FIELD: medicine.
SUBSTANCE: following steps are performed sequentially: gathering of impedance data and auxiliary data from the user's body part; data pre-processing, wherein the pre-processing includes data filtering and removal of artifacts from the obtained impedance data by detecting non-food physiological factors on the basis of the auxiliary data; dynamics glucose curve recovery by machine learning algorithm application, glycemic index evaluation based on the glucose curve dynamics, providing the user with the results of eating habits assessment and automated monitoring on the basis the said evaluation for a certain period of time.
EFFECT: group of inventions allows to increase the effectiveness of glycemic indices non-invasive monitoring and to correct eating habits.
42 cl, 5 dwg
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