. For example, the concentration estimating apparatus may update Equation <NUM> to Equation <NUM> as shown elsewhere herein.

The concentration estimating apparatus may measure an analysis spectrum in operation <NUM>.

The concentration estimating apparatus may estimate an analyte concentration by using the analysis spectrum and the updated concentration estimation model in operation <NUM>. For example, the concentration estimating apparatus may calculate ΔC by applying a regression analysis algorithm (e.g., a least square method) to Equation <NUM> as shown elsewhere herein, and may estimate the analyte concentration using Equation <NUM> shown elsewhere herein. In the process of calculating ΔC by applying the regression analysis algorithm, ai and aLED may also be calculated.

<FIG> is a diagram illustrating another example of a method of estimating a concentration of an analyte. The concentration estimating method of <FIG> may be performed by the concentration estimating apparatus <NUM> of <FIG>. Operations <NUM> to <NUM> of <FIG> may be substantially similar as the operations <NUM> to <NUM> of <FIG> respectively, such that the description thereof may be briefly made below.

Referring to <FIG>, the concentration estimating apparatus may measure a plurality of training spectra during an interval in which an analyte concentration of an object is substantially constant in operation <NUM>.

The concentration estimating apparatus may generate a concentration estimation model based on the measured plurality of training spectra in operation <NUM>. In this case, examples of the analyte may include glucose, triglyceride, urea, uric acid, lactate, protein, cholesterol, ethanol, and the like, but the analyte is not limited thereto. In the case where an in vivo analyte is glucose, an analyte concentration may indicate a blood glucose level; and an interval in which an analyte is substantially constant may indicate a fasting interval in which glucose is not consumed by an object.

The concentration estimating apparatus may generate a concentration estimation model by using the NAS algorithm and the plurality of training spectra. More specifically, the concentration estimating apparatus may identify a spectrum change factor, which is relatively less relevant to a change in the analyte concentration, by using the plurality of training spectra as training data. For example, the concentration estimating apparatus may extract a principal component spectrum vector from the plurality of training spectra by using various dimension reduction algorithms described above. In addition, the concentration estimating apparatus may generate the concentration estimation model based on a result of the training, i.e., the extracted principal component spectrum vector. In this case, the generated concentration estimation model may be represented by the Equations <NUM> and <NUM> shown elsewhere herein.

The concentration estimating apparatus may measure a plurality of temperature correction spectra based on a temperature change of each light source in operation <NUM>, and may obtain a light source temperature drift vector by analyzing the measured plurality of temperature correction spectra in operation <NUM>.

The concentration estimating apparatus may update the concentration estimation model by using the obtained light source temperature drift vector in operation <NUM>, and may measure an analysis spectrum in operation <NUM>.

The concentration estimating apparatus may estimate an analyte concentration by using the analysis spectrum and the updated concentration estimation model in operation <NUM>.

<FIG> is a block diagram illustrating another example of an apparatus for estimating a concentration of an analyte. The concentration estimating apparatus <NUM> of <FIG> is an apparatus for estimating an analyte concentration by analyzing an in vivo spectrum of an object, and may be embedded in an electronic device, or may be enclosed in a housing to be provided as a separate device.

Referring to <FIG>, the concentration estimating apparatus <NUM> includes the light source array <NUM>, the photodetector <NUM>, the processor <NUM>, an input interface <NUM>, a memory <NUM>, a communication interface <NUM>, and an output interface <NUM>. Here, the light source array <NUM>, the photodetector <NUM>, and the processor <NUM> may be substantially similar as described above with reference to <FIG>, such that detailed description thereof may be omitted.

The input interface <NUM> may receive input of various operation signals based on a user input. In an embodiment, the input interface <NUM> may include a keypad, a dome switch, a touch pad (static pressure/capacitance), a jog wheel, a jog switch, a hardware (H/W) button, and the like. Particularly, the touch pad, which forms a layer structure with a display, may be referred to as a touch screen.

The memory <NUM> may store programs or commands for operation of the concentration estimating apparatus <NUM>, and may store data input to and output from the concentration estimating apparatus <NUM>. Further, the memory <NUM> may store an in vivo spectrum, a concentration estimation model, an estimated analyte concentration value, and the like. The memory <NUM> may 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., a secure digital (SD) memory, an eXtreme digital (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. Further, the concentration estimating apparatus <NUM> may communicate with an external storage medium, such as web storage and the like, which performs a storage function of the memory <NUM> via the Internet.

The communication interface <NUM> may perform communication with an external device. For example, the communication interface <NUM> may transmit, to the external device, the data input to the concentration estimating apparatus <NUM>, data stored in and processed by the concentration estimating apparatus <NUM>, and the like, or may receive, from the external device, various data for generating/updating a concentration estimation model and estimating an analyte concentration.

In this case, the external device may be medical equipment that uses the data input to the concentration estimating apparatus <NUM>, the data stored in and processed by the concentration estimating apparatus <NUM>, and the like, a printer to print out results, or a display to display the results. In addition, the external device may be a digital television (TV), a desktop computer, a cellular phone, a smartphone, a tablet PC, a laptop computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation, an MP3 player, a digital camera, a wearable device, and the like, but is not limited thereto.

The communication interface <NUM> may communicate with an external device by using Bluetooth communication, Bluetooth Low Energy (BLE) communication, Near Field Communication (NFC), wireless local area network (WLAN) communication, Zigbee communication, Infrared Data Association (IrDA) communication, wireless fidelity (Wi-Fi) communication, Ultra-Wideband (UWB) communication, Ant+ communication, Wi-Fi Direct (WFD) communication, Radio Frequency Identification (RFID) communication, third generation (<NUM>) communication, fourth generation (<NUM>) communication, fifth generation (<NUM>) communication, and the like. However, this is merely exemplary and is not intended to be limiting.

The output interface <NUM> may output the data input to the concentration estimating apparatus <NUM>, the data stored in and processed by the concentration estimating apparatus <NUM>, and the like. In an embodiment, the output interface <NUM> may output the data input to the concentration estimating apparatus <NUM>, the data stored in and processed by the concentration estimating apparatus <NUM>, and the like, by using at least one of an acoustic method, a visual method, and a tactile method. To this end, the output interface <NUM> may include a speaker, a display, a vibrator, and the like.

<FIG> is a diagram illustrating an example of a wrist-type wearable device.

Referring to <FIG>, the wrist-type wearable device <NUM> includes a strap <NUM> and a main body <NUM>.

The strap <NUM> may be connected to both ends of the main body <NUM> so as to be fastened in a detachable manner or may be integrally formed therewith as a smart band. The strap <NUM> may be made of a flexible material to be wrapped around a user's wrist so that the main body <NUM> may be worn on the wrist.

The main body <NUM> may include the aforementioned spectrum measuring apparatuses <NUM> and <NUM> and/or the aforementioned concentration estimating apparatuses <NUM> and <NUM>. Further, the main body <NUM> may include a battery which supplies power to the spectrum measuring apparatuses <NUM> and <NUM>, the concentration estimating apparatuses <NUM> and <NUM>, and/or the wrist-type wearable device <NUM>.

An optical sensor may be disposed at the bottom of the main body <NUM> to be exposed to a user's wrist. Accordingly, when a user wears the wrist-type wearable device <NUM>, the optical sensor may naturally come into contact with the user's skin. In this case, the optical sensor may obtain an in vivo spectrum by emitting light towards the user's wrist and receiving light reflected by or scattered by the user's wrist.

The wrist-type wearable device <NUM> may further include a display <NUM> and an input interface <NUM> which are disposed in the main body <NUM>. The display <NUM> may display data processed by the spectrum measuring apparatuses <NUM> and <NUM>, the concentration estimating apparatuses <NUM> and <NUM>, and/or the wrist-type wearable device <NUM>, processing result data thereof, and the like. The input interface <NUM> may receive various operation signals from a user based on a user input.

The present disclosure can be realized as computer-readable code stored in a non-transitory computer-readable recording medium. The computer-readable medium may be any type of recording device in which data is stored in a computer-readable manner. Examples of the computer-readable 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 medium may be distributed via a plurality of computer systems connected to a network so that computer-readable code is written thereto and executed therefrom in a decentralized manner.

Claim 1:
An apparatus (<NUM>; <NUM>) for measuring a spectrum, the apparatus (<NUM>; <NUM>) comprising:
a light source array (<NUM>) configured to emit light towards an object;
a photodetector (<NUM>) configured to detect light reflected by the object; and
a processor (<NUM>; <NUM>) configured to:
measure a first temperature correction spectrum by driving the light source array (<NUM>) based on a first driving condition;
induce a temperature change of the light source array (<NUM>) by changing the first driving condition to a second driving condition,
wherein the temperature change of the light source array (<NUM>) is induced by changing at least one of a pulse width, a cooling delay, and an intensity of an applied current; and
measure a second temperature correction spectrum by driving the light source array (<NUM>) based on the second driving condition,
obtain a light source temperature drift vector by calculating a difference spectrum between the first temperature correction spectrum and the second temperature correction spectrum and extracting a principal component spectrum vector of the calculated difference spectrum as the light source temperature drift vector, by using one of Principal Component Analysis, PCA, Independent Component Analysis, ICA, Non-negative Matrix Factorization, NMF, and Singular Value Decomposition, SVD;
measure, using the light source array (<NUM>) and the photodetector (<NUM>), an analysis spectrum by using the light source array (<NUM>) and the photodetector (<NUM>); and
adjust the measured analysis spectrum to reduce an effect of the temperature change of the light source array (<NUM>) by using the obtained light source temperature drift vector.