Abstract: A method for quantifying a glucose concentration is a method for quantifying a glucose concentration in which near-infrared light is emitted onto a living organism and a glucose concentration in a biological tissue is measured using a signal obtained by receiving diffusely reflected light or transmitted light from the biological tissue. A concentration calculation step calculates a concentration index of a glucose component by using at least a spectrum of a water component, a spectrum of a glucose component, and a spectrum of a fat component to synthesize a difference spectrum between a measurement spectrum at a time of measurement of a glucose concentration and a spectrum serving as a reference obtained previous to the measurement spectrum. A glucose concentration calculation step calculates a glucose concentration in the living organism using the calculated concentration index.

Abstract: A method for quantifying a glucose concentration is a method for quantifying a glucose concentration in which near-infrared light is emitted onto a living organism and a glucose concentration in a biological tissue is measured using a signal obtained by receiving diffusely reflected light or transmitted light from the biological tissue. A concentration calculation step calculates a concentration index of a glucose component by using at least a spectrum of a water component, a spectrum of a glucose component, and a spectrum of a fat component to synthesize a difference spectrum between a measurement spectrum at a time of measurement of a glucose concentration and a spectrum serving as a reference obtained previous to the measurement spectrum. A glucose concentration calculation step calculates a glucose concentration in the living organism using the calculated concentration index.

Abstract: The blood sugar value estimation apparatus is configured to non-invasively calculate the blood sugar value with time on the basis of the optical spectrum of the living body measured with time and the calibration model. The apparatus comprises a calibration model creating means configured to create a calibration model from the calibration models or a calibration model from a plurality of the datasets for creating the calibration model. The apparatus is configured to measure a bio-spectrum of a person being tested to set the reference spectrum, and to measure a difference spectrum of a difference between the reference spectrum and a measurement spectrum measured at a time other than a time when the reference spectrum is measured, and to change the calibration model for calculating according to the variation of the difference spectrum. Consequently, the blood sugar value is estimated, especially monitored with time, with high-accuracy.

Abstract: A method of non-invasively determining a concentration of an in-vivo component such as blood sugar level (glucose) of a subject is provided. An absorption spectrum of the subject is measured by use of near-infrared light. The concentration of the in-vivo component is determined by use of the absorption spectrum of the subject and a calibration curve. The calibration curve is prepared by determining a plurality of difference absorption spectra that are differences between a plurality of near-infrared absorption spectra of a living body and a reference absorption spectrum selected from the near-infrared absorption spectra, determining a plurality of synthetic absorption spectra, which are obtained by synthesizing each of the difference absorption spectra with a previously measured reference absorption spectrum of the subject, and performing a multivariate analysis with use of the obtained synthetic absorption spectra.

Abstract: A method for calculating a biological component density of this invention comprises steps of irradiating a light of NIR spectrum to a skin of a subject, receiving the light of NIR reflected from the skin to obtain NIR spectrum data thereof, and substituting the NIR spectrum data into a predetermined calibrating equation to obtain a biological component density of the subject such as glucose density.

Abstract: A method of non-invasively determining a concentration of an in-vivo component such as blood sugar level (glucose) of a subject is provided. An absorption spectrum of the subject is measured by use of near-infrared light. The concentration of the in-vivo component is determined by use of the absorption spectrum of the subject and a calibration curve. The calibration curve is prepared by determining a plurality of difference absorption spectra that are differences between a plurality of near-infrared absorption spectra of a living body and a reference absorption spectrum selected from the near-infrared absorption spectra, determining a plurality of synthetic absorption spectra, which are obtained by synthesizing each of the difference absorption spectra with a previously measured reference absorption spectrum of the subject, and performing a multivariate analysis with use of the obtained synthetic absorption spectra.

Abstract: A method for calculating a biological component density of this invention comprises steps of irradiating a light of NIR spectrum to a skin of a subject, receiving the light of NIR reflected from the skin to obtain NIR spectrum data thereof, and substituting the NIR spectrum data into a predetermined calibrating equation to obtain a biological component density of the subject such as glucose density.

Abstract: A device for non-invasive determination of a glucose concentration in the blood of a subject which includes a light source for producing near-infrared radiation having successive wavelengths within a range of 1300 nm to 2500 nm, a light projecting unit for projecting the near-infrared radiation on a skin of the subject, a light receiving unit for receiving a resulting radiation emitted from the inside of the skin, and a spectrum analyzing unit for making a spectrum analysis of the resulting radiation, and determining the glucose concentration according to the spectrum analysis. The light receiving unit is separated from the light projecting unit by a distance defined within a range of 0.1 mm to 2 mm to selectively sense the resulting radiation emitted from a dermis layer positioned under an epidermis layer of the skin.

Abstract: A glucose concentration in a living tissue as a target is determined by the following method. Near-infrared radiation is projected on the living tissue, and a resulting radiation emitted from the living tissue is received. A spectrum analysis of the resulting radiation is performed to detect a first absorption signal from a wavelength region, e.g., 1550 nm to 1650 nm, having an absorption peak of OH group derived from glucose molecule, a second absorption signal from a wavelength region, e.g., 1480 nm to 1550 nm, having an absorption peak of NH group in the living tissue, and a third absorption signal from a wavelength region, e.g., 1650 nm to 1880 nm, having an absorption peak of CH group in the living tissue. The glucose concentration is determined by a multivariate analysis of results of the spectrum analysis, in which the first, second and third absorption signals are used as explanatory variables, and the glucose concentration is a criterion variable.

Abstract: An optimum workload corresponding to a target heart rate of an user is determined by the following method, which is preferably utilized for providing a safe training or an accurate examination of physical strength to the user. After the target heart rate is set, a first steady heart rate of the user is measured during an initial exercise cycle in which a first workload is applied to the user. The first workload is derived in accordance with the target heart rate and a statistically obtained workload versus heart rate correlation corresponding to at least one factor selected from the group consisting of the user's age, gender, body weight, and body height, etc. In addition, a second steady heart rate of the user is measured during at least one subsequent exercise cycle in which a second workload is applied to the user. The second workload is derived by entering as input parameters the applied workload and the measured heart rate at the immediately previous exercise cycle into a multiple variate model equation.