Machine learning-based wearable apparatus for blood glucose measurement using Raman spectroscopy
Blood glucose level measurement includes a light source configured to irradiate light to a subject; a monochrome part configured to separate wavelength components of the light that is reflected and scattered from the subject; a light receiver configured to receive the light transmitted via the monochrome part and to generate electrical signals based on the received light; and a processor configured to extract information on the blood glucose level of the subject based on a frequency shift of the light due to the Raman effect.
1 . A wearable apparatus comprising:
a light source configured to irradiate light to a subject;
a monochrome part configured to separate wavelength components of the light that is reflected and scattered from the subject;
a light receiver configured to receive the light transmitted via the monochrome part and to generate electrical signals representative of a Raman spectrum of the light that is reflected and scattered from the subject;
a processor configured to determine a glucose level of the subject based on the Raman spectrum; and
a battery that provides electrical power to the apparatus,
wherein the glucose level is determined based on a machine learning model that has been trained to correlate the glucose level with areas of (a) a peak at about 1450 cm −1 , (b) a peak at about 1660 cm −1 , and (c) a peak at about 1125 cm −1 in the Raman spectrum, and
wherein, for (c) the peak at about 1125 cm −1 , the glucose level is correlated based on each of at least three ranges (c-i) between 1089 cm −1 and 1160 cm −1 , (c-ii) between 1115 cm −1 and 1140 cm −1 , and (c-iii) between 1120 cm −1 and 1130 cm −1 .
2 . The wearable apparatus of claim 1 , wherein the machine learning model utilizes partial least squares (PLS), support vector machine (SVM), autoencoder, ResNet, or any combination thereof.
3 . The wearable apparatus of claim 2 , wherein the machine learning model comprises a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, a generative adversarial network (GAN), or any combination thereof.
4 . The wearable apparatus of claim 1 , wherein, for (a) the peak at about 1450 cm −1 , the area is obtained in a range between 1415 cm −1 and 1480 cm −1 .
5 . The wearable apparatus of claim 1 , wherein, for (b) the peak at about 1660 cm −1 , the area is obtained in a range between 1630 cm −1 to 1685 cm −1 .